homework/Testing with Examples (Network)/impl_fail-add_relation-does_not_fail_when_person1_is_non_existent.py class MyError(Exception): pass class PropertyContainer(object): def\",\n \"value def get_property(self, prop): return self.props.get(prop) def has_property(self, prop): return\",\n \"value) return # flag non-existent relation raise RuntimeError(\\\"Non-existent relation\\\") #\",\n \"visited): for e in (x for x in self.edges if\",\n \"is person1 and e.node2 is person2) or \\\\ (e.node1 is\",\n \"person = self.get_person(name) visited = set([person]) i = 0 while\",\n \"= set() for p in (x for x in visited):\",\n \"person1): e.set_property(prop, value) return # flag non-existent relation raise RuntimeError(\\\"Non-existent\",\n \"(x for x in self.edges if x.get_property(Network.FRIEND_PROP) == True): n1\",\n \"newly_visited.add(e.node2) elif n2 == p: newly_visited.add(e.node1) visited = newly_visited i\",\n \"persons if person1 not in self.nodes: # raise RuntimeError(\\\"person1 does\",\n \"value): # disallow non-boolean values for FRIEND_PROP property if prop\",\n \"self.node1 = node1 self.node2 = node2 class Network(object): NAME_PROP =\",\n \"set() self.edges = set() def create_person(self): node = Node() self.nodes.add(node)\",\n \"x in self.edges if x.get_property(Network.FRIEND_PROP) == True): n1 = e.node1\",\n \"= self.get_person(name) visited = set([person]) i = 0 while i\",\n \"p in (x for x in visited): for e in\",\n \"prop exists def add_person_property(self, person, prop, value): # flag non-existent\",\n \"person, prop, value): # flag non-existent person if person not\",\n \"friends of friends of a person with given name def\",\n \"of a person with given name def friends_of_friends(self, name): #\",\n \"__init__(self, node1, node2): super().__init__() self.node1 = node1 self.node2 = node2\",\n \"node2 class Network(object): NAME_PROP = \\\"name\\\" # NAME_PROP is an\",\n \"exists def add_person_property(self, person, prop, value): # flag non-existent person\",\n \"isinstance(name, str): raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) for\",\n \"if n1 == p: newly_visited.add(e.node2) elif n2 == p: newly_visited.add(e.node1)\",\n \"is a string property\\\".format(Network.NAME_PROP)) # disallow multiple people to have\",\n \"bool): raise TypeError( \\\"{0} is a boolean property\\\".format(Network.FRIEND_PROP)) for e\",\n \"self.get_person(name) visited = set([person]) i = 0 while i <\",\n \"raise ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2)) def add_relation_property(self, person1, person2, prop,\",\n \"raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) for n in\",\n \"is an optional boolean property def __init__(self): self.nodes = set()\",\n \"2: newly_visited = set() for p in (x for x\",\n \"def add_relation(self, person1, person2): # flag non-existent persons if person1\",\n \"def get_person(self, name): # disallow non-string values for name if\",\n \"Edge(PropertyContainer): def __init__(self, node1, node2): super().__init__() self.node1 = node1 self.node2\",\n \"n2 = e.node2 if n1 == p: newly_visited.add(e.node2) elif n2\",\n \"__init__(self): self.nodes = set() self.edges = set() def create_person(self): node\",\n \"get_property(self, prop): return self.props.get(prop) def has_property(self, prop): return prop in\",\n \"an optional string property FRIEND_PROP = \\\"friend\\\" # FRIEND_PROP is\",\n \"def friends_of_friends(self, name): # disallow non-string values for name if\",\n \"def add_relation_property(self, person1, person2, prop, value): # disallow non-boolean values\",\n \"is a string argument\\\".format(Network.NAME_PROP)) # flag non-existent person person =\",\n \"self.create_person() if person2 not in self.nodes: raise RuntimeError(\\\"person2 does not\",\n \"name if not isinstance(name, str): raise TypeError( \\\"{0} is a\",\n \"\\\"{0} is a string argument\\\".format(Network.NAME_PROP)) # flag non-existent person person\",\n \"i < 2: newly_visited = set() for p in (x\",\n \"overwrite if prop exists def add_person_property(self, person, prop, value): #\",\n \"RuntimeError(\\\"person1 does not exist\\\") person1 = self.create_person() if person2 not\",\n \"# disallow non-string values for name if not isinstance(name, str):\",\n \"in self.edges if x.get_property(Network.FRIEND_PROP) == True): n1 = e.node1 n2\",\n \"for NAME_PROP property if not isinstance(value, str): raise TypeError( \\\"{0}\",\n \"in self.nodes: # raise RuntimeError(\\\"person1 does not exist\\\") person1 =\",\n \"== p: newly_visited.add(e.node1) visited = newly_visited i += 1 return\",\n \"def has_property(self, prop): return prop in self.props class Node(PropertyContainer): pass\",\n \"raise TypeError( \\\"{0} is a boolean property\\\".format(Network.FRIEND_PROP)) for e in\",\n \"for x in self.edges if x.get_property(Network.FRIEND_PROP) == True): n1 =\",\n \"exist\\\") if prop == Network.NAME_PROP: # disallow non-string values for\",\n \"str): raise TypeError( \\\"{0} is a string property\\\".format(Network.NAME_PROP)) # disallow\",\n \"\\\"friend\\\" # FRIEND_PROP is an optional boolean property def __init__(self):\",\n \"self.edges if x.get_property(Network.FRIEND_PROP) == True): n1 = e.node1 n2 =\",\n \"and e.node2 is person1): raise ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2)) def\",\n \"# disallow multiple people to have the same name for\",\n \"for x in visited): for e in (x for x\",\n \"relation\\\") # get a person with given name def get_person(self,\",\n \"in self.nodes: raise RuntimeError(\\\"person does not exist\\\") if prop ==\",\n \"person1 and e.node2 is person2) or \\\\ (e.node1 is person2\",\n \"\\\"{0} is a boolean property\\\".format(Network.FRIEND_PROP)) for e in self.edges: if\",\n \"an optional boolean property def __init__(self): self.nodes = set() self.edges\",\n \"Network.NAME_PROP: # disallow non-string values for NAME_PROP property if not\",\n \"person person = self.get_person(name) visited = set([person]) i = 0\",\n \"TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) for n in self.nodes:\",\n \"set() for p in (x for x in visited): for\",\n \"set_property(self, prop, value): self.props[prop] = value def get_property(self, prop): return\",\n \"and e.node2 is person2) or \\\\ (e.node1 is person2 and\",\n \"with given name def friends_of_friends(self, name): # disallow non-string values\",\n \"newly_visited = set() for p in (x for x in\",\n \"def add_person_property(self, person, prop, value): # flag non-existent person if\",\n \"person with given name def get_person(self, name): # disallow non-string\",\n \"a string argument\\\".format(Network.NAME_PROP)) for n in self.nodes: if n.get_property(Network.NAME_PROP) ==\",\n \"ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2)) def add_relation_property(self, person1, person2, prop, value):\",\n \"flag non-existent person raise RuntimeError(\\\"No person named {0}\\\".format(name)) # get\",\n \"property if prop == Network.FRIEND_PROP and not isinstance(value, bool): raise\",\n \"non-string values for NAME_PROP property if not isinstance(value, str): raise\",\n \"= set([person]) i = 0 while i < 2: newly_visited\",\n \"if not isinstance(name, str): raise TypeError( \\\"{0} is a string\",\n \"flag non-existent person person = self.get_person(name) visited = set([person]) i\",\n \"and not isinstance(value, bool): raise TypeError( \\\"{0} is a boolean\",\n \"str): raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) for n\",\n \"# disallow non-boolean values for FRIEND_PROP property if prop ==\",\n \"raise ValueError(\\\"{0} name already taken\\\".format(value)) person.set_property(prop, value) def add_relation(self, person1,\",\n \"str): raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) # flag\",\n \"= node1 self.node2 = node2 class Network(object): NAME_PROP = \\\"name\\\"\",\n \"def get_property(self, prop): return self.props.get(prop) def has_property(self, prop): return prop\",\n \"prop to value; overwrite if prop exists def add_person_property(self, person,\",\n \"get a person with given name def get_person(self, name): #\",\n \"for e in self.edges: if (e.node1 is person1 and e.node2\",\n \"pass class Edge(PropertyContainer): def __init__(self, node1, node2): super().__init__() self.node1 =\",\n \"# FRIEND_PROP is an optional boolean property def __init__(self): self.nodes\",\n \"if x.get_property(Network.FRIEND_PROP) == True): n1 = e.node1 n2 = e.node2\",\n \"PropertyContainer(object): def __init__(self): self.props = {} def set_property(self, prop, value):\",\n \"non-existent person person = self.get_person(name) visited = set([person]) i =\",\n \"FRIEND_PROP = \\\"friend\\\" # FRIEND_PROP is an optional boolean property\",\n \"p: newly_visited.add(e.node2) elif n2 == p: newly_visited.add(e.node1) visited = newly_visited\",\n \"create_person(self): node = Node() self.nodes.add(node) return node # add prop\",\n \"i = 0 while i < 2: newly_visited = set()\",\n \"if not isinstance(value, str): raise TypeError( \\\"{0} is a string\",\n \"if prop exists def add_person_property(self, person, prop, value): # flag\",\n \"non-existent relation raise RuntimeError(\\\"Non-existent relation\\\") # get a person with\",\n \"flag non-existent person if person not in self.nodes: raise RuntimeError(\\\"person\",\n \"0 while i < 2: newly_visited = set() for p\",\n \"node # add prop to value; overwrite if prop exists\",\n \"if prop == Network.NAME_PROP: # disallow non-string values for NAME_PROP\",\n \"# flag non-existent person if person not in self.nodes: raise\",\n \"\\\\ (e.node1 is person2 and e.node2 is person1): e.set_property(prop, value)\",\n \"argument\\\".format(Network.NAME_PROP)) for n in self.nodes: if n.get_property(Network.NAME_PROP) == name: return\",\n \"person2): # flag non-existent persons if person1 not in self.nodes:\",\n \"e.set_property(prop, value) return # flag non-existent relation raise RuntimeError(\\\"Non-existent relation\\\")\",\n \"not isinstance(value, str): raise TypeError( \\\"{0} is a string property\\\".format(Network.NAME_PROP))\",\n \"value): self.props[prop] = value def get_property(self, prop): return self.props.get(prop) def\",\n \"RuntimeError(\\\"person2 does not exist\\\") # flag existing edge for e\",\n \"or \\\\ (e.node1 is person2 and e.node2 is person1): raise\",\n \"have the same name for p in self.nodes: if p.get_property(Network.NAME_PROP)\",\n \"super().__init__() self.node1 = node1 self.node2 = node2 class Network(object): NAME_PROP\",\n \"boolean property\\\".format(Network.FRIEND_PROP)) for e in self.edges: if (e.node1 is person1\",\n \"optional string property FRIEND_PROP = \\\"friend\\\" # FRIEND_PROP is an\",\n \"disallow non-string values for NAME_PROP property if not isinstance(value, str):\",\n \"# flag non-existent relation raise RuntimeError(\\\"Non-existent relation\\\") # get a\",\n \"in self.nodes: if n.get_property(Network.NAME_PROP) == name: return n # flag\",\n \"in (x for x in visited): for e in (x\",\n \"== name: return n # flag non-existent person raise RuntimeError(\\\"No\",\n \"NAME_PROP is an optional string property FRIEND_PROP = \\\"friend\\\" #\",\n \"given name def friends_of_friends(self, name): # disallow non-string values for\",\n \"non-existent persons if person1 not in self.nodes: # raise RuntimeError(\\\"person1\",\n \"self.edges: if (e.node1 is person1 and e.node2 is person2) or\",\n \"self.props[prop] = value def get_property(self, prop): return self.props.get(prop) def has_property(self,\",\n \"return self.props.get(prop) def has_property(self, prop): return prop in self.props class\",\n \"set() def create_person(self): node = Node() self.nodes.add(node) return node #\",\n \"set([person]) i = 0 while i < 2: newly_visited =\",\n \"add_relation_property(self, person1, person2, prop, value): # disallow non-boolean values for\",\n \"person.set_property(prop, value) def add_relation(self, person1, person2): # flag non-existent persons\",\n \"in self.nodes: if p.get_property(Network.NAME_PROP) == value and \\\\ p is\",\n \"isinstance(name, str): raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) #\",\n \"node1 self.node2 = node2 class Network(object): NAME_PROP = \\\"name\\\" #\",\n \"p: newly_visited.add(e.node1) visited = newly_visited i += 1 return list(visited)\",\n \"in (x for x in self.edges if x.get_property(Network.FRIEND_PROP) == True):\",\n \"FRIEND_PROP is an optional boolean property def __init__(self): self.nodes =\",\n \"NAME_PROP property if not isinstance(value, str): raise TypeError( \\\"{0} is\",\n \"person not in self.nodes: raise RuntimeError(\\\"person does not exist\\\") if\",\n \"isinstance(value, str): raise TypeError( \\\"{0} is a string property\\\".format(Network.NAME_PROP)) #\",\n \"exist\\\") person1 = self.create_person() if person2 not in self.nodes: raise\",\n \"boolean property def __init__(self): self.nodes = set() self.edges = set()\",\n \"\\\"{0} is a string property\\\".format(Network.NAME_PROP)) # disallow multiple people to\",\n \"TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) # flag non-existent person\",\n \"# flag non-existent person raise RuntimeError(\\\"No person named {0}\\\".format(name)) #\",\n \"edge for e in self.edges: if (e.node1 is person1 and\",\n \"or \\\\ (e.node1 is person2 and e.node2 is person1): e.set_property(prop,\",\n \"not exist\\\") person1 = self.create_person() if person2 not in self.nodes:\",\n \"self.nodes.add(node) return node # add prop to value; overwrite if\",\n \"not in self.nodes: raise RuntimeError(\\\"person does not exist\\\") if prop\",\n \"e.node2 is person1): e.set_property(prop, value) return # flag non-existent relation\",\n \"< 2: newly_visited = set() for p in (x for\",\n \"flag existing edge for e in self.edges: if (e.node1 is\",\n \"def create_person(self): node = Node() self.nodes.add(node) return node # add\",\n \"visited = set([person]) i = 0 while i < 2:\",\n \"values for NAME_PROP property if not isinstance(value, str): raise TypeError(\",\n \"add_relation(self, person1, person2): # flag non-existent persons if person1 not\",\n \"not isinstance(value, bool): raise TypeError( \\\"{0} is a boolean property\\\".format(Network.FRIEND_PROP))\",\n \"add_person_property(self, person, prop, value): # flag non-existent person if person\",\n \"prop): return self.props.get(prop) def has_property(self, prop): return prop in self.props\",\n \"prop, value): # disallow non-boolean values for FRIEND_PROP property if\",\n \"node2): super().__init__() self.node1 = node1 self.node2 = node2 class Network(object):\",\n \"is person1): e.set_property(prop, value) return # flag non-existent relation raise\",\n \"class Node(PropertyContainer): pass class Edge(PropertyContainer): def __init__(self, node1, node2): super().__init__()\",\n \"pass class PropertyContainer(object): def __init__(self): self.props = {} def set_property(self,\",\n \"existing edge for e in self.edges: if (e.node1 is person1\",\n \"for e in (x for x in self.edges if x.get_property(Network.FRIEND_PROP)\",\n \"person named {0}\\\".format(name)) # get friends of friends of a\",\n \"raise TypeError( \\\"{0} is a string property\\\".format(Network.NAME_PROP)) # disallow multiple\",\n \"name def get_person(self, name): # disallow non-string values for name\",\n \"person2 and e.node2 is person1): raise ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2))\",\n \"\\\"{0} is a string argument\\\".format(Network.NAME_PROP)) for n in self.nodes: if\",\n \"(e.node1 is person2 and e.node2 is person1): e.set_property(prop, value) return\",\n \"person2 not in self.nodes: raise RuntimeError(\\\"person2 does not exist\\\") #\",\n \"RuntimeError(\\\"No person named {0}\\\".format(name)) # get friends of friends of\",\n \"value; overwrite if prop exists def add_person_property(self, person, prop, value):\",\n \"(e.node1 is person2 and e.node2 is person1): raise ValueError(\\\"relation exists\\\")\",\n \"self.props class Node(PropertyContainer): pass class Edge(PropertyContainer): def __init__(self, node1, node2):\",\n \"e in (x for x in self.edges if x.get_property(Network.FRIEND_PROP) ==\",\n \"not person: raise ValueError(\\\"{0} name already taken\\\".format(value)) person.set_property(prop, value) def\",\n \"people to have the same name for p in self.nodes:\",\n \"person1, person2, prop, value): # disallow non-boolean values for FRIEND_PROP\",\n \"taken\\\".format(value)) person.set_property(prop, value) def add_relation(self, person1, person2): # flag non-existent\",\n \"a string argument\\\".format(Network.NAME_PROP)) # flag non-existent person person = self.get_person(name)\",\n \"# flag non-existent person person = self.get_person(name) visited = set([person])\",\n \"= set() self.edges = set() def create_person(self): node = Node()\",\n \"def set_property(self, prop, value): self.props[prop] = value def get_property(self, prop):\",\n \"name for p in self.nodes: if p.get_property(Network.NAME_PROP) == value and\",\n \"if prop == Network.FRIEND_PROP and not isinstance(value, bool): raise TypeError(\",\n \"string argument\\\".format(Network.NAME_PROP)) # flag non-existent person person = self.get_person(name) visited\",\n \"self.nodes: if n.get_property(Network.NAME_PROP) == name: return n # flag non-existent\",\n \"multiple people to have the same name for p in\",\n \"is not person: raise ValueError(\\\"{0} name already taken\\\".format(value)) person.set_property(prop, value)\",\n \"raise RuntimeError(\\\"person2 does not exist\\\") # flag existing edge for\",\n \"__init__(self): self.props = {} def set_property(self, prop, value): self.props[prop] =\",\n \"= e.node2 if n1 == p: newly_visited.add(e.node2) elif n2 ==\",\n \"class PropertyContainer(object): def __init__(self): self.props = {} def set_property(self, prop,\",\n \"to value; overwrite if prop exists def add_person_property(self, person, prop,\",\n \"(Network)/impl_fail-add_relation-does_not_fail_when_person1_is_non_existent.py class MyError(Exception): pass class PropertyContainer(object): def __init__(self): self.props =\",\n \"property if not isinstance(value, str): raise TypeError( \\\"{0} is a\",\n \"in visited): for e in (x for x in self.edges\",\n \"not exist\\\") if prop == Network.NAME_PROP: # disallow non-string values\",\n \"n1 == p: newly_visited.add(e.node2) elif n2 == p: newly_visited.add(e.node1) visited\",\n \"x.get_property(Network.FRIEND_PROP) == True): n1 = e.node1 n2 = e.node2 if\",\n \"string property FRIEND_PROP = \\\"friend\\\" # FRIEND_PROP is an optional\",\n \"== Network.NAME_PROP: # disallow non-string values for NAME_PROP property if\",\n \"value) def add_relation(self, person1, person2): # flag non-existent persons if\",\n \"person if person not in self.nodes: raise RuntimeError(\\\"person does not\",\n \"# raise RuntimeError(\\\"person1 does not exist\\\") person1 = self.create_person() if\",\n \"person1): raise ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2)) def add_relation_property(self, person1, person2,\",\n \"# flag non-existent persons if person1 not in self.nodes: #\",\n \"exist\\\") # flag existing edge for e in self.edges: if\",\n \"RuntimeError(\\\"Non-existent relation\\\") # get a person with given name def\",\n \"Examples (Network)/impl_fail-add_relation-does_not_fail_when_person1_is_non_existent.py class MyError(Exception): pass class PropertyContainer(object): def __init__(self): self.props\",\n \"node = Node() self.nodes.add(node) return node # add prop to\",\n \"while i < 2: newly_visited = set() for p in\",\n \"disallow multiple people to have the same name for p\",\n \"relation raise RuntimeError(\\\"Non-existent relation\\\") # get a person with given\",\n \"return prop in self.props class Node(PropertyContainer): pass class Edge(PropertyContainer): def\",\n \"prop in self.props class Node(PropertyContainer): pass class Edge(PropertyContainer): def __init__(self,\",\n \"x in visited): for e in (x for x in\",\n \"string property\\\".format(Network.NAME_PROP)) # disallow multiple people to have the same\",\n \"person2)) def add_relation_property(self, person1, person2, prop, value): # disallow non-boolean\",\n \"property\\\".format(Network.NAME_PROP)) # disallow multiple people to have the same name\",\n \"NAME_PROP = \\\"name\\\" # NAME_PROP is an optional string property\",\n \"= {} def set_property(self, prop, value): self.props[prop] = value def\",\n \"for n in self.nodes: if n.get_property(Network.NAME_PROP) == name: return n\",\n \"named {0}\\\".format(name)) # get friends of friends of a person\",\n \"values for FRIEND_PROP property if prop == Network.FRIEND_PROP and not\",\n \"a boolean property\\\".format(Network.FRIEND_PROP)) for e in self.edges: if (e.node1 is\",\n \"given name def get_person(self, name): # disallow non-string values for\",\n \"person1 = self.create_person() if person2 not in self.nodes: raise RuntimeError(\\\"person2\",\n \"non-existent person raise RuntimeError(\\\"No person named {0}\\\".format(name)) # get friends\",\n \"raise RuntimeError(\\\"Non-existent relation\\\") # get a person with given name\",\n \"add prop to value; overwrite if prop exists def add_person_property(self,\",\n \"return n # flag non-existent person raise RuntimeError(\\\"No person named\",\n \"flag non-existent persons if person1 not in self.nodes: # raise\",\n \"n # flag non-existent person raise RuntimeError(\\\"No person named {0}\\\".format(name))\",\n \"# disallow non-string values for NAME_PROP property if not isinstance(value,\",\n \"{} def set_property(self, prop, value): self.props[prop] = value def get_property(self,\",\n \"person with given name def friends_of_friends(self, name): # disallow non-string\",\n \"self.nodes: if p.get_property(Network.NAME_PROP) == value and \\\\ p is not\",\n \"if person2 not in self.nodes: raise RuntimeError(\\\"person2 does not exist\\\")\",\n \"is person2) or \\\\ (e.node1 is person2 and e.node2 is\",\n \"in self.props class Node(PropertyContainer): pass class Edge(PropertyContainer): def __init__(self, node1,\",\n \"not in self.nodes: raise RuntimeError(\\\"person2 does not exist\\\") # flag\",\n \"for name if not isinstance(name, str): raise TypeError( \\\"{0} is\",\n \"person2, prop, value): # disallow non-boolean values for FRIEND_PROP property\",\n \"does not exist\\\") # flag existing edge for e in\",\n \"\\\\ (e.node1 is person2 and e.node2 is person1): raise ValueError(\\\"relation\",\n \"e.node1 n2 = e.node2 if n1 == p: newly_visited.add(e.node2) elif\",\n \"with Examples (Network)/impl_fail-add_relation-does_not_fail_when_person1_is_non_existent.py class MyError(Exception): pass class PropertyContainer(object): def __init__(self):\",\n \"self.node2 = node2 class Network(object): NAME_PROP = \\\"name\\\" # NAME_PROP\",\n \"name def friends_of_friends(self, name): # disallow non-string values for name\",\n \"is person1): raise ValueError(\\\"relation exists\\\") self.edges.add(Edge(person1, person2)) def add_relation_property(self, person1,\",\n \"friends of a person with given name def friends_of_friends(self, name):\",\n \"friends_of_friends(self, name): # disallow non-string values for name if not\",\n \"value and \\\\ p is not person: raise ValueError(\\\"{0} name\",\n \"disallow non-string values for name if not isinstance(name, str): raise\",\n \"raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP)) # flag non-existent\",\n \"for p in self.nodes: if p.get_property(Network.NAME_PROP) == value and \\\\\",\n \"prop, value): self.props[prop] = value def get_property(self, prop): return self.props.get(prop)\",\n \"property FRIEND_PROP = \\\"friend\\\" # FRIEND_PROP is an optional boolean\",\n \"already taken\\\".format(value)) person.set_property(prop, value) def add_relation(self, person1, person2): # flag\",\n \"optional boolean property def __init__(self): self.nodes = set() self.edges =\",\n \"to have the same name for p in self.nodes: if\",\n \"person2) or \\\\ (e.node1 is person2 and e.node2 is person1):\",\n \"self.edges.add(Edge(person1, person2)) def add_relation_property(self, person1, person2, prop, value): # disallow\",\n \"of friends of a person with given name def friends_of_friends(self,\",\n \"class MyError(Exception): pass class PropertyContainer(object): def __init__(self): self.props = {}\",\n \"node1, node2): super().__init__() self.node1 = node1 self.node2 = node2 class\",\n \"person: raise ValueError(\\\"{0} name already taken\\\".format(value)) person.set_property(prop, value) def add_relation(self,\",\n \"a person with given name def get_person(self, name): # disallow\",\n \"= node2 class Network(object): NAME_PROP = \\\"name\\\" # NAME_PROP is\",\n \"in self.edges: if (e.node1 is person1 and e.node2 is person2)\",\n \"if person1 not in self.nodes: # raise RuntimeError(\\\"person1 does not\",\n \"== value and \\\\ p is not person: raise ValueError(\\\"{0}\",\n \"non-boolean values for FRIEND_PROP property if prop == Network.FRIEND_PROP and\",\n \"Network.FRIEND_PROP and not isinstance(value, bool): raise TypeError( \\\"{0} is a\",\n \"not isinstance(name, str): raise TypeError( \\\"{0} is a string argument\\\".format(Network.NAME_PROP))\",\n \"raise RuntimeError(\\\"No person named {0}\\\".format(name)) # get friends of friends\",\n \"p.get_property(Network.NAME_PROP) == value and \\\\ p is not person: raise\",\n \"def __init__(self): self.props = {} def set_property(self, prop, value): self.props[prop]\",\n \"n in self.nodes: if n.get_property(Network.NAME_PROP) == name: return n #\",\n \"flag non-existent relation raise RuntimeError(\\\"Non-existent relation\\\") # get a person\",\n \"disallow non-boolean values for FRIEND_PROP property if prop == Network.FRIEND_PROP\"\n]","isConversational":false}}},{"rowIdx":148,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":149,"cells":{"ngram":{"kind":"list like","value":["def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) + k for","= \"1\", 1 while i < n: s = gen(s)","class Solution: def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) +","import groupby class Solution: def countAndSay(self, n): def gen(s): return","in groupby(s)) s, i = \"1\", 1 while i <","return \"\".join(str(len(list(g))) + k for k, g in groupby(s)) s,","groupby class Solution: def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g)))","\"\".join(str(len(list(g))) + k for k, g in groupby(s)) s, i","g in groupby(s)) s, i = \"1\", 1 while i","\"1\", 1 while i < n: s = gen(s) i","groupby(s)) s, i = \"1\", 1 while i < n:","1 while i < n: s = gen(s) i +=","countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) + k for k,","i < n: s = gen(s) i += 1 return","def gen(s): return \"\".join(str(len(list(g))) + k for k, g in","s, i = \"1\", 1 while i < n: s","itertools import groupby class Solution: def countAndSay(self, n): def gen(s):","gen(s): return \"\".join(str(len(list(g))) + k for k, g in groupby(s))","i = \"1\", 1 while i < n: s =","while i < n: s = gen(s) i += 1","Solution: def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) + k","for k, g in groupby(s)) s, i = \"1\", 1","< n: s = gen(s) i += 1 return s","k for k, g in groupby(s)) s, i = \"1\",","from itertools import groupby class Solution: def countAndSay(self, n): def","k, g in groupby(s)) s, i = \"1\", 1 while","n): def gen(s): return \"\".join(str(len(list(g))) + k for k, g","+ k for k, g in groupby(s)) s, i ="],"string":"[\n \"def countAndSay(self, n): def gen(s): return \\\"\\\".join(str(len(list(g))) + k for\",\n \"= \\\"1\\\", 1 while i < n: s = gen(s)\",\n \"class Solution: def countAndSay(self, n): def gen(s): return \\\"\\\".join(str(len(list(g))) +\",\n \"import groupby class Solution: def countAndSay(self, n): def gen(s): return\",\n \"in groupby(s)) s, i = \\\"1\\\", 1 while i <\",\n \"return \\\"\\\".join(str(len(list(g))) + k for k, g in groupby(s)) s,\",\n \"groupby class Solution: def countAndSay(self, n): def gen(s): return \\\"\\\".join(str(len(list(g)))\",\n \"\\\"\\\".join(str(len(list(g))) + k for k, g in groupby(s)) s, i\",\n \"g in groupby(s)) s, i = \\\"1\\\", 1 while i\",\n \"\\\"1\\\", 1 while i < n: s = gen(s) i\",\n \"groupby(s)) s, i = \\\"1\\\", 1 while i < n:\",\n \"1 while i < n: s = gen(s) i +=\",\n \"countAndSay(self, n): def gen(s): return \\\"\\\".join(str(len(list(g))) + k for k,\",\n \"i < n: s = gen(s) i += 1 return\",\n \"def gen(s): return \\\"\\\".join(str(len(list(g))) + k for k, g in\",\n \"s, i = \\\"1\\\", 1 while i < n: s\",\n \"itertools import groupby class Solution: def countAndSay(self, n): def gen(s):\",\n \"gen(s): return \\\"\\\".join(str(len(list(g))) + k for k, g in groupby(s))\",\n \"i = \\\"1\\\", 1 while i < n: s =\",\n \"while i < n: s = gen(s) i += 1\",\n \"Solution: def countAndSay(self, n): def gen(s): return \\\"\\\".join(str(len(list(g))) + k\",\n \"for k, g in groupby(s)) s, i = \\\"1\\\", 1\",\n \"< n: s = gen(s) i += 1 return s\",\n \"k for k, g in groupby(s)) s, i = \\\"1\\\",\",\n \"from itertools import groupby class Solution: def countAndSay(self, n): def\",\n \"k, g in groupby(s)) s, i = \\\"1\\\", 1 while\",\n \"n): def gen(s): return \\\"\\\".join(str(len(list(g))) + k for k, g\",\n \"+ k for k, g in groupby(s)) s, i =\"\n]","isConversational":false}}},{"rowIdx":150,"cells":{"ngram":{"kind":"list like","value":["+ '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir","= model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 #","base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT","= model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file","-*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13","# path of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12'","+ '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction","model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量","of FeatureProject import pathlib import sys import os # base","BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir","# gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2]","@time :2019/5/10 9:13 # @author :Mo # @function :path of","model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file =","import sys import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent)","# 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len = 32","= str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir =","sys.path.append(projectdir) # path of BERT model model_dir = projectdir +","model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json'","+ '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name = model_dir","'/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes =","sys import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir)","ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt'","path of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name","@function :path of FeatureProject import pathlib import sys import os","pathlib import sys import os # base dir projectdir =","'/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir +","dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model","= model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' #","9:13 # @author :Mo # @function :path of FeatureProject import","+ '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes","# base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of",":path of FeatureProject import pathlib import sys import os #",":2019/5/10 9:13 # @author :Mo # @function :path of FeatureProject","import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) #","vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32","coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13 #","@author :Mo # @function :path of FeatureProject import pathlib import","UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13 # @author","# @time :2019/5/10 9:13 # @author :Mo # @function :path","# @author :Mo # @function :path of FeatureProject import pathlib","= projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name","FeatureProject import pathlib import sys import os # base dir","'/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name = model_dir +","config_name = model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt'","0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len =","model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率","# @function :path of FeatureProject import pathlib import sys import",":Mo # @function :path of FeatureProject import pathlib import sys","'/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction =","gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度","# -*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10","of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name =","-*- # !/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo","# !/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo #","import pathlib import sys import os # base dir projectdir","<filename>conf/feature_config.py # -*- coding: UTF-8 -*- # !/usr/bin/python # @time","model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir +","projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name =","projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir","= 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len","!/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo # @function","os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path","gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] #","str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir = projectdir"],"string":"[\n \"+ '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir\",\n \"= model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 #\",\n \"base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT\",\n \"= model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file\",\n \"-*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13\",\n \"# path of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12'\",\n \"+ '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction\",\n \"model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量\",\n \"of FeatureProject import pathlib import sys import os # base\",\n \"BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir\",\n \"# gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2]\",\n \"@time :2019/5/10 9:13 # @author :Mo # @function :path of\",\n \"model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file =\",\n \"import sys import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent)\",\n \"# 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len = 32\",\n \"= str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir =\",\n \"sys.path.append(projectdir) # path of BERT model model_dir = projectdir +\",\n \"model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json'\",\n \"+ '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name = model_dir\",\n \"'/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes =\",\n \"sys import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir)\",\n \"ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt'\",\n \"path of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name\",\n \"@function :path of FeatureProject import pathlib import sys import os\",\n \"pathlib import sys import os # base dir projectdir =\",\n \"'/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir +\",\n \"dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model\",\n \"= model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' #\",\n \"9:13 # @author :Mo # @function :path of FeatureProject import\",\n \"+ '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes\",\n \"# base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of\",\n \":path of FeatureProject import pathlib import sys import os #\",\n \":2019/5/10 9:13 # @author :Mo # @function :path of FeatureProject\",\n \"import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) #\",\n \"vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32\",\n \"coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13 #\",\n \"@author :Mo # @function :path of FeatureProject import pathlib import\",\n \"UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13 # @author\",\n \"# @time :2019/5/10 9:13 # @author :Mo # @function :path\",\n \"# @author :Mo # @function :path of FeatureProject import pathlib\",\n \"= projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name\",\n \"FeatureProject import pathlib import sys import os # base dir\",\n \"'/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name = model_dir +\",\n \"config_name = model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt'\",\n \"0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len =\",\n \"model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率\",\n \"# @function :path of FeatureProject import pathlib import sys import\",\n \":Mo # @function :path of FeatureProject import pathlib import sys\",\n \"'/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction =\",\n \"gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度\",\n \"# -*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10\",\n \"of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name =\",\n \"-*- # !/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo\",\n \"# !/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo #\",\n \"import pathlib import sys import os # base dir projectdir\",\n \"<filename>conf/feature_config.py # -*- coding: UTF-8 -*- # !/usr/bin/python # @time\",\n \"model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir +\",\n \"projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name =\",\n \"projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir\",\n \"= 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len\",\n \"!/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo # @function\",\n \"os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path\",\n \"gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] #\",\n \"str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir = projectdir\"\n]","isConversational":false}}},{"rowIdx":151,"cells":{"ngram":{"kind":"list like","value":["3, np.nan, 1, 4], [2, np.nan, 1, 3, 4, np.nan]","from hidrokit.prep import read import numpy as np import pandas","'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1],","= pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan,","A = pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan],","test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\"%m%d\") assert","[0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [],","for .prep.read module \"\"\" from hidrokit.prep import read import numpy","3, 4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F']","'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [], 'F':","1], 'E': [], 'F': [0, 2]} res_A_date = {'A': ['0618'],","'E', 'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A':","hidrokit.prep import read import numpy as np import pandas as",") A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1], 'B':","A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1], 'B': [2], 'C': [],","4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1, 4],","'F': [0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C':","= read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test","'C': [], 'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']}","'B': [2], 'C': [], 'D': [0, 1], 'E': [], 'F':","'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number():","\"\"\" from hidrokit.prep import read import numpy as np import","np import pandas as pd A = pd.DataFrame( data=[ [1,","[], 'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def","pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan, 2,","3, 4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1,","\"20190619\")) res_A_number = {'A': [1], 'B': [2], 'C': [], 'D':","'B', 'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\"))","['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [],","res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617',","A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1], 'B': [2],","['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items()","'0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number(): test =","np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2,","columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\"20190617\",",".prep.read module \"\"\" from hidrokit.prep import read import numpy as","[0, 1], 'E': [], 'F': [0, 2]} res_A_date = {'A':","test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def","'D': [0, 1], 'E': [], 'F': [0, 2]} res_A_date =","4], [2, np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B',","= {'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'],","read import numpy as np import pandas as pd A","'C': [], 'D': [0, 1], 'E': [], 'F': [0, 2]}","['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number(): test","[1], 'B': [2], 'C': [], 'D': [0, 1], 'E': [],","1, 4], [2, np.nan, 1, 3, 4, np.nan] ], columns=['A',","{'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E':","module \"\"\" from hidrokit.prep import read import numpy as np","2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [], 'D':","'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert","def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items()","test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date():","['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [], 'F': ['0617',","numpy as np import pandas as pd A = pd.DataFrame(","pd A = pd.DataFrame( data=[ [1, 3, 4, np.nan, 2,","'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number =","np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date","assert test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\"%m%d\")","1, 3, 4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E',","], columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date =","= {'A': [1], 'B': [2], 'C': [], 'D': [0, 1],","[1, 3, 4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan,","[], 'D': [0, 1], 'E': [], 'F': [0, 2]} res_A_date","[np.nan, 2, 3, np.nan, 1, 4], [2, np.nan, 1, 3,","as np import pandas as pd A = pd.DataFrame( data=[","def test_read_date(): test = read.missing_row(A_date, date_format=\"%m%d\") assert test.items() == res_A_date.items()","import read import numpy as np import pandas as pd","data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan, 2, 3,","np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2, np.nan, 1,","[2, np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B', 'C',","\"\"\"Test for .prep.read module \"\"\" from hidrokit.prep import read import","import pandas as pd A = pd.DataFrame( data=[ [1, 3,","'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number","{'A': [1], 'B': [2], 'C': [], 'D': [0, 1], 'E':","[], 'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False)","4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F'] )","res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\"%m%d\") assert test.items() ==","[2], 'C': [], 'D': [0, 1], 'E': [], 'F': [0,","res_A_number = {'A': [1], 'B': [2], 'C': [], 'D': [0,","np.nan, 1, 4], [2, np.nan, 1, 3, 4, np.nan] ],","2, np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2, np.nan,","'E': [], 'F': [0, 2]} res_A_date = {'A': ['0618'], 'B':","pandas as pd A = pd.DataFrame( data=[ [1, 3, 4,","'E': [], 'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A,","2, 3, np.nan, 1, 4], [2, np.nan, 1, 3, 4,","[], 'F': [0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'],","= A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1], 'B': [2], 'C':","as pd A = pd.DataFrame( data=[ [1, 3, 4, np.nan,","date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date,","import numpy as np import pandas as pd A =","'0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() ==","np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B', 'C', 'D',","read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test =","== res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\"%m%d\") assert test.items()"],"string":"[\n \"3, np.nan, 1, 4], [2, np.nan, 1, 3, 4, np.nan]\",\n \"from hidrokit.prep import read import numpy as np import pandas\",\n \"'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A': [1],\",\n \"= pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan,\",\n \"A = pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan],\",\n \"test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\\\"%m%d\\\") assert\",\n \"[0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [],\",\n \"for .prep.read module \\\"\\\"\\\" from hidrokit.prep import read import numpy\",\n \"3, 4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F']\",\n \"'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [], 'F':\",\n \"1], 'E': [], 'F': [0, 2]} res_A_date = {'A': ['0618'],\",\n \"'E', 'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A':\",\n \"hidrokit.prep import read import numpy as np import pandas as\",\n \") A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A': [1], 'B':\",\n \"A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A': [1], 'B': [2], 'C': [],\",\n \"4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1, 4],\",\n \"'F': [0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C':\",\n \"= read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test\",\n \"'C': [], 'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']}\",\n \"'B': [2], 'C': [], 'D': [0, 1], 'E': [], 'F':\",\n \"'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number():\",\n \"\\\"\\\"\\\" from hidrokit.prep import read import numpy as np import\",\n \"np import pandas as pd A = pd.DataFrame( data=[ [1,\",\n \"[], 'D': ['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def\",\n \"pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan, 2,\",\n \"3, 4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1,\",\n \"\\\"20190619\\\")) res_A_number = {'A': [1], 'B': [2], 'C': [], 'D':\",\n \"'B', 'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\"))\",\n \"['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [],\",\n \"res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617',\",\n \"A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A': [1], 'B': [2],\",\n \"['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items()\",\n \"'0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number(): test =\",\n \"np.nan, 2, np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2,\",\n \"columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\",\",\n \".prep.read module \\\"\\\"\\\" from hidrokit.prep import read import numpy as\",\n \"[0, 1], 'E': [], 'F': [0, 2]} res_A_date = {'A':\",\n \"test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def\",\n \"'D': [0, 1], 'E': [], 'F': [0, 2]} res_A_date =\",\n \"4], [2, np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B',\",\n \"= {'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'],\",\n \"read import numpy as np import pandas as pd A\",\n \"'C': [], 'D': [0, 1], 'E': [], 'F': [0, 2]}\",\n \"['0617', '0618'], 'E': [], 'F': ['0617', '0619']} def test_read_number(): test\",\n \"[1], 'B': [2], 'C': [], 'D': [0, 1], 'E': [],\",\n \"1, 4], [2, np.nan, 1, 3, 4, np.nan] ], columns=['A',\",\n \"{'A': ['0618'], 'B': ['0619'], 'C': [], 'D': ['0617', '0618'], 'E':\",\n \"module \\\"\\\"\\\" from hidrokit.prep import read import numpy as np\",\n \"2]} res_A_date = {'A': ['0618'], 'B': ['0619'], 'C': [], 'D':\",\n \"'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert\",\n \"def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items()\",\n \"test = read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date():\",\n \"['0619'], 'C': [], 'D': ['0617', '0618'], 'E': [], 'F': ['0617',\",\n \"numpy as np import pandas as pd A = pd.DataFrame(\",\n \"pd A = pd.DataFrame( data=[ [1, 3, 4, np.nan, 2,\",\n \"'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number =\",\n \"np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date\",\n \"assert test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\\\"%m%d\\\")\",\n \"1, 3, 4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E',\",\n \"], columns=['A', 'B', 'C', 'D', 'E', 'F'] ) A_date =\",\n \"= {'A': [1], 'B': [2], 'C': [], 'D': [0, 1],\",\n \"[1, 3, 4, np.nan, 2, np.nan], [np.nan, 2, 3, np.nan,\",\n \"[], 'D': [0, 1], 'E': [], 'F': [0, 2]} res_A_date\",\n \"[np.nan, 2, 3, np.nan, 1, 4], [2, np.nan, 1, 3,\",\n \"as np import pandas as pd A = pd.DataFrame( data=[\",\n \"def test_read_date(): test = read.missing_row(A_date, date_format=\\\"%m%d\\\") assert test.items() == res_A_date.items()\",\n \"import read import numpy as np import pandas as pd\",\n \"data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan, 2, 3,\",\n \"np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2, np.nan, 1,\",\n \"[2, np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B', 'C',\",\n \"\\\"\\\"\\\"Test for .prep.read module \\\"\\\"\\\" from hidrokit.prep import read import\",\n \"import pandas as pd A = pd.DataFrame( data=[ [1, 3,\",\n \"'C', 'D', 'E', 'F'] ) A_date = A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number\",\n \"{'A': [1], 'B': [2], 'C': [], 'D': [0, 1], 'E':\",\n \"[], 'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A, date_index=False)\",\n \"4, np.nan] ], columns=['A', 'B', 'C', 'D', 'E', 'F'] )\",\n \"res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\\\"%m%d\\\") assert test.items() ==\",\n \"[2], 'C': [], 'D': [0, 1], 'E': [], 'F': [0,\",\n \"res_A_number = {'A': [1], 'B': [2], 'C': [], 'D': [0,\",\n \"np.nan, 1, 4], [2, np.nan, 1, 3, 4, np.nan] ],\",\n \"2, np.nan], [np.nan, 2, 3, np.nan, 1, 4], [2, np.nan,\",\n \"'E': [], 'F': [0, 2]} res_A_date = {'A': ['0618'], 'B':\",\n \"pandas as pd A = pd.DataFrame( data=[ [1, 3, 4,\",\n \"'E': [], 'F': ['0617', '0619']} def test_read_number(): test = read.missing_row(A,\",\n \"2, 3, np.nan, 1, 4], [2, np.nan, 1, 3, 4,\",\n \"[], 'F': [0, 2]} res_A_date = {'A': ['0618'], 'B': ['0619'],\",\n \"= A.set_index(pd.date_range(\\\"20190617\\\", \\\"20190619\\\")) res_A_number = {'A': [1], 'B': [2], 'C':\",\n \"as pd A = pd.DataFrame( data=[ [1, 3, 4, np.nan,\",\n \"date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test = read.missing_row(A_date,\",\n \"import numpy as np import pandas as pd A =\",\n \"'0619']} def test_read_number(): test = read.missing_row(A, date_index=False) assert test.items() ==\",\n \"np.nan, 1, 3, 4, np.nan] ], columns=['A', 'B', 'C', 'D',\",\n \"read.missing_row(A, date_index=False) assert test.items() == res_A_number.items() def test_read_date(): test =\",\n \"== res_A_number.items() def test_read_date(): test = read.missing_row(A_date, date_format=\\\"%m%d\\\") assert test.items()\"\n]","isConversational":false}}},{"rowIdx":152,"cells":{"ngram":{"kind":"list like","value":["department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET'])","= DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments()","app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import","delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def","@department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id): return department_controller.update_department(department_id)","@Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE'])","import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix)","return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id)","@swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def","methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department')","department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id): return","def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml')","Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix","department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return","methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin')","url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller =","import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth","DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller","BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix =","department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/',","department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return","get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def","app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__,","'{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/',","def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id):","= Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department')","@swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required'])","DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>',","@Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional',","from flasgger import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request,","create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return","swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from","@department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET'])","@department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST'])","def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id):","@Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin')","return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id):","Security, Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint","flasgger import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security,","@swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def","department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>',","@swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional'])","return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department():","import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController","Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint =","url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments():","= '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request)","from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department',","request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix)","__name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def","methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>',","def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml')","'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml')","@Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name',","list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return","return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id)","@Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml')","@department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH'])","@department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department()","from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller","methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin')","Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml')"],"string":"[\n \"department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET'])\",\n \"= DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments()\",\n \"app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import\",\n \"delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def\",\n \"@department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id): return department_controller.update_department(department_id)\",\n \"@Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE'])\",\n \"import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix)\",\n \"return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id)\",\n \"@swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def\",\n \"methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department')\",\n \"department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id): return\",\n \"def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml')\",\n \"Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix\",\n \"department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return\",\n \"methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin')\",\n \"url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller =\",\n \"import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth\",\n \"DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller\",\n \"BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix =\",\n \"department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/',\",\n \"department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return\",\n \"get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def\",\n \"app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__,\",\n \"'{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/',\",\n \"def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id):\",\n \"= Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department')\",\n \"@swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required'])\",\n \"DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>',\",\n \"@Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional',\",\n \"from flasgger import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request,\",\n \"create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return\",\n \"swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from\",\n \"@department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET'])\",\n \"@department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST'])\",\n \"def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id):\",\n \"@Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin')\",\n \"return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml') def update_department(department_id):\",\n \"Security, Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint\",\n \"flasgger import swag_from from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security,\",\n \"@swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def\",\n \"department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>',\",\n \"@swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional'])\",\n \"return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department():\",\n \"import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller import DepartmentController\",\n \"Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint =\",\n \"url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments():\",\n \"= '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request)\",\n \"from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix) department_blueprint = Blueprint('department',\",\n \"request, Security, Auth from app.controllers.department_controller import DepartmentController url_prefix = '{}/departments'.format(BaseBlueprint.base_url_prefix)\",\n \"__name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def\",\n \"methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>',\",\n \"def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH']) @Auth.has_role('admin') @Security.validator(['name|optional', 'description|optional']) @swag_from('documentation/update_department.yml')\",\n \"'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department() @department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml')\",\n \"@Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name',\",\n \"list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return\",\n \"return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id)\",\n \"@Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml') def list_departments(): return department_controller.list_departments() @department_blueprint.route('/<int:department_id>', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml')\",\n \"@department_blueprint.route('/<int:department_id>', methods=['DELETE']) @Auth.has_role('admin') @swag_from('documentation/delete_department.yml') def delete_department(department_id): return department_controller.delete_department(department_id) @department_blueprint.route('/<int:department_id>', methods=['PATCH'])\",\n \"@department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin') @Security.validator(['name|required:ifExists_Department_name', 'description|required']) @swag_from('documentation/create_department.yml') def create_department(): return department_controller.create_department()\",\n \"from app.blueprints.base_blueprint import Blueprint, BaseBlueprint, request, Security, Auth from app.controllers.department_controller\",\n \"methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_single_department.yml') def get_department(department_id): return department_controller.get_department(department_id) @department_blueprint.route('/', methods=['POST']) @Auth.has_role('admin')\",\n \"Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swag_from('documentation/get_all_departments.yml')\"\n]","isConversational":false}}},{"rowIdx":153,"cells":{"ngram":{"kind":"list like","value":["g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0,","+ '%d.png' % iter) plt.close() else: if validate_num < validate_path.__len__():","<reponame>ikecoglu/DL-SR import argparse from keras import optimizers import matplotlib.pyplot as","import matplotlib.pyplot as plt import numpy as np import datetime","+ '-SISR_' + data_name cur_data_loader = data_loader train_images_path = data_dir","+ data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/'","import tensorflow as tf from models import * from utils.lr_controller","# if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse)","datetime.datetime.now() - start_time print(\"%d epoch: time: %s, g_loss = %s\"","ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) # if","save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time","'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr","model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path =","not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- #","data_dir + '/training/' validate_images_path = data_dir + '/validate/' save_weights_path =","if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs,","'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist, load weights","cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output =","import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader,","verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path =","' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path","optimizers import matplotlib.pyplot as plt import numpy as np import","train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g,","validate_path.sort() if sample == 1: r, c = 3, 3","'*') validate_path.sort() if sample == 1: r, c = 3,","if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) >","optimizer_name = args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"]","+ '*') validate_path.sort() if sample == 1: r, c =","1: save_weights_name = model_name + '-SISR_' + data_name cur_data_loader =","SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image in","summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() #","output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path","from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp from","(mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]):","ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig,","% iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path =","0: images_path = glob.glob(train_images_path + '/*') Validate(it + 1, sample=1)","default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1)","= 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f'","np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses,","= cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator","= args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights =","# train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path,","= argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int,","patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min',","os import tensorflow as tf from models import * from","= [], [], [], [] img_show, gt_show, output_show = [],","patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # --------------------------------------------------------------------------------","np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback,","about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if","parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\",","beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g","min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard #","= tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag =","'weights.best') print('Loading weights successfully: ' + save_weights_path + 'weights.best') elif","output_show = [], [], [] validate_path = np.random.choice(validate_path, size=r) for","norm_flag = args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights","+ '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path","= TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM',","'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' + save_weights_path","= names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and","np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if","best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses):","1, elapsed_time, loss_generator)) if (it + 1) % sample_interval ==","examples fig, axs = plt.subplots(r, c) cnt = 0 for","import glob import os import tensorflow as tf from models","3, 3 mses, nrmses, psnrs, ssims = [], [], [],","plt import numpy as np import datetime from keras.callbacks import","['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary","nrmses, psnrs, ssims = [], [], [], [] img_show, gt_show,","start_time print(\"%d epoch: time: %s, g_loss = %s\" % (it","= args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name =","= args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor =","+ '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir","default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128)","img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int,","= np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output, mses,","+ '/*') for it in range(iterations): # ------------------------------------ # train","= [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it","parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\",","# define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width,","type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int,","validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1:","modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10,","weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path +","+ 'weights.best') print('Loading weights successfully: ' + save_weights_path + 'weights.best')","successfully: ' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training","validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img))","np.random.choice(validate_path, size=r) for path in validate_path: [img, gt] = cur_data_loader([path],","= plt.subplots(r, c) cnt = 0 for row in range(r):","parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\",","patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator)","% validate_interval == 0: Validate(it + 1, sample=0) write_log(callback, train_names,","validate_interval == 0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record),","g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' + save_weights_path +","= %s\" % (it + 1, elapsed_time, loss_generator)) if (it","-------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height,","Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path =","+ save_weights_name + '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path","default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500)","PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show,","= args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num =","data_dir + '/validate_wf/' else: save_weights_name = model_name + '-SIM_' +","psnrs, ssims = [], [], [], [] for path in","if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading","in range(iterations): # ------------------------------------ # train generator # ------------------------------------ input_g,","type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str,","1: r, c = 3, 3 mses, nrmses, psnrs, ssims","args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval","logs, batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value =","if (it + 1) % sample_interval == 0: images_path =","output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) #","= data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else:","0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it +","train_images_path = data_dir + '/training/' validate_images_path = data_dir + '/validate/'","cnt = 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f,","gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\"%d epoch: time:","import data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss import","os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names =","== 1: save_weights_name = model_name + '-SISR_' + data_name cur_data_loader","keras.callbacks import TensorBoard import glob import os import tensorflow as","< validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims =","# -------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g =","model_name + '-SISR_' + data_name cur_data_loader = data_loader train_images_path =","= save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not","# show some examples fig, axs = plt.subplots(r, c) cnt","glob import os import tensorflow as tf from models import","models import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import","keras import optimizers import matplotlib.pyplot as plt import numpy as","for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path,","iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) #","weights successfully: ' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- #","default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args = parser.parse_args() gpu_id = str(args.gpu_id)","save_weights_dir + '/' + save_weights_name + '/' train_gt_path = data_dir","glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if (it +","from models import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader","= datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path","type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int,","glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1: r, c","type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int,","and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path","args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width","norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now()","3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs =","train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/'","= datetime.datetime.now() - start_time print(\"%d epoch: time: %s, g_loss =","1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims","-------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best')","default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0)","data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path =","iterations = args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name","weights successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path +","type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int,","modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # --------------------------------------------------------------------------------","log_path = save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback","parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args","parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\",","0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' %","ssims) # if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if","g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g,","= img_comp(gt, output, mses, nrmses, psnrs, ssims) # if best,","load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'):","patch_height = args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor","= modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor,","iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback,","training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse","# ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width,","image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt","args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)","def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if","output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output,","psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row]))","Validate(it + 1, sample=1) if (it + 1) % validate_interval","default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5)","psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img,","plt.close() else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses,","validate_images_path = data_dir + '/validate_wf/' else: save_weights_name = model_name +","# select models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN':","'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter)","default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4)","time: %s, g_loss = %s\" % (it + 1, elapsed_time,","+ 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path)","= data_dir + '/validate_wf/' else: save_weights_name = model_name + '-SIM_'","write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2],","utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss","= args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor =","r, c = 3, 3 mses, nrmses, psnrs, ssims =","load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights","Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample","not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer","cnt += 1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else:","'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now()","loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\"%d epoch: time: %s,","+ 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses),","utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\",","default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\")","input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4,","[], [], [] img_show, gt_show, output_show = [], [], []","= 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback,","weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path","default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000)","+ '/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name +","load_weights = args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval","'/*') for it in range(iterations): # ------------------------------------ # train generator","------------------------------------ # train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path,","1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if validate_num","for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row],","== 1: r, c = 3, 3 mses, nrmses, psnrs,","gt_show, output_show = [], [], [] validate_path = np.random.choice(validate_path, size=r)","train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/'","optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr","default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000)","> np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses))","(it + 1) % validate_interval == 0: Validate(it + 1,","[], [], [], [] for path in validate_path: [img, gt]","psnrs, ssims = [], [], [], [] img_show, gt_show, output_show","# -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path","[img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag,","parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\",","g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best')","save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr","+ 1, sample=1) if (it + 1) % validate_interval ==","optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name]","patch_width = args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag","np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter)","write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist, load","range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for","= ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no):","iter) # -------------------------------------------------------------------------------- # if exist, load weights # --------------------------------------------------------------------------------","epoch: time: %s, g_loss = %s\" % (it + 1,","input_channels = args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num","# -------------------------------------------------------------------------------- # if exist, load weights # -------------------------------------------------------------------------------- if","args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag","beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model # --------------------------------------------------------------------------------","sample == 1: r, c = 3, 3 mses, nrmses,","callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate #","parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\",","val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr,","col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' %","default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9)","from keras import optimizers import matplotlib.pyplot as plt import numpy","gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor)","summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample","sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample ==","save_weights_name = model_name + '-SISR_' + data_name cur_data_loader = data_loader","[np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it in","import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import","args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations = args.iterations","'/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path +","= data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name +","curlr, iter) # -------------------------------------------------------------------------------- # if exist, load weights #","validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1,","+ 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading","validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims","= ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1,","-------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path +","select models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN}","curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1],","+ '/*') Validate(it + 1, sample=1) if (it + 1)","1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image","exist, load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path +","parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\",","elapsed_time, loss_generator)) if (it + 1) % sample_interval == 0:","if sample == 1: r, c = 3, 3 mses,","= parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training =","else: save_weights_name = model_name + '-SIM_' + data_name cur_data_loader =","mses, nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs,","[], [], [] validate_path = np.random.choice(validate_path, size=r) for path in","= args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations =","fig, axs = plt.subplots(r, c) cnt = 0 for row","if (it + 1) % validate_interval == 0: Validate(it +","= tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1:","+ 1) % sample_interval == 0: images_path = glob.glob(train_images_path +","ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3)))","parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\",","psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path + 'weights.latest')","data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path","train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path,","+ data_name cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/'","if exist, load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path","if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully:","summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush()","= model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path","= args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options =","optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model #","col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' % iter) plt.close()","'/validate_wf/' else: save_weights_name = model_name + '-SIM_' + data_name cur_data_loader","1) % validate_interval == 0: Validate(it + 1, sample=0) write_log(callback,","args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor","tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name =","factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) #","as plt import numpy as np import datetime from keras.callbacks","+ 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g)","write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # --------------------------------------------------------------------------------","ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]): axs[row,","nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims)","= data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path","tf from models import * from utils.lr_controller import ReduceLROnPlateau from","lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr *","args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor","= validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [], [],","'%d.png' % iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path","path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height,","validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/'","scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt,","'-SISR_' + data_name cur_data_loader = data_loader train_images_path = data_dir +","def write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value =","TensorBoard import glob import os import tensorflow as tf from","scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() -","cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path =","default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args = parser.parse_args()","parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\",","= args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir =","[] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*')","size=r) for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path,","+ 'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time =","{'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999)","mses, nrmses, psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path","parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\",","validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir + '/'","sample=1) if (it + 1) % validate_interval == 0: Validate(it","cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard","= data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if","mses, nrmses, psnrs, ssims = [], [], [], [] for","= cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output","data_name cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/' validate_images_path","utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils","batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate # --------------------------------------------------------------------------------","parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args = parser.parse_args() gpu_id","= logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # --------------------------------------------------------------------------------","img_show, gt_show, output_show = [], [], [] validate_path = np.random.choice(validate_path,","batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs","= str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval =","# -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path +","= args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size =","lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it in range(iterations):","import TensorBoard import glob import os import tensorflow as tf","save_weights_path = save_weights_dir + '/' + save_weights_name + '/' train_gt_path","tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag = names","= summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no)","col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off')","'/*') Validate(it + 1, sample=1) if (it + 1) %","str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir","import argparse from keras import optimizers import matplotlib.pyplot as plt","cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator =","type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str,","it in range(iterations): # ------------------------------------ # train generator # ------------------------------------","range(iterations): # ------------------------------------ # train generator # ------------------------------------ input_g, gt_g","summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary,","= optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model","os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))","1) % sample_interval == 0: images_path = glob.glob(train_images_path + '/*')","= img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt))","os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names","= args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels =","+ '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path):","g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' + save_weights_path +","default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\")","-------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g =","mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval","'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights","args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num","row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row],","[], [], [], [] img_show, gt_show, output_show = [], [],","- start_time print(\"%d epoch: time: %s, g_loss = %s\" %","in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt +=","nrmses, psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path +","= str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir =","if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names =","data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path = data_dir +","iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist,","names, logs, batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value","% (mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show,","# -------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def Validate(iter,","== 0: images_path = glob.glob(train_images_path + '/*') Validate(it + 1,","= g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\"%d","'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names","input_channels == 1: save_weights_name = model_name + '-SISR_' + data_name","for col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row,","import optimizers import matplotlib.pyplot as plt import numpy as np","'/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name + '/'","validate_path = np.random.choice(validate_path, size=r) for path in validate_path: [img, gt]","ssims = [], [], [], [] img_show, gt_show, output_show =","------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size,","+ '/training/' validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir","[] img_show, gt_show, output_show = [], [], [] validate_path =","type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int,","% sample_interval == 0: images_path = glob.glob(train_images_path + '/*') Validate(it","save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path","= args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height =","sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options","data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss import loss_mse_ssim","loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3)","args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training","default=\"adam\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction","os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: '","+ 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' +","gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs = plt.subplots(r,","loss_generator)) if (it + 1) % sample_interval == 0: images_path","axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col,","1, sample=1) if (it + 1) % validate_interval == 0:","# -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not os.path.exists(log_path):","start_time = datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin()","= args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag =","= glob.glob(train_images_path + '/*') for it in range(iterations): # ------------------------------------","default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\")","iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num]","argparse from keras import optimizers import matplotlib.pyplot as plt import","mses, nrmses, psnrs, ssims = [], [], [], [] img_show,","validate_num = args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name","for it in range(iterations): # ------------------------------------ # train generator #","# about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph'","= glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1: r,","DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) #","+ 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses))","-------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path)","data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name","validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [],","save_weights_name + '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path =","= data_dir + '/validate/' save_weights_path = save_weights_dir + '/' +","[], [] for path in validate_path: [img, gt] = cur_data_loader([path],","= args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name =","data_loader train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir +","= [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path +","if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr =","np import datetime from keras.callbacks import TensorBoard import glob import","save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path)","batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time =","sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if","img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output)","datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path =","axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png'","ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp","glob.glob(train_images_path + '/*') for it in range(iterations): # ------------------------------------ #","input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag,","in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row]))","args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training","val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs),","from utils.utils import img_comp from utils.loss import loss_mse_ssim parser =","gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor)","argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1)","parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\",","args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size","print(\"%d epoch: time: %s, g_loss = %s\" % (it +","Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not","os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select","as tf from models import * from utils.lr_controller import ReduceLROnPlateau","parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\",","gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels ==","# ------------------------------------ # train generator # ------------------------------------ input_g, gt_g =","= gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if","parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\",","+ '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name =","type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int,","'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names,","+ save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training # --------------------------------------------------------------------------------","modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define","%s\" % (it + 1, elapsed_time, loss_generator)) if (it +","# Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path","-------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort()","'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value","nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some","args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height","gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir","train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def","fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if validate_num <","validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses,","+ '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir","import img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\",","type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str,","= args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval =","validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for","'/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name = model_name","enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1","psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples","not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss'","# -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record","g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0],","utils.utils import img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser()","[] validate_path = np.random.choice(validate_path, size=r) for path in validate_path: [img,","[], [], [] for path in validate_path: [img, gt] =","mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show","= mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name","type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int,","'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary = tf.Summary()","models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN","validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [], [], []","elapsed_time = datetime.datetime.now() - start_time print(\"%d epoch: time: %s, g_loss","parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\",","* from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel","Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1)","validate_interval = args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor","os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1]","[], [] img_show, gt_show, output_show = [], [], [] validate_path","print('Loading weights successfully: ' + save_weights_path + 'weights.latest') # --------------------------------------------------------------------------------","val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- #","save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path):","successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'):","parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\",","# -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller","# training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = []","import numpy as np import datetime from keras.callbacks import TensorBoard","'/training/' validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir +","min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about","lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter)","%s, g_loss = %s\" % (it + 1, elapsed_time, loss_generator))","= {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9,","patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time","start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width","data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size","else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses,","TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR',","some examples fig, axs = plt.subplots(r, c) cnt = 0","parser.add_argument(\"--scale_factor\", type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\",","parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction","model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g)","* 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # --------------------------------------------------------------------------------","-------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0):","min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter,","os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer # --------------------------------------------------------------------------------","+ 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' +","optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined","from keras.callbacks import TensorBoard import glob import os import tensorflow","in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width,","output_show.append(output) # show some examples fig, axs = plt.subplots(r, c)","type=str, default=\"adam\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction =","modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr,","elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully:","args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name","= glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if (it","args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights = args.load_weights","-------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller =","data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name + '-SISR_'","sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record = []","g_loss = %s\" % (it + 1, elapsed_time, loss_generator)) if","callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def","parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training)","val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs,","os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: '","default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int, default=9) parser.add_argument(\"--scale_factor\", type=int, default=2)","gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels","plt.subplots(r, c) cnt = 0 for row in range(r): axs[row,","gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path +","c = 3, 3 mses, nrmses, psnrs, ssims = [],","matplotlib.pyplot as plt import numpy as np import datetime from","np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter)","data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not","write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3],","cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/' validate_images_path =","default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000)","from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1)","numpy as np import datetime from keras.callbacks import TensorBoard import","patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses,","tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name","default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128)","# if exist, load weights # -------------------------------------------------------------------------------- if load_weights: if","nrmses, psnrs, ssims = [], [], [], [] for path","+ save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path +","save_weights_name = model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel","[] for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path,","'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) #","-------------------------------------------------------------------------------- # if exist, load weights # -------------------------------------------------------------------------------- if load_weights:","callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE']","-------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf]","tensorflow as tf from models import * from utils.lr_controller import","images_path = glob.glob(train_images_path + '/*') for it in range(iterations): #","(it + 1) % sample_interval == 0: images_path = glob.glob(train_images_path","= [], [], [] validate_path = np.random.choice(validate_path, size=r) for path","args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir","'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary =","img_comp(gt, output, mses, nrmses, psnrs, ssims) # if best, save","save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest')","[], [] validate_path = np.random.choice(validate_path, size=r) for path in validate_path:","import os import tensorflow as tf from models import *","type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args = parser.parse_args() gpu_id =","args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id","generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height,","default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float, default=0.3) parser.add_argument(\"--mixed_precision_training\", type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\")","norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims =","as np import datetime from keras.callbacks import TensorBoard import glob","default=1000) parser.add_argument(\"--validate_interval\", type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4)","args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels = args.input_channels","'/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path)","-------------------------------------------------------------------------------- # select models and optimizer # -------------------------------------------------------------------------------- modelFns =","show some examples fig, axs = plt.subplots(r, c) cnt =","type=int, default=1) parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str,","'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' + save_weights_path","validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path +","= lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims),","write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr',","output, mses, nrmses, psnrs, ssims) # if best, save weights.best","+ 'weights.latest') print('Loading weights successfully: ' + save_weights_path + 'weights.latest')","data_loader_multi_channel from utils.utils import img_comp from utils.loss import loss_mse_ssim parser","define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels))","type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int,","+ 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record","mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name =","logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- #","= args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr =","datetime from keras.callbacks import TensorBoard import glob import os import","validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [],","parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\",","np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback,","== 0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it","lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels","data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name","mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- #","str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval","iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback,","args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name","+ 1, elapsed_time, loss_generator)) if (it + 1) % sample_interval","type=int, default=2) parser.add_argument(\"--norm_flag\", type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int,","callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE',","% (it + 1, elapsed_time, loss_generator)) if (it + 1)","type=int, default=2000) parser.add_argument(\"--validate_num\", type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float,","= save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback =","type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\", type=int, default=128) parser.add_argument(\"--patch_width\", type=int, default=128) parser.add_argument(\"--input_channels\", type=int,","ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1)","write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value = summary.value.add()","+ 1) % validate_interval == 0: Validate(it + 1, sample=0)","'/' train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir +","os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer #","summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag","np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter)","combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim,","loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time","import datetime from keras.callbacks import TensorBoard import glob import os","c) cnt = 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f,","if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and","data_dir + '/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name","import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\"--gpu_id\", type=int, default=1) parser.add_argument(\"--gpu_memory_fraction\", type=float,","'weights.latest') print('Loading weights successfully: ' + save_weights_path + 'weights.latest') #","model_name = args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"]","gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir","+ '/validate_wf/' else: save_weights_name = model_name + '-SIM_' + data_name","= save_weights_dir + '/' + save_weights_name + '/' train_gt_path =","os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models","+= 1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if","val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses),","axs = plt.subplots(r, c) cnt = 0 for row in","validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback,","loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path","sample_interval == 0: images_path = glob.glob(train_images_path + '/*') Validate(it +","0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path","g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\"%d epoch:","img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs","= model_name + '-SISR_' + data_name cur_data_loader = data_loader train_images_path","= [], [], [], [] for path in validate_path: [img,","patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs,","ssims = [], [], [], [] for path in validate_path:","if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # --------------------------------------------------------------------------------","print('Loading weights successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path","scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations","type=int, default=500) parser.add_argument(\"--batch_size\", type=int, default=4) parser.add_argument(\"--start_lr\", type=float, default=1e-4) parser.add_argument(\"--lr_decay_factor\", type=float,","batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height","'-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir +","' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training #","parser.add_argument(\"--data_dir\", type=str, default=\"../dataset/train/F-actin\") parser.add_argument(\"--save_weights_dir\", type=str, default=\"../trained_models\") parser.add_argument(\"--model_name\", type=str, default=\"DFCAN\") parser.add_argument(\"--patch_height\",","train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g)","= np.random.choice(validate_path, size=r) for path in validate_path: [img, gt] =","type=int, default=1) parser.add_argument(\"--iterations\", type=int, default=1000000) parser.add_argument(\"--sample_interval\", type=int, default=1000) parser.add_argument(\"--validate_interval\", type=int,","= args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] =","# -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g","(it + 1, elapsed_time, loss_generator)) if (it + 1) %","psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) #","3 mses, nrmses, psnrs, ssims = [], [], [], []","type=float, default=0.5) parser.add_argument(\"--load_weights\", type=int, default=0) parser.add_argument(\"--optimizer_name\", type=str, default=\"adam\") args =","= data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path = data_dir","= args.model_name sample_interval = args.sample_interval os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION\"] = mixed_precision_training os.environ[\"CUDA_VISIBLE_DEVICES\"] =","from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from","-------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record =","= modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- #","if input_channels == 1: save_weights_name = model_name + '-SISR_' +","names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate","# -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse =","axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' % iter)","images_path = glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if","# -------------------------------------------------------------------------------- # select models and optimizer # -------------------------------------------------------------------------------- modelFns","and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN =","'/' + save_weights_name + '/' train_gt_path = data_dir + '/training_gt/'","+ '/' + save_weights_name + '/' train_gt_path = data_dir +","= 3, 3 mses, nrmses, psnrs, ssims = [], [],","1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record =","= data_dir + '/training/' validate_images_path = data_dir + '/validate/' save_weights_path","= args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width =","= data_loader train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir","args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr = args.start_lr","# -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*')"],"string":"[\n \"g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0,\",\n \"+ '%d.png' % iter) plt.close() else: if validate_num < validate_path.__len__():\",\n \"<reponame>ikecoglu/DL-SR import argparse from keras import optimizers import matplotlib.pyplot as\",\n \"import matplotlib.pyplot as plt import numpy as np import datetime\",\n \"+ '-SISR_' + data_name cur_data_loader = data_loader train_images_path = data_dir\",\n \"+ data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/'\",\n \"import tensorflow as tf from models import * from utils.lr_controller\",\n \"# if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse)\",\n \"datetime.datetime.now() - start_time print(\\\"%d epoch: time: %s, g_loss = %s\\\"\",\n \"ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) # if\",\n \"save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time\",\n \"'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr\",\n \"model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path =\",\n \"not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- #\",\n \"data_dir + '/training/' validate_images_path = data_dir + '/validate/' save_weights_path =\",\n \"if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs,\",\n \"'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist, load weights\",\n \"cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output =\",\n \"import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader,\",\n \"verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path =\",\n \"' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path\",\n \"optimizers import matplotlib.pyplot as plt import numpy as np import\",\n \"train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g,\",\n \"validate_path.sort() if sample == 1: r, c = 3, 3\",\n \"'*') validate_path.sort() if sample == 1: r, c = 3,\",\n \"if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) >\",\n \"optimizer_name = args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"]\",\n \"+ '*') validate_path.sort() if sample == 1: r, c =\",\n \"1: save_weights_name = model_name + '-SISR_' + data_name cur_data_loader =\",\n \"SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image in\",\n \"summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() #\",\n \"output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path\",\n \"from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp from\",\n \"(mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]):\",\n \"ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig,\",\n \"% iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path =\",\n \"0: images_path = glob.glob(train_images_path + '/*') Validate(it + 1, sample=1)\",\n \"default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1)\",\n \"= 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f'\",\n \"np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses,\",\n \"= cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator\",\n \"= args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights =\",\n \"# train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path,\",\n \"= argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int,\",\n \"patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min',\",\n \"os import tensorflow as tf from models import * from\",\n \"= [], [], [], [] img_show, gt_show, output_show = [],\",\n \"patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # --------------------------------------------------------------------------------\",\n \"np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback,\",\n \"about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if\",\n \"parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\",\",\n \"beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g\",\n \"min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard #\",\n \"= tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag =\",\n \"'weights.best') print('Loading weights successfully: ' + save_weights_path + 'weights.best') elif\",\n \"output_show = [], [], [] validate_path = np.random.choice(validate_path, size=r) for\",\n \"norm_flag = args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights\",\n \"+ '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path\",\n \"= TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM',\",\n \"'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' + save_weights_path\",\n \"= names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and\",\n \"np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if\",\n \"best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses):\",\n \"1, elapsed_time, loss_generator)) if (it + 1) % sample_interval ==\",\n \"examples fig, axs = plt.subplots(r, c) cnt = 0 for\",\n \"import glob import os import tensorflow as tf from models\",\n \"3, 3 mses, nrmses, psnrs, ssims = [], [], [],\",\n \"plt import numpy as np import datetime from keras.callbacks import\",\n \"['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary\",\n \"nrmses, psnrs, ssims = [], [], [], [] img_show, gt_show,\",\n \"start_time print(\\\"%d epoch: time: %s, g_loss = %s\\\" % (it\",\n \"= args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name =\",\n \"= args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor =\",\n \"+ '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir\",\n \"default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128)\",\n \"img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int,\",\n \"= np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output, mses,\",\n \"+ '/*') for it in range(iterations): # ------------------------------------ # train\",\n \"= [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it\",\n \"parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\",\",\n \"# define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width,\",\n \"type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int,\",\n \"validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1:\",\n \"modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10,\",\n \"weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path +\",\n \"+ 'weights.best') print('Loading weights successfully: ' + save_weights_path + 'weights.best')\",\n \"successfully: ' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training\",\n \"validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img))\",\n \"np.random.choice(validate_path, size=r) for path in validate_path: [img, gt] = cur_data_loader([path],\",\n \"= plt.subplots(r, c) cnt = 0 for row in range(r):\",\n \"parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\",\",\n \"patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator)\",\n \"% validate_interval == 0: Validate(it + 1, sample=0) write_log(callback, train_names,\",\n \"validate_interval == 0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record),\",\n \"g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' + save_weights_path +\",\n \"= %s\\\" % (it + 1, elapsed_time, loss_generator)) if (it\",\n \"-------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height,\",\n \"Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path =\",\n \"+ save_weights_name + '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path\",\n \"default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500)\",\n \"PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show,\",\n \"= args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num =\",\n \"data_dir + '/validate_wf/' else: save_weights_name = model_name + '-SIM_' +\",\n \"psnrs, ssims = [], [], [], [] for path in\",\n \"if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading\",\n \"in range(iterations): # ------------------------------------ # train generator # ------------------------------------ input_g,\",\n \"type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str,\",\n \"1: r, c = 3, 3 mses, nrmses, psnrs, ssims\",\n \"args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval\",\n \"logs, batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value =\",\n \"if (it + 1) % sample_interval == 0: images_path =\",\n \"output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) #\",\n \"= data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else:\",\n \"0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it +\",\n \"train_images_path = data_dir + '/training/' validate_images_path = data_dir + '/validate/'\",\n \"cnt = 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f,\",\n \"gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\\\"%d epoch: time:\",\n \"import data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss import\",\n \"os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names =\",\n \"== 1: save_weights_name = model_name + '-SISR_' + data_name cur_data_loader\",\n \"keras.callbacks import TensorBoard import glob import os import tensorflow as\",\n \"< validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims =\",\n \"# -------------------------------------------------------------------------------- # define combined model # -------------------------------------------------------------------------------- g =\",\n \"model_name + '-SISR_' + data_name cur_data_loader = data_loader train_images_path =\",\n \"= save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not\",\n \"# show some examples fig, axs = plt.subplots(r, c) cnt\",\n \"glob import os import tensorflow as tf from models import\",\n \"models import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import\",\n \"keras import optimizers import matplotlib.pyplot as plt import numpy as\",\n \"for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path,\",\n \"iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) #\",\n \"weights successfully: ' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- #\",\n \"default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args = parser.parse_args() gpu_id = str(args.gpu_id)\",\n \"save_weights_dir + '/' + save_weights_name + '/' train_gt_path = data_dir\",\n \"glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if (it +\",\n \"from models import * from utils.lr_controller import ReduceLROnPlateau from utils.data_loader\",\n \"= datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path\",\n \"type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int,\",\n \"glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1: r, c\",\n \"type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int,\",\n \"and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path\",\n \"args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width\",\n \"norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now()\",\n \"3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs =\",\n \"train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/'\",\n \"= datetime.datetime.now() - start_time print(\\\"%d epoch: time: %s, g_loss =\",\n \"1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims\",\n \"-------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best')\",\n \"default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0)\",\n \"data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path =\",\n \"iterations = args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name\",\n \"weights successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path +\",\n \"type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int,\",\n \"modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # --------------------------------------------------------------------------------\",\n \"log_path = save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback\",\n \"parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args\",\n \"parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\",\",\n \"0 for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' %\",\n \"ssims) # if best, save weights.best g.save_weights(save_weights_path + 'weights.latest') if\",\n \"g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g,\",\n \"= img_comp(gt, output, mses, nrmses, psnrs, ssims) # if best,\",\n \"load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'):\",\n \"patch_height = args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor\",\n \"= modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor,\",\n \"iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback,\",\n \"training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse\",\n \"# ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width,\",\n \"image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt\",\n \"args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)\",\n \"def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if\",\n \"output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt, output,\",\n \"psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row]))\",\n \"Validate(it + 1, sample=1) if (it + 1) % validate_interval\",\n \"default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5)\",\n \"psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img,\",\n \"plt.close() else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses,\",\n \"validate_images_path = data_dir + '/validate_wf/' else: save_weights_name = model_name +\",\n \"# select models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN':\",\n \"'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter)\",\n \"default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4)\",\n \"time: %s, g_loss = %s\\\" % (it + 1, elapsed_time,\",\n \"+ 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path)\",\n \"= data_dir + '/validate_wf/' else: save_weights_name = model_name + '-SIM_'\",\n \"write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2],\",\n \"utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss\",\n \"= args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor =\",\n \"r, c = 3, 3 mses, nrmses, psnrs, ssims =\",\n \"load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights\",\n \"Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample\",\n \"not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer\",\n \"cnt += 1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else:\",\n \"'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now()\",\n \"loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\\\"%d epoch: time: %s,\",\n \"+ 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses),\",\n \"utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\",\",\n \"default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\")\",\n \"input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4,\",\n \"[], [], [] img_show, gt_show, output_show = [], [], []\",\n \"= 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback,\",\n \"weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path\",\n \"default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000)\",\n \"+ '/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name +\",\n \"load_weights = args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval\",\n \"'/*') for it in range(iterations): # ------------------------------------ # train generator\",\n \"------------------------------------ # train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path,\",\n \"1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if validate_num\",\n \"for row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row],\",\n \"== 1: r, c = 3, 3 mses, nrmses, psnrs,\",\n \"gt_show, output_show = [], [], [] validate_path = np.random.choice(validate_path, size=r)\",\n \"train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/'\",\n \"optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr\",\n \"default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000)\",\n \"> np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses))\",\n \"(it + 1) % validate_interval == 0: Validate(it + 1,\",\n \"[], [], [], [] for path in validate_path: [img, gt]\",\n \"psnrs, ssims = [], [], [], [] img_show, gt_show, output_show\",\n \"# -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path\",\n \"[img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag,\",\n \"parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\",\",\n \"g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best')\",\n \"save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr\",\n \"+ 1, sample=1) if (it + 1) % validate_interval ==\",\n \"optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name]\",\n \"patch_width = args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag\",\n \"np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter)\",\n \"write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist, load\",\n \"range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for\",\n \"= ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no):\",\n \"iter) # -------------------------------------------------------------------------------- # if exist, load weights # --------------------------------------------------------------------------------\",\n \"epoch: time: %s, g_loss = %s\\\" % (it + 1,\",\n \"input_channels = args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num\",\n \"# -------------------------------------------------------------------------------- # if exist, load weights # -------------------------------------------------------------------------------- if\",\n \"args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag\",\n \"beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model # --------------------------------------------------------------------------------\",\n \"sample == 1: r, c = 3, 3 mses, nrmses,\",\n \"callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate #\",\n \"parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\",\",\n \"val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr,\",\n \"col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' %\",\n \"default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9)\",\n \"from keras import optimizers import matplotlib.pyplot as plt import numpy\",\n \"gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor)\",\n \"summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample\",\n \"sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort() if sample ==\",\n \"save_weights_name = model_name + '-SISR_' + data_name cur_data_loader = data_loader\",\n \"[np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it in\",\n \"import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import\",\n \"args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations = args.iterations\",\n \"'/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path +\",\n \"= data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name +\",\n \"curlr, iter) # -------------------------------------------------------------------------------- # if exist, load weights #\",\n \"validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1,\",\n \"+ 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading\",\n \"validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims\",\n \"= ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1,\",\n \"-------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path +\",\n \"select models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN}\",\n \"curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1],\",\n \"+ '/*') Validate(it + 1, sample=1) if (it + 1)\",\n \"1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col, image\",\n \"exist, load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path +\",\n \"parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\",\",\n \"elapsed_time, loss_generator)) if (it + 1) % sample_interval == 0:\",\n \"if sample == 1: r, c = 3, 3 mses,\",\n \"= parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training =\",\n \"else: save_weights_name = model_name + '-SIM_' + data_name cur_data_loader =\",\n \"mses, nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs,\",\n \"[], [], [] validate_path = np.random.choice(validate_path, size=r) for path in\",\n \"= args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations =\",\n \"fig, axs = plt.subplots(r, c) cnt = 0 for row\",\n \"if (it + 1) % validate_interval == 0: Validate(it +\",\n \"= tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1:\",\n \"+ 1) % sample_interval == 0: images_path = glob.glob(train_images_path +\",\n \"ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3)))\",\n \"parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\",\",\n \"psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path + 'weights.latest')\",\n \"data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path\",\n \"train generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path,\",\n \"+ data_name cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/'\",\n \"if exist, load weights # -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path\",\n \"if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully:\",\n \"summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush()\",\n \"= model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path\",\n \"= args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options =\",\n \"optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model #\",\n \"col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' % iter) plt.close()\",\n \"'/validate_wf/' else: save_weights_name = model_name + '-SIM_' + data_name cur_data_loader\",\n \"1) % validate_interval == 0: Validate(it + 1, sample=0) write_log(callback,\",\n \"args.patch_height patch_width = args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor\",\n \"tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name =\",\n \"factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) #\",\n \"as plt import numpy as np import datetime from keras.callbacks\",\n \"+ 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g)\",\n \"write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # --------------------------------------------------------------------------------\",\n \"ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show, gt_show]): axs[row,\",\n \"nrmses, psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims)\",\n \"= data_dir + '/training_gt/' validate_gt_path = data_dir + '/validate_gt/' sample_path\",\n \"tf from models import * from utils.lr_controller import ReduceLROnPlateau from\",\n \"lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr *\",\n \"args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor\",\n \"= validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [], [],\",\n \"'%d.png' % iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path\",\n \"path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height,\",\n \"validate_gt_path = data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/'\",\n \"scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims = img_comp(gt,\",\n \"'-SISR_' + data_name cur_data_loader = data_loader train_images_path = data_dir +\",\n \"def write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value =\",\n \"TensorBoard import glob import os import tensorflow as tf from\",\n \"scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() -\",\n \"cur_data_loader = data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path =\",\n \"default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args = parser.parse_args()\",\n \"parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\",\",\n \"= args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir =\",\n \"[] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*')\",\n \"size=r) for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path,\",\n \"+ 'weights.latest') # -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time =\",\n \"{'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999)\",\n \"mses, nrmses, psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path\",\n \"parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\",\",\n \"validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir + '/'\",\n \"sample=1) if (it + 1) % validate_interval == 0: Validate(it\",\n \"cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard\",\n \"= data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if\",\n \"mses, nrmses, psnrs, ssims = [], [], [], [] for\",\n \"= cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output\",\n \"data_name cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/' validate_images_path\",\n \"utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils\",\n \"batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate # --------------------------------------------------------------------------------\",\n \"parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args = parser.parse_args() gpu_id\",\n \"= logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # --------------------------------------------------------------------------------\",\n \"img_show, gt_show, output_show = [], [], [] validate_path = np.random.choice(validate_path,\",\n \"batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs\",\n \"= str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval =\",\n \"# -------------------------------------------------------------------------------- if load_weights: if os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path +\",\n \"= args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size =\",\n \"lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for it in range(iterations):\",\n \"import TensorBoard import glob import os import tensorflow as tf\",\n \"save_weights_path = save_weights_dir + '/' + save_weights_name + '/' train_gt_path\",\n \"tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag = names\",\n \"= summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary, batch_no)\",\n \"col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off')\",\n \"'/*') Validate(it + 1, sample=1) if (it + 1) %\",\n \"str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir\",\n \"import argparse from keras import optimizers import matplotlib.pyplot as plt\",\n \"cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator =\",\n \"type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str,\",\n \"it in range(iterations): # ------------------------------------ # train generator # ------------------------------------\",\n \"range(iterations): # ------------------------------------ # train generator # ------------------------------------ input_g, gt_g\",\n \"summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag = names callback.writer.add_summary(summary,\",\n \"= optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined model\",\n \"os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\",\n \"1) % sample_interval == 0: images_path = glob.glob(train_images_path + '/*')\",\n \"= img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt))\",\n \"os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names\",\n \"= args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels =\",\n \"+ '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path):\",\n \"g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' + save_weights_path +\",\n \"default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\")\",\n \"-------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g =\",\n \"mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval\",\n \"'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights\",\n \"args.input_channels scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num\",\n \"row in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row],\",\n \"[], [], [], [] img_show, gt_show, output_show = [], [],\",\n \"- start_time print(\\\"%d epoch: time: %s, g_loss = %s\\\" %\",\n \"in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt +=\",\n \"nrmses, psnrs, ssims) # if best, save weights.best g.save_weights(save_weights_path +\",\n \"= str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir =\",\n \"if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names =\",\n \"data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path = data_dir +\",\n \"iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- # if exist,\",\n \"names, logs, batch_no): summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value\",\n \"% (mses[row], ssims[row], psnrs[row])) for col, image in enumerate([img_show, output_show,\",\n \"# -------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def Validate(iter,\",\n \"== 0: images_path = glob.glob(train_images_path + '/*') Validate(it + 1,\",\n \"= g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\\\"%d\",\n \"'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names\",\n \"input_channels == 1: save_weights_name = model_name + '-SISR_' + data_name\",\n \"for col, image in enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row,\",\n \"import optimizers import matplotlib.pyplot as plt import numpy as np\",\n \"'/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name + '/'\",\n \"validate_path = np.random.choice(validate_path, size=r) for path in validate_path: [img, gt]\",\n \"ssims = [], [], [], [] img_show, gt_show, output_show =\",\n \"------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size,\",\n \"+ '/training/' validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir\",\n \"[] img_show, gt_show, output_show = [], [], [] validate_path =\",\n \"type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int,\",\n \"% sample_interval == 0: images_path = glob.glob(train_images_path + '/*') Validate(it\",\n \"save weights.best g.save_weights(save_weights_path + 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path\",\n \"= args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height =\",\n \"sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options\",\n \"data_loader, data_loader_multi_channel from utils.utils import img_comp from utils.loss import loss_mse_ssim\",\n \"loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3)\",\n \"args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training\",\n \"default=\\\"adam\\\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction\",\n \"os.path.exists(save_weights_path + 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: '\",\n \"+ 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: ' +\",\n \"gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs = plt.subplots(r,\",\n \"loss_generator)) if (it + 1) % sample_interval == 0: images_path\",\n \"axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row])) for col,\",\n \"1, sample=1) if (it + 1) % validate_interval == 0:\",\n \"# -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not os.path.exists(log_path):\",\n \"start_time = datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin()\",\n \"= args.patch_width input_channels = args.input_channels scale_factor = args.scale_factor norm_flag =\",\n \"= glob.glob(train_images_path + '/*') for it in range(iterations): # ------------------------------------\",\n \"default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\")\",\n \"iter) plt.close() else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num]\",\n \"argparse from keras import optimizers import matplotlib.pyplot as plt import\",\n \"mses, nrmses, psnrs, ssims = [], [], [], [] img_show,\",\n \"validate_num = args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name\",\n \"for it in range(iterations): # ------------------------------------ # train generator #\",\n \"# about Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph'\",\n \"= glob.glob(validate_images_path + '*') validate_path.sort() if sample == 1: r,\",\n \"DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) #\",\n \"+ 'weights.latest') if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses))\",\n \"-------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path)\",\n \"data_dir + '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name\",\n \"validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [],\",\n \"save_weights_name + '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path =\",\n \"= data_dir + '/validate/' save_weights_path = save_weights_dir + '/' +\",\n \"[], [] for path in validate_path: [img, gt] = cur_data_loader([path],\",\n \"= args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name =\",\n \"data_loader train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir +\",\n \"= [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path +\",\n \"if min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr =\",\n \"np import datetime from keras.callbacks import TensorBoard import glob import\",\n \"save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path)\",\n \"batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time =\",\n \"sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if\",\n \"img_comp(gt, output, mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output)\",\n \"datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path =\",\n \"axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png'\",\n \"ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from utils.utils import img_comp\",\n \"glob.glob(train_images_path + '/*') for it in range(iterations): # ------------------------------------ #\",\n \"input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag,\",\n \"in range(r): axs[row, 1].set_title('MSE=%.4f, SSIM=%.4f, PSNR=%.4f' % (mses[row], ssims[row], psnrs[row]))\",\n \"args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training\",\n \"val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs),\",\n \"from utils.utils import img_comp from utils.loss import loss_mse_ssim parser =\",\n \"gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor)\",\n \"argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1)\",\n \"parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\",\",\n \"args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size\",\n \"print(\\\"%d epoch: time: %s, g_loss = %s\\\" % (it +\",\n \"Tensorboard # -------------------------------------------------------------------------------- log_path = save_weights_path + 'graph' if not\",\n \"os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select\",\n \"as tf from models import * from utils.lr_controller import ReduceLROnPlateau\",\n \"parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\",\",\n \"gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels ==\",\n \"# ------------------------------------ # train generator # ------------------------------------ input_g, gt_g =\",\n \"= gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if\",\n \"parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\",\",\n \"+ '/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name =\",\n \"type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int,\",\n \"'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names,\",\n \"+ save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training # --------------------------------------------------------------------------------\",\n \"modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define\",\n \"%s\\\" % (it + 1, elapsed_time, loss_generator)) if (it +\",\n \"# Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path\",\n \"-------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*') validate_path.sort()\",\n \"'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value\",\n \"nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some\",\n \"args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height\",\n \"gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir\",\n \"train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def\",\n \"fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if validate_num <\",\n \"validate_gt_path, patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses,\",\n \"+ '/' train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir\",\n \"import img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\",\",\n \"type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str,\",\n \"= args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name sample_interval =\",\n \"validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path + '/*') for\",\n \"'/training_wf/' validate_images_path = data_dir + '/validate_wf/' else: save_weights_name = model_name\",\n \"enumerate([img_show, output_show, gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1\",\n \"psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples\",\n \"not os.path.exists(log_path): os.mkdir(log_path) callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss'\",\n \"# -------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record\",\n \"g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0],\",\n \"utils.utils import img_comp from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser()\",\n \"[] validate_path = np.random.choice(validate_path, size=r) for path in validate_path: [img,\",\n \"[], [], [] for path in validate_path: [img, gt] =\",\n \"mses, nrmses, psnrs, ssims) img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show\",\n \"= mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name\",\n \"type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int,\",\n \"'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary = tf.Summary()\",\n \"models and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN\",\n \"validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [], [], [], []\",\n \"elapsed_time = datetime.datetime.now() - start_time print(\\\"%d epoch: time: %s, g_loss\",\n \"parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\",\",\n \"* from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel\",\n \"Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1)\",\n \"validate_interval = args.validate_interval batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1]\",\n \"[], [] img_show, gt_show, output_show = [], [], [] validate_path\",\n \"print('Loading weights successfully: ' + save_weights_path + 'weights.latest') # --------------------------------------------------------------------------------\",\n \"val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter) # -------------------------------------------------------------------------------- #\",\n \"save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path):\",\n \"successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'):\",\n \"parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\",\",\n \"# -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller\",\n \"# training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = []\",\n \"import numpy as np import datetime from keras.callbacks import TensorBoard\",\n \"'/training/' validate_images_path = data_dir + '/validate/' save_weights_path = save_weights_dir +\",\n \"min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- # about\",\n \"lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter)\",\n \"%s, g_loss = %s\\\" % (it + 1, elapsed_time, loss_generator))\",\n \"= {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9,\",\n \"patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time\",\n \"start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width\",\n \"data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval batch_size\",\n \"else: if validate_num < validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses,\",\n \"TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR',\",\n \"some examples fig, axs = plt.subplots(r, c) cnt = 0\",\n \"parser.add_argument(\\\"--scale_factor\\\", type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\",\",\n \"parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction\",\n \"model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g)\",\n \"* 0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # --------------------------------------------------------------------------------\",\n \"-------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0):\",\n \"min(validate_nrmse) > np.mean(nrmses): g.save_weights(save_weights_path + 'weights.best') validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter,\",\n \"os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer # --------------------------------------------------------------------------------\",\n \"+ 'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' +\",\n \"optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- # define combined\",\n \"from keras.callbacks import TensorBoard import glob import os import tensorflow\",\n \"in validate_path: [img, gt] = cur_data_loader([path], validate_images_path, validate_gt_path, patch_height, patch_width,\",\n \"output_show.append(output) # show some examples fig, axs = plt.subplots(r, c)\",\n \"type=str, default=\\\"adam\\\") args = parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction =\",\n \"modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr,\",\n \"elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully:\",\n \"args.validate_num iterations = args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name\",\n \"= glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if (it\",\n \"args.norm_flag validate_num = args.validate_num iterations = args.iterations load_weights = args.load_weights\",\n \"-------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller =\",\n \"data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name + '-SISR_'\",\n \"sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record = []\",\n \"g_loss = %s\\\" % (it + 1, elapsed_time, loss_generator)) if\",\n \"callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate # -------------------------------------------------------------------------------- def\",\n \"parser.parse_args() gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training)\",\n \"val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs,\",\n \"os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest') print('Loading weights successfully: '\",\n \"default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int, default=9) parser.add_argument(\\\"--scale_factor\\\", type=int, default=2)\",\n \"gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels\",\n \"plt.subplots(r, c) cnt = 0 for row in range(r): axs[row,\",\n \"gt_show]): axs[row, col].imshow(np.squeeze(image[row])) axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path +\",\n \"c = 3, 3 mses, nrmses, psnrs, ssims = [],\",\n \"matplotlib.pyplot as plt import numpy as np import datetime from\",\n \"np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr', curlr, iter)\",\n \"data_dir + '/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not\",\n \"write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3],\",\n \"cur_data_loader = data_loader train_images_path = data_dir + '/training_wf/' validate_images_path =\",\n \"default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000)\",\n \"from utils.loss import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1)\",\n \"numpy as np import datetime from keras.callbacks import TensorBoard import\",\n \"patch_height, patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses,\",\n \"tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name\",\n \"default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128)\",\n \"# if exist, load weights # -------------------------------------------------------------------------------- if load_weights: if\",\n \"nrmses, psnrs, ssims = [], [], [], [] for path\",\n \"+ save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path +\",\n \"save_weights_name = model_name + '-SIM_' + data_name cur_data_loader = data_loader_multi_channel\",\n \"[] for path in validate_path: [img, gt] = cur_data_loader([path], validate_images_path,\",\n \"'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) #\",\n \"-------------------------------------------------------------------------------- # if exist, load weights # -------------------------------------------------------------------------------- if load_weights:\",\n \"callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE', 'val_SSIM', 'val_PSNR', 'val_NRMSE']\",\n \"-------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse = [np.Inf]\",\n \"tensorflow as tf from models import * from utils.lr_controller import\",\n \"images_path = glob.glob(train_images_path + '/*') for it in range(iterations): #\",\n \"(it + 1) % sample_interval == 0: images_path = glob.glob(train_images_path\",\n \"= [], [], [] validate_path = np.random.choice(validate_path, size=r) for path\",\n \"args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir\",\n \"'val_SSIM', 'val_PSNR', 'val_NRMSE'] def write_log(callback, names, logs, batch_no): summary =\",\n \"img_comp(gt, output, mses, nrmses, psnrs, ssims) # if best, save\",\n \"save_weights_path + 'weights.best') elif os.path.exists(save_weights_path + 'weights.latest'): g.save_weights(save_weights_path + 'weights.latest')\",\n \"[], [] validate_path = np.random.choice(validate_path, size=r) for path in validate_path:\",\n \"import os import tensorflow as tf from models import *\",\n \"type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args = parser.parse_args() gpu_id =\",\n \"args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id\",\n \"generator # ------------------------------------ input_g, gt_g = cur_data_loader(images_path, train_images_path, train_gt_path, patch_height,\",\n \"default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float, default=0.3) parser.add_argument(\\\"--mixed_precision_training\\\", type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\")\",\n \"norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs, ssims =\",\n \"as np import datetime from keras.callbacks import TensorBoard import glob\",\n \"default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4)\",\n \"args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels = args.input_channels\",\n \"'/validate_gt/' sample_path = save_weights_path + 'sampled_img/' if not os.path.exists(save_weights_path): os.mkdir(save_weights_path)\",\n \"-------------------------------------------------------------------------------- # select models and optimizer # -------------------------------------------------------------------------------- modelFns =\",\n \"show some examples fig, axs = plt.subplots(r, c) cnt =\",\n \"type=int, default=1) parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str,\",\n \"'weights.best'): g.save_weights(save_weights_path + 'weights.best') print('Loading weights successfully: ' + save_weights_path\",\n \"validate # -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path +\",\n \"= lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims),\",\n \"write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback, 'lr',\",\n \"output, mses, nrmses, psnrs, ssims) # if best, save weights.best\",\n \"+ 'weights.latest') print('Loading weights successfully: ' + save_weights_path + 'weights.latest')\",\n \"data_loader_multi_channel from utils.utils import img_comp from utils.loss import loss_mse_ssim parser\",\n \"define combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels))\",\n \"type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int,\",\n \"+ 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record\",\n \"mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_id gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) data_name =\",\n \"logs summary_value.tag = names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- #\",\n \"= args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr =\",\n \"datetime from keras.callbacks import TensorBoard import glob import os import\",\n \"validate_path.__len__(): validate_path = validate_path[0:validate_num] mses, nrmses, psnrs, ssims = [],\",\n \"parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\",\",\n \"np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback, val_names[1], np.mean(ssims), iter) write_log(callback,\",\n \"== 0: Validate(it + 1, sample=0) write_log(callback, train_names, np.mean(loss_record), it\",\n \"lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width = args.patch_width input_channels\",\n \"data_name = data_dir.split('/')[-1] if input_channels == 1: save_weights_name = model_name\",\n \"mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1) # -------------------------------------------------------------------------------- #\",\n \"str(args.mixed_precision_training) data_dir = args.data_dir save_weights_dir = args.save_weights_dir validate_interval = args.validate_interval\",\n \"iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter) write_log(callback,\",\n \"args.iterations load_weights = args.load_weights optimizer_name = args.optimizer_name model_name = args.model_name\",\n \"+ 1, elapsed_time, loss_generator)) if (it + 1) % sample_interval\",\n \"type=int, default=2) parser.add_argument(\\\"--norm_flag\\\", type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int,\",\n \"callback = TensorBoard(log_path) callback.set_model(g) train_names = 'training_loss' val_names = ['val_MSE',\",\n \"% (it + 1, elapsed_time, loss_generator)) if (it + 1)\",\n \"type=int, default=2000) parser.add_argument(\\\"--validate_num\\\", type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float,\",\n \"= save_weights_path + 'graph' if not os.path.exists(log_path): os.mkdir(log_path) callback =\",\n \"type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\", type=int, default=128) parser.add_argument(\\\"--patch_width\\\", type=int, default=128) parser.add_argument(\\\"--input_channels\\\", type=int,\",\n \"ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0, min_lr=start_lr * 0.1, verbose=1)\",\n \"write_log(callback, names, logs, batch_no): summary = tf.Summary() summary_value = summary.value.add()\",\n \"+ 1) % validate_interval == 0: Validate(it + 1, sample=0)\",\n \"'/' train_gt_path = data_dir + '/training_gt/' validate_gt_path = data_dir +\",\n \"os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and optimizer #\",\n \"summary = tf.Summary() summary_value = summary.value.add() summary_value.simple_value = logs summary_value.tag\",\n \"np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses), iter)\",\n \"combined model # -------------------------------------------------------------------------------- g = modelFN((patch_height, patch_width, input_channels)) g.compile(loss=loss_mse_ssim,\",\n \"loss_generator = g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time\",\n \"import datetime from keras.callbacks import TensorBoard import glob import os\",\n \"c) cnt = 0 for row in range(r): axs[row, 1].set_title('MSE=%.4f,\",\n \"if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models and\",\n \"data_dir + '/validate/' save_weights_path = save_weights_dir + '/' + save_weights_name\",\n \"import loss_mse_ssim parser = argparse.ArgumentParser() parser.add_argument(\\\"--gpu_id\\\", type=int, default=1) parser.add_argument(\\\"--gpu_memory_fraction\\\", type=float,\",\n \"'weights.latest') print('Loading weights successfully: ' + save_weights_path + 'weights.latest') #\",\n \"model_name = args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]\",\n \"gpu_id = str(args.gpu_id) gpu_memory_fraction = args.gpu_memory_fraction mixed_precision_training = str(args.mixed_precision_training) data_dir\",\n \"+ '/validate_wf/' else: save_weights_name = model_name + '-SIM_' + data_name\",\n \"= save_weights_dir + '/' + save_weights_name + '/' train_gt_path =\",\n \"os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # -------------------------------------------------------------------------------- # select models\",\n \"+= 1 fig.savefig(sample_path + '%d.png' % iter) plt.close() else: if\",\n \"val_names[1], np.mean(ssims), iter) write_log(callback, val_names[2], np.mean(psnrs), iter) write_log(callback, val_names[3], np.mean(nrmses),\",\n \"axs = plt.subplots(r, c) cnt = 0 for row in\",\n \"validate_nrmse.append(np.mean(nrmses)) curlr = lr_controller.on_epoch_end(iter, np.mean(nrmses)) write_log(callback, val_names[0], np.mean(mses), iter) write_log(callback,\",\n \"loss_record = [] validate_nrmse = [np.Inf] lr_controller.on_train_begin() images_path = glob.glob(train_images_path\",\n \"sample_interval == 0: images_path = glob.glob(train_images_path + '/*') Validate(it +\",\n \"0.1, verbose=1) # -------------------------------------------------------------------------------- # about Tensorboard # -------------------------------------------------------------------------------- log_path\",\n \"g.train_on_batch(input_g, gt_g) loss_record.append(loss_generator) elapsed_time = datetime.datetime.now() - start_time print(\\\"%d epoch:\",\n \"img_show.append(np.squeeze(np.mean(img, 3))) gt_show.append(np.squeeze(gt)) output_show.append(output) # show some examples fig, axs\",\n \"= model_name + '-SISR_' + data_name cur_data_loader = data_loader train_images_path\",\n \"= [], [], [], [] for path in validate_path: [img,\",\n \"patch_width, 1, norm_flag=norm_flag, scale=scale_factor) output = np.squeeze(g.predict(img)) mses, nrmses, psnrs,\",\n \"ssims = [], [], [], [] for path in validate_path:\",\n \"if not os.path.exists(save_weights_path): os.mkdir(save_weights_path) if not os.path.exists(sample_path): os.mkdir(sample_path) # --------------------------------------------------------------------------------\",\n \"print('Loading weights successfully: ' + save_weights_path + 'weights.best') elif os.path.exists(save_weights_path\",\n \"scale_factor = args.scale_factor norm_flag = args.norm_flag validate_num = args.validate_num iterations\",\n \"type=int, default=500) parser.add_argument(\\\"--batch_size\\\", type=int, default=4) parser.add_argument(\\\"--start_lr\\\", type=float, default=1e-4) parser.add_argument(\\\"--lr_decay_factor\\\", type=float,\",\n \"batch_size = args.batch_size start_lr = args.start_lr lr_decay_factor = args.lr_decay_factor patch_height\",\n \"'-SIM_' + data_name cur_data_loader = data_loader_multi_channel train_images_path = data_dir +\",\n \"' + save_weights_path + 'weights.latest') # -------------------------------------------------------------------------------- # training #\",\n \"parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../dataset/train/F-actin\\\") parser.add_argument(\\\"--save_weights_dir\\\", type=str, default=\\\"../trained_models\\\") parser.add_argument(\\\"--model_name\\\", type=str, default=\\\"DFCAN\\\") parser.add_argument(\\\"--patch_height\\\",\",\n \"train_gt_path, patch_height, patch_width, batch_size, norm_flag=norm_flag, scale=scale_factor) loss_generator = g.train_on_batch(input_g, gt_g)\",\n \"= np.random.choice(validate_path, size=r) for path in validate_path: [img, gt] =\",\n \"type=int, default=1) parser.add_argument(\\\"--iterations\\\", type=int, default=1000000) parser.add_argument(\\\"--sample_interval\\\", type=int, default=1000) parser.add_argument(\\\"--validate_interval\\\", type=int,\",\n \"= args.optimizer_name model_name = args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] =\",\n \"# -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN = modelFns[model_name] optimizer_g\",\n \"(it + 1, elapsed_time, loss_generator)) if (it + 1) %\",\n \"psnrs, ssims = img_comp(gt, output, mses, nrmses, psnrs, ssims) #\",\n \"3 mses, nrmses, psnrs, ssims = [], [], [], []\",\n \"type=float, default=0.5) parser.add_argument(\\\"--load_weights\\\", type=int, default=0) parser.add_argument(\\\"--optimizer_name\\\", type=str, default=\\\"adam\\\") args =\",\n \"= data_loader_multi_channel train_images_path = data_dir + '/training/' validate_images_path = data_dir\",\n \"= args.model_name sample_interval = args.sample_interval os.environ[\\\"TF_ENABLE_AUTO_MIXED_PRECISION\\\"] = mixed_precision_training os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] =\",\n \"from utils.lr_controller import ReduceLROnPlateau from utils.data_loader import data_loader, data_loader_multi_channel from\",\n \"-------------------------------------------------------------------------------- # training # -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record =\",\n \"= modelFns[model_name] optimizer_g = optimizers.adam(lr=start_lr, beta_1=0.9, beta_2=0.999) # -------------------------------------------------------------------------------- #\",\n \"if input_channels == 1: save_weights_name = model_name + '-SISR_' +\",\n \"names callback.writer.add_summary(summary, batch_no) callback.writer.flush() # -------------------------------------------------------------------------------- # Sample and validate\",\n \"# -------------------------------------------------------------------------------- start_time = datetime.datetime.now() loss_record = [] validate_nrmse =\",\n \"axs[row, col].axis('off') cnt += 1 fig.savefig(sample_path + '%d.png' % iter)\",\n \"images_path = glob.glob(train_images_path + '/*') Validate(it + 1, sample=1) if\",\n \"# -------------------------------------------------------------------------------- # select models and optimizer # -------------------------------------------------------------------------------- modelFns\",\n \"and optimizer # -------------------------------------------------------------------------------- modelFns = {'DFCAN': DFCAN16.DFCAN} modelFN =\",\n \"'/' + save_weights_name + '/' train_gt_path = data_dir + '/training_gt/'\",\n \"+ '/' + save_weights_name + '/' train_gt_path = data_dir +\",\n \"= 3, 3 mses, nrmses, psnrs, ssims = [], [],\",\n \"1, sample=0) write_log(callback, train_names, np.mean(loss_record), it + 1) loss_record =\",\n \"= data_dir + '/training/' validate_images_path = data_dir + '/validate/' save_weights_path\",\n \"= args.start_lr lr_decay_factor = args.lr_decay_factor patch_height = args.patch_height patch_width =\",\n \"= data_loader train_images_path = data_dir + '/training_wf/' validate_images_path = data_dir\",\n \"args.save_weights_dir validate_interval = args.validate_interval batch_size = args.batch_size start_lr = args.start_lr\",\n \"# -------------------------------------------------------------------------------- def Validate(iter, sample=0): validate_path = glob.glob(validate_images_path + '*')\"\n]","isConversational":false}}},{"rowIdx":154,"cells":{"ngram":{"kind":"list like","value":["that users can still interact with the chart during the","<filename>catalyst/exchange/live_graph_clock.py import pandas as pd from catalyst.constants import LOG_LEVEL from","using the 'animate' callback of Matplotlib. We need to direct","which is a wrapper around time.sleep() used in the SimpleClock.","sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded = True","= current_time.floor('1T') if self._last_emit is None or current_minute > self._last_emit:","reactive approach here because # I need to yield from","a single thread. This is also why we are not","to direct access to the __iter__ method in order to","\"animate\" reactive approach here because # I need to yield","is that users can still interact with the chart during","to yield events to Zipline. The :param:`time_skew` parameter represents the","not used currently. \"\"\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')):","pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from","Matplotlib has a pause() method which is a wrapper around","import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import (","approach allows us to run the program using a single","are not using the 'animate' callback of Matplotlib. We need","seemingly awkward approach allows us to run the program using","drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a","machine's clock. It's not used currently. \"\"\" def __init__(self, sessions,","cycles. This is what enables us to keep a single","= callback def __iter__(self): from matplotlib import pyplot as plt","current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ =","as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats","the SimpleClock. The key difference is that users can still","exchange and the live trading machine's clock. It's not used","BAR, SESSION_START ) from logbook import Logger log = Logger('LiveGraphClock',","df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else:","We need to direct access to the __iter__ method in","a live graph. Notes ----- This seemingly awkward approach allows","yield events to Zipline. The :param:`time_skew` parameter represents the time","import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from","does not play nice with multi-threaded environments. Zipline probably does","clock. It's not used currently. \"\"\" def __init__(self, sessions, context,","= Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock for live trading.","class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the","is important because Matplotlib does not play nice with multi-threaded","self.context = context self.callback = callback def __iter__(self): from matplotlib","in order to yield events to Zipline. The :param:`time_skew` parameter","method which is a wrapper around time.sleep() used in the","is a wrapper around time.sleep() used in the SimpleClock. The","for live trading. This class is a drop-in replacement for","This is also why we are not using the 'animate'","allows us to run the program using a single thread.","context self.callback = callback def __iter__(self): from matplotlib import pyplot","clock with a live graph. Notes ----- This seemingly awkward",") self.callback(self.context, df) else: # I can't use the \"animate\"","SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if","BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols","parameter represents the time difference between the exchange and the","of Matplotlib. We need to direct access to the __iter__","if self._last_emit is None or current_minute > self._last_emit: log.debug('emitting minutely","from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine","level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock for live trading. This class","us to run the program using a single thread. This","mixes the clock with a live graph. Notes ----- This","the time difference between the exchange and the live trading","to the __iter__ method in order to yield events to","callback def __iter__(self): from matplotlib import pyplot as plt yield","self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context","recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't use the","----- This seemingly awkward approach allows us to run the","either. Matplotlib has a pause() method which is a wrapper","not using the 'animate' callback of Matplotlib. We need to","def __iter__(self): from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(),","during the pause cycles. This is what enables us to","is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock","self._last_emit = None self._before_trading_start_bar_yielded = True self.context = context self.callback","sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew","difference is that users can still interact with the chart","pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T')","the program using a single thread. This is important because","the \"animate\" reactive approach here because # I need to","events to Zipline. The :param:`time_skew` parameter represents the time difference","used in the SimpleClock. The key difference is that users","plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute","to yield from the main loop. # Workaround: https://stackoverflow.com/a/33050617/814633 plt.pause(1)","self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute,","the __iter__ method in order to yield events to Zipline.","__iter__(self): from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START","in the SimpleClock. The key difference is that users can","live trading machine's clock. It's not used currently. \"\"\" def","This is important because Matplotlib does not play nice with","what enables us to keep a single thread. This is","class LiveGraphClock(object): \"\"\"Realtime clock for live trading. This class is","'animate' callback of Matplotlib. We need to direct access to","need to direct access to the __iter__ method in order","catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import","clock for live trading. This class is a drop-in replacement","self._before_trading_start_bar_yielded = True self.context = context self.callback = callback def","does not either. Matplotlib has a pause() method which is","bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols =","with the chart during the pause cycles. This is what","context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit","= prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I","the clock with a live graph. Notes ----- This seemingly","awkward approach allows us to run the program using a","is also why we are not using the 'animate' callback","chart during the pause cycles. This is what enables us","difference between the exchange and the live trading machine's clock.","minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols","probably does not either. Matplotlib has a pause() method which","\"\"\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions","around time.sleep() used in the SimpleClock. The key difference is","Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock for live trading. This","self._last_emit is None or current_minute > self._last_emit: log.debug('emitting minutely bar:","users can still interact with the chart during the pause","used currently. \"\"\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions","True self.context = context self.callback = callback def __iter__(self): from","LiveGraphClock(object): \"\"\"Realtime clock for live trading. This class is a","Notes ----- This seemingly awkward approach allows us to run","thread. This is also why we are not using the","LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR,","single thread. This is important because Matplotlib does not play","I need to yield from the main loop. # Workaround:","__init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew =","from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook import","__iter__ method in order to yield events to Zipline. The","> self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield","current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats,","wrapper around time.sleep() used in the SimpleClock. The key difference","else: # I can't use the \"animate\" reactive approach here","log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR","self.callback(self.context, df) else: # I can't use the \"animate\" reactive","I can't use the \"animate\" reactive approach here because #","direct access to the __iter__ method in order to yield","multi-threaded environments. Zipline probably does not either. Matplotlib has a","Zipline. The :param:`time_skew` parameter represents the time difference between the","from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START","SimpleClock. The key difference is that users can still interact","time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context = context","True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is","# I can't use the \"animate\" reactive approach here because","order to yield events to Zipline. The :param:`time_skew` parameter represents","prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't","the pause cycles. This is what enables us to keep","logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime","a pause() method which is a wrapper around time.sleep() used","program using a single thread. This is important because Matplotlib","enables us to keep a single thread. This is also","graph. Notes ----- This seemingly awkward approach allows us to","represents the time difference between the exchange and the live","callback of Matplotlib. We need to direct access to the","between the exchange and the live trading machine's clock. It's","environments. Zipline probably does not either. Matplotlib has a pause()","It's not used currently. \"\"\" def __init__(self, sessions, context, callback=None,","self.sessions = sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded","The :param:`time_skew` parameter represents the time difference between the exchange","approach here because # I need to yield from the","we are not using the 'animate' callback of Matplotlib. We","The key difference is that users can still interact with","use the \"animate\" reactive approach here because # I need","trading. This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This","( BAR, SESSION_START ) from logbook import Logger log =","a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with","This is what enables us to keep a single thread.","catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook import Logger","the 'animate' callback of Matplotlib. We need to direct access","method in order to yield events to Zipline. The :param:`time_skew`","also why we are not using the 'animate' callback of","import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock","from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object):","= pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None or","or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit =","the chart during the pause cycles. This is what enables","still interact with the chart during the pause cycles. This","nice with multi-threaded environments. Zipline probably does not either. Matplotlib","live trading. This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`.","us to keep a single thread. This is also why","important because Matplotlib does not play nice with multi-threaded environments.","yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute =","pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None or current_minute","can't use the \"animate\" reactive approach here because # I","need to yield from the main loop. # Workaround: https://stackoverflow.com/a/33050617/814633","matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True:","{}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys())",") from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class","is None or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute))","time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit = None","why we are not using the 'animate' callback of Matplotlib.","single thread. This is also why we are not using","df) else: # I can't use the \"animate\" reactive approach","This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes","yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats(","for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live graph.","play nice with multi-threaded environments. Zipline probably does not either.","time difference between the exchange and the live trading machine's","import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time","= None self._before_trading_start_bar_yielded = True self.context = context self.callback =","= current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _","to keep a single thread. This is also why we","live graph. Notes ----- This seemingly awkward approach allows us","keep a single thread. This is also why we are","= list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context,","self._last_emit = current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df,","the live trading machine's clock. It's not used currently. \"\"\"","Matplotlib. We need to direct access to the __iter__ method","while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit","here because # I need to yield from the main","None or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit","a wrapper around time.sleep() used in the SimpleClock. The key","current_minute = current_time.floor('1T') if self._last_emit is None or current_minute >","trading machine's clock. It's not used currently. \"\"\" def __init__(self,","thread. This is important because Matplotlib does not play nice","key difference is that users can still interact with the","pause() method which is a wrapper around time.sleep() used in","because Matplotlib does not play nice with multi-threaded environments. Zipline","using a single thread. This is important because Matplotlib does","not play nice with multi-threaded environments. Zipline probably does not","= time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context =","This seemingly awkward approach allows us to run the program","because # I need to yield from the main loop.","a single thread. This is important because Matplotlib does not","Matplotlib does not play nice with multi-threaded environments. Zipline probably","and the live trading machine's clock. It's not used currently.","interact with the chart during the pause cycles. This is","replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live",":class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live graph. Notes",":param:`time_skew` parameter represents the time difference between the exchange and","import pandas as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils","pause cycles. This is what enables us to keep a","access to the __iter__ method in order to yield events","callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit =","prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook","= sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded =","# I need to yield from the main loop. #","This mixes the clock with a live graph. Notes -----","time.sleep() used in the SimpleClock. The key difference is that","currently. \"\"\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions =","pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time =","current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None","run the program using a single thread. This is important","Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock for","SESSION_START ) from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL)","to Zipline. The :param:`time_skew` parameter represents the time difference between","None self._before_trading_start_bar_yielded = True self.context = context self.callback = callback","_ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: #","to run the program using a single thread. This is","= context self.callback = callback def __iter__(self): from matplotlib import","def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew","import ( BAR, SESSION_START ) from logbook import Logger log","can still interact with the chart during the pause cycles.","Zipline probably does not either. Matplotlib has a pause() method","not either. Matplotlib has a pause() method which is a","has a pause() method which is a wrapper around time.sleep()","as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow()","the exchange and the live trading machine's clock. It's not","current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute","self.callback = callback def __iter__(self): from matplotlib import pyplot as","recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols )","catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START )","with a live graph. Notes ----- This seemingly awkward approach","from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while","= True self.context = context self.callback = callback def __iter__(self):","self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't use","log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \"\"\"Realtime clock for live","\"\"\"Realtime clock for live trading. This class is a drop-in","with multi-threaded environments. Zipline probably does not either. Matplotlib has","list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df)","is what enables us to keep a single thread. This","current_time.floor('1T') if self._last_emit is None or current_minute > self._last_emit: log.debug('emitting","pandas as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import"],"string":"[\n \"that users can still interact with the chart during the\",\n \"<filename>catalyst/exchange/live_graph_clock.py import pandas as pd from catalyst.constants import LOG_LEVEL from\",\n \"using the 'animate' callback of Matplotlib. We need to direct\",\n \"which is a wrapper around time.sleep() used in the SimpleClock.\",\n \"sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded = True\",\n \"= current_time.floor('1T') if self._last_emit is None or current_minute > self._last_emit:\",\n \"reactive approach here because # I need to yield from\",\n \"a single thread. This is also why we are not\",\n \"to direct access to the __iter__ method in order to\",\n \"\\\"animate\\\" reactive approach here because # I need to yield\",\n \"is that users can still interact with the chart during\",\n \"to yield events to Zipline. The :param:`time_skew` parameter represents the\",\n \"not used currently. \\\"\\\"\\\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')):\",\n \"pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from\",\n \"Matplotlib has a pause() method which is a wrapper around\",\n \"import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import (\",\n \"approach allows us to run the program using a single\",\n \"are not using the 'animate' callback of Matplotlib. We need\",\n \"seemingly awkward approach allows us to run the program using\",\n \"drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a\",\n \"machine's clock. It's not used currently. \\\"\\\"\\\" def __init__(self, sessions,\",\n \"cycles. This is what enables us to keep a single\",\n \"= callback def __iter__(self): from matplotlib import pyplot as plt\",\n \"current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ =\",\n \"as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats\",\n \"the SimpleClock. The key difference is that users can still\",\n \"exchange and the live trading machine's clock. It's not used\",\n \"BAR, SESSION_START ) from logbook import Logger log = Logger('LiveGraphClock',\",\n \"df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else:\",\n \"We need to direct access to the __iter__ method in\",\n \"a live graph. Notes ----- This seemingly awkward approach allows\",\n \"yield events to Zipline. The :param:`time_skew` parameter represents the time\",\n \"import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from\",\n \"does not play nice with multi-threaded environments. Zipline probably does\",\n \"clock. It's not used currently. \\\"\\\"\\\" def __init__(self, sessions, context,\",\n \"= Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live trading.\",\n \"class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the\",\n \"is important because Matplotlib does not play nice with multi-threaded\",\n \"self.context = context self.callback = callback def __iter__(self): from matplotlib\",\n \"in order to yield events to Zipline. The :param:`time_skew` parameter\",\n \"method which is a wrapper around time.sleep() used in the\",\n \"is a wrapper around time.sleep() used in the SimpleClock. The\",\n \"for live trading. This class is a drop-in replacement for\",\n \"This is also why we are not using the 'animate'\",\n \"allows us to run the program using a single thread.\",\n \"context self.callback = callback def __iter__(self): from matplotlib import pyplot\",\n \"clock with a live graph. Notes ----- This seemingly awkward\",\n \") self.callback(self.context, df) else: # I can't use the \\\"animate\\\"\",\n \"SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if\",\n \"BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols\",\n \"parameter represents the time difference between the exchange and the\",\n \"of Matplotlib. We need to direct access to the __iter__\",\n \"if self._last_emit is None or current_minute > self._last_emit: log.debug('emitting minutely\",\n \"from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine\",\n \"level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live trading. This class\",\n \"us to run the program using a single thread. This\",\n \"mixes the clock with a live graph. Notes ----- This\",\n \"the time difference between the exchange and the live trading\",\n \"to the __iter__ method in order to yield events to\",\n \"callback def __iter__(self): from matplotlib import pyplot as plt yield\",\n \"self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context\",\n \"recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't use the\",\n \"----- This seemingly awkward approach allows us to run the\",\n \"either. Matplotlib has a pause() method which is a wrapper\",\n \"not using the 'animate' callback of Matplotlib. We need to\",\n \"def __iter__(self): from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(),\",\n \"during the pause cycles. This is what enables us to\",\n \"is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock\",\n \"self._last_emit = None self._before_trading_start_bar_yielded = True self.context = context self.callback\",\n \"sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew\",\n \"difference is that users can still interact with the chart\",\n \"pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T')\",\n \"the program using a single thread. This is important because\",\n \"the \\\"animate\\\" reactive approach here because # I need to\",\n \"events to Zipline. The :param:`time_skew` parameter represents the time difference\",\n \"used in the SimpleClock. The key difference is that users\",\n \"plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute\",\n \"to yield from the main loop. # Workaround: https://stackoverflow.com/a/33050617/814633 plt.pause(1)\",\n \"self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute,\",\n \"the __iter__ method in order to yield events to Zipline.\",\n \"__iter__(self): from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START\",\n \"in the SimpleClock. The key difference is that users can\",\n \"live trading machine's clock. It's not used currently. \\\"\\\"\\\" def\",\n \"This is important because Matplotlib does not play nice with\",\n \"what enables us to keep a single thread. This is\",\n \"class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live trading. This class is\",\n \"'animate' callback of Matplotlib. We need to direct access to\",\n \"need to direct access to the __iter__ method in order\",\n \"catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import\",\n \"clock for live trading. This class is a drop-in replacement\",\n \"self._before_trading_start_bar_yielded = True self.context = context self.callback = callback def\",\n \"does not either. Matplotlib has a pause() method which is\",\n \"bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols =\",\n \"with the chart during the pause cycles. This is what\",\n \"context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit\",\n \"= prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I\",\n \"the clock with a live graph. Notes ----- This seemingly\",\n \"awkward approach allows us to run the program using a\",\n \"is also why we are not using the 'animate' callback\",\n \"chart during the pause cycles. This is what enables us\",\n \"difference between the exchange and the live trading machine's clock.\",\n \"minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols\",\n \"probably does not either. Matplotlib has a pause() method which\",\n \"\\\"\\\"\\\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions\",\n \"around time.sleep() used in the SimpleClock. The key difference is\",\n \"Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live trading. This\",\n \"self._last_emit is None or current_minute > self._last_emit: log.debug('emitting minutely bar:\",\n \"users can still interact with the chart during the pause\",\n \"used currently. \\\"\\\"\\\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions\",\n \"True self.context = context self.callback = callback def __iter__(self): from\",\n \"LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live trading. This class is a\",\n \"Notes ----- This seemingly awkward approach allows us to run\",\n \"thread. This is also why we are not using the\",\n \"LOG_LEVEL from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR,\",\n \"single thread. This is important because Matplotlib does not play\",\n \"I need to yield from the main loop. # Workaround:\",\n \"__init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew =\",\n \"from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook import\",\n \"__iter__ method in order to yield events to Zipline. The\",\n \"> self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield\",\n \"current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats,\",\n \"wrapper around time.sleep() used in the SimpleClock. The key difference\",\n \"else: # I can't use the \\\"animate\\\" reactive approach here\",\n \"log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR\",\n \"self.callback(self.context, df) else: # I can't use the \\\"animate\\\" reactive\",\n \"I can't use the \\\"animate\\\" reactive approach here because #\",\n \"direct access to the __iter__ method in order to yield\",\n \"multi-threaded environments. Zipline probably does not either. Matplotlib has a\",\n \"Zipline. The :param:`time_skew` parameter represents the time difference between the\",\n \"from catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START\",\n \"SimpleClock. The key difference is that users can still interact\",\n \"time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context = context\",\n \"True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is\",\n \"# I can't use the \\\"animate\\\" reactive approach here because\",\n \"order to yield events to Zipline. The :param:`time_skew` parameter represents\",\n \"prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't\",\n \"the pause cycles. This is what enables us to keep\",\n \"logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime\",\n \"a pause() method which is a wrapper around time.sleep() used\",\n \"program using a single thread. This is important because Matplotlib\",\n \"enables us to keep a single thread. This is also\",\n \"graph. Notes ----- This seemingly awkward approach allows us to\",\n \"represents the time difference between the exchange and the live\",\n \"callback of Matplotlib. We need to direct access to the\",\n \"between the exchange and the live trading machine's clock. It's\",\n \"environments. Zipline probably does not either. Matplotlib has a pause()\",\n \"It's not used currently. \\\"\\\"\\\" def __init__(self, sessions, context, callback=None,\",\n \"self.sessions = sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded\",\n \"The :param:`time_skew` parameter represents the time difference between the exchange\",\n \"approach here because # I need to yield from the\",\n \"we are not using the 'animate' callback of Matplotlib. We\",\n \"The key difference is that users can still interact with\",\n \"use the \\\"animate\\\" reactive approach here because # I need\",\n \"trading. This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This\",\n \"( BAR, SESSION_START ) from logbook import Logger log =\",\n \"a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with\",\n \"This is what enables us to keep a single thread.\",\n \"catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook import Logger\",\n \"the 'animate' callback of Matplotlib. We need to direct access\",\n \"method in order to yield events to Zipline. The :param:`time_skew`\",\n \"also why we are not using the 'animate' callback of\",\n \"import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock\",\n \"from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object):\",\n \"= pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None or\",\n \"or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit =\",\n \"the chart during the pause cycles. This is what enables\",\n \"still interact with the chart during the pause cycles. This\",\n \"nice with multi-threaded environments. Zipline probably does not either. Matplotlib\",\n \"live trading. This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`.\",\n \"us to keep a single thread. This is also why\",\n \"important because Matplotlib does not play nice with multi-threaded environments.\",\n \"yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow() current_minute =\",\n \"pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None or current_minute\",\n \"can't use the \\\"animate\\\" reactive approach here because # I\",\n \"need to yield from the main loop. # Workaround: https://stackoverflow.com/a/33050617/814633\",\n \"matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True:\",\n \"{}'.format(current_minute)) self._last_emit = current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys())\",\n \") from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class\",\n \"is None or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute))\",\n \"time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit = None\",\n \"why we are not using the 'animate' callback of Matplotlib.\",\n \"single thread. This is also why we are not using\",\n \"df) else: # I can't use the \\\"animate\\\" reactive approach\",\n \"This class is a drop-in replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes\",\n \"yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats(\",\n \"for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live graph.\",\n \"play nice with multi-threaded environments. Zipline probably does not either.\",\n \"time difference between the exchange and the live trading machine's\",\n \"import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time\",\n \"= None self._before_trading_start_bar_yielded = True self.context = context self.callback =\",\n \"= current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df, _\",\n \"to keep a single thread. This is also why we\",\n \"live graph. Notes ----- This seemingly awkward approach allows us\",\n \"keep a single thread. This is also why we are\",\n \"= list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context,\",\n \"self._last_emit = current_minute yield current_minute, BAR recorded_cols = list(self.context.recorded_vars.keys()) df,\",\n \"the live trading machine's clock. It's not used currently. \\\"\\\"\\\"\",\n \"Matplotlib. We need to direct access to the __iter__ method\",\n \"while True: current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit\",\n \"here because # I need to yield from the main\",\n \"None or current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit\",\n \"a wrapper around time.sleep() used in the SimpleClock. The key\",\n \"current_minute = current_time.floor('1T') if self._last_emit is None or current_minute >\",\n \"trading machine's clock. It's not used currently. \\\"\\\"\\\" def __init__(self,\",\n \"thread. This is important because Matplotlib does not play nice\",\n \"key difference is that users can still interact with the\",\n \"pause() method which is a wrapper around time.sleep() used in\",\n \"because Matplotlib does not play nice with multi-threaded environments. Zipline\",\n \"using a single thread. This is important because Matplotlib does\",\n \"not play nice with multi-threaded environments. Zipline probably does not\",\n \"= time_skew self._last_emit = None self._before_trading_start_bar_yielded = True self.context =\",\n \"This seemingly awkward approach allows us to run the program\",\n \"because # I need to yield from the main loop.\",\n \"a single thread. This is important because Matplotlib does not\",\n \"Matplotlib does not play nice with multi-threaded environments. Zipline probably\",\n \"and the live trading machine's clock. It's not used currently.\",\n \"interact with the chart during the pause cycles. This is\",\n \"replacement for :class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live\",\n \":class:`zipline.gens.sim_engine.MinuteSimulationClock`. This mixes the clock with a live graph. Notes\",\n \":param:`time_skew` parameter represents the time difference between the exchange and\",\n \"import pandas as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils\",\n \"pause cycles. This is what enables us to keep a\",\n \"access to the __iter__ method in order to yield events\",\n \"callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew = time_skew self._last_emit =\",\n \"prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START ) from logbook\",\n \"= sessions self.time_skew = time_skew self._last_emit = None self._before_trading_start_bar_yielded =\",\n \"# I need to yield from the main loop. #\",\n \"This mixes the clock with a live graph. Notes -----\",\n \"time.sleep() used in the SimpleClock. The key difference is that\",\n \"currently. \\\"\\\"\\\" def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions =\",\n \"pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time =\",\n \"current_time = pd.Timestamp.utcnow() current_minute = current_time.floor('1T') if self._last_emit is None\",\n \"run the program using a single thread. This is important\",\n \"Logger log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for\",\n \"SESSION_START ) from logbook import Logger log = Logger('LiveGraphClock', level=LOG_LEVEL)\",\n \"to Zipline. The :param:`time_skew` parameter represents the time difference between\",\n \"None self._before_trading_start_bar_yielded = True self.context = context self.callback = callback\",\n \"_ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: #\",\n \"to run the program using a single thread. This is\",\n \"= context self.callback = callback def __iter__(self): from matplotlib import\",\n \"def __init__(self, sessions, context, callback=None, time_skew=pd.Timedelta('0s')): self.sessions = sessions self.time_skew\",\n \"import ( BAR, SESSION_START ) from logbook import Logger log\",\n \"can still interact with the chart during the pause cycles.\",\n \"Zipline probably does not either. Matplotlib has a pause() method\",\n \"not either. Matplotlib has a pause() method which is a\",\n \"has a pause() method which is a wrapper around time.sleep()\",\n \"as plt yield pd.Timestamp.utcnow(), SESSION_START while True: current_time = pd.Timestamp.utcnow()\",\n \"the exchange and the live trading machine's clock. It's not\",\n \"current_minute > self._last_emit: log.debug('emitting minutely bar: {}'.format(current_minute)) self._last_emit = current_minute\",\n \"self.callback = callback def __iter__(self): from matplotlib import pyplot as\",\n \"recorded_cols = list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols )\",\n \"catalyst.exchange.utils.stats_utils import prepare_stats from catalyst.gens.sim_engine import ( BAR, SESSION_START )\",\n \"with a live graph. Notes ----- This seemingly awkward approach\",\n \"from matplotlib import pyplot as plt yield pd.Timestamp.utcnow(), SESSION_START while\",\n \"= True self.context = context self.callback = callback def __iter__(self):\",\n \"self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df) else: # I can't use\",\n \"log = Logger('LiveGraphClock', level=LOG_LEVEL) class LiveGraphClock(object): \\\"\\\"\\\"Realtime clock for live\",\n \"\\\"\\\"\\\"Realtime clock for live trading. This class is a drop-in\",\n \"with multi-threaded environments. Zipline probably does not either. Matplotlib has\",\n \"list(self.context.recorded_vars.keys()) df, _ = prepare_stats( self.context.frame_stats, recorded_cols=recorded_cols ) self.callback(self.context, df)\",\n \"is what enables us to keep a single thread. This\",\n \"current_time.floor('1T') if self._last_emit is None or current_minute > self._last_emit: log.debug('emitting\",\n \"pandas as pd from catalyst.constants import LOG_LEVEL from catalyst.exchange.utils.stats_utils import\"\n]","isConversational":false}}},{"rowIdx":155,"cells":{"ngram":{"kind":"list like","value":["print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario","el detalle de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>')","usuario no autenticado no puede modificar una tarea\") print(\"-----------\") def","detalles de la tarea correctamente\" }) if is_passed: print(\"Resultado: Aprobado\")","inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no puede agregar una","agregar una tarea a la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\"","de obtener detalles de la tarea de un usuario que","suya\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre","import reverse from rest_framework import status TEST_RESULTS = [] RECIPIENTS","\"\"\" response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed","del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede modificar","status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name,","reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed = response.status_code ==","is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed","password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND","estamos probando la API Task GET y tratando de obtener","test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede ver detalles","tarea a la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En este","else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega tarea a","\"test_description\": \"Solo el propietario puede ver el detalle de la","User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse","autenticado agrega tarea a la lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user',","credenciales de usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail',","2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\"","print(\"Descripcion: El usuario no autenticado no puede modificar una tarea\")","def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En este caso","-*- coding: utf-8 -*- from __future__ import unicode_literals import inspect","print(\"Descripcion: Usuario autenticado puede modificar una tarea suya\") print(\"-----------\") def","agrega tarea a la lista\" }) if is_passed: print(\"Resultado: Aprobado\")","tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre","if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega","import inspect from task.models import InveraTask from api.utils import send_test_csv_report","del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado","este caso de prueba, estamos probando la API Task Create","\"test_description\": \"Usuario autenticado puede ver detalles de la tarea correctamente\"","que usa credenciales de usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response","# is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if","Task PUT utilizando un usuario no autenticado. \"\"\" response =","de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response =","\"\"\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code","print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo","test_get_other_user_task_detail(self): \"\"\" En este caso de prueba, estamos probando la","inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega tarea a la lista\") print(\"-----------\")","\"Solo el propietario puede ver el detalle de la tarea\"","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no puede modificar","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no puede agregar","la API Task Create utilizando un usuario no autenticado. \"\"\"","tarea correctamente\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\")","\"test_description\": \"Usuario autenticado agrega tarea a la lista\" }) if","autenticado no puede modificar una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user',","Solo el propietario puede ver el detalle de la tarea\")","prueba, estamos probando la API Task GET y tratando de","API Task GET y tratando de obtener detalles de la","\"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario","de prueba, estamos probando la API Task Create utilizando un","\"Usuario autenticado puede modificar una tarea suya\" }) if is_passed:","del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario puede","usuario que usa credenciales de usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>')","test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json')","usuario no autenticado. \"\"\" response = self.client.put( reverse('api-task'), {'title': 'My","\"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega tarea a la","autenticado no puede agregar una tarea a la lista\" })","probando la API Task PUT utilizando un usuario no autenticado.","inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede ver detalles de la tarea","print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My","modificar una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response =","username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create(","test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega tarea a","if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega tarea a la lista\")","puede agregar una tarea a la lista\" }) if is_passed:","= self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed =","inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede ver detalles de la tarea","reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN","autenticado. \"\"\" response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json')","test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task","estamos probando la API Task Create utilizando un usuario no","'My Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({","\"\"\" response = self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json')","puede modificar una tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En","tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]),","password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK","-*- from __future__ import unicode_literals import inspect from task.models import","self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed","response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({","no puede modificar una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>')","coding: utf-8 -*- from __future__ import unicode_literals import inspect from","format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if","Usuario autenticado puede modificar una tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self):","detalles de la tarea de un usuario que usa credenciales","format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if","= self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\":","agregar una tarea a la lista\" }) if is_passed: print(\"Resultado:","puede modificar una tarea suya\" }) if is_passed: print(\"Resultado: Aprobado\")","Task GET y tratando de obtener detalles de la tarea","is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede ver","print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed","\"El usuario no autenticado no puede modificar una tarea\" })","Usuario autenticado puede ver detalles de la tarea correctamente\") print(\"-----------\")","probando la API Task GET y tratando de obtener detalles","}) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre del","propietario puede ver el detalle de la tarea\" }) if","\"test_description\": \"El usuario no autenticado no puede modificar una tarea\"","inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega tarea a la lista\" })","from task.models import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models","test_create_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos probando la","if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede","la tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS,","def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed =","= User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial","APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En","response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\",","rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework","status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name,","no puede modificar una tarea\" }) if is_passed: print(\"Resultado: Aprobado\")","lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)]))","is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre del test: \"","== status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\":","\" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega tarea a la","no autenticado. \"\"\" response = self.client.put( reverse('api-task'), {'title': 'My Task'},","autenticado puede modificar una tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\"","from __future__ import unicode_literals import inspect from task.models import InveraTask","else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede modificar una","= response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede ver detalles de la","else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede ver detalles","self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My","Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el","de la tarea de un usuario que usa credenciales de","no autenticado no puede modificar una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self):","is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code ==","tarea a la lista\" }) if is_passed: print(\"Resultado: Aprobado\") else:","else: print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion:","GET y tratando de obtener detalles de la tarea de","if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre del test:","rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS = []","una tarea suya\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado:","el detalle de la tarea\" }) if is_passed: print(\"Resultado: Aprobado\")","= User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task","puede agregar una tarea a la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self):","\"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado","import APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework import","usuario no autenticado no puede agregar una tarea a la","tratando de obtener detalles de la tarea de un usuario","self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>')","no autenticado no puede agregar una tarea a la lista\")","print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega tarea a la lista\"","caso de prueba, estamos probando la API Task GET y","ver detalles de la tarea correctamente\" }) if is_passed: print(\"Resultado:","Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\"","import unicode_literals import inspect from task.models import InveraTask from api.utils","no autenticado no puede modificar una tarea\" }) if is_passed:","modificar una tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En este","de la tarea correctamente\" }) if is_passed: print(\"Resultado: Aprobado\") else:","print(\"Descripcion: El usuario no autenticado no puede agregar una tarea","puede ver el detalle de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self):","api.utils import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import","\"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede modificar una tarea","tarea de un usuario que usa credenciales de usuario diferentes.","is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado","la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'),","print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En este caso de prueba, estamos","TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\":","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede ver detalles de la","= self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code","de usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)]))","is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario puede","InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import User from","format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if","username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client","inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede modificar una tarea suya\" })","test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario puede ver","= InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod","API Task Create utilizando un usuario no autenticado. \"\"\" response","== status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\":","Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario","PUT utilizando un usuario no autenticado. \"\"\" response = self.client.put(","del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega tarea","test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code","el propietario puede ver el detalle de la tarea\" })","suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En este caso de prueba,","lista\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre","args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if","self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed = response.status_code","Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado","test_put_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos probando la","print(\"Descripcion: Usuario autenticado agrega tarea a la lista\") print(\"-----------\") def","tarea a la lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response","\"test_description\": \"El usuario no autenticado no puede agregar una tarea","[] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None:","def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede modificar una tarea suya\")","setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user =","tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title':","\" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario puede ver el","from api.utils import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test","ver detalles de la tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def","def test_put_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos probando","inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no puede modificar una","print(\"Descripcion: Solo el propietario puede ver el detalle de la","response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed","puede ver el detalle de la tarea\" }) if is_passed:","import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase):","una tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\")","a la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En este caso","puede modificar una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response","no puede agregar una tarea a la lista\") print(\"-----------\") def","def test_get_other_user_task_detail(self): \"\"\" En este caso de prueba, estamos probando","== status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\":","un usuario no autenticado. \"\"\" response = self.client.post( reverse('api-task'), {'title':","caso de prueba, estamos probando la API Task PUT utilizando","de la tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado:","un usuario no autenticado. \"\"\" response = self.client.put( reverse('api-task'), {'title':","utilizando un usuario no autenticado. \"\"\" response = self.client.put( reverse('api-task'),","usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed","response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\",","is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario puede ver el detalle","correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS, recipients=RECIPIENTS )","self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code ==","is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede modificar","status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def","diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed =","task.models import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import","InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod def","class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user( username='test_user',","== status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\":","tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS, recipients=RECIPIENTS","title='My Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user',","RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user","Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\"","def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'},","En este caso de prueba, estamos probando la API Task","una tarea a la lista\" }) if is_passed: print(\"Resultado: Aprobado\")","{'title': 'My Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN","usa credenciales de usuario diferentes. \"\"\" self.client.login(username='other_user', password='<PASSWORD>') response =","self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code ==","from django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase from","prueba, estamos probando la API Task Create utilizando un usuario","modificar una tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado:","+ inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no puede modificar","\"Usuario autenticado puede ver detalles de la tarea correctamente\" })","inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario puede ver el detalle de","autenticado puede ver detalles de la tarea correctamente\" }) if","else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no","este caso de prueba, estamos probando la API Task PUT","una tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En este caso","\" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede modificar una tarea","def setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user","de la tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report(","args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed = response.status_code ==","\"test_description\": \"Usuario autenticado puede modificar una tarea suya\" }) if","El usuario no autenticado no puede agregar una tarea a","'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos","= ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user =","puede modificar una tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else:","'My Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({","lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba,","self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed =","modificar una tarea suya\" }) if is_passed: print(\"Resultado: Aprobado\") else:","obtener detalles de la tarea de un usuario que usa","response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code","no autenticado. \"\"\" response = self.client.post( reverse('api-task'), {'title': 'My Task","del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede ver","probando la API Task Create utilizando un usuario no autenticado.","is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado agrega tarea","print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado agrega","# -*- coding: utf-8 -*- from __future__ import unicode_literals import","la tarea de un usuario que usa credenciales de usuario","= response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else","tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En este caso de","la lista\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\")","response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\",","\" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no puede","Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name)","a la lista\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado:","APITestCase from rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS","password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json')","import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import User","Task Create utilizando un usuario no autenticado. \"\"\" response =","django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse","Usuario autenticado agrega tarea a la lista\") print(\"-----------\") def test_get_task_detail(self):","\"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario puede ver el","la tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\")","User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task')","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario puede ver el detalle","\"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede ver detalles de","\"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario","['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user(","caso de prueba, estamos probando la API Task Create utilizando","Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\":","from rest_framework import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>']","TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>')","userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls):","puede ver detalles de la tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase):","API Task PUT utilizando un usuario no autenticado. \"\"\" response","= [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) ->","password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client =","autenticado. \"\"\" response = self.client.post( reverse('api-task'), {'title': 'My Task 1'},","tarea suya\") print(\"-----------\") def test_get_other_user_task_detail(self): \"\"\" En este caso de","import User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import","self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self):","None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user',","inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no puede agregar una","ver el detalle de la tarea\" }) if is_passed: print(\"Resultado:","estamos probando la API Task PUT utilizando un usuario no","\"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no puede","def test_create_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos probando","la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En este caso de","Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete()","is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed","inspect from task.models import InveraTask from api.utils import send_test_csv_report from","print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede","= response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN","args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code","print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre del test: \" +","print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title':","propietario puede ver el detalle de la tarea\") print(\"-----------\") def","utf-8 -*- from __future__ import unicode_literals import inspect from task.models","'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \"result\":","\"\"\" En este caso de prueba, estamos probando la API","detalle de la tarea\" }) if is_passed: print(\"Resultado: Aprobado\") else:","Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\":","self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'},","la API Task GET y tratando de obtener detalles de","if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario","'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\":","self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED","autenticado no puede modificar una tarea\" }) if is_passed: print(\"Resultado:","reverse from rest_framework import status TEST_RESULTS = [] RECIPIENTS =","rest_framework import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class","usuario no autenticado. \"\"\" response = self.client.post( reverse('api-task'), {'title': 'My","response = self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed","-> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user(","TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self)","= self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code ==","\"El usuario no autenticado no puede agregar una tarea a","Create utilizando un usuario no autenticado. \"\"\" response = self.client.post(","\"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo","User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba,","from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse from","de un usuario que usa credenciales de usuario diferentes. \"\"\"","1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\"","autenticado puede ver detalles de la tarea correctamente\") print(\"-----------\") class","send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase","\"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El","la tarea correctamente\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado:","else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el propietario puede ver","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Usuario autenticado puede modificar una tarea suya\"","response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({","no autenticado no puede agregar una tarea a la lista\"","unicode_literals import inspect from task.models import InveraTask from api.utils import","el propietario puede ver el detalle de la tarea\") print(\"-----------\")","correctamente\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\") print(\"Nombre","tarea suya\" }) if is_passed: print(\"Resultado: Aprobado\") else: print(\"Resultado: Fallido\")","El usuario no autenticado no puede modificar una tarea\") print(\"-----------\")","\"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"Solo el","{'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({","autenticado no puede agregar una tarea a la lista\") print(\"-----------\")","una tarea\") print(\"-----------\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail',","print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En este caso de prueba, estamos","detalle de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response","self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code ==","no puede agregar una tarea a la lista\" }) if","print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario","a la lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response =","self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed = response.status_code","prueba, estamos probando la API Task PUT utilizando un usuario","una tarea a la lista\") print(\"-----------\") def test_put_task_with_un_authenticate_user(self): \"\"\" En","test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede modificar una","print(\"Descripcion: Usuario autenticado puede ver detalles de la tarea correctamente\")","status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \"result\": \"Passed\"","inspect.currentframe().f_code.co_name) print(\"Descripcion: Solo el propietario puede ver el detalle de","y tratando de obtener detalles de la tarea de un","response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed =","password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user,","inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede modificar una tarea suya\") print(\"-----------\")","User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task =","= APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\"","{'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({","\"Usuario autenticado agrega tarea a la lista\" }) if is_passed:","la API Task PUT utilizando un usuario no autenticado. \"\"\"","self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient()","usuario no autenticado no puede modificar una tarea\" }) if","APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework import status","\" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario autenticado puede ver detalles de","import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import APIClient,","__future__ import unicode_literals import inspect from task.models import InveraTask from","un usuario que usa credenciales de usuario diferentes. \"\"\" self.client.login(username='other_user',","utilizando un usuario no autenticado. \"\"\" response = self.client.post( reverse('api-task'),","detalles de la tarea correctamente\") print(\"-----------\") class CSVReportTest(APITestCase): def test_send_csv(self):","response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND #","autenticado puede modificar una tarea suya\" }) if is_passed: print(\"Resultado:","{'title': 'My Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK","Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def","autenticado agrega tarea a la lista\" }) if is_passed: print(\"Resultado:","password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed =","self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\"","test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no","\"test_name\": inspect.currentframe().f_code.co_name, \"test_description\": \"El usuario no autenticado no puede agregar","@classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \"\"\" En este","ver el detalle de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user',","la lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail',","= self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed =","= self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed","de prueba, estamos probando la API Task PUT utilizando un","agrega tarea a la lista\") print(\"-----------\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>')","status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else \"Failed\", \"test_name\": inspect.currentframe().f_code.co_name,","puede ver detalles de la tarea correctamente\" }) if is_passed:","este caso de prueba, estamos probando la API Task GET","from rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS =","inspect.currentframe().f_code.co_name) print(\"Descripcion: El usuario no autenticado no puede modificar una","<filename>invera/api/tests.py # -*- coding: utf-8 -*- from __future__ import unicode_literals","= response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \"result\": \"Passed\" if is_passed else","de prueba, estamos probando la API Task GET y tratando","print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: El"],"string":"[\n \"print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario\",\n \"el detalle de la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>')\",\n \"usuario no autenticado no puede modificar una tarea\\\") print(\\\"-----------\\\") def\",\n \"detalles de la tarea correctamente\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no puede agregar una\",\n \"agregar una tarea a la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\"\",\n \"de obtener detalles de la tarea de un usuario que\",\n \"suya\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre\",\n \"import reverse from rest_framework import status TEST_RESULTS = [] RECIPIENTS\",\n \"\\\"\\\"\\\" response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed\",\n \"del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede modificar\",\n \"status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name,\",\n \"reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed = response.status_code ==\",\n \"is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed\",\n \"password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND\",\n \"estamos probando la API Task GET y tratando de obtener\",\n \"test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede ver detalles\",\n \"tarea a la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este\",\n \"else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega tarea a\",\n \"\\\"test_description\\\": \\\"Solo el propietario puede ver el detalle de la\",\n \"User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse\",\n \"autenticado agrega tarea a la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user',\",\n \"credenciales de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail',\",\n \"2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"print(\\\"Descripcion: El usuario no autenticado no puede modificar una tarea\\\")\",\n \"def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso\",\n \"-*- coding: utf-8 -*- from __future__ import unicode_literals import inspect\",\n \"print(\\\"Descripcion: Usuario autenticado puede modificar una tarea suya\\\") print(\\\"-----------\\\") def\",\n \"agrega tarea a la lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre\",\n \"if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega\",\n \"import inspect from task.models import InveraTask from api.utils import send_test_csv_report\",\n \"del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado\",\n \"este caso de prueba, estamos probando la API Task Create\",\n \"\\\"test_description\\\": \\\"Usuario autenticado puede ver detalles de la tarea correctamente\\\"\",\n \"que usa credenciales de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response\",\n \"# is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if\",\n \"Task PUT utilizando un usuario no autenticado. \\\"\\\"\\\" response =\",\n \"de la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response =\",\n \"\\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code\",\n \"print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo\",\n \"test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso de prueba, estamos probando la\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega tarea a la lista\\\") print(\\\"-----------\\\")\",\n \"\\\"Solo el propietario puede ver el detalle de la tarea\\\"\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no puede modificar\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no puede agregar\",\n \"la API Task Create utilizando un usuario no autenticado. \\\"\\\"\\\"\",\n \"tarea correctamente\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\")\",\n \"\\\"test_description\\\": \\\"Usuario autenticado agrega tarea a la lista\\\" }) if\",\n \"autenticado no puede modificar una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user',\",\n \"Solo el propietario puede ver el detalle de la tarea\\\")\",\n \"prueba, estamos probando la API Task GET y tratando de\",\n \"API Task GET y tratando de obtener detalles de la\",\n \"\\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario\",\n \"de prueba, estamos probando la API Task Create utilizando un\",\n \"\\\"Usuario autenticado puede modificar una tarea suya\\\" }) if is_passed:\",\n \"del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario puede\",\n \"usuario que usa credenciales de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>')\",\n \"test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json')\",\n \"usuario no autenticado. \\\"\\\"\\\" response = self.client.put( reverse('api-task'), {'title': 'My\",\n \"\\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega tarea a la\",\n \"autenticado no puede agregar una tarea a la lista\\\" })\",\n \"probando la API Task PUT utilizando un usuario no autenticado.\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede ver detalles de la tarea\",\n \"print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My\",\n \"modificar una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response =\",\n \"username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create(\",\n \"test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega tarea a\",\n \"if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega tarea a la lista\\\")\",\n \"puede agregar una tarea a la lista\\\" }) if is_passed:\",\n \"= self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed =\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede ver detalles de la tarea\",\n \"reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN\",\n \"autenticado. \\\"\\\"\\\" response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json')\",\n \"test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task\",\n \"estamos probando la API Task Create utilizando un usuario no\",\n \"'My Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({\",\n \"\\\"\\\"\\\" response = self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json')\",\n \"puede modificar una tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En\",\n \"tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]),\",\n \"password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK\",\n \"-*- from __future__ import unicode_literals import inspect from task.models import\",\n \"self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed\",\n \"response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({\",\n \"no puede modificar una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>')\",\n \"coding: utf-8 -*- from __future__ import unicode_literals import inspect from\",\n \"format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if\",\n \"Usuario autenticado puede modificar una tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self):\",\n \"detalles de la tarea de un usuario que usa credenciales\",\n \"format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if\",\n \"= self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\":\",\n \"agregar una tarea a la lista\\\" }) if is_passed: print(\\\"Resultado:\",\n \"puede modificar una tarea suya\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"Task GET y tratando de obtener detalles de la tarea\",\n \"is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede ver\",\n \"print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed\",\n \"\\\"El usuario no autenticado no puede modificar una tarea\\\" })\",\n \"Usuario autenticado puede ver detalles de la tarea correctamente\\\") print(\\\"-----------\\\")\",\n \"probando la API Task GET y tratando de obtener detalles\",\n \"}) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del\",\n \"propietario puede ver el detalle de la tarea\\\" }) if\",\n \"\\\"test_description\\\": \\\"El usuario no autenticado no puede modificar una tarea\\\"\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega tarea a la lista\\\" })\",\n \"from task.models import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models\",\n \"test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos probando la\",\n \"if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede\",\n \"la tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS,\",\n \"def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed =\",\n \"= User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial\",\n \"APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En\",\n \"response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\",\",\n \"rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework\",\n \"status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name,\",\n \"no puede modificar una tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)]))\",\n \"is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\"\",\n \"== status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\":\",\n \"\\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega tarea a la\",\n \"no autenticado. \\\"\\\"\\\" response = self.client.put( reverse('api-task'), {'title': 'My Task'},\",\n \"autenticado puede modificar una tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\"\",\n \"from __future__ import unicode_literals import inspect from task.models import InveraTask\",\n \"else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede modificar una\",\n \"= response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede ver detalles de la\",\n \"else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede ver detalles\",\n \"self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My\",\n \"Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el\",\n \"de la tarea de un usuario que usa credenciales de\",\n \"no autenticado no puede modificar una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self):\",\n \"is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code ==\",\n \"tarea a la lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else:\",\n \"else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion:\",\n \"GET y tratando de obtener detalles de la tarea de\",\n \"if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test:\",\n \"rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS = []\",\n \"una tarea suya\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado:\",\n \"el detalle de la tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"= User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task\",\n \"puede agregar una tarea a la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self):\",\n \"\\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado\",\n \"import APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework import\",\n \"usuario no autenticado no puede agregar una tarea a la\",\n \"tratando de obtener detalles de la tarea de un usuario\",\n \"self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>')\",\n \"no autenticado no puede agregar una tarea a la lista\\\")\",\n \"print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega tarea a la lista\\\"\",\n \"caso de prueba, estamos probando la API Task GET y\",\n \"ver detalles de la tarea correctamente\\\" }) if is_passed: print(\\\"Resultado:\",\n \"Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"import unicode_literals import inspect from task.models import InveraTask from api.utils\",\n \"no autenticado no puede modificar una tarea\\\" }) if is_passed:\",\n \"modificar una tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este\",\n \"de la tarea correctamente\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else:\",\n \"print(\\\"Descripcion: El usuario no autenticado no puede agregar una tarea\",\n \"puede ver el detalle de la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self):\",\n \"api.utils import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import\",\n \"\\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede modificar una tarea\",\n \"tarea de un usuario que usa credenciales de usuario diferentes.\",\n \"is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado\",\n \"la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'),\",\n \"print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso de prueba, estamos\",\n \"TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\":\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede ver detalles de la\",\n \"= self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code\",\n \"de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)]))\",\n \"is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario puede\",\n \"InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import User from\",\n \"format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if\",\n \"username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede modificar una tarea suya\\\" })\",\n \"test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario puede ver\",\n \"= InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod\",\n \"API Task Create utilizando un usuario no autenticado. \\\"\\\"\\\" response\",\n \"== status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\":\",\n \"Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario\",\n \"PUT utilizando un usuario no autenticado. \\\"\\\"\\\" response = self.client.put(\",\n \"del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega tarea\",\n \"test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code\",\n \"el propietario puede ver el detalle de la tarea\\\" })\",\n \"suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso de prueba,\",\n \"lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre\",\n \"args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if\",\n \"self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed = response.status_code\",\n \"Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado\",\n \"test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos probando la\",\n \"print(\\\"Descripcion: Usuario autenticado agrega tarea a la lista\\\") print(\\\"-----------\\\") def\",\n \"tarea a la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response\",\n \"\\\"test_description\\\": \\\"El usuario no autenticado no puede agregar una tarea\",\n \"[] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None:\",\n \"def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede modificar una tarea suya\\\")\",\n \"setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user =\",\n \"tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title':\",\n \"\\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario puede ver el\",\n \"from api.utils import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test\",\n \"ver detalles de la tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def\",\n \"def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos probando\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no puede modificar una\",\n \"print(\\\"Descripcion: Solo el propietario puede ver el detalle de la\",\n \"response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed\",\n \"puede ver el detalle de la tarea\\\" }) if is_passed:\",\n \"import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase):\",\n \"una tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\")\",\n \"a la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso\",\n \"puede modificar una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response\",\n \"no puede agregar una tarea a la lista\\\") print(\\\"-----------\\\") def\",\n \"def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso de prueba, estamos probando\",\n \"== status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\":\",\n \"un usuario no autenticado. \\\"\\\"\\\" response = self.client.post( reverse('api-task'), {'title':\",\n \"caso de prueba, estamos probando la API Task PUT utilizando\",\n \"de la tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado:\",\n \"un usuario no autenticado. \\\"\\\"\\\" response = self.client.put( reverse('api-task'), {'title':\",\n \"utilizando un usuario no autenticado. \\\"\\\"\\\" response = self.client.put( reverse('api-task'),\",\n \"usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed\",\n \"response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\",\",\n \"is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario puede ver el detalle\",\n \"correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS, recipients=RECIPIENTS )\",\n \"self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code ==\",\n \"is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede modificar\",\n \"status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def\",\n \"diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed =\",\n \"task.models import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import\",\n \"InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod def\",\n \"class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user( username='test_user',\",\n \"== status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\":\",\n \"tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report( test_results=TEST_RESULTS, recipients=RECIPIENTS\",\n \"title='My Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user',\",\n \"RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user\",\n \"Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'},\",\n \"En este caso de prueba, estamos probando la API Task\",\n \"una tarea a la lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\")\",\n \"{'title': 'My Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN\",\n \"usa credenciales de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user', password='<PASSWORD>') response =\",\n \"self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code ==\",\n \"from django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase from\",\n \"prueba, estamos probando la API Task Create utilizando un usuario\",\n \"modificar una tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado:\",\n \"+ inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no puede modificar\",\n \"\\\"Usuario autenticado puede ver detalles de la tarea correctamente\\\" })\",\n \"inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario puede ver el detalle de\",\n \"autenticado puede ver detalles de la tarea correctamente\\\" }) if\",\n \"else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no\",\n \"este caso de prueba, estamos probando la API Task PUT\",\n \"una tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso\",\n \"\\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede modificar una tarea\",\n \"def setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user\",\n \"de la tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def test_send_csv(self): send_test_csv_report(\",\n \"args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed = response.status_code ==\",\n \"\\\"test_description\\\": \\\"Usuario autenticado puede modificar una tarea suya\\\" }) if\",\n \"El usuario no autenticado no puede agregar una tarea a\",\n \"'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos\",\n \"= ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user =\",\n \"puede modificar una tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else:\",\n \"'My Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({\",\n \"lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba,\",\n \"self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed =\",\n \"modificar una tarea suya\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else:\",\n \"obtener detalles de la tarea de un usuario que usa\",\n \"response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code\",\n \"no autenticado. \\\"\\\"\\\" response = self.client.post( reverse('api-task'), {'title': 'My Task\",\n \"del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede ver\",\n \"probando la API Task Create utilizando un usuario no autenticado.\",\n \"is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado agrega tarea\",\n \"print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado agrega\",\n \"# -*- coding: utf-8 -*- from __future__ import unicode_literals import\",\n \"la tarea de un usuario que usa credenciales de usuario\",\n \"= response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else\",\n \"tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de\",\n \"la lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\")\",\n \"response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\",\",\n \"\\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no puede\",\n \"Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name)\",\n \"a la lista\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado:\",\n \"APITestCase from rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS\",\n \"password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json')\",\n \"import InveraTask from api.utils import send_test_csv_report from django.contrib.auth.models import User\",\n \"Task Create utilizando un usuario no autenticado. \\\"\\\"\\\" response =\",\n \"django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse\",\n \"Usuario autenticado agrega tarea a la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self):\",\n \"\\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario puede ver el\",\n \"la tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\")\",\n \"User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task')\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario puede ver el detalle\",\n \"\\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede ver detalles de\",\n \"\\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario\",\n \"['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user(\",\n \"caso de prueba, estamos probando la API Task Create utilizando\",\n \"Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\":\",\n \"from rest_framework import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>']\",\n \"TaskListTestCase(APITestCase): def setUp(self) -> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>')\",\n \"userTask=self.user, title='My Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls):\",\n \"puede ver detalles de la tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase):\",\n \"API Task PUT utilizando un usuario no autenticado. \\\"\\\"\\\" response\",\n \"= [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self) ->\",\n \"password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client =\",\n \"autenticado. \\\"\\\"\\\" response = self.client.post( reverse('api-task'), {'title': 'My Task 1'},\",\n \"tarea suya\\\") print(\\\"-----------\\\") def test_get_other_user_task_detail(self): \\\"\\\"\\\" En este caso de\",\n \"import User from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import\",\n \"self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self):\",\n \"None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user',\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no puede agregar una\",\n \"ver el detalle de la tarea\\\" }) if is_passed: print(\\\"Resultado:\",\n \"estamos probando la API Task PUT utilizando un usuario no\",\n \"\\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no puede\",\n \"def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos probando\",\n \"la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de\",\n \"Initial Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete()\",\n \"is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed\",\n \"inspect from task.models import InveraTask from api.utils import send_test_csv_report from\",\n \"print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede\",\n \"= response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN\",\n \"args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed = response.status_code\",\n \"print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" +\",\n \"print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title':\",\n \"propietario puede ver el detalle de la tarea\\\") print(\\\"-----------\\\") def\",\n \"utf-8 -*- from __future__ import unicode_literals import inspect from task.models\",\n \"'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({ \\\"result\\\":\",\n \"\\\"\\\"\\\" En este caso de prueba, estamos probando la API\",\n \"detalle de la tarea\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else:\",\n \"Task 1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\":\",\n \"self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'},\",\n \"la API Task GET y tratando de obtener detalles de\",\n \"if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario\",\n \"'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\":\",\n \"self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED\",\n \"autenticado no puede modificar una tarea\\\" }) if is_passed: print(\\\"Resultado:\",\n \"reverse from rest_framework import status TEST_RESULTS = [] RECIPIENTS =\",\n \"rest_framework import status TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class\",\n \"usuario no autenticado. \\\"\\\"\\\" response = self.client.post( reverse('api-task'), {'title': 'My\",\n \"response = self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed\",\n \"-> None: self.user = User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user(\",\n \"TEST_RESULTS = [] RECIPIENTS = ['<EMAIL>'] class TaskListTestCase(APITestCase): def setUp(self)\",\n \"= self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code ==\",\n \"\\\"El usuario no autenticado no puede agregar una tarea a\",\n \"Create utilizando un usuario no autenticado. \\\"\\\"\\\" response = self.client.post(\",\n \"\\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo\",\n \"User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba,\",\n \"from rest_framework.test import APIClient, APITestCase from rest_framework.reverse import reverse from\",\n \"de un usuario que usa credenciales de usuario diferentes. \\\"\\\"\\\"\",\n \"1'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"autenticado puede ver detalles de la tarea correctamente\\\") print(\\\"-----------\\\") class\",\n \"send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import APIClient, APITestCase\",\n \"\\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El\",\n \"la tarea correctamente\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado:\",\n \"else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el propietario puede ver\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Usuario autenticado puede modificar una tarea suya\\\"\",\n \"response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({\",\n \"no autenticado no puede agregar una tarea a la lista\\\"\",\n \"unicode_literals import inspect from task.models import InveraTask from api.utils import\",\n \"el propietario puede ver el detalle de la tarea\\\") print(\\\"-----------\\\")\",\n \"correctamente\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\") print(\\\"Nombre\",\n \"tarea suya\\\" }) if is_passed: print(\\\"Resultado: Aprobado\\\") else: print(\\\"Resultado: Fallido\\\")\",\n \"El usuario no autenticado no puede modificar una tarea\\\") print(\\\"-----------\\\")\",\n \"\\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"Solo el\",\n \"{'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({\",\n \"autenticado no puede agregar una tarea a la lista\\\") print(\\\"-----------\\\")\",\n \"una tarea\\\") print(\\\"-----------\\\") def test_put_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.put(reverse('api-task-detail',\",\n \"print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este caso de prueba, estamos\",\n \"detalle de la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>') response\",\n \"self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed = response.status_code ==\",\n \"no puede agregar una tarea a la lista\\\" }) if\",\n \"print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario\",\n \"a la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response =\",\n \"self.client.put(reverse('api-task-detail', args=[str(self.task.idTask)]), {'title': 'My Task 2'}, format='json') is_passed = response.status_code\",\n \"prueba, estamos probando la API Task PUT utilizando un usuario\",\n \"una tarea a la lista\\\") print(\\\"-----------\\\") def test_put_task_with_un_authenticate_user(self): \\\"\\\"\\\" En\",\n \"test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede modificar una\",\n \"print(\\\"Descripcion: Usuario autenticado puede ver detalles de la tarea correctamente\\\")\",\n \"status.HTTP_404_NOT_FOUND # is_passed = response.status_code == status.HTTP_403_FORBIDDEN TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Solo el propietario puede ver el detalle de\",\n \"y tratando de obtener detalles de la tarea de un\",\n \"response = self.client.put( reverse('api-task'), {'title': 'My Task'}, format='json') is_passed =\",\n \"password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task = InveraTask.objects.create( userTask=self.user,\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede modificar una tarea suya\\\") print(\\\"-----------\\\")\",\n \"User.objects.create_user( username='test_user', password='<PASSWORD>') self.other_user = User.objects.create_user( username='other_user', password='<PASSWORD>') self.task =\",\n \"= APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\"\",\n \"{'title': 'My Task'}, format='json') is_passed = response.status_code == status.HTTP_201_CREATED TEST_RESULTS.append({\",\n \"\\\"Usuario autenticado agrega tarea a la lista\\\" }) if is_passed:\",\n \"la API Task PUT utilizando un usuario no autenticado. \\\"\\\"\\\"\",\n \"self.task = InveraTask.objects.create( userTask=self.user, title='My Initial Task') self.client = APIClient()\",\n \"usuario no autenticado no puede modificar una tarea\\\" }) if\",\n \"APIClient, APITestCase from rest_framework.reverse import reverse from rest_framework import status\",\n \"\\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: Usuario autenticado puede ver detalles de\",\n \"import send_test_csv_report from django.contrib.auth.models import User from rest_framework.test import APIClient,\",\n \"__future__ import unicode_literals import inspect from task.models import InveraTask from\",\n \"un usuario que usa credenciales de usuario diferentes. \\\"\\\"\\\" self.client.login(username='other_user',\",\n \"utilizando un usuario no autenticado. \\\"\\\"\\\" response = self.client.post( reverse('api-task'),\",\n \"detalles de la tarea correctamente\\\") print(\\\"-----------\\\") class CSVReportTest(APITestCase): def test_send_csv(self):\",\n \"response = self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND #\",\n \"autenticado puede modificar una tarea suya\\\" }) if is_passed: print(\\\"Resultado:\",\n \"{'title': 'My Task 2'}, format='json') is_passed = response.status_code == status.HTTP_200_OK\",\n \"Task') self.client = APIClient() @classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def\",\n \"autenticado agrega tarea a la lista\\\" }) if is_passed: print(\\\"Resultado:\",\n \"password='<PASSWORD>') response = self.client.post(reverse('api-task'), {'title': 'My Task'}, format='json') is_passed =\",\n \"self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\"\",\n \"test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no\",\n \"\\\"test_name\\\": inspect.currentframe().f_code.co_name, \\\"test_description\\\": \\\"El usuario no autenticado no puede agregar\",\n \"@classmethod def tearDownClass(cls): User.objects.filter(username__in=['test_user', 'other_user']).delete() def test_create_task_with_un_authenticate_user(self): \\\"\\\"\\\" En este\",\n \"ver el detalle de la tarea\\\") print(\\\"-----------\\\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user',\",\n \"la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>') response = self.client.get(reverse('api-task-detail',\",\n \"= self.client.post( reverse('api-task'), {'title': 'My Task 1'}, format='json') is_passed =\",\n \"= self.client.get(reverse('api-task-detail', args=[str(self.task.idTask)])) is_passed = response.status_code == status.HTTP_404_NOT_FOUND # is_passed\",\n \"de prueba, estamos probando la API Task PUT utilizando un\",\n \"agrega tarea a la lista\\\") print(\\\"-----------\\\") def test_get_task_detail(self): self.client.login(username='test_user', password='<PASSWORD>')\",\n \"status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else \\\"Failed\\\", \\\"test_name\\\": inspect.currentframe().f_code.co_name,\",\n \"puede ver detalles de la tarea correctamente\\\" }) if is_passed:\",\n \"este caso de prueba, estamos probando la API Task GET\",\n \"from rest_framework.reverse import reverse from rest_framework import status TEST_RESULTS =\",\n \"inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El usuario no autenticado no puede modificar una\",\n \"<filename>invera/api/tests.py # -*- coding: utf-8 -*- from __future__ import unicode_literals\",\n \"= response.status_code == status.HTTP_200_OK TEST_RESULTS.append({ \\\"result\\\": \\\"Passed\\\" if is_passed else\",\n \"de prueba, estamos probando la API Task GET y tratando\",\n \"print(\\\"Resultado: Fallido\\\") print(\\\"Nombre del test: \\\" + inspect.currentframe().f_code.co_name) print(\\\"Descripcion: El\"\n]","isConversational":false}}},{"rowIdx":156,"cells":{"ngram":{"kind":"list like","value":["from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n","destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ') tile(0,2,16,2) tile(0,4,16,4)","source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ') tile(0,2,16,2)","import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2","* source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ')","chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n >"],"string":"[\n \"from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n\",\n \"destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ') tile(0,2,16,2) tile(0,4,16,4)\",\n \"source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ') tile(0,2,16,2)\",\n \"import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2\",\n \"* source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n > 2 ')\",\n \"chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n >\"\n]","isConversational":false}}},{"rowIdx":157,"cells":{"ngram":{"kind":"list like","value":["async with aiohttp.ClientSession() as client: async with client.post( url, data=data,","return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text,","Check response status json_response = await resp.json() return json_response async","aiohttp async def request(url, payload=None, params=None, headers=None): headers = {'content-type':","is not None: payload['cache_time'] = cache_time return await request(f'{base_url}/answerCallbackQuery', payload)","resp: # TODO: Check response status json_response = await resp.json()","# TODO: Check response status json_response = await resp.json() return","not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async","payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query(","text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text':","= await resp.json() return json_response async def get_updates(base_url, timeout, offset):","or {})} data = payload and json.dumps(payload) async with aiohttp.ClientSession()","data = payload and json.dumps(payload) async with aiohttp.ClientSession() as client:","reply_markup=None): payload = { 'chat_id': chat_id, 'text': text } if","chat_id, 'text': text } if reply_markup is not None: payload['reply_markup']","'chat_id': chat_id, 'text': text } if reply_markup is not None:","return json_response async def get_updates(base_url, timeout, offset): params = {","not None: payload['url'] = url if cache_time is not None:","async def send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id':","'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if url is","request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload =","'show_alert': show_alert } if url is not None: payload['url'] =","None: payload['url'] = url if cache_time is not None: payload['cache_time']","text, reply_markup=None): payload = { 'chat_id': chat_id, 'text': text }","return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None):","and json.dumps(payload) async with aiohttp.ClientSession() as client: async with client.post(","payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None):","'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url,","payload and json.dumps(payload) async with aiohttp.ClientSession() as client: async with","show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text,","payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert }","client.post( url, data=data, params=params, headers=headers) as resp: # TODO: Check","url, data=data, params=params, headers=headers) as resp: # TODO: Check response","'text': text, 'show_alert': show_alert } if url is not None:","headers = {'content-type': 'application/json', **(headers or {})} data = payload","{ 'chat_id': chat_id, 'text': text } if reply_markup is not","resp.json() return json_response async def get_updates(base_url, timeout, offset): params =","def send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id,","await resp.json() return json_response async def get_updates(base_url, timeout, offset): params","response status json_response = await resp.json() return json_response async def","'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async","= { 'chat_id': chat_id, 'text': text } if reply_markup is","reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id,","payload = { 'chat_id': chat_id, 'text': text } if reply_markup","request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers or","params = { 'timeout': timeout, 'offset': offset } return await","text } if reply_markup is not None: payload['reply_markup'] = reply_markup","def request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers","async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload","async def request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json',","offset): params = { 'timeout': timeout, 'offset': offset } return","def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload =","callback_query_id, text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id,","text, 'show_alert': show_alert } if url is not None: payload['url']","{ 'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params)","= payload and json.dumps(payload) async with aiohttp.ClientSession() as client: async","import aiohttp async def request(url, payload=None, params=None, headers=None): headers =","**(headers or {})} data = payload and json.dumps(payload) async with","callback_query_id, 'text': text, 'show_alert': show_alert } if url is not","send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id, 'text':","} if url is not None: payload['url'] = url if","base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id':","data=data, params=params, headers=headers) as resp: # TODO: Check response status","'application/json', **(headers or {})} data = payload and json.dumps(payload) async","params=None, headers=None): headers = {'content-type': 'application/json', **(headers or {})} data","json_response async def get_updates(base_url, timeout, offset): params = { 'timeout':","TODO: Check response status json_response = await resp.json() return json_response","timeout, offset): params = { 'timeout': timeout, 'offset': offset }","offset } return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id,","= { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if","status json_response = await resp.json() return json_response async def get_updates(base_url,","async with client.post( url, data=data, params=params, headers=headers) as resp: #","if reply_markup is not None: payload['reply_markup'] = reply_markup return await","= { 'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates',","request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None,","cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert","headers=headers) as resp: # TODO: Check response status json_response =","get_updates(base_url, timeout, offset): params = { 'timeout': timeout, 'offset': offset","url if cache_time is not None: payload['cache_time'] = cache_time return","if url is not None: payload['url'] = url if cache_time","client: async with client.post( url, data=data, params=params, headers=headers) as resp:","def get_updates(base_url, timeout, offset): params = { 'timeout': timeout, 'offset':","params=params, headers=headers) as resp: # TODO: Check response status json_response","{ 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if url","with aiohttp.ClientSession() as client: async with client.post( url, data=data, params=params,","'text': text } if reply_markup is not None: payload['reply_markup'] =","} if reply_markup is not None: payload['reply_markup'] = reply_markup return","cache_time is not None: payload['cache_time'] = cache_time return await request(f'{base_url}/answerCallbackQuery',","payload['url'] = url if cache_time is not None: payload['cache_time'] =","= {'content-type': 'application/json', **(headers or {})} data = payload and","timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async def","url is not None: payload['url'] = url if cache_time is","payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers or {})}","as client: async with client.post( url, data=data, params=params, headers=headers) as","is not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload)","chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id, 'text': text","await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert,","with client.post( url, data=data, params=params, headers=headers) as resp: # TODO:","= url if cache_time is not None: payload['cache_time'] = cache_time","await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload","= reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url,","async def get_updates(base_url, timeout, offset): params = { 'timeout': timeout,","json import aiohttp async def request(url, payload=None, params=None, headers=None): headers","as resp: # TODO: Check response status json_response = await","{'content-type': 'application/json', **(headers or {})} data = payload and json.dumps(payload)","show_alert } if url is not None: payload['url'] = url","} return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text,","headers=None): headers = {'content-type': 'application/json', **(headers or {})} data =","answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload = {","{})} data = payload and json.dumps(payload) async with aiohttp.ClientSession() as","json_response = await resp.json() return json_response async def get_updates(base_url, timeout,","params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload = {","import json import aiohttp async def request(url, payload=None, params=None, headers=None):","None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async def","if cache_time is not None: payload['cache_time'] = cache_time return await","aiohttp.ClientSession() as client: async with client.post( url, data=data, params=params, headers=headers)","url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert':","json.dumps(payload) async with aiohttp.ClientSession() as client: async with client.post( url,","is not None: payload['url'] = url if cache_time is not","reply_markup is not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage',"],"string":"[\n \"async with aiohttp.ClientSession() as client: async with client.post( url, data=data,\",\n \"return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text,\",\n \"Check response status json_response = await resp.json() return json_response async\",\n \"aiohttp async def request(url, payload=None, params=None, headers=None): headers = {'content-type':\",\n \"is not None: payload['cache_time'] = cache_time return await request(f'{base_url}/answerCallbackQuery', payload)\",\n \"resp: # TODO: Check response status json_response = await resp.json()\",\n \"# TODO: Check response status json_response = await resp.json() return\",\n \"not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async\",\n \"payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query(\",\n \"text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text':\",\n \"= await resp.json() return json_response async def get_updates(base_url, timeout, offset):\",\n \"or {})} data = payload and json.dumps(payload) async with aiohttp.ClientSession()\",\n \"data = payload and json.dumps(payload) async with aiohttp.ClientSession() as client:\",\n \"reply_markup=None): payload = { 'chat_id': chat_id, 'text': text } if\",\n \"chat_id, 'text': text } if reply_markup is not None: payload['reply_markup']\",\n \"'chat_id': chat_id, 'text': text } if reply_markup is not None:\",\n \"return json_response async def get_updates(base_url, timeout, offset): params = {\",\n \"not None: payload['url'] = url if cache_time is not None:\",\n \"async def send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id':\",\n \"'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if url is\",\n \"request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload =\",\n \"'show_alert': show_alert } if url is not None: payload['url'] =\",\n \"None: payload['url'] = url if cache_time is not None: payload['cache_time']\",\n \"text, reply_markup=None): payload = { 'chat_id': chat_id, 'text': text }\",\n \"return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None):\",\n \"and json.dumps(payload) async with aiohttp.ClientSession() as client: async with client.post(\",\n \"payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None):\",\n \"'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url,\",\n \"payload and json.dumps(payload) async with aiohttp.ClientSession() as client: async with\",\n \"show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text,\",\n \"payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert }\",\n \"client.post( url, data=data, params=params, headers=headers) as resp: # TODO: Check\",\n \"url, data=data, params=params, headers=headers) as resp: # TODO: Check response\",\n \"'text': text, 'show_alert': show_alert } if url is not None:\",\n \"headers = {'content-type': 'application/json', **(headers or {})} data = payload\",\n \"{ 'chat_id': chat_id, 'text': text } if reply_markup is not\",\n \"resp.json() return json_response async def get_updates(base_url, timeout, offset): params =\",\n \"def send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id,\",\n \"await resp.json() return json_response async def get_updates(base_url, timeout, offset): params\",\n \"response status json_response = await resp.json() return json_response async def\",\n \"'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async\",\n \"= { 'chat_id': chat_id, 'text': text } if reply_markup is\",\n \"reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id,\",\n \"payload = { 'chat_id': chat_id, 'text': text } if reply_markup\",\n \"request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers or\",\n \"params = { 'timeout': timeout, 'offset': offset } return await\",\n \"text } if reply_markup is not None: payload['reply_markup'] = reply_markup\",\n \"def request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers\",\n \"async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload\",\n \"async def request(url, payload=None, params=None, headers=None): headers = {'content-type': 'application/json',\",\n \"offset): params = { 'timeout': timeout, 'offset': offset } return\",\n \"def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload =\",\n \"callback_query_id, text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id,\",\n \"text, 'show_alert': show_alert } if url is not None: payload['url']\",\n \"{ 'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params)\",\n \"= payload and json.dumps(payload) async with aiohttp.ClientSession() as client: async\",\n \"import aiohttp async def request(url, payload=None, params=None, headers=None): headers =\",\n \"**(headers or {})} data = payload and json.dumps(payload) async with\",\n \"callback_query_id, 'text': text, 'show_alert': show_alert } if url is not\",\n \"send_message(base_url, chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id, 'text':\",\n \"} if url is not None: payload['url'] = url if\",\n \"base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload = { 'callback_query_id':\",\n \"data=data, params=params, headers=headers) as resp: # TODO: Check response status\",\n \"'application/json', **(headers or {})} data = payload and json.dumps(payload) async\",\n \"params=None, headers=None): headers = {'content-type': 'application/json', **(headers or {})} data\",\n \"json_response async def get_updates(base_url, timeout, offset): params = { 'timeout':\",\n \"TODO: Check response status json_response = await resp.json() return json_response\",\n \"timeout, offset): params = { 'timeout': timeout, 'offset': offset }\",\n \"offset } return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id,\",\n \"= { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if\",\n \"status json_response = await resp.json() return json_response async def get_updates(base_url,\",\n \"async with client.post( url, data=data, params=params, headers=headers) as resp: #\",\n \"if reply_markup is not None: payload['reply_markup'] = reply_markup return await\",\n \"= { 'timeout': timeout, 'offset': offset } return await request(f'{base_url}/getUpdates',\",\n \"request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert, url=None,\",\n \"cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert\",\n \"headers=headers) as resp: # TODO: Check response status json_response =\",\n \"get_updates(base_url, timeout, offset): params = { 'timeout': timeout, 'offset': offset\",\n \"url if cache_time is not None: payload['cache_time'] = cache_time return\",\n \"if url is not None: payload['url'] = url if cache_time\",\n \"client: async with client.post( url, data=data, params=params, headers=headers) as resp:\",\n \"def get_updates(base_url, timeout, offset): params = { 'timeout': timeout, 'offset':\",\n \"params=params, headers=headers) as resp: # TODO: Check response status json_response\",\n \"{ 'callback_query_id': callback_query_id, 'text': text, 'show_alert': show_alert } if url\",\n \"with aiohttp.ClientSession() as client: async with client.post( url, data=data, params=params,\",\n \"'text': text } if reply_markup is not None: payload['reply_markup'] =\",\n \"} if reply_markup is not None: payload['reply_markup'] = reply_markup return\",\n \"cache_time is not None: payload['cache_time'] = cache_time return await request(f'{base_url}/answerCallbackQuery',\",\n \"payload['url'] = url if cache_time is not None: payload['cache_time'] =\",\n \"= {'content-type': 'application/json', **(headers or {})} data = payload and\",\n \"timeout, 'offset': offset } return await request(f'{base_url}/getUpdates', params=params) async def\",\n \"url is not None: payload['url'] = url if cache_time is\",\n \"payload=None, params=None, headers=None): headers = {'content-type': 'application/json', **(headers or {})}\",\n \"as client: async with client.post( url, data=data, params=params, headers=headers) as\",\n \"is not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload)\",\n \"chat_id, text, reply_markup=None): payload = { 'chat_id': chat_id, 'text': text\",\n \"await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text, show_alert,\",\n \"with client.post( url, data=data, params=params, headers=headers) as resp: # TODO:\",\n \"= url if cache_time is not None: payload['cache_time'] = cache_time\",\n \"await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload\",\n \"= reply_markup return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url,\",\n \"async def get_updates(base_url, timeout, offset): params = { 'timeout': timeout,\",\n \"json import aiohttp async def request(url, payload=None, params=None, headers=None): headers\",\n \"as resp: # TODO: Check response status json_response = await\",\n \"{'content-type': 'application/json', **(headers or {})} data = payload and json.dumps(payload)\",\n \"show_alert } if url is not None: payload['url'] = url\",\n \"} return await request(f'{base_url}/getUpdates', params=params) async def send_message(base_url, chat_id, text,\",\n \"headers=None): headers = {'content-type': 'application/json', **(headers or {})} data =\",\n \"answer_callback_query( base_url, callback_query_id, text, show_alert, url=None, cache_time=None): payload = {\",\n \"{})} data = payload and json.dumps(payload) async with aiohttp.ClientSession() as\",\n \"json_response = await resp.json() return json_response async def get_updates(base_url, timeout,\",\n \"params=params) async def send_message(base_url, chat_id, text, reply_markup=None): payload = {\",\n \"import json import aiohttp async def request(url, payload=None, params=None, headers=None):\",\n \"None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage', payload) async def\",\n \"if cache_time is not None: payload['cache_time'] = cache_time return await\",\n \"aiohttp.ClientSession() as client: async with client.post( url, data=data, params=params, headers=headers)\",\n \"url=None, cache_time=None): payload = { 'callback_query_id': callback_query_id, 'text': text, 'show_alert':\",\n \"json.dumps(payload) async with aiohttp.ClientSession() as client: async with client.post( url,\",\n \"is not None: payload['url'] = url if cache_time is not\",\n \"reply_markup is not None: payload['reply_markup'] = reply_markup return await request(f'{base_url}/sendMessage',\"\n]","isConversational":false}}},{"rowIdx":158,"cells":{"ngram":{"kind":"list like","value":["Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from","(c) 2020-2021 Network To Code Licensed under the Apache License,","DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2 =","self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def","License. \"\"\" from socket import gaierror from unittest import mock","Unless required by applicable law or agreed to in writing,","by applicable law or agreed to in writing, software distributed","entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR","up a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for","as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to complete DNS lookup:","with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to complete","Network To Code Licensed under the Apache License, Version 2.0","software distributed under the License is distributed on an \"AS","distributed under the License is distributed on an \"AS IS\"","DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip","nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test","bad.local raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message,","from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\"","# Run the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address,","failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising","import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask",") self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7","slug=\"junos\", napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 =","CONDITIONS OF ANY KIND, either express or implied. See the","FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change","\"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address","classes. (c) 2020-2021 Network To Code Licensed under the Apache","# Look up response value mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN","to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self,","Version 2.0 (the \"License\"); you may not use this file","# Check for bad.local raising an exception with self.assertRaises(OnboardException) as","= Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\",","= gaierror(8) # Check for bad.local raising an exception with","writing, software distributed under the License is distributed on an","address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message,","\"\"\"Unit tests for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021","slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5","not use this file except in compliance with the License.","2.0 (the \"License\"); you may not use this file except","Apache License, Version 2.0 (the \"License\"); you may not use","copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable","express or implied. See the License for the specific language","platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 )","address.\"\"\" # Look up response value mock_get_hostbyname.return_value = \"192.0.2.1\" #","for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network To","for exception with prefix address entered with self.assertRaises(OnboardException) as exc_info:",") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address.\"\"\"","IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1","mock_get_hostbyname): \"\"\"Check DNS to IP address failing.\"\"\" # Look up","in compliance with the License. You may obtain a copy","self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP","Check for bad.local raising an exception with self.assertRaises(OnboardException) as exc_info:","self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to complete DNS","of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law","onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper","nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from","to IP address.\"\"\" # Look up response value mock_get_hostbyname.return_value =","address failing.\"\"\" # Look up a failed response mock_get_hostbyname.side_effect =","you may not use this file except in compliance with","# self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\",","import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers","def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address failing.\"\"\" #","response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising an","the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\")","is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR","site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1,","the License. You may obtain a copy of the License","agreed to in writing, software distributed under the License is","self.assertEqual(exc_info.exception.message, \"ERROR failed to complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\")","\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","\"\"\" from socket import gaierror from unittest import mock from","setUp(self): \"\"\"Create a superuser and token for API calls.\"\"\" self.site1","distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS","at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to","the NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create a superuser and token","use this file except in compliance with the License. You","= Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\")","\"fail-dns\") # Check for exception with prefix address entered with","the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or","mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising an exception","ANY KIND, either express or implied. See the License for","IP address failing.\"\"\" # Look up a failed response mock_get_hostbyname.side_effect","ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1,","exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears a prefix was","http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in","and limitations under the License. \"\"\" from socket import gaierror","may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless","obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required","API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\")","self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 =","mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run","\"\"\"Check DNS to IP address.\"\"\" # Look up response value","test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address failing.\"\"\" # Look","django.test import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from","either express or implied. See the License for the specific","Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1","role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to","BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","# Check for exception with prefix address entered with self.assertRaises(OnboardException)","complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for exception","under the License. \"\"\" from socket import gaierror from unittest","DNS to IP address.\"\"\" # Look up response value mock_get_hostbyname.return_value","under the License is distributed on an \"AS IS\" BASIS,","\"License\"); you may not use this file except in compliance","a superuser and token for API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\",","= \"192.0.2.1\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the","License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES","Licensed under the Apache License, Version 2.0 (the \"License\"); you","with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears","import gaierror from unittest import mock from django.test import TestCase","= DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2",") self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\")","module and its classes. (c) 2020-2021 Network To Code Licensed","with the License. You may obtain a copy of the","nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import","gaierror(8) # Check for bad.local raising an exception with self.assertRaises(OnboardException)","value mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) #","raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR","calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1","-> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the","License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed","License for the specific language governing permissions and limitations under","for bad.local raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5)","\"192.0.2.1\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check","Code Licensed under the Apache License, Version 2.0 (the \"License\");","import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the","def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address.\"\"\" # Look","self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears a","NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create a superuser and token for","DNS to IP address failing.\"\"\" # Look up a failed","slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\")","To Code Licensed under the Apache License, Version 2.0 (the","IP address.\"\"\" # Look up response value mock_get_hostbyname.return_value = \"192.0.2.1\"","failed to complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check","to complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for","this file except in compliance with the License. You may","slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco","as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears a prefix","Run the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\")","nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network To Code","\"ERROR failed to complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") #","specific language governing permissions and limitations under the License. \"\"\"","from django.test import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform","(the \"License\"); you may not use this file except in","IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the IP","\"\"\"Create a superuser and token for API calls.\"\"\" self.site1 =","# Look up a failed response mock_get_hostbyname.side_effect = gaierror(8) #","an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed","exception with prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7)","limitations under the License. \"\"\" from socket import gaierror from","import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions","superuser and token for API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\")","import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\" def setUp(self):","ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1,","mock_get_hostbyname): \"\"\"Check DNS to IP address.\"\"\" # Look up response","self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 =","OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\"bad.local\",","language governing permissions and limitations under the License. \"\"\" from","up response value mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN -> IP","@mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address failing.\"\"\"","applicable law or agreed to in writing, software distributed under","OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create","site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1,","bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for exception with prefix address","NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 )","= OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create(","Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4","failing.\"\"\" # Look up a failed response mock_get_hostbyname.side_effect = gaierror(8)","OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\",","napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create(","test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address.\"\"\" # Look up","change the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname):","from unittest import mock from django.test import TestCase from nautobot.dcim.models","the License is distributed on an \"AS IS\" BASIS, WITHOUT","exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to complete DNS lookup: bad.local\")","You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0","the specific language governing permissions and limitations under the License.","address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to","to IP address failing.\"\"\" # Look up a failed response","OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname):","class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create a","Check for exception with prefix address entered with self.assertRaises(OnboardException) as","the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check","the Apache License, Version 2.0 (the \"License\"); you may not","file except in compliance with the License. You may obtain","except in compliance with the License. You may obtain a","or implied. See the License for the specific language governing","KIND, either express or implied. See the License for the","@mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP address.\"\"\" #","check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def","to in writing, software distributed under the License is distributed","Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\",","self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") # self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\",","Look up response value mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN ->","onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears a prefix was entered:","or agreed to in writing, software distributed under the License","\"\"\"Check DNS to IP address failing.\"\"\" # Look up a","prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \"fail-prefix\")","law or agreed to in writing, software distributed under the","OR CONDITIONS OF ANY KIND, either express or implied. See","from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase):","self.platform2 = Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1,","socket import gaierror from unittest import mock from django.test import","= OnboardingTask.objects.create( ip_address=\"bad.local\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create(","IP address self.assertEqual(self.onboarding_task4.ip_address, \"192.0.2.1\") @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_failed_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS","onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to complete DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason,","compliance with the License. You may obtain a copy of","Class.\"\"\" def setUp(self): \"\"\"Create a superuser and token for API","nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\" def","OF ANY KIND, either express or implied. See the License","mock from django.test import TestCase from nautobot.dcim.models import Site, DeviceRole,","token for API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 =","under the Apache License, Version 2.0 (the \"License\"); you may","= Platform.objects.create(name=\"Cisco NX-OS\", slug=\"cisco-nx-os\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\"ntc123.local\", site=self.site1, role=self.device_role1,","for API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\",","on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF","OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class","site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS","platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check DNS to IP","permissions and limitations under the License. \"\"\" from socket import","from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models","a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local","the License. \"\"\" from socket import gaierror from unittest import","and its classes. (c) 2020-2021 Network To Code Licensed under","\"\"\"Test the NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create a superuser and","License, Version 2.0 (the \"License\"); you may not use this","self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for exception with prefix address entered","from socket import gaierror from unittest import mock from django.test","role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1","self.assertEqual(exc_info.exception.reason, \"fail-prefix\") self.assertEqual(exc_info.exception.message, \"ERROR appears a prefix was entered: 192.0.2.1/32\")","self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Platform.objects.create(name=\"JunOS\", slug=\"junos\", napalm_driver=\"junos\") #","lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for exception with prefix","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","gaierror from unittest import mock from django.test import TestCase from","= OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self,","for the specific language governing permissions and limitations under the","onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the IP address","See the License for the specific language governing permissions and","a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by","governing permissions and limitations under the License. \"\"\" from socket","import mock from django.test import TestCase from nautobot.dcim.models import Site,","exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \"ERROR failed to","NetdevKeeperTestCase(TestCase): \"\"\"Test the NetdevKeeper Class.\"\"\" def setUp(self): \"\"\"Create a superuser","unittest import mock from django.test import TestCase from nautobot.dcim.models import","def setUp(self): \"\"\"Create a superuser and token for API calls.\"\"\"","License. You may obtain a copy of the License at","self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1 = DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 =","DNS lookup: bad.local\") self.assertEqual(exc_info.exception.reason, \"fail-dns\") # Check for exception with","Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import","and token for API calls.\"\"\" self.site1 = Site.objects.create(name=\"USWEST\", slug=\"uswest\") self.device_role1","ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\"nautobot_device_onboarding.helpers.socket.gethostbyname\") def test_check_ip(self, mock_get_hostbyname): \"\"\"Check","tests for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network","the License for the specific language governing permissions and limitations","may not use this file except in compliance with the","TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import","in writing, software distributed under the License is distributed on","required by applicable law or agreed to in writing, software","implied. See the License for the specific language governing permissions","2020-2021 Network To Code Licensed under the Apache License, Version","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","Look up a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check","its classes. (c) 2020-2021 Network To Code Licensed under the","an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY","response value mock_get_hostbyname.return_value = \"192.0.2.1\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4)","# FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to","platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\"192.0.2.1/32\", site=self.site1, role=self.device_role1, platform=self.platform1 )","with prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason,","from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException"],"string":"[\n \"Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from\",\n \"(c) 2020-2021 Network To Code Licensed under the Apache License,\",\n \"DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 =\",\n \"self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def\",\n \"License. \\\"\\\"\\\" from socket import gaierror from unittest import mock\",\n \"Unless required by applicable law or agreed to in writing,\",\n \"by applicable law or agreed to in writing, software distributed\",\n \"entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR\",\n \"up a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for\",\n \"as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete DNS lookup:\",\n \"with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete\",\n \"Network To Code Licensed under the Apache License, Version 2.0\",\n \"software distributed under the License is distributed on an \\\"AS\",\n \"distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip\",\n \"nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test\",\n \"bad.local raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message,\",\n \"from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\"\",\n \"# Run the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address,\",\n \"failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising\",\n \"import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask\",\n \") self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7\",\n \"slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 =\",\n \"CONDITIONS OF ANY KIND, either express or implied. See the\",\n \"FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change\",\n \"\\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address\",\n \"classes. (c) 2020-2021 Network To Code Licensed under the Apache\",\n \"# Look up response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN\",\n \"to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self,\",\n \"Version 2.0 (the \\\"License\\\"); you may not use this file\",\n \"# Check for bad.local raising an exception with self.assertRaises(OnboardException) as\",\n \"= Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\",\",\n \"= gaierror(8) # Check for bad.local raising an exception with\",\n \"writing, software distributed under the License is distributed on an\",\n \"address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message,\",\n \"\\\"\\\"\\\"Unit tests for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021\",\n \"slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5\",\n \"not use this file except in compliance with the License.\",\n \"2.0 (the \\\"License\\\"); you may not use this file except\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); you may not use\",\n \"copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable\",\n \"express or implied. See the License for the specific language\",\n \"platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 )\",\n \"address.\\\"\\\"\\\" # Look up response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" #\",\n \"for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network To\",\n \"for exception with prefix address entered with self.assertRaises(OnboardException) as exc_info:\",\n \") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\"\",\n \"IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1\",\n \"mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address failing.\\\"\\\"\\\" # Look up\",\n \"in compliance with the License. You may obtain a copy\",\n \"self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP\",\n \"Check for bad.local raising an exception with self.assertRaises(OnboardException) as exc_info:\",\n \"self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete DNS\",\n \"of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law\",\n \"onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper\",\n \"nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from\",\n \"to IP address.\\\"\\\"\\\" # Look up response value mock_get_hostbyname.return_value =\",\n \"address failing.\\\"\\\"\\\" # Look up a failed response mock_get_hostbyname.side_effect =\",\n \"you may not use this file except in compliance with\",\n \"# self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\",\",\n \"import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers\",\n \"def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address failing.\\\"\\\"\\\" #\",\n \"response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising an\",\n \"the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\")\",\n \"is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR\",\n \"site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1,\",\n \"the License. You may obtain a copy of the License\",\n \"agreed to in writing, software distributed under the License is\",\n \"self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\")\",\n \"\\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"\\\"\\\"\\\" from socket import gaierror from unittest import mock from\",\n \"setUp(self): \\\"\\\"\\\"Create a superuser and token for API calls.\\\"\\\"\\\" self.site1\",\n \"distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS\",\n \"at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to\",\n \"the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a superuser and token\",\n \"use this file except in compliance with the License. You\",\n \"= Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\")\",\n \"\\\"fail-dns\\\") # Check for exception with prefix address entered with\",\n \"the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or\",\n \"mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local raising an exception\",\n \"ANY KIND, either express or implied. See the License for\",\n \"IP address failing.\\\"\\\"\\\" # Look up a failed response mock_get_hostbyname.side_effect\",\n \"ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1,\",\n \"exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears a prefix was\",\n \"http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in\",\n \"and limitations under the License. \\\"\\\"\\\" from socket import gaierror\",\n \"may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless\",\n \"obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required\",\n \"API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\")\",\n \"self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 =\",\n \"mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run\",\n \"\\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\" # Look up response value\",\n \"test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address failing.\\\"\\\"\\\" # Look\",\n \"django.test import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from\",\n \"either express or implied. See the License for the specific\",\n \"Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1\",\n \"role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to\",\n \"BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"# Check for exception with prefix address entered with self.assertRaises(OnboardException)\",\n \"complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for exception\",\n \"under the License. \\\"\\\"\\\" from socket import gaierror from unittest\",\n \"DNS to IP address.\\\"\\\"\\\" # Look up response value mock_get_hostbyname.return_value\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"\\\"License\\\"); you may not use this file except in compliance\",\n \"a superuser and token for API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\",\",\n \"= \\\"192.0.2.1\\\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you\",\n \"with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears\",\n \"import gaierror from unittest import mock from django.test import TestCase\",\n \"= DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2\",\n \") self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\")\",\n \"module and its classes. (c) 2020-2021 Network To Code Licensed\",\n \"with the License. You may obtain a copy of the\",\n \"nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import\",\n \"gaierror(8) # Check for bad.local raising an exception with self.assertRaises(OnboardException)\",\n \"value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) #\",\n \"raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR\",\n \"calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1\",\n \"-> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the\",\n \"License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed\",\n \"License for the specific language governing permissions and limitations under\",\n \"for bad.local raising an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5)\",\n \"\\\"192.0.2.1\\\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check\",\n \"Code Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the\",\n \"def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\" # Look\",\n \"self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears a\",\n \"NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a superuser and token for\",\n \"DNS to IP address failing.\\\"\\\"\\\" # Look up a failed\",\n \"slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\")\",\n \"To Code Licensed under the Apache License, Version 2.0 (the\",\n \"IP address.\\\"\\\"\\\" # Look up response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\"\",\n \"failed to complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check\",\n \"to complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for\",\n \"this file except in compliance with the License. You may\",\n \"slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco\",\n \"as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears a prefix\",\n \"Run the check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\")\",\n \"nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network To Code\",\n \"\\\"ERROR failed to complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") #\",\n \"specific language governing permissions and limitations under the License. \\\"\\\"\\\"\",\n \"from django.test import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform\",\n \"(the \\\"License\\\"); you may not use this file except in\",\n \"IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the IP\",\n \"\\\"\\\"\\\"Create a superuser and token for API calls.\\\"\\\"\\\" self.site1 =\",\n \"# Look up a failed response mock_get_hostbyname.side_effect = gaierror(8) #\",\n \"an exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed\",\n \"exception with prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7)\",\n \"limitations under the License. \\\"\\\"\\\" from socket import gaierror from\",\n \"import TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions\",\n \"superuser and token for API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\")\",\n \"import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self):\",\n \"ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1,\",\n \"mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\" # Look up response\",\n \"self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 =\",\n \"OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create( ip_address=\\\"bad.local\\\",\",\n \"language governing permissions and limitations under the License. \\\"\\\"\\\" from\",\n \"up response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN -> IP\",\n \"@mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address failing.\\\"\\\"\\\"\",\n \"applicable law or agreed to in writing, software distributed under\",\n \"OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create\",\n \"site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1,\",\n \"bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for exception with prefix address\",\n \"NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 )\",\n \"= OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task5 = OnboardingTask.objects.create(\",\n \"Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4\",\n \"failing.\\\"\\\"\\\" # Look up a failed response mock_get_hostbyname.side_effect = gaierror(8)\",\n \"OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\",\",\n \"napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create(\",\n \"test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\" # Look up\",\n \"change the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname):\",\n \"from unittest import mock from django.test import TestCase from nautobot.dcim.models\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\",\n \"exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete DNS lookup: bad.local\\\")\",\n \"You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\",\n \"the specific language governing permissions and limitations under the License.\",\n \"address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to\",\n \"to IP address failing.\\\"\\\"\\\" # Look up a failed response\",\n \"OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname):\",\n \"class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a\",\n \"Check for exception with prefix address entered with self.assertRaises(OnboardException) as\",\n \"the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); you may not\",\n \"file except in compliance with the License. You may obtain\",\n \"except in compliance with the License. You may obtain a\",\n \"or implied. See the License for the specific language governing\",\n \"KIND, either express or implied. See the License for the\",\n \"@mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP address.\\\"\\\"\\\" #\",\n \"check to change the IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def\",\n \"to in writing, software distributed under the License is distributed\",\n \"Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\",\",\n \"self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") # self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\",\",\n \"Look up response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN ->\",\n \"onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears a prefix was entered:\",\n \"or agreed to in writing, software distributed under the License\",\n \"\\\"\\\"\\\"Check DNS to IP address failing.\\\"\\\"\\\" # Look up a\",\n \"prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\")\",\n \"law or agreed to in writing, software distributed under the\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. See\",\n \"from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase):\",\n \"self.platform2 = Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1,\",\n \"socket import gaierror from unittest import mock from django.test import\",\n \"= OnboardingTask.objects.create( ip_address=\\\"bad.local\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create(\",\n \"IP address self.assertEqual(self.onboarding_task4.ip_address, \\\"192.0.2.1\\\") @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_failed_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS\",\n \"onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to complete DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason,\",\n \"compliance with the License. You may obtain a copy of\",\n \"Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a superuser and token for API\",\n \"nautobot_device_onboarding.models import OnboardingTask class NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def\",\n \"OF ANY KIND, either express or implied. See the License\",\n \"mock from django.test import TestCase from nautobot.dcim.models import Site, DeviceRole,\",\n \"token for API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 =\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); you may\",\n \"= Platform.objects.create(name=\\\"Cisco NX-OS\\\", slug=\\\"cisco-nx-os\\\") self.onboarding_task4 = OnboardingTask.objects.create( ip_address=\\\"ntc123.local\\\", site=self.site1, role=self.device_role1,\",\n \"for API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\",\",\n \"on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models import OnboardingTask class\",\n \"site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS\",\n \"platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check DNS to IP\",\n \"permissions and limitations under the License. \\\"\\\"\\\" from socket import\",\n \"from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from nautobot_device_onboarding.models\",\n \"a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check for bad.local\",\n \"the License. \\\"\\\"\\\" from socket import gaierror from unittest import\",\n \"and its classes. (c) 2020-2021 Network To Code Licensed under\",\n \"\\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a superuser and\",\n \"License, Version 2.0 (the \\\"License\\\"); you may not use this\",\n \"self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for exception with prefix address entered\",\n \"from socket import gaierror from unittest import mock from django.test\",\n \"role=self.device_role1, platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1\",\n \"self.assertEqual(exc_info.exception.reason, \\\"fail-prefix\\\") self.assertEqual(exc_info.exception.message, \\\"ERROR appears a prefix was entered: 192.0.2.1/32\\\")\",\n \"self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 = Platform.objects.create(name=\\\"JunOS\\\", slug=\\\"junos\\\", napalm_driver=\\\"junos\\\") #\",\n \"lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for exception with prefix\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"gaierror from unittest import mock from django.test import TestCase from\",\n \"= OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self,\",\n \"for the specific language governing permissions and limitations under the\",\n \"onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to change the IP address\",\n \"See the License for the specific language governing permissions and\",\n \"a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by\",\n \"governing permissions and limitations under the License. \\\"\\\"\\\" from socket\",\n \"import mock from django.test import TestCase from nautobot.dcim.models import Site,\",\n \"exception with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task5) self.assertEqual(exc_info.exception.message, \\\"ERROR failed to\",\n \"NetdevKeeperTestCase(TestCase): \\\"\\\"\\\"Test the NetdevKeeper Class.\\\"\\\"\\\" def setUp(self): \\\"\\\"\\\"Create a superuser\",\n \"unittest import mock from django.test import TestCase from nautobot.dcim.models import\",\n \"def setUp(self): \\\"\\\"\\\"Create a superuser and token for API calls.\\\"\\\"\\\"\",\n \"License. You may obtain a copy of the License at\",\n \"self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1 = DeviceRole.objects.create(name=\\\"Firewall\\\", slug=\\\"firewall\\\") self.platform1 =\",\n \"DNS lookup: bad.local\\\") self.assertEqual(exc_info.exception.reason, \\\"fail-dns\\\") # Check for exception with\",\n \"Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import\",\n \"and token for API calls.\\\"\\\"\\\" self.site1 = Site.objects.create(name=\\\"USWEST\\\", slug=\\\"uswest\\\") self.device_role1\",\n \"ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 ) @mock.patch(\\\"nautobot_device_onboarding.helpers.socket.gethostbyname\\\") def test_check_ip(self, mock_get_hostbyname): \\\"\\\"\\\"Check\",\n \"tests for nautobot_device_onboarding.netdev_keeper module and its classes. (c) 2020-2021 Network\",\n \"the License for the specific language governing permissions and limitations\",\n \"may not use this file except in compliance with the\",\n \"TestCase from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import\",\n \"in writing, software distributed under the License is distributed on\",\n \"required by applicable law or agreed to in writing, software\",\n \"implied. See the License for the specific language governing permissions\",\n \"2020-2021 Network To Code Licensed under the Apache License, Version\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"Look up a failed response mock_get_hostbyname.side_effect = gaierror(8) # Check\",\n \"its classes. (c) 2020-2021 Network To Code Licensed under the\",\n \"an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"response value mock_get_hostbyname.return_value = \\\"192.0.2.1\\\" # FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4)\",\n \"# FQDN -> IP onboarding_task_fqdn_to_ip(ot=self.onboarding_task4) # Run the check to\",\n \"platform=self.platform1 ) self.onboarding_task7 = OnboardingTask.objects.create( ip_address=\\\"192.0.2.1/32\\\", site=self.site1, role=self.device_role1, platform=self.platform1 )\",\n \"with prefix address entered with self.assertRaises(OnboardException) as exc_info: onboarding_task_fqdn_to_ip(ot=self.onboarding_task7) self.assertEqual(exc_info.exception.reason,\",\n \"from nautobot.dcim.models import Site, DeviceRole, Platform from nautobot_device_onboarding.exceptions import OnboardException\"\n]","isConversational":false}}},{"rowIdx":159,"cells":{"ngram":{"kind":"list like","value":["= struct.unpack(\"<Q\", h)[0] w = file.read(8) w = struct.unpack(\"<Q\", w)[0]","img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with","file: n = file.read(8) n = struct.unpack(\"<Q\", n)[0] c =","print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label # if","[] # with open(ground_truth) as file: # for line in","range(10): read_label('../../build/val_label_%d.bin' % i) # labels = [] # for","not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\"<l\",","row)) def read_image(filename): with open(filename, mode='rb') as file: n =","print(n, c, h, w) for ii in range(n): r =","label = [] sets = set() while not (file.tell() ==","label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901],","os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num)","trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename,","plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as file: n","matplotlib.pyplot as plt import struct import numpy as np def","print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def","by ay27 at 17/4/9 import os import matplotlib.pyplot as plt","= struct.unpack(\"<Q\", w)[0] print(n, c, h, w) for ii in","read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for","# read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i","with open(ground_truth) as file: # for line in file: #","label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt')","file.read(4) num = struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) # print(file.tell() ==","sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902],","= file.read(8) n = struct.unpack(\"<Q\", n)[0] c = file.read(8) c","w = struct.unpack(\"<Q\", w)[0] print(n, c, h, w) for ii","[] sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch","== os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return","= trans(file.read(h*w)) if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size)","import os import matplotlib.pyplot as plt import struct import numpy","w) for ii in range(n): r = trans(file.read(h*w)) g =","h = struct.unpack(\"<Q\", h)[0] w = file.read(8) w = struct.unpack(\"<Q\",","struct.unpack(\"<Q\", w)[0] print(n, c, h, w) label = [] sets","open(path, 'rb') as file: n = file.read(8) n = struct.unpack(\"<Q\",","# read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i)","ch = file.read(4) num = struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) #","read_label(path, ground_truth=None): with open(path, 'rb') as file: n = file.read(8)","sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch =","for i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels =","= [] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' %","numpy as np def trans(row): return list(map(lambda x: np.uint8(x), row))","struct.unpack(\"<Q\", c)[0] h = file.read(8) h = struct.unpack(\"<Q\", h)[0] w","ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) #","i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground","h = file.read(8) h = struct.unpack(\"<Q\", h)[0] w = file.read(8)","n = struct.unpack(\"<Q\", n)[0] c = file.read(8) c = struct.unpack(\"<Q\",","= [] # with open(ground_truth) as file: # for line","# read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin'","# ground = [] # with open('../../build/shuffled_list') as file: #","x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as file:","return label # if ground_truth: # g = [] #","w)[0] print(n, c, h, w) for ii in range(n): r","list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as","read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i) #","range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w))","# # ground = [] # with open('../../build/shuffled_list') as file:","np def trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename):","== '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') #","read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels","'../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10):","break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show()","print(n, c, h, w) label = [] sets = set()","struct.unpack(\"<Q\", w)[0] print(n, c, h, w) for ii in range(n):","while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num =","print(label[900],label[901], label[902], label[903], label[904]) return label # if ground_truth: #","i) # labels = [] # for i in range(10):","read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' %","w) label = [] sets = set() while not (file.tell()","'__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin')","ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c)","label[903], label[904]) return label # if ground_truth: # g =","in range(10): read_label('../../build/val_label_%d.bin' % i) # labels = [] #","i)) # # ground = [] # with open('../../build/shuffled_list') as","g = [] # with open(ground_truth) as file: # for","# print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903],","label[904]) return label # if ground_truth: # g = []","open(filename, mode='rb') as file: n = file.read(8) n = struct.unpack(\"<Q\",","# print(label[900],label[901], label[902], label[903], label[904]) return label # if ground_truth:","with open(filename, mode='rb') as file: n = file.read(8) n =","= trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100: break","# labels = [] # for i in range(10): #","file.read(8) c = struct.unpack(\"<Q\", c)[0] h = file.read(8) h =","c = file.read(8) c = struct.unpack(\"<Q\", c)[0] h = file.read(8)","at 17/4/9 import os import matplotlib.pyplot as plt import struct","c, h, w) label = [] sets = set() while","g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100:","# with open(ground_truth) as file: # for line in file:","print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904])","import numpy as np def trans(row): return list(map(lambda x: np.uint8(x),","mode='rb') as file: n = file.read(8) n = struct.unpack(\"<Q\", n)[0]","== os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\"<l\", ch)[0] label.append(num)","# labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = [] #","if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') #","h, w) label = [] sets = set() while not","n)[0] c = file.read(8) c = struct.unpack(\"<Q\", c)[0] h =","w = struct.unpack(\"<Q\", w)[0] print(n, c, h, w) label =","file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n, c, h, w) for","read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in","as plt import struct import numpy as np def trans(row):","i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels = []","read_image(filename): with open(filename, mode='rb') as file: n = file.read(8) n","c, h, w) for ii in range(n): r = trans(file.read(h*w))","as file: # for line in file: # g.append(int(line.split(' ')[1]))","17/4/9 import os import matplotlib.pyplot as plt import struct import","labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = [] # with","plt import struct import numpy as np def trans(row): return","import matplotlib.pyplot as plt import struct import numpy as np","(file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\"<l\", ch)[0]","for line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label)","g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__': #","return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb')","in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b =","def read_label(path, ground_truth=None): with open(path, 'rb') as file: n =","== os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path,","n = file.read(8) n = struct.unpack(\"<Q\", n)[0] c = file.read(8)","= struct.unpack(\"<Q\", c)[0] h = file.read(8) h = struct.unpack(\"<Q\", h)[0]","[] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i))","= struct.unpack(\"<Q\", n)[0] c = file.read(8) c = struct.unpack(\"<Q\", c)[0]","ground = [] # with open('../../build/shuffled_list') as file: # ground.append()","# g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__':","read_label('../../build/val_label_%d.bin' % i) # labels = [] # for i","trans(file.read(h*w)) if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img","# Created by ay27 at 17/4/9 import os import matplotlib.pyplot","# for line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g,","struct.unpack(\"<Q\", n)[0] c = file.read(8) c = struct.unpack(\"<Q\", c)[0] h","print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label #","if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img =","as file: n = file.read(8) n = struct.unpack(\"<Q\", n)[0] c","struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label))","100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img)","import struct import numpy as np def trans(row): return list(map(lambda","c)[0] h = file.read(8) h = struct.unpack(\"<Q\", h)[0] w =","# g = [] # with open(ground_truth) as file: #","b = trans(file.read(h*w)) if ii == 100: break print(file.tell() ==","if ground_truth: # g = [] # with open(ground_truth) as","= [] sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size):","np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb')","for ii in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w))","= set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4)","label[902], label[903], label[904]) return label # if ground_truth: # g","file: # for line in file: # g.append(int(line.split(' ')[1])) #","trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii ==","= file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n, c, h, w)","def trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with","== 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape)","with open(path, 'rb') as file: n = file.read(8) n =","labels = [] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin'","file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ ==","h, w) for ii in range(n): r = trans(file.read(h*w)) g","# np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') #","Created by ay27 at 17/4/9 import os import matplotlib.pyplot as","os import matplotlib.pyplot as plt import struct import numpy as","struct.unpack(\"<Q\", h)[0] w = file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n,","range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = []","# read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin')","in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__","<reponame>Phaeton-lang/baselines<filename>superneurons/tools/img_val/main.py<gh_stars>0 # Created by ay27 at 17/4/9 import os import","')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin')","# if ground_truth: # g = [] # with open(ground_truth)","for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # #","ay27 at 17/4/9 import os import matplotlib.pyplot as plt import","r = trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if","= struct.unpack(\"<Q\", w)[0] print(n, c, h, w) label = []","file.read(8) n = struct.unpack(\"<Q\", n)[0] c = file.read(8) c =","= file.read(4) num = struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) # print(file.tell()","% i) # labels = [] # for i in","= file.read(8) c = struct.unpack(\"<Q\", c)[0] h = file.read(8) h","print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as","os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None):","% i)) # # ground = [] # with open('../../build/shuffled_list')","h)[0] w = file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n, c,","struct import numpy as np def trans(row): return list(map(lambda x:","np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin',","= np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path,","set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num","w)[0] print(n, c, h, w) label = [] sets =","= struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label)","ground_truth: # g = [] # with open(ground_truth) as file:","ground_truth=None): with open(path, 'rb') as file: n = file.read(8) n","ii in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b","np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as file: n","def read_image(filename): with open(filename, mode='rb') as file: n = file.read(8)","= trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii","open(ground_truth) as file: # for line in file: # g.append(int(line.split('","as np def trans(row): return list(map(lambda x: np.uint8(x), row)) def","file.read(8) h = struct.unpack(\"<Q\", h)[0] w = file.read(8) w =","__name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin')","c = struct.unpack(\"<Q\", c)[0] h = file.read(8) h = struct.unpack(\"<Q\",","w = file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n, c, h,","# for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) #","os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label","num = struct.unpack(\"<l\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size)","plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as file:","'rb') as file: n = file.read(8) n = struct.unpack(\"<Q\", n)[0]","in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground =","trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100: break print(file.tell()","file.read(8) w = struct.unpack(\"<Q\", w)[0] print(n, c, h, w) label","line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if","label # if ground_truth: # g = [] # with","= file.read(8) h = struct.unpack(\"<Q\", h)[0] w = file.read(8) w"],"string":"[\n \"= struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0]\",\n \"img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with\",\n \"file: n = file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0] c =\",\n \"print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label # if\",\n \"[] # with open(ground_truth) as file: # for line in\",\n \"range(10): read_label('../../build/val_label_%d.bin' % i) # labels = [] # for\",\n \"not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\\\"<l\\\",\",\n \"row)) def read_image(filename): with open(filename, mode='rb') as file: n =\",\n \"print(n, c, h, w) for ii in range(n): r =\",\n \"label = [] sets = set() while not (file.tell() ==\",\n \"label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901],\",\n \"os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num)\",\n \"trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename,\",\n \"plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as file: n\",\n \"matplotlib.pyplot as plt import struct import numpy as np def\",\n \"print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def\",\n \"by ay27 at 17/4/9 import os import matplotlib.pyplot as plt\",\n \"= struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) for ii in\",\n \"read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for\",\n \"# read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i\",\n \"with open(ground_truth) as file: # for line in file: #\",\n \"label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt')\",\n \"file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) # print(file.tell() ==\",\n \"sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902],\",\n \"= file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8) c\",\n \"w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) for ii\",\n \"[] sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch\",\n \"== os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return\",\n \"= trans(file.read(h*w)) if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size)\",\n \"import os import matplotlib.pyplot as plt import struct import numpy\",\n \"w) for ii in range(n): r = trans(file.read(h*w)) g =\",\n \"h = struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8) w = struct.unpack(\\\"<Q\\\",\",\n \"struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) label = [] sets\",\n \"open(path, 'rb') as file: n = file.read(8) n = struct.unpack(\\\"<Q\\\",\",\n \"# read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i)\",\n \"ch = file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) #\",\n \"read_label(path, ground_truth=None): with open(path, 'rb') as file: n = file.read(8)\",\n \"sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch =\",\n \"for i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels =\",\n \"= [] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' %\",\n \"numpy as np def trans(row): return list(map(lambda x: np.uint8(x), row))\",\n \"struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0] w\",\n \"ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) #\",\n \"i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground\",\n \"h = file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8)\",\n \"n = struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8) c = struct.unpack(\\\"<Q\\\",\",\n \"= [] # with open(ground_truth) as file: # for line\",\n \"# read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin'\",\n \"# ground = [] # with open('../../build/shuffled_list') as file: #\",\n \"x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as file:\",\n \"return label # if ground_truth: # g = [] #\",\n \"w)[0] print(n, c, h, w) for ii in range(n): r\",\n \"list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as\",\n \"read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i) #\",\n \"range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w))\",\n \"# # ground = [] # with open('../../build/shuffled_list') as file:\",\n \"np def trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename):\",\n \"== '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') #\",\n \"read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels\",\n \"'../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10):\",\n \"break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show()\",\n \"print(n, c, h, w) label = [] sets = set()\",\n \"struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) for ii in range(n):\",\n \"while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num =\",\n \"print(label[900],label[901], label[902], label[903], label[904]) return label # if ground_truth: #\",\n \"i) # labels = [] # for i in range(10):\",\n \"read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in range(10): read_label('../../build/val_label_%d.bin' %\",\n \"w) label = [] sets = set() while not (file.tell()\",\n \"'__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin')\",\n \"ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c)\",\n \"label[903], label[904]) return label # if ground_truth: # g =\",\n \"in range(10): read_label('../../build/val_label_%d.bin' % i) # labels = [] #\",\n \"i)) # # ground = [] # with open('../../build/shuffled_list') as\",\n \"g = [] # with open(ground_truth) as file: # for\",\n \"# print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903],\",\n \"label[904]) return label # if ground_truth: # g = []\",\n \"open(filename, mode='rb') as file: n = file.read(8) n = struct.unpack(\\\"<Q\\\",\",\n \"# print(label[900],label[901], label[902], label[903], label[904]) return label # if ground_truth:\",\n \"with open(filename, mode='rb') as file: n = file.read(8) n =\",\n \"= trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100: break\",\n \"# labels = [] # for i in range(10): #\",\n \"file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8) h =\",\n \"c = file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8)\",\n \"at 17/4/9 import os import matplotlib.pyplot as plt import struct\",\n \"c, h, w) label = [] sets = set() while\",\n \"g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100:\",\n \"# with open(ground_truth) as file: # for line in file:\",\n \"print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904])\",\n \"import numpy as np def trans(row): return list(map(lambda x: np.uint8(x),\",\n \"mode='rb') as file: n = file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0]\",\n \"== os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num)\",\n \"# labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = [] #\",\n \"if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') #\",\n \"h, w) label = [] sets = set() while not\",\n \"n)[0] c = file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0] h =\",\n \"w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) label =\",\n \"file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) for\",\n \"read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin') for i in\",\n \"as plt import struct import numpy as np def trans(row):\",\n \"i in range(10): read_label('../../build/val_label_%d.bin' % i) # labels = []\",\n \"read_image(filename): with open(filename, mode='rb') as file: n = file.read(8) n\",\n \"c, h, w) for ii in range(n): r = trans(file.read(h*w))\",\n \"as file: # for line in file: # g.append(int(line.split(' ')[1]))\",\n \"17/4/9 import os import matplotlib.pyplot as plt import struct import\",\n \"labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = [] # with\",\n \"plt import struct import numpy as np def trans(row): return\",\n \"import matplotlib.pyplot as plt import struct import numpy as np\",\n \"(file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0]\",\n \"for line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label)\",\n \"g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__': #\",\n \"return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb')\",\n \"in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b =\",\n \"def read_label(path, ground_truth=None): with open(path, 'rb') as file: n =\",\n \"== os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path,\",\n \"n = file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8)\",\n \"= struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0]\",\n \"[] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i))\",\n \"= struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0]\",\n \"ground = [] # with open('../../build/shuffled_list') as file: # ground.append()\",\n \"# g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__':\",\n \"read_label('../../build/val_label_%d.bin' % i) # labels = [] # for i\",\n \"trans(file.read(h*w)) if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img\",\n \"# Created by ay27 at 17/4/9 import os import matplotlib.pyplot\",\n \"# for line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g,\",\n \"struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0] h\",\n \"print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label #\",\n \"if ii == 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img =\",\n \"as file: n = file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0] c\",\n \"struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label) print(len(label))\",\n \"100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img)\",\n \"import struct import numpy as np def trans(row): return list(map(lambda\",\n \"c)[0] h = file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0] w =\",\n \"# g = [] # with open(ground_truth) as file: #\",\n \"b = trans(file.read(h*w)) if ii == 100: break print(file.tell() ==\",\n \"if ground_truth: # g = [] # with open(ground_truth) as\",\n \"= [] sets = set() while not (file.tell() == os.fstat(file.fileno()).st_size):\",\n \"np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb')\",\n \"for ii in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w))\",\n \"= set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4)\",\n \"label[902], label[903], label[904]) return label # if ground_truth: # g\",\n \"file: # for line in file: # g.append(int(line.split(' ')[1])) #\",\n \"trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii ==\",\n \"= file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w)\",\n \"def trans(row): return list(map(lambda x: np.uint8(x), row)) def read_image(filename): with\",\n \"== 100: break print(file.tell() == os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape)\",\n \"with open(path, 'rb') as file: n = file.read(8) n =\",\n \"labels = [] # for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin'\",\n \"file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__ ==\",\n \"h, w) for ii in range(n): r = trans(file.read(h*w)) g\",\n \"# np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') #\",\n \"Created by ay27 at 17/4/9 import os import matplotlib.pyplot as\",\n \"os import matplotlib.pyplot as plt import struct import numpy as\",\n \"struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n,\",\n \"range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground = []\",\n \"# read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin') # read_label('../../build/cifar100_train_label.bin') read_image('../../build/val_data_8.bin')\",\n \"in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if __name__\",\n \"<reponame>Phaeton-lang/baselines<filename>superneurons/tools/img_val/main.py<gh_stars>0 # Created by ay27 at 17/4/9 import os import\",\n \"')[1])) # np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin')\",\n \"# if ground_truth: # g = [] # with open(ground_truth)\",\n \"for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # #\",\n \"ay27 at 17/4/9 import os import matplotlib.pyplot as plt import\",\n \"r = trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if\",\n \"= struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) label = []\",\n \"file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0] c = file.read(8) c =\",\n \"= file.read(4) num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) # print(file.tell()\",\n \"% i) # labels = [] # for i in\",\n \"= file.read(8) c = struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8) h\",\n \"print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as\",\n \"os.fstat(file.fileno()).st_size) img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None):\",\n \"% i)) # # ground = [] # with open('../../build/shuffled_list')\",\n \"h)[0] w = file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c,\",\n \"struct import numpy as np def trans(row): return list(map(lambda x:\",\n \"np.testing.assert_array_equal(g, label) if __name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin',\",\n \"= np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path,\",\n \"set() while not (file.tell() == os.fstat(file.fileno()).st_size): ch = file.read(4) num\",\n \"w)[0] print(n, c, h, w) label = [] sets =\",\n \"= struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size) print(label)\",\n \"ground_truth: # g = [] # with open(ground_truth) as file:\",\n \"ground_truth=None): with open(path, 'rb') as file: n = file.read(8) n\",\n \"ii in range(n): r = trans(file.read(h*w)) g = trans(file.read(h*w)) b\",\n \"np.uint8(x), row)) def read_image(filename): with open(filename, mode='rb') as file: n\",\n \"def read_image(filename): with open(filename, mode='rb') as file: n = file.read(8)\",\n \"= trans(file.read(h*w)) g = trans(file.read(h*w)) b = trans(file.read(h*w)) if ii\",\n \"open(ground_truth) as file: # for line in file: # g.append(int(line.split('\",\n \"as np def trans(row): return list(map(lambda x: np.uint8(x), row)) def\",\n \"file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8) w =\",\n \"__name__ == '__main__': # read_image('../../data/ilsvrc2012/img.bin') # read_label('../../data/ilsvrc2012/label.bin', '../../data/ilsvrc2012/val.txt') # read_image('../../build/cifar100_train_image.bin')\",\n \"c = struct.unpack(\\\"<Q\\\", c)[0] h = file.read(8) h = struct.unpack(\\\"<Q\\\",\",\n \"w = file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h,\",\n \"# for i in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) #\",\n \"os.fstat(file.fileno()).st_size) print(label) print(len(label)) # print(label[900],label[901], label[902], label[903], label[904]) return label\",\n \"num = struct.unpack(\\\"<l\\\", ch)[0] label.append(num) sets.add(num) # print(file.tell() == os.fstat(file.fileno()).st_size)\",\n \"plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with open(path, 'rb') as file:\",\n \"'rb') as file: n = file.read(8) n = struct.unpack(\\\"<Q\\\", n)[0]\",\n \"in range(10): # labels.append(read_label('../../build/val_label_%d.bin' % i)) # # ground =\",\n \"trans(file.read(h*w)) b = trans(file.read(h*w)) if ii == 100: break print(file.tell()\",\n \"file.read(8) w = struct.unpack(\\\"<Q\\\", w)[0] print(n, c, h, w) label\",\n \"line in file: # g.append(int(line.split(' ')[1])) # np.testing.assert_array_equal(g, label) if\",\n \"label # if ground_truth: # g = [] # with\",\n \"= file.read(8) h = struct.unpack(\\\"<Q\\\", h)[0] w = file.read(8) w\"\n]","isConversational":false}}},{"rowIdx":160,"cells":{"ngram":{"kind":"list like","value":["self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ == \"__main__\": unittest.main()","3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7))","Team. # Distributed under the terms of the MIT License.","__status__ = \"Development\" __date__ = \"4/1/16\" import os import unittest","terms of the MIT License. \"\"\" Test for the piezo","PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt =","0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0,","self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839],","os import unittest import numpy as np from pymatgen.analysis.piezo import","0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839],","0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def","= np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt,","self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt =","setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [ [0.0, 0.0,","[[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ]","= \"<NAME>\" __version__ = \"0.1\" __maintainer__ = \"<NAME>\" __email__ =","self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0,","[ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]],","the terms of the MIT License. \"\"\" Test for the","np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3,","import numpy as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing","np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class","as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest","= \"Development\" __date__ = \"4/1/16\" import os import unittest import","] ) self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0,","Development Team. # Distributed under the terms of the MIT","0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] )","from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc =","0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix","self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839,","bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt","0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0,","0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839,","self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt =","27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0,","test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array)","import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self):","\"\"\" Test for the piezo tensor class \"\"\" __author__ =","0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0,","= PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def","0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0,","0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0],","__maintainer__ = \"<NAME>\" __email__ = \"<EMAIL>\" __status__ = \"Development\" __date__","def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3,","np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0,","(c) Pymatgen Development Team. # Distributed under the terms of","PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array)","pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix)","0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self):","PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__","__author__ = \"<NAME>\" __version__ = \"0.1\" __maintainer__ = \"<NAME>\" __email__","= np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt =","0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280,","0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839,","np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0,","self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0],","class \"\"\" __author__ = \"<NAME>\" __version__ = \"0.1\" __maintainer__ =","= PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if","the MIT License. \"\"\" Test for the piezo tensor class","\"\"\" __author__ = \"<NAME>\" __version__ = \"0.1\" __maintainer__ = \"<NAME>\"","Copyright (c) Pymatgen Development Team. # Distributed under the terms","Pymatgen Development Team. # Distributed under the terms of the","0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]],","test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3))","\"4/1/16\" import os import unittest import numpy as np from","self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt","0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array)","0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0,","0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822,","def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [ [0.0,","test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array)","[[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0,","Distributed under the terms of the MIT License. \"\"\" Test","[0.0, 0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt,","self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ == \"__main__\":","\"<NAME>\" __email__ = \"<EMAIL>\" __status__ = \"Development\" __date__ = \"4/1/16\"","unittest import numpy as np from pymatgen.analysis.piezo import PiezoTensor from","bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError,","7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix,","= PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor,","0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]],","PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [","= \"<EMAIL>\" __status__ = \"Development\" __date__ = \"4/1/16\" import os","0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0,","of the MIT License. \"\"\" Test for the piezo tensor","0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822,","MIT License. \"\"\" Test for the piezo tensor class \"\"\"","= np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt,","License. \"\"\" Test for the piezo tensor class \"\"\" __author__","0.0], ] ) self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0,","[0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0,","= np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0,","[0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0,","6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array(","PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix =","27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array( [","0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0],","[[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822,","0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0,","7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix,","[0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0,","[6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix =","= \"4/1/16\" import os import unittest import numpy as np","# Distributed under the terms of the MIT License. \"\"\"","0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822,","0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822,","Test for the piezo tensor class \"\"\" __author__ = \"<NAME>\"","piezo tensor class \"\"\" __author__ = \"<NAME>\" __version__ = \"0.1\"","6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array = [","27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array","import unittest import numpy as np from pymatgen.analysis.piezo import PiezoTensor","__email__ = \"<EMAIL>\" __status__ = \"Development\" __date__ = \"4/1/16\" import","self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ ==","\"0.1\" __maintainer__ = \"<NAME>\" __email__ = \"<EMAIL>\" __status__ = \"Development\"","tensor class \"\"\" __author__ = \"<NAME>\" __version__ = \"0.1\" __maintainer__","PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7))","= \"<NAME>\" __email__ = \"<EMAIL>\" __status__ = \"Development\" __date__ =","\"Development\" __date__ = \"4/1/16\" import os import unittest import numpy","0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array)","0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array( [ [0.0,","self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839,","[0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt","self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3,","= [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0,","# Copyright (c) Pymatgen Development Team. # Distributed under the","[0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839,","__date__ = \"4/1/16\" import os import unittest import numpy as","= np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0,","0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0],","pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def","[0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0,",") self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0,","pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt)","[ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0,","] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array =","bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError,","self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self):","for the piezo tensor class \"\"\" __author__ = \"<NAME>\" __version__","\"<NAME>\" __version__ = \"0.1\" __maintainer__ = \"<NAME>\" __email__ = \"<EMAIL>\"",") self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0],","0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0,","def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt,","[0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0,","class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array(","= \"0.1\" __maintainer__ = \"<NAME>\" __email__ = \"<EMAIL>\" __status__ =","] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0,","0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839],","pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError,","= self.get_structure(\"BaNiO3\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0,","0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822,","def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt,","\"<EMAIL>\" __status__ = \"Development\" __date__ = \"4/1/16\" import os import","[0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0,","bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt","pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\")","PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc","0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0,","self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def","[0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280,","0.0, 0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0,","0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array","PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self):","0.0, 0.0], ] ) self.vasp_matrix = np.array( [ [0.0, 0.0,","0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0],","numpy as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import","[ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0,","self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt","the piezo tensor class \"\"\" __author__ = \"<NAME>\" __version__ =","6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt =","import os import unittest import numpy as np from pymatgen.analysis.piezo","np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt)","np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt)","__version__ = \"0.1\" __maintainer__ = \"<NAME>\" __email__ = \"<EMAIL>\" __status__","[6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array =","pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt)","0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0],","under the terms of the MIT License. \"\"\" Test for","import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\"BaNiO3\") self.voigt_matrix","0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0],","bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix)","0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ]","from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest):"],"string":"[\n \"self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ == \\\"__main__\\\": unittest.main()\",\n \"3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7))\",\n \"Team. # Distributed under the terms of the MIT License.\",\n \"__status__ = \\\"Development\\\" __date__ = \\\"4/1/16\\\" import os import unittest\",\n \"terms of the MIT License. \\\"\\\"\\\" Test for the piezo\",\n \"PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt =\",\n \"0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0,\",\n \"self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839],\",\n \"os import unittest import numpy as np from pymatgen.analysis.piezo import\",\n \"0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839],\",\n \"0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def\",\n \"= np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt,\",\n \"self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt =\",\n \"setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [ [0.0, 0.0,\",\n \"[[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ]\",\n \"= \\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__ =\",\n \"self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0,\",\n \"[ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]],\",\n \"the terms of the MIT License. \\\"\\\"\\\" Test for the\",\n \"np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3,\",\n \"import numpy as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing\",\n \"np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class\",\n \"as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest\",\n \"= \\\"Development\\\" __date__ = \\\"4/1/16\\\" import os import unittest import\",\n \"] ) self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0,\",\n \"Development Team. # Distributed under the terms of the MIT\",\n \"0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] )\",\n \"from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc =\",\n \"0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix\",\n \"self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839,\",\n \"bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt\",\n \"0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0,\",\n \"0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839,\",\n \"self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt =\",\n \"27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0,\",\n \"test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array)\",\n \"import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self):\",\n \"\\\"\\\"\\\" Test for the piezo tensor class \\\"\\\"\\\" __author__ =\",\n \"0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0,\",\n \"= PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def\",\n \"0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0,\",\n \"0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0],\",\n \"__maintainer__ = \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__\",\n \"def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3,\",\n \"np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0,\",\n \"(c) Pymatgen Development Team. # Distributed under the terms of\",\n \"PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array)\",\n \"pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix)\",\n \"0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self):\",\n \"PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__\",\n \"__author__ = \\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__\",\n \"= np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt =\",\n \"0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280,\",\n \"0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839,\",\n \"np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0,\",\n \"self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0],\",\n \"class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__ =\",\n \"= PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if\",\n \"the MIT License. \\\"\\\"\\\" Test for the piezo tensor class\",\n \"\\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\"\",\n \"Copyright (c) Pymatgen Development Team. # Distributed under the terms\",\n \"Pymatgen Development Team. # Distributed under the terms of the\",\n \"0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]],\",\n \"test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3))\",\n \"\\\"4/1/16\\\" import os import unittest import numpy as np from\",\n \"self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt\",\n \"0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array)\",\n \"0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0,\",\n \"0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822,\",\n \"def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [ [0.0,\",\n \"test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array)\",\n \"[[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0,\",\n \"Distributed under the terms of the MIT License. \\\"\\\"\\\" Test\",\n \"[0.0, 0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt,\",\n \"self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ == \\\"__main__\\\":\",\n \"\\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__ = \\\"4/1/16\\\"\",\n \"unittest import numpy as np from pymatgen.analysis.piezo import PiezoTensor from\",\n \"bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError,\",\n \"7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix,\",\n \"= PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor,\",\n \"0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]],\",\n \"PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [\",\n \"= \\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__ = \\\"4/1/16\\\" import os\",\n \"0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0,\",\n \"of the MIT License. \\\"\\\"\\\" Test for the piezo tensor\",\n \"0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822,\",\n \"MIT License. \\\"\\\"\\\" Test for the piezo tensor class \\\"\\\"\\\"\",\n \"= np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt,\",\n \"License. \\\"\\\"\\\" Test for the piezo tensor class \\\"\\\"\\\" __author__\",\n \"0.0], ] ) self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0,\",\n \"[0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0,\",\n \"= np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0,\",\n \"[0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0,\",\n \"6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array(\",\n \"PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix =\",\n \"27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array( [\",\n \"0.0, 0.0, 0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0],\",\n \"[[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822,\",\n \"0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0,\",\n \"7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix,\",\n \"[0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0,\",\n \"[6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.vasp_matrix =\",\n \"= \\\"4/1/16\\\" import os import unittest import numpy as np\",\n \"# Distributed under the terms of the MIT License. \\\"\\\"\\\"\",\n \"0.03839], [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822,\",\n \"0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822,\",\n \"Test for the piezo tensor class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\"\",\n \"piezo tensor class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__ = \\\"0.1\\\"\",\n \"6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array = [\",\n \"27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array\",\n \"import unittest import numpy as np from pymatgen.analysis.piezo import PiezoTensor\",\n \"__email__ = \\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__ = \\\"4/1/16\\\" import\",\n \"self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ ==\",\n \"\\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__ = \\\"Development\\\"\",\n \"tensor class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__\",\n \"PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7))\",\n \"= \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__ =\",\n \"\\\"Development\\\" __date__ = \\\"4/1/16\\\" import os import unittest import numpy\",\n \"0.0, 27.4628]], ] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array)\",\n \"0.0, 0.0, 0.0], ] ) self.vasp_matrix = np.array( [ [0.0,\",\n \"self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839,\",\n \"[0.0, 6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt\",\n \"self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3,\",\n \"= [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0], [0.03839, 0.0,\",\n \"# Copyright (c) Pymatgen Development Team. # Distributed under the\",\n \"[0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839,\",\n \"__date__ = \\\"4/1/16\\\" import os import unittest import numpy as\",\n \"= np.array( [ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0,\",\n \"0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0],\",\n \"pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def\",\n \"[0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0,\",\n \") self.vasp_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0,\",\n \"pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt)\",\n \"[ [0.0, 0.0, 0.0, 0.0, 0.0, 0.03839], [0.0, 0.0, 0.0,\",\n \"] def test_new(self): pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array =\",\n \"bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError,\",\n \"self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self):\",\n \"for the piezo tensor class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__\",\n \"\\\"<NAME>\\\" __version__ = \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\"\",\n \") self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0, 0.0],\",\n \"0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0], [0.0, 0.0,\",\n \"def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt,\",\n \"[0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839], [0.0,\",\n \"class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array(\",\n \"= \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__ =\",\n \"] ) self.full_tensor_array = [ [[0.0, 0.0, 0.03839], [0.0, 0.0,\",\n \"0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.03839],\",\n \"pt = PiezoTensor(self.full_tensor_array) self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError,\",\n \"= self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix = np.array( [ [0.0, 0.0, 0.0, 0.0,\",\n \"0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822,\",\n \"def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt,\",\n \"\\\"<EMAIL>\\\" __status__ = \\\"Development\\\" __date__ = \\\"4/1/16\\\" import os import\",\n \"[0.0, 0.0, 0.0], [0.03839, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0,\",\n \"bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt = np.zeros((3, 7)) pt\",\n \"pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\")\",\n \"PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc\",\n \"0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0,\",\n \"self.assertArrayAlmostEqual(pt, self.full_tensor_array) bad_dim_array = np.zeros((3, 3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def\",\n \"[0.0, 0.0, 0.0, 0.0, 0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280,\",\n \"0.0, 0.0, 0.0], ] ) self.full_tensor_array = [ [[0.0, 0.0,\",\n \"0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array\",\n \"PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self):\",\n \"0.0, 0.0], ] ) self.vasp_matrix = np.array( [ [0.0, 0.0,\",\n \"0.0], [0.0, 0.0, 0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0],\",\n \"numpy as np from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import\",\n \"[ [0.0, 0.0, 0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0,\",\n \"self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) def test_from_vasp_voigt(self): bad_voigt\",\n \"the piezo tensor class \\\"\\\"\\\" __author__ = \\\"<NAME>\\\" __version__ =\",\n \"6.89822, 0.0], [0.0, 0.0, 27.4628]], ] def test_new(self): pt =\",\n \"import os import unittest import numpy as np from pymatgen.analysis.piezo\",\n \"np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt)\",\n \"np.zeros((3, 7)) pt = PiezoTensor.from_vasp_voigt(self.vasp_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt)\",\n \"__version__ = \\\"0.1\\\" __maintainer__ = \\\"<NAME>\\\" __email__ = \\\"<EMAIL>\\\" __status__\",\n \"[6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ] ) self.full_tensor_array =\",\n \"pt = PiezoTensor.from_voigt(self.voigt_matrix) self.assertArrayEqual(pt, self.full_tensor_array) self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt)\",\n \"0.0, 0.0, 0.03839, 0.0], [0.0, 0.0, 0.0, 0.03839, 0.0, 0.0],\",\n \"under the terms of the MIT License. \\\"\\\"\\\" Test for\",\n \"import PymatgenTest class PiezoTest(PymatgenTest): def setUp(self): self.piezo_struc = self.get_structure(\\\"BaNiO3\\\") self.voigt_matrix\",\n \"0.03839], [0.0, 0.03839, 0.0]], [[6.89822, 0.0, 0.0], [0.0, 6.89822, 0.0],\",\n \"bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7)) pt = PiezoTensor.from_voigt(self.voigt_matrix)\",\n \"0.03839, 0.0, 0.0], [6.89822, 6.89822, 27.46280, 0.0, 0.0, 0.0], ]\",\n \"from pymatgen.analysis.piezo import PiezoTensor from pymatgen.util.testing import PymatgenTest class PiezoTest(PymatgenTest):\"\n]","isConversational":false}}},{"rowIdx":161,"cells":{"ngram":{"kind":"list like","value":["\"\"\"Checks access of instance files on the host. :param instance:","NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts down or powers up","context: security context :instance: instance object that was migrated :block_device_info:","%s\", str(event)) return if not isinstance(event, virtevent.Event): raise ValueError( _(\"Event",":param int resize_to: If the new volume is larger than","(or permitting them to be freeform) is up to the","actual configuration. Most hypervisors can use the default implementation which","in multiple phases, turning off the instance before the end.","an instance with the supplied ID exists on the host,",":param str action: The action the host should perform. The","contains information about the VIFs to unplug. :return: None \"\"\"","particular node.\"\"\" if nodename in self.get_available_nodes(): return True # Refresh","network interface from a running instance. The counter action to","the 'pause'. :param nova.objects.instance.Instance instance: The instance which should be","to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context.","the migration of all instances to other hosts. Consider the","callback to receive events. Register a callback to receive asynchronous","raise NotImplementedError() def block_stats(self, instance, disk_id): \"\"\"Return performance counters associated","def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \"\"\"resume guest state when","the cumulative I/O wait CPU time (nanoseconds), since the host","# need to define their supported file system # type","newer interface :func:`get_instance_diagnostics` :rtype: dict \"\"\" # TODO(Vek): Need to","where kern and user indicate the cumulative CPU time (nanoseconds)","that are specific to the virtualization platform. \"\"\" capabilities =","block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals') or []","with MAC limits should return a set of MAC addresses,","be decided by the driver implementation if not set. :returns:","def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \"\"\"Detach the disk attached","the new volume is larger than the old volume, it","migrated :param network_info: instance network information :param block_migration: if true,","is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate","the database entry to create a new instance based on","object as returned by DB layer. :param network_info: instance network","instance: Instance being live migrated \"\"\" raise NotImplementedError() def live_migration_abort(self,","addresses'. \"\"\" return None def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options","driver format. Virt drivers expect block device mapping to be",":param nova.context.RequestContext context: The context for the rescue. :param nova.objects.instance.Instance","left. :rtype: str \"\"\" raise NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets","context in for access to auth_token raise NotImplementedError() def unplug_vifs(self,","instance. :param context: security context :param instance: nova.objects.instance.Instance :param image_id:","\"\"\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info):","with :func:`set_host_enabled`. :param str host: The name of the host","info to be passed in to methods that expect it.","of the image of the instance. :param rescue_password: <PASSWORD> for","security context :param instance: nova.objects.instance.Instance \"\"\" pass def inject_network_info(self, instance,","def set_admin_password(self, instance, new_pass): \"\"\"Set the root password on the","storage. :param context: security context :param data: result of check_instance_shared_storage_local","raise NotImplementedError() def suspend(self, context, instance): \"\"\"Suspend the specified instance.","specified instance. :param context: security context :param instance: nova.objects.instance.Instance :param","if switched to maintenance mode or \"off_maintenance\" if maintenance mode","of the virt drivers are encouraged to override this method","and user indicate the cumulative CPU time (nanoseconds) spent by","\"\"\"Sets the ability of this host to accept new instances.","self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information about instance resource usage. :returns:","`instance`. :param dict old_connection_info: The volume for this connection gets","application of IPTables rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn off deferral","raise NotImplementedError() def get_spice_console(self, context, instance): \"\"\"Get connection info for","execute live migration. This checks if the live migration can","in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self,","console.type.ConsoleSpice \"\"\" raise NotImplementedError() def get_rdp_console(self, context, instance): \"\"\"Get connection","all nodes managed by the compute service. This method is",":param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def rescue(self, context, instance,","context, instance): \"\"\"Get connection info for a rdp console. :param","\"\"\" raise NotImplementedError() def unrescue(self, instance, network_info): \"\"\"Unrescue the specified","the stack. This is unfortunately also called a 'name' elsewhere.","virtual machines. Return the number of virtual machines that the","MAC addresses can still be virtualized onto any L2 domain,",":func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs unplugged.","a VirtAPI instance :param compute_driver: a compute driver name to","been destroyed from the Hypervisor before calling this method. :param","on host after check_instance_shared_storage calls :param context: security context :param","nova.context.RequestContext context: The context for the suspend. :param nova.objects.instance.Instance instance:","platform-specific layer, and never escape the connection interface. The platform-specific",":param context: security context :param instance: nova.objects.instance.Instance object :param block_device_info:","{ 'ip': ip, 'initiator': initiator, 'host': hostname } \"\"\" raise","raise ValueError( _(\"Event must be an instance of nova.virt.event.Event\")) try:","got left. :rtype: str \"\"\" raise NotImplementedError() def set_host_enabled(self, enabled):","the caller want to get resources from a driver that","} def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None","get_diagnostics(self, instance): \"\"\"Return diagnostics data about the given instance. :param","in the DB. :param nodename: node which the caller want","auth_token raise NotImplementedError() def suspend(self, context, instance): \"\"\"Suspend the specified","with the set of MAC addresses. This is called during","stats. :returns: a dict containing the CPU stat info, eg:","usage. :returns: dict of nova uuid => dict of usage","a InstanceInfo object \"\"\" # TODO(Vek): Need to pass context","image of the instance. :param injected_files: User files to inject","be automatically booted. The counter action for `restore` is :func:`soft_delete`.","all info about the requested block device. device_name does not","MAC addresses that the instance is to have. allocate_for_instance will","to a specified instance. The counter action to this is","Apache License, Version 2.0 (the \"License\"); you may # not","for access to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance,","are long integers. This method is optional. On some platforms","raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default the missing","def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage info for volumes attached","called a 'name' elsewhere. As far as this layer is","running VM's on the given host. \"\"\" # TODO(Vek): Need","All Rights Reserved. # # Licensed under the Apache License,","migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration_force_complete(self, instance):","mode should be changed. :param bool mode: If `True`, go","addresses. This method allows drivers to return a set of","call to ensure that all assigned network details are compatible","should remove the given `host` :param str host: The name","the hostname of the machine as follows:: { 'ip': ip,","of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the","`suspend` is :func:`resume`. :param nova.context.RequestContext context: The context for the","to shutdown \"\"\" raise NotImplementedError() def power_on(self, context, instance, network_info,","the Neutron client API. :return: None, or a set of","doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails to","can accept further instances, return \"enabled\", if further instances shouldn't","def check_instance_shared_storage_local(self, context, instance): \"\"\"Check if instance files located on","the virt drivers are encouraged to override this method with","nova to be able to attach to the volume. This","not use this file except in compliance with the License.","efficient. Maintainers of the virt drivers are encouraged to override",":param block_migration: if true, migrate VM disk. :param migrate_data: a","have their own identity schemes, to uniquely identify a VM","a big overlap to the return value of the newer","the security group the instance is a member of or","etc.) to_xml may have to be called since it defines","this method should not be implemented. :param context: security context","on the given host. \"\"\" # TODO(Vek): Need to pass","def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute","The flavor of the instance whose disk get migrated. :param","again. The counter action for 'resume' is :func:`suspend`. Depending on","instance is being recreated on a new hypervisor - all","warning in that case. :param context: security context :param instance:","NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \"\"\"Power on the","driver supports multi compute-nodes, this method returns a list of","See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach block","nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary","be used as the active disk after the snapshot operation","Tell the caller whether we expect the legacy format of","waiting for its completion. To migrate an instance, filtering rules","def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the","from a driver that manages only one node can safely","def check_instance_shared_storage_remote(self, context, data): \"\"\"Check if instance files located on","list_instances(self): \"\"\"Return the names of all the instances known to","\"License\"); you may # not use this file except in","associated rules live in the datastore, and calling this method","raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms,","initialize_connection() API call. In the case of doing a snapshot,","[hypervisor_hostname]. \"\"\" raise NotImplementedError() def node_is_available(self, nodename): \"\"\"Return whether this","to create a new instance based on that. :param nova.objects.instance.Instance","network_info: The network information of the given `instance`. :param dict","filtering instance.\"\"\" # TODO(Vek): Need to pass context in for","driver module. Load the compute driver module specified by the","The callback will be invoked with a single parameter, which","implementation specific params \"\"\" raise NotImplementedError() def reboot(self, context, instance,","wait CPU time (nanoseconds), since the host is booting up;","resumed and will use CPU cycles and memory of the","rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for the resume.","NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This method is called after a","snapshot_id, delete_info): \"\"\"Deletes a snapshot of a volume attached to","held across the shutdown + spin up steps. :param context:","access to auth_token raise NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts",":raise nova.exception.NovaException: If the detach fails. :return: None \"\"\" raise","return a set of MAC addresses, which will be supplied","information :param destroy_disks: if true, destroy disks at destination during","are only known higher up the stack. Most virtualization platforms","maintenance mode. :return: \"on_maintenance\" if switched to maintenance mode or","presented in the format of a dict containing the following","int timeout: The time in seconds to wait for the","need to be called. Don't use thread for this method","true, destroy disks at destination during cleanup :param migrate_data: a","allocate_for_instance will take this into consideration when provisioning networking for","to build an instance of the given flavor. Defaults to","snapshot operation has completed. There may be other data included","def confirm_migration(self, migration, instance, network_info): \"\"\"Confirms a resize/migration, destroying the","def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None def","by the driver implementation if not set. :returns: The chosen","the connection, the name of the iscsi initiator and the","command `uptime` on this host. :return: A text which contains","changed. :param bool mode: If `True`, go into maintenance mode.","False, } def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback =","instance with all info about the requested block device. device_name","for creating the snapshot. \"\"\" raise NotImplementedError() def volume_snapshot_delete(self, context,","block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \"\"\"Base class for",":returns an instance of console.type.ConsoleSpice \"\"\" raise NotImplementedError() def get_rdp_console(self,","as returned by DB layer. :param network_info: instance network information","caller if the driver requires legacy block device info. Tell","disk to the instance at mountpoint using info.\"\"\" raise NotImplementedError()","it's more specialized subclasses. \"\"\" from nova.virt import block_device as","platform-specific ID, and vice versa. Some methods here take an","\"\"\" raise NotImplementedError() def restore(self, instance): \"\"\"Restore the specified soft-deleted","validation of access to instance shared disk files \"\"\" return","check_can_live_migrate_destination \"\"\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None):","\"\"\"Setting up filtering rules and waiting for its completion. To","ip of the machine that will be making the connection,","of console.type.ConsoleSerial \"\"\" raise NotImplementedError() def get_mks_console(self, context, instance): \"\"\"Get","thingy... :return: None \"\"\" raise NotImplementedError() def undo_aggregate_operation(self, context, op,","the current CPU frequency (MHz). All values are long integers.","an instance on the host. :param instance: The instance to","\"\"\"Defer application of IPTables rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn off","Exception as ex: LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"), {'event': event,","ID' and 'instance name' are synonyms. Note that the instance","from the Hypervisor. If the instance is not found (for","object :param block_device_info: instance block device information :param network_info: instance","\"off_maintenance\" if maintenance mode got left. :rtype: str \"\"\" raise","def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context in","node can safely ignore this :returns: Dictionary describing resources \"\"\"","raise NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get connector information for the","of an instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can","return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to load the virtualization","an aggregate. The counter action to this is :func:`add_to_aggregate` :param","the given size (in Gigabyte) of `resize_to`. :return: None \"\"\"","timeout, instances): \"\"\"Perform a reboot on all given 'instances'. Reboots","context :param data: result of check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self,","] \"\"\" return None def manage_image_cache(self, context, all_instances): \"\"\"Manage the","new VM afterwards. It may be overridden by hypervisors that","ability of this host to accept new instances. :param bool","This is unfortunately also called a 'name' elsewhere. As far","files to inject into instance. :param admin_password: <PASSWORD> to set","instances to other hosts. Consider the combination with :func:`set_host_enabled`. :param","str action: The action the host should perform. The valid","domain. These IDs must stay internal to the platform-specific layer,","the cache and remove images which are no longer of",":param nova.objects.instance.Instance instance: The instance to soft-delete. :return: None \"\"\"","EC2 and internal Nova terminology), by which we mean 'running","new instances. If it is `False`, the host won't accept","<PASSWORD> to set in instance. :param bdms: block-device-mappings to use","\"\"\"Manage the driver's local image cache. Some drivers chose to","destroying the source VM. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek):","NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get connector information for the instance","for the driver. :param nova.objects.instance.Instance instance: The instance to get",":returns: True if the instance was deleted from disk, False","on rebuild \"\"\" raise NotImplementedError() def spawn(self, context, instance, image_meta,","information about the interface to detach. :raise nova.exception.NovaException: If the","time to wait for GuestOS to shutdown :param retry_interval: How","check_instance_shared_storage_local \"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup","that instances boot from the network, and manage their own","context in for access to auth_token raise NotImplementedError() def list_instance_uuids(self):","this compute service manages a particular node.\"\"\" if nodename in","set. :returns: The chosen device name. \"\"\" raise NotImplementedError() def","except Exception as ex: LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"), {'event':","A nova.objects.BlockDeviceMapping instance with all info about the requested block","support \" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False,","details are compatible with the set of MAC addresses. This","instance. The counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext","the host will accept new instances. If it is `False`,","block_device_info: instance block device information :param network_info: instance network information","get_mks_console(self, context, instance): \"\"\"Get connection info for a MKS console.","should be detached from the instance. :param destroy_disks: Indicates if","supplied as an argument. Compute drivers constructors take a VirtAPI","manage their own TFTP service. This requires passing the appropriate","True if the instance is being recreated on a new","pause(self, instance): \"\"\"Pause the given instance. A paused instance doesn't","the ability of this host to accept new instances. :param","based on that. :param nova.objects.instance.Instance instance: The instance to soft-delete.","instance: The instance which gets a network interface removed. :param","validate values are defined at disk API module. \"\"\" #","is `False`, the host won't accept new instances. :return: If","the specified instance. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need","result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a","of an instance, by name (not ID!) :param instance: nova.objects.instance.Instance","request context :param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta","attached to the instance. :param preserve_ephemeral: True if the default","an instance ID. \"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does","given `instances` which are longer in the rebooting state than","will match the actual configuration. Most hypervisors can use the","called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.)","Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get connector information for","or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or","supplied. :param virtapi: a VirtAPI instance :param compute_driver: a compute","access to auth_token raise NotImplementedError() def get_console_output(self, context, instance): \"\"\"Get","guest state when a host is booted. :param instance: nova.objects.instance.Instance","soft-deleted instance. The restored instance will be automatically booted. The","network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make this instance. A","creation of the new instance. :param nova.objects.ImageMeta image_meta: The metadata",":param instance: nova.objects.instance.Instance that the network ports will be associated","instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to pass context in","\"\"\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce the","NotImplementedError() def detach_interface(self, instance, vif): \"\"\"Use hotunplug to remove a","Necessary network information for the resume. :param dict block_device_info: Instance","identifier chosen by Nova to represent the instance further up","driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver)","snapshot. :param context: security context :param instance: nova.objects.instance.Instance \"\"\" pass","from networks at source. :param context: security context :param instance:","context in for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self,","this method is unused. Note that this function takes an","backed snapshots, this would include the file being merged, and","layer is responsible for keeping track of which instance ID","has the same state as before the 'suspend'. :param nova.context.RequestContext","instance, by name (not ID!) :param instance: nova.objects.instance.Instance object Returns","doesn't use CPU cycles or memory of the host anymore.","Function to handle any bad volumes encountered \"\"\" raise NotImplementedError()","metadata of the image of the instance. :param resize_instance: True","and \"reboot\". :return: The result of the power action :rtype:","A \"stronger\" version of `pause` is :func:'suspend'. The counter action","instance: The instance to which the diagnostic data should be","may obtain # a copy of the License at #","was being migrated :param network_info: instance network information :param block_device_info:","nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param dest: destination","longer in the rebooting state than `timeout` seconds. :param int","Volume to be snapshotted :param create_info: The data needed for","a HARD or SOFT reboot :param block_device_info: Info pertaining to","supported by this driver :param fs_type: the file system type","disk_over_commit=False): \"\"\"Check if it is possible to execute live migration.","filtering rules to hypervisor, since filtering rules to firewall rules","the configuration of it. :param nova.objects.instance.Instance instance: The instance where","The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance","{} def instance_on_disk(self, instance): \"\"\"Checks access of instance files on","and this must be supplied. :param virtapi: a VirtAPI instance","the memory or storage space of the host, depending on","thread. \"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event)) return","def get_serial_console(self, context, instance): \"\"\"Get connection info for a serial","given paused instance. The paused instance gets unpaused and will","is the total number of I/O requests made, bytes is","root device. \"\"\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists):","counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The","hypervisors that need to - e.g. for optimisations, or when",":func:`set_host_enabled`. :param str host: The name of the host whose","for GuestOS to shutdown :param retry_interval: How often to signal","successfully even in cases in which the underlying domain/vm is","layer, and never escape the connection interface. The platform-specific layer","Don't use thread for this method since migration should not","This is the data structure used by nova.compute to store","methods that expect it. \"\"\" return True def volume_snapshot_create(self, context,","b64_contents): \"\"\"Writes a file on the specified instance. The first","may # not use this file except in compliance with","self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization overhead required to","current status of an instance, by name (not ID!) :param","list_instance_uuids(self): \"\"\"Return the UUIDS of all the instances known to",":param nova.objects.instance.Instance instance: The instance where the crash dump should","compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to load","the total number of I/O requests made, bytes is the","default implementation which returns None. Hypervisors with MAC limits should","dump the crash data. How the guest OS reacts in","and allocate storage space this way. A \"softer\" way of","case. :param context: security context :param instance: Instance object as","user, | 'iowait': wait, | 'frequency': freq}, where kern and","first parameter is an instance of nova.compute.service.Instance, and so the","that should be detached from the instance. :param destroy_disks: Indicates","be raised if the operation cannot complete. \"\"\" # TODO(Vek):","'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ]","which the diagnostic data should be returned. :return: Has a","state when a host is booted. :param instance: nova.objects.instance.Instance \"\"\"","service manages a particular node.\"\"\" if nodename in self.get_available_nodes(): return","dest, post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration of an instance","This is called during spawn_instance by the compute manager. :return:","NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self,","self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event)) return if not isinstance(event, virtevent.Event):","node.\"\"\" if nodename in self.get_available_nodes(): return True # Refresh and","known higher up the stack. Most virtualization platforms will also","booted. The counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance",":return: None \"\"\" raise NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve resource","multi compute-nodes support. If a driver supports multi compute-nodes, this",":param nova.objects.instance.Instance instance: The instance which should be unpaused. :return:","the image of the instance. \"\"\" raise NotImplementedError() def network_binding_host_id(self,","interfaces (VIFs) from networks. The counter action is :func:`plug_vifs`. :param","where rd indicates read, wr indicates write, req is the","compute service manages a particular node.\"\"\" if nodename in self.get_available_nodes():","Instance/flavor to calculate overhead for. :returns: Dict of estimated overhead","no need to be called. Don't use thread for this","for access to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info):","check_instance_shared_storage calls :param context: security context :param data: result of","\"\"\"Load a compute driver module. Load the compute driver module","Currently only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context,","snapshot, it is the image file Cinder expects to be","does not need to be set, and should be decided","action :func:`restore` uses the database entry to create a new","import importutils import nova.conf from nova.i18n import _, _LE, _LI",":param instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created image","example is to clean the cache and remove images which","the network ports will be associated with :returns: a string","about the VIFs to plug. :return: None \"\"\" # TODO(Vek):","of DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'},","which contains the uptime of this host since the last","context: security context :param instance: instance object reference :param network_info:","data): \"\"\"Check if instance files located on shared storage. :param","networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The","mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the disk to the instance","access of instance files on the host. :param instance: nova.objects.instance.Instance","may be other data included as well that is needed","and their associated rules live in the datastore, and calling","the specified instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def","the instance should be running (power_state.RUNNING). If this fails, any","power_state.RUNNING. The virtualization platform should ensure that the reboot action","use CPU cycles of the host anymore. The state of","create_info): \"\"\"Snapshots volumes attached to a specified instance. The counter","in which the underlying domain/vm is paused or halted/stopped. :param","kwargs: A free-form thingy... :return: None \"\"\" raise NotImplementedError() def","image_id, update_task_state): \"\"\"Snapshots the specified instance. :param context: security context","VM could be stored in the memory or storage space","aggregate. The counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext","nova.objects.instance.Instance instance: The instance whose disk should be migrated. :param","the context for the migration/resize :param migration: the migrate/resize information","- root_device_name: device name of the root disk - ephemerals:","block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified instance. After this is called","uniquely identify a VM or domain. These IDs must stay","instance.\"\"\" # TODO(Vek): Need to pass context in for access","The counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context:","root device name for the driver. :param nova.objects.instance.Instance instance: The","VirtAPI instance :param compute_driver: a compute driver name to override","information :param block_migration: if true, post operation of block_migration. \"\"\"","(Xen or libvirt terminology). An instance has an ID, which","check_instance_shared_storage_local(self, context, instance): \"\"\"Check if instance files located on shared","implemented. :param context: security context :param instance: nova.objects.instance.Instance \"\"\" pass","the results in the DB. :param nodename: node which the",":param attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for","the given `host` :param str host: The name of the","host is booting up; freq indicates the current CPU frequency",":returns an instance of console.type.ConsoleVNC \"\"\" raise NotImplementedError() def get_spice_console(self,","the instance.\"\"\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint,","context: request context :param instance: nova.objects.instance.Instance that the network ports","devices to be attached to the instance. :param preserve_ephemeral: True","host after check_can_live_migrate calls :param context: security context :param dest_check_data:","of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData","network information :param block_migration: if true, post operation of block_migration.","be attached to the instance. \"\"\" raise NotImplementedError() def destroy(self,","of live migration at destination host. :param context: security context","device mapping to be presented in the format of a",":param nova.objects.instance.Instance instance: The instance which gets a network interface","when nova-compute launches, and as part of a periodic task","ID to associate with network ports. :param context: request context","information about instance resource usage. :returns: dict of nova uuid","actual disk sizes of an instance. :param instance: nova.objects.Instance :param","the resume. :param nova.objects.ImageMeta image_meta: The metadata of the image","block_device_info: Information about block devices that should be detached from","specific params \"\"\" raise NotImplementedError() def reboot(self, context, instance, network_info,","Maintainers of the virt drivers are encouraged to override this","often to signal guest while waiting for it to shutdown.","instance :param compute_driver: a compute driver name to override the","The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should","Nova terminology), by which we mean 'running virtual machine' (XenAPI","raise NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False,","disk information :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError()","filter_defer_apply_off(self): \"\"\"Turn off deferral of IPTables rules and apply the","mappings for an instance to driver format. Virt drivers expect","instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger crash","again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information about",":param dict kwargs: A free-form thingy... :return: None \"\"\" #",":param nova.objects.instance.Instance instance: The instance whose volume gets replaced by","def trigger_crash_dump(self, instance): \"\"\"Trigger crash dump mechanism on the given","NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force live migration to complete :param","instances. :param bool enabled: If this is `True`, the host","to accept new instances. :param bool enabled: If this is","instance. \"\"\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \"\"\"Get host","NotImplementedError() def plug_vifs(self, instance, network_info): \"\"\"Plug virtual interfaces (VIFs) into","migration. This runs checks on the destination host, and then","\"\"\"Attach the disk to the instance at mountpoint using info.\"\"\"","\"\"\" return len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks existence of an","the allocate_for_instance call by the compute manager, and it is","\"\"\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \"\"\"Confirms a","should be paused. :return: None \"\"\" # TODO(Vek): Need to","contents of the file, also base64-encoded. NOTE(russellb) This method is","of this host since the last boot. :rtype: str \"\"\"","the specific language governing permissions and limitations # under the","action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security","on the results from check_can_live_migrate_destination. :param context: security context :param","instance is being specified as instance.name. The second parameter is","The restored instance will be automatically booted. The counter action","if the live migration can succeed, based on the results","network_info: instance network information :param block_device_info: instance volume block device","the destination host. :param nova.objects.flavor.Flavor flavor: The flavor of the","migration of all instances to other hosts. Consider the combination","The interface to this class talks in terms of 'instances'","method is an opportunity to do management of that cache","\"\"\"Perform a reboot on all given 'instances'. Reboots the given","connection info for a rdp console. :param context: security context","the updated rules to instances running the specified security group.","def get_rdp_console(self, context, instance): \"\"\"Get connection info for a rdp","instance, image_id, update_task_state): \"\"\"Snapshots the specified instance. :param context: security","given 'instances'. Reboots the given `instances` which are longer in","set of DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value':","NOTE(markus_z): 2015-08-18 # The compute manager doesn't use this interface,","this host since the last boot. :rtype: str \"\"\" #","migrated. :param nova.network.model.NetworkInfo network_info: The network information of the given","instance. These are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs],","aggregate, host, **kwargs): \"\"\"Add a compute host to an aggregate.","nova uuid => dict of usage info \"\"\" return {}",":param new_pass: the <PASSWORD> \"\"\" raise NotImplementedError() def inject_file(self, instance,","fails. :return: None \"\"\" raise NotImplementedError() def detach_interface(self, instance, vif):","\"\"\"Refresh security group rules Gets called when an instance gets","driver method which is used to publish changes to the","{'kernel': kern, | 'idle': idle, | 'user': user, | 'iowait':","rules to instances running the specified security group. An error","backend technology specific data needed to be able to complete","volume block device info :param power_on: True if the instance","to_xml may have to be called since it defines PROJNET,","the instance. :param resize_instance: True if the instance is being","value is specific to the Neutron client API. :return: None,","is not human-readable or customer-controlled -- it's an internal ID","should still succeed. It's probably a good idea to log","CONDITIONS OF ANY KIND, either express or implied. See the","which is the identifier chosen by Nova to represent the","instance): \"\"\"Get connection info for a serial console. :param context:","event %s\", str(event)) return if not isinstance(event, virtevent.Event): raise ValueError(","missing device names in the block device mapping.\"\"\" raise NotImplementedError()","nova.context.RequestContext context: The context for the resume. :param nova.objects.instance.Instance instance:","this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param","\"supports_device_tagging\": False, } def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback",":return: If the host can accept further instances, return \"enabled\",","this method should apply the updated rules to instances running","to auth_token raise NotImplementedError() def suspend(self, context, instance): \"\"\"Suspend the","the system and remakes the VM with given 'metadata' and","live migration. This checks if the live migration can succeed,","now.\"\"\" pass def unfilter_instance(self, instance, network_info): \"\"\"Stop filtering instance.\"\"\" #","the image of the instance. :param rescue_password: <PASSWORD> for rescue.","network_info, block_device_info=None): \"\"\"Power on the specified instance. :param instance: nova.objects.instance.Instance","be unpaused. :return: None \"\"\" # TODO(Vek): Need to pass","the virtualization layer, as a list. \"\"\" raise NotImplementedError() def","is called after a change to security groups. All security","def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace the volume","\"\"\"Destroy and re-make this instance. A 'rebuild' effectively purges all","'metadata' and 'personalities'. This base class method shuts down the","post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug VIFs from networks at source.","to remove a network interface from a running instance. The","is the image file Cinder expects to be used as","specific to the virtualization platform. \"\"\" capabilities = { \"has_imagecache\":","the config opt :returns: a ComputeDriver instance \"\"\" if not","data: result of check_instance_shared_storage_local \"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context,","block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or any of","block_migration=False, block_device_info=None): \"\"\"Post operation of live migration at destination host.","case of doing a snapshot, it is the image file","IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND,","into the given `instance` at instance boot time. The counter","and errs is the number of requests held up due","compute manager. Invokes the event callback registered by the compute","\"\"\"Retrieves the IP address of the dom0 \"\"\" # TODO(Vek):","raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \"\"\"Check if instance files","by the service. Otherwise, this method should return [hypervisor_hostname]. \"\"\"","held up due to a full pipeline. All counters are","context, instance, network_info, block_device_info=None): \"\"\"resume guest state when a host","bare metal) require that instances boot from the network, and","network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a resize/migration. :param context:","invoked with a single parameter, which will be an instance","to attach. :raise nova.exception.NovaException: If the attach fails. :return: None",":param context: the context for the finish_revert_migration :param instance: nova.objects.instance.Instance","an instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can be","new instance based on that. :param nova.objects.instance.Instance instance: The instance","to the virtualization layer, as a list. \"\"\" # TODO(Vek):","ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo object \"\"\"","layer. This layer is responsible for translating that generic data","migrates those value through migrateToURI(), so , no need to","be invoked from a green thread. \"\"\" if not self._compute_event_callback:","\"\"\"Turn off deferral of IPTables rules and apply the rules","is not found (for example if networking failed), this function","chosen by Nova to represent the instance further up the","doing a snapshot, it is the image file Cinder expects","I/O requests made, bytes is the total number of bytes","be associated with :returns: a string representing the host ID","security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS","spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create a","instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes a snapshot of a volume","'pause'. :param nova.objects.instance.Instance instance: The instance which should be unpaused.","All values are long integers. \"\"\" raise NotImplementedError() def block_stats(self,","a callback to receive asynchronous event notifications from hypervisors. The","init_host(self, host): \"\"\"Initialize anything that is necessary for the driver","The state of the instance could be persisted on the","host. :return: A text which contains the uptime of this","that the reboot action has completed successfully even in cases","supported file system # type and implement this function at","by another one. :param str mountpoint: The mountpoint in the","_LI from nova import utils from nova.virt import event as","host from an aggregate. The counter action to this is","devices. :param int timeout: The time in seconds to wait","auth_token raise NotImplementedError() def cleanup_host(self, host): \"\"\"Clean up anything that","be set, and should be decided by the driver implementation","the crash data. How the guest OS reacts in details,",":param instance: nova.objects.instance.Instance :returns: True if the instance was deleted",":func:'suspend'. The counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance","nova.exception.NovaException: If the attach fails. :return: None \"\"\" raise NotImplementedError()","nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to","of a periodic task that records the results in the","context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated.","instance, then this method should not be implemented. :param context:","attach to the volume. This is the same data format","dest host after check_can_live_migrate calls :param context: security context :param","def is_supported_fs_format(self, fs_type): \"\"\"Check whether the file format is supported",":param network_info: instance network information :param block_device_info: instance volume block","to ensure that all assigned network details are compatible with","prepare for block migration :param disk_over_commit: if true, allow disk","The metadata of the image of the instance. :param resize_instance:","for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all","return True def volume_snapshot_create(self, context, instance, volume_id, create_info): \"\"\"Snapshots volumes","the nova.virt layer, instances do not have human-readable names at","should use the data there to guide the creation of","migrated. :param dest: destination host :param post_method: post operation method.","method since migration should not be started when setting-up filtering","False otherwise. .. note:: This implementation works for all drivers,","nova.objects.instance.Instance \"\"\" pass def finish_migration(self, context, migration, instance, disk_info, network_info,","the host and allocate storage space this way. A \"softer\"","| 'user': user, | 'iowait': wait, | 'frequency': freq}, where","instance object that was migrated :block_device_info: instance block device information","context in for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self,","the VM could be stored in the memory or storage","domain, with arbitrary VLANs etc, but regular switches require pre-configured","delete_info: Volume backend technology specific data needed to be able","context, all_instances): \"\"\"Manage the driver's local image cache. Some drivers","to the instance.\"\"\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance,","\"\"\"Unquiesce the specified instance after snapshots. If the specified instance","by nova.compute to store details regarding an instance, and pass","only for filtering rules to hypervisor, since filtering rules to","mean 'running virtual machine' (XenAPI terminology) or domain (Xen or","context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that","to a running instance. The counter action to this is","host: The name of the host to add to the","up the new VM afterwards. It may be overridden by","first object and this must be supplied. :param virtapi: a","resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \"\"\"resume guest state when a","to the instance's metadata to the hypervisor. If the hypervisor","power_on=True): \"\"\"Finish reverting a resize/migration. :param context: the context for","from the given `instance`. :param dict new_connection_info: The volume for","of check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info,","destroyed :param migrate_data: implementation specific params \"\"\" raise NotImplementedError() def","context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \"\"\"","of console.type.ConsoleMKS \"\"\" raise NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return diagnostics","and so the instance is being specified as instance.name. The","power_on=True): \"\"\"Completes a resize/migration. :param context: the context for the","| 'opt_value': '192.168.127.12'} | ] \"\"\" return None def manage_image_cache(self,","should be unpaused. :return: None \"\"\" # TODO(Vek): Need to","None \"\"\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide","method is optional. On some platforms (e.g. XenAPI) performance statistics","nova.objects.instance.Instance \"\"\" pass def inject_network_info(self, instance, nw_info): \"\"\"inject network info","the volume attached. :param uuid volume_id: Attached volume associated with","is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate","of publishing the instance metadata to the instance, then this","\"\"\"Get the currently known host CPU stats. :returns: a dict","about block devices to be attached to the instance. \"\"\"","migration, instance, network_info): \"\"\"Confirms a resize/migration, destroying the source VM.","storage space this way. A \"softer\" way of `suspend` is","security context :param data: result of check_instance_shared_storage_local \"\"\" raise NotImplementedError()",":param dict block_device_info: Instance volume block device info. :return: None","responsible for translating that generic data structure into terms that","turning off the instance before the end. :param nova.objects.instance.Instance instance:","VirtAPI object as their first object and this must be","only be invoked from a green thread. \"\"\" if not","security context :param instance: nova.objects.instance.Instance :param image_id: Reference to a","that compute drivers must follow, and shared types that support","call. In the case of doing a snapshot, it is","host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host maintenance window. On start, it","not isinstance(event, virtevent.Event): raise ValueError( _(\"Event must be an instance","is :func:`resume`. :param nova.context.RequestContext context: The context for the suspend.","or \"off_maintenance\" if maintenance mode got left. :rtype: str \"\"\"","block_device_info: Information about block devices to be attached to the","options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name':","False otherwise :param block_device_info: instance volume block device info :param","statistics can be retrieved directly in aggregate form, without Nova","host won't accept new instances. :return: If the host can","'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'}","The instance that has the volume attached. :param uuid volume_id:","result of _get_instance_block_device_info :returns: a LiveMigrateData object \"\"\" raise NotImplementedError()","(such as bare metal) cannot do freeform virtualization of MAC",":param block_migration: if true, post operation of block_migration. \"\"\" raise",":param dict delete_info: Volume backend technology specific data needed to","instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if it is possible","block_device_obj): \"\"\"Get the next device name based on the block","block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \"\"\"Base","by kernel and user processes respectively, idle indicates the cumulative","exists on the host, False otherwise. .. note:: This implementation","raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \"\"\"Confirms a resize/migration,","transferred, and errs is the number of requests held up","specified instance from the Hypervisor. If the instance is not","be an instance of the nova.virt.event.Event class. \"\"\" self._compute_event_callback =","has the volume attached :param uuid volume_id: Volume to be","injected_files: User files to inject into instance. :param admin_password: Administrator","in cases in which the underlying domain/vm is paused or","and the hostname of the machine as follows:: { 'ip':","file being merged into (if appropriate). :return: None \"\"\" raise","for usage. :param network_info: instance network information :param recreate: True","This is an optional driver method which is used to","means 'these and only these MAC addresses'. \"\"\" return None","for a rdp console. :param context: security context :param instance:","in-progress live migration. :param instance: instance that is live migrating","network information :param block_device_info: Information about block devices to be","virtualization layer, as a list. \"\"\" raise NotImplementedError() def rebuild(self,","aggregate form, without Nova having to do the aggregation. On","nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \"\"\" raise NotImplementedError() def","a compute host to an aggregate. The counter action to","= block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \"\"\"Base class","deferral of IPTables rules and apply the rules now.\"\"\" pass","\"\"\"Check if it is possible to execute live migration. This","\"\"\"Unplug VIFs from networks at source. :param context: security context","machine :param dst_compute_info: Info about the receiving machine :param block_migration:","block_device_info or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0}","= dict() for driver_str in named_driver_config: driver_type, _sep, driver =","The network information of the given `instance`. :param dict block_device_info:","str \"\"\" raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state):","the power action :rtype: : str \"\"\" raise NotImplementedError() def","get_host_uptime(self): \"\"\"Returns the result of calling the Linux command `uptime`","instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return","ID chosen by Nova. At the nova.virt layer, instances do","instance ID maps to which platform-specific ID, and vice versa.","connection gets detached from the given `instance`. :param dict new_connection_info:","NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace the","> 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals","at mountpoint using info.\"\"\" raise NotImplementedError() def detach_volume(self, connection_info, instance,","the same state as before the 'pause'. :param nova.objects.instance.Instance instance:","context, instance): \"\"\"Check if instance files located on shared storage.","destroy_disks: if true, destroy disks at destination during cleanup :param",":param int timeout: The timeout (in seconds) for considering rebooting","required for validation of access to instance shared disk files","unpause(self, instance): \"\"\"Unpause the given paused instance. The paused instance","the uptime of this host since the last boot. :rtype:","their own identity schemes, to uniquely identify a VM or","hypervisors and firewalls are inevitable on destination host. ( Waiting","is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets a","for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which","= block_device_info or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size':","manager, and it is up to that call to ensure","'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name':","data included as well that is needed for creating the","is an optional driver method which is used to publish","\"\"\"Finish reverting a resize/migration. :param context: the context for the","the given 'instance'. :param nova.objects.instance.Instance instance: The instance whose volume","out to the DHCP service. Most hypervisors can use the","all -- such things are only known higher up the","instance, network_info, block_device_info=None): \"\"\"Power on the specified instance. :param instance:","to override this method with something more efficient. \"\"\" return","this fails, any partial instance should be completely cleaned up,","context: security context :param instance: nova.objects.instance.Instance :returns an instance of","instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \"\"\" raise NotImplementedError()","If the attach fails. :return: None \"\"\" raise NotImplementedError() def","groups and their associated rules live in the datastore, and","libvirt migrates those value through migrateToURI(), so , no need","for each interface on each running VM. :param instances: nova.objects.instance.InstanceList","to suspend. :return: None \"\"\" raise NotImplementedError() def resume(self, context,","since the host is booting up; freq indicates the current","disk. This method is an opportunity to do management of","given size (in Gigabyte) of `resize_to`. :return: None \"\"\" raise","pass them into this layer. This layer is responsible for","as a list. \"\"\" # TODO(Vek): Need to pass context","raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None):","\"\"\"Snapshots volumes attached to a specified instance. The counter action","\"\"\"Get host ID to associate with network ports. :param context:","the data structure used by nova.compute to store details regarding","suspend. :return: None \"\"\" raise NotImplementedError() def resume(self, context, instance,","virtualization of MAC addresses. This method allows drivers to return","console. :param context: security context :param instance: nova.objects.instance.Instance :returns an",":block_device_info: instance block device information :param migrate_data: a LiveMigrateData object","safely ignore this :returns: Dictionary describing resources \"\"\" raise NotImplementedError()","instance, root_device_name, *block_device_lists): \"\"\"Default the missing device names in the","uses the database entry to create a new instance based","destination node after a failed live migration. :param context: security","= None def init_host(self, host): \"\"\"Initialize anything that is necessary","\"\"\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of all","except in compliance with the License. You may obtain #","note:: This implementation works for all drivers, but it is","'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This","instance): \"\"\"Get console output for an instance :param context: security","the Linux command `uptime` on this host. :return: A text","'disk_gb': 0} def list_instances(self): \"\"\"Return the names of all the","raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None):","object that was migrated :block_device_info: instance block device information :param","NotImplementedError() def node_is_available(self, nodename): \"\"\"Return whether this compute service manages","a specified instance. The counter action to this is :func:`volume_snapshot_delete`","this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param","volume, it gets resized to the given size (in Gigabyte)","if not set. :returns: The chosen device name. \"\"\" raise","back to power_state.RUNNING. The virtualization platform should ensure that the","dict of usage info \"\"\" return {} def instance_on_disk(self, instance):","def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \"\"\"Undo for Resource",":param context: security context :param instance: nova.objects.instance.Instance :param image_id: Reference","src_compute_info: Info about the sending machine :param dst_compute_info: Info about","the maintenance mode. :return: \"on_maintenance\" if switched to maintenance mode","context, instance, network_info, image_meta, rescue_password): \"\"\"Rescue the specified instance. :param","available as a database entry. The counter action :func:`restore` uses","the block device mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms,","indicates the cumulative I/O wait CPU time (nanoseconds), since the","*block_device_lists): \"\"\"Default the missing device names in the block device","network interface. :param nova.objects.ImageMeta image_meta: The metadata of the image","the driver requires legacy block device info. Tell the caller","call began. :param context: security context :param instance: nova.objects.instance.Instance This","# type and implement this function at their # virt","get_spice_console(self, context, instance): \"\"\"Get connection info for a spice console.","serial console. :param context: security context :param instance: nova.objects.instance.Instance :returns",":param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance: The","nova.objects.instance.Instance instance: The instance that has the volume attached :param","to the Neutron client API. :return: None, or a set","return None def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options for this",":return: None \"\"\" # NOTE(jogo) Currently only used for XenAPI-Pool","be started when setting-up filtering rules operations are not completed.","indicates read, wr indicates write, req is the total number","do freeform virtualization of MAC addresses. This method allows drivers","specific language governing permissions and limitations # under the License.","# not use this file except in compliance with the","compute manager, and it is up to that call to","network information :param recreate: True if the instance is being","the image of the instance. :param injected_files: User files to","is migrated :param network_info: instance network information :param block_migration: if","on a new hypervisor - all the cleanup of old","CPU cycles of the host anymore. The state of the","the manager should be the controlling thing here. \"\"\"Unplug virtual","network information for the resume. :param dict block_device_info: Instance volume","of the host to remove from the given `aggregate`. :param","about the interface to detach. :raise nova.exception.NovaException: If the detach","specific to the Neutron client API. :return: None, or a","context for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param","security group the instance is a member of or if","capabilities = { \"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\":","password on the specified instance. :param instance: nova.objects.instance.Instance :param new_pass:","after a failed live migration. :param context: security context :param","the old volume, it gets resized to the given size","an instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def unrescue(self,","nova.objects.flavor.Flavor flavor: The flavor of the instance whose disk get",":param nova.objects.instance.Instance instance: The instance that has the volume attached","that every hypervisor # need to define their supported file","NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data about the given","nova.objects.instance.Instance \"\"\" raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta,","security context :param instance: instance object that is migrated :param","host after check_instance_shared_storage calls :param context: security context :param data:","method should return [hypervisor_hostname]. \"\"\" raise NotImplementedError() def node_is_available(self, nodename):","be passed in to methods that expect it. \"\"\" return","None \"\"\" raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the currently known","the compute_driver configuration option or, if supplied, the driver name","are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd",":param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \"\"\" raise NotImplementedError()","that is necessary for the driver to function, including catching","Reference to a pre-created image that will hold the snapshot.","block device. device_name does not need to be set, and","updated rules to instances running the specified security group. An","supports multi compute-nodes, this method returns a list of nodenames","that manages only one node can safely ignore this :returns:","nova.objects.instance.Instance that volume is requesting a device name :param bdms:","information :param block_device_info: instance block device information :param destroy_disks: if","check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information","block_device_info: Info pertaining to attached volumes :param bad_volumes_callback: Function to","the instances known to the virtualization layer, as a list.","'instance'. :param nova.objects.instance.Instance instance: The instance whose volume gets replaced","instance resources . Instance should have been destroyed from the","block device mappings for an instance to driver format. Virt","need to be set, and should be decided by the","block device info to be passed in to methods that","name. \"\"\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check whether the","paused instance gets unpaused and will use CPU cycles of","has no means of publishing the instance metadata to the","the set of MAC addresses. This is called during spawn_instance","the image of the instance. :param nova.network.model.NetworkInfo vif: The object","snapshot_id: The snapshot to delete. :param dict delete_info: Volume backend","the instance is a member of or if the group","of the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The","image_meta, rescue_password): \"\"\"Rescue the specified instance. :param nova.context.RequestContext context: The","chose to cache images for instances on disk. This method","legacy block device info. Tell the caller whether we expect","specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \"\"\"","This runs checks on the destination host, and then calls","specialized subclasses. \"\"\" from nova.virt import block_device as virt_block_device block_device_info","Cinder expects to be used as the active disk after","the iscsi initiator and the hostname of the machine as","nova.objects.instance.Instance :param new_pass: the <PASSWORD> \"\"\" raise NotImplementedError() def inject_file(self,","raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \"\"\"resume guest","network_info: instance network information :param block_migration: if true, post operation","or any of it's more specialized subclasses. \"\"\" from nova.virt","\"\"\"Detach the disk attached to the instance.\"\"\" raise NotImplementedError() def","to auth_token pass def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a reboot","driver. :param nova.objects.instance.Instance instance: The instance to get the root","# Copyright 2011 <NAME> # All Rights Reserved. # #","this host, return \"disabled\". :rtype: str \"\"\" # TODO(Vek): Need","\"\"\"Undo for Resource Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get","old volume, it gets resized to the given size (in","way. A \"softer\" way of `suspend` is :func:`pause`. The counter","auth_token raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns the result of calling","CPU stats. :returns: a dict containing the CPU stat info,","ex: LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"), {'event': event, 'ex': ex})","values are defined at disk API module. \"\"\" # NOTE(jichenjc):","not found (for example if networking failed), this function should","bad volumes encountered \"\"\" raise NotImplementedError() def get_console_pool_info(self, console_type): #","this is `True`, the host will accept new instances. If","human-readable names at all -- such things are only known","will use CPU cycles of the host again. The counter","power_on(self, context, instance, network_info, block_device_info=None): \"\"\"Power on the specified instance.","instance): \"\"\"Checks existence of an instance on the host. :param","result of check_can_live_migrate_destination \"\"\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance,","instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a resize/migration.","def live_migration_abort(self, instance): \"\"\"Abort an in-progress live migration. :param instance:","a big overlap to the return value of the older","and shared types that support that contract \"\"\" import sys",":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \"\"\" raise","the newer interface :func:`get_instance_diagnostics` :rtype: dict \"\"\" # TODO(Vek): Need","to be set, and should be decided by the driver",":param block_device_info: Information about block devices that should be detached","instance of console.type.ConsoleMKS \"\"\" raise NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return","results. :param context: security context :param instance: nova.objects.instance.Instance object \"\"\"","rescue. \"\"\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \"\"\"Set the","writing, software # distributed under the License is distributed on","a new instance based on that. :param nova.objects.instance.Instance instance: The",", no need to be called. Don't use thread for","up steps. :param context: security context :param instance: nova.objects.instance.Instance This","def get_diagnostics(self, instance): \"\"\"Return diagnostics data about the given instance.","instance): \"\"\"Get connection info for a vnc console. :param context:","if true, allow disk over commit :returns: a LiveMigrateData object","driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device mappings for an","context :param instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created","instance to prepare for snapshots. If the specified instance doesn't","module. Load the compute driver module specified by the compute_driver","the VIFs to unplug. :return: None \"\"\" raise NotImplementedError() def","dict containing the CPU stat info, eg: | {'kernel': kern,","block device info. Tell the caller whether we expect the","not particularly efficient. Maintainers of the virt drivers are encouraged","the hypervisor has no means of publishing the instance metadata","total number of bytes transferred, and errs is the number","def get_host_uptime(self): \"\"\"Returns the result of calling the Linux command","context :param instance: instance object reference :param network_info: instance network","context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create a new","is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance","quiesce(self, context, instance, image_meta): \"\"\"Quiesce the specified instance to prepare","a full pipeline. All counters are long integers. This method","raise NotImplementedError() def get_serial_console(self, context, instance): \"\"\"Get connection info for","on the destination host, and then calls back to the","details regarding an instance, and pass them into this layer.","operation of live migration at destination host. :param context: security","\"\"\" raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \"\"\"Snapshots","that is live migrating \"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context,","suspended instance gets resumed and will use CPU cycles and","(e.g. XenAPI) performance statistics can be retrieved directly in aggregate","define their supported file system # type and implement this","up filtering rules and waiting for its completion. To migrate","the instance resources . Instance should have been destroyed from",":param injected_files: User files to inject into instance. :param admin_password:","aggregate: The aggregate which should add the given `host` :param","get_available_resource(self, nodename): \"\"\"Retrieve resource information. This method is called when","instance): \"\"\"Delete any lingering instance files for an instance. :param","\"\"\"Checks existence of an instance on the host. :param instance:","raise NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage counters for","request context :param instance: nova.objects.instance.Instance that the network ports will","files located on shared storage. :param context: security context :param","terms of 'instances' (Amazon EC2 and internal Nova terminology), by","enabled): \"\"\"Sets the ability of this host to accept new","for. :returns: Dict of estimated overhead values. \"\"\" return {'memory_mb':","which will be supplied to the allocate_for_instance call by the","method is for multi compute-nodes support. If a driver supports","which contains information about the VIFs to unplug. :return: None","error should be raised if the operation cannot complete. \"\"\"","block_device_mapping class ComputeDriver(object): \"\"\"Base class for compute drivers. The interface","bool mode: If `True`, go into maintenance mode. If `False`,","import nova.conf from nova.i18n import _, _LE, _LI from nova","default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide a default root device name","this way. A \"softer\" way of `suspend` is :func:`pause`. The","guest OS reacts in details, depends on the configuration of","False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\": False, } def __init__(self,","\"enabled\", if further instances shouldn't be scheduled to this host,","instance, network_info): \"\"\"Confirms a resize/migration, destroying the source VM. :param","at source host. :param context: security context :instance: instance object","are desired. :param instance_info: Instance/flavor to calculate overhead for. :returns:","instance: The soft-deleted instance which should be restored from the","instance which will get an additional network interface. :param nova.objects.ImageMeta","dict \"\"\" # TODO(Vek): Need to pass context in for","is attached to. :param int resize_to: If the new volume","`old_connection_info` is attached to. :param int resize_to: If the new","The action the host should perform. The valid actions are:","MAC addresses must this instance have? Some hypervisors (such as","__init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None def init_host(self,","which has the information about the interface to detach. :raise","a new instance/VM/domain on the virtualization platform. Once this successfully","if the default ephemeral storage partition must be preserved on","License. \"\"\" Driver base-classes: (Beginning of) the contract that compute","for access to auth_token raise NotImplementedError() def get_console_output(self, context, instance):",":param context: security context :param instance: Instance object as returned","of it. :param nova.objects.instance.Instance instance: The instance where the crash","new_pass: the <PASSWORD> \"\"\" raise NotImplementedError() def inject_file(self, instance, b64_path,","to represent the instance further up the stack. This is","admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and","is :func:`pause`. The counter action for `suspend` is :func:`resume`. :param","is booted. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def rescue(self,","accessible on the host, False otherwise. .. note:: Used in","should be raised if the operation cannot complete. \"\"\" #","device info :param power_on: True if the instance should be","(nanoseconds), since the host is booting up; freq indicates the","after snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported","since migration should not be started when setting-up filtering rules","rules and waiting for its completion. To migrate an instance,","of block_migration. \"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \"\"\"Check","raise NotImplementedError() def get_rdp_console(self, context, instance): \"\"\"Get connection info for","catching up with currently running VM's on the given host.","instance): \"\"\"Checks access of instance files on the host. :param",":param instance: nova.objects.instance.Instance that volume is requesting a device name","bytes is the total number of bytes transferred, and errs","instance: nova.objects.instance.Instance object :param block_device_info: instance block device information :param","a set of DHCP options, eg: | [{'opt_name': 'bootfile-name', |","context, instance): \"\"\"Get connection info for a MKS console. :param","\"\"\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \"\"\"Post","the volume. This is the same data format returned by","A paused instance doesn't use CPU cycles of the host","set of MAC addresses that the instance is to have.",":param str host: The name of the host to add","so , no need to be called. Don't use thread","value of the newer interface :func:`get_instance_diagnostics` :rtype: dict \"\"\" #","pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare an instance","empty) list of DriverVolumeBlockDevice or any of it's more specialized","leave the maintenance mode. :return: \"on_maintenance\" if switched to maintenance","True if the instance was deleted from disk, False otherwise.","\"\"\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to","Rights Reserved. # # Licensed under the Apache License, Version","to the return value of the newer interface :func:`get_instance_diagnostics` :rtype:","instance): \"\"\"Restore the specified soft-deleted instance. The restored instance will","this successfully completes, the instance should be running (power_state.RUNNING). If","layer, as a list. \"\"\" raise NotImplementedError() def rebuild(self, context,","nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \"\"\" raise NotImplementedError() def","the VIFs to plug. :return: None \"\"\" # TODO(Vek): Need","for keeping track of which instance ID maps to which","this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets","existing data from the system and remakes the VM with","device_name does not need to be set, and should be","overhead required to build an instance of the given flavor.","metadata of the image of the instance. :param injected_files: User","network interface to a running instance. The counter action to","pass context in for access to auth_token pass def poll_rebooting_instances(self,","of the return value is specific to the Neutron client","anything that is necessary for the driver gracefully stop, including","host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance","other data included as well that is needed for creating","'frequency': freq}, where kern and user indicate the cumulative CPU","the instance at mountpoint using info.\"\"\" raise NotImplementedError() def detach_volume(self,","ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to load the virtualization driver\")) sys.exit(1)","def network_binding_host_id(self, context, instance): \"\"\"Get host ID to associate with","checks if the live migration can succeed, based on the","ID, and vice versa. Some methods here take an instance",":param bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A","instances shouldn't be scheduled to this host, return \"disabled\". :rtype:","context: security context :param instance: nova.objects.instance.Instance \"\"\" pass def inject_network_info(self,","be making the connection, the name of the iscsi initiator","context for the migration/resize :param migration: the migrate/resize information :param","network information :param block_device_info: Information about block devices that should","nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred disk information","raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \"\"\"Check if","instance, nw_info): \"\"\"inject network info for specified instance.\"\"\" # TODO(Vek):","'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This method","uuid => dict of usage info \"\"\" return {} def","the soft-deleted data. :return: None \"\"\" raise NotImplementedError() def get_available_resource(self,","volumes encountered \"\"\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek):","nodename: node which the caller want to get resources from","the given `aggregate`. :param dict kwargs: A free-form thingy... :return:","security context :param instance: nova.objects.instance.Instance This function should use the","hypervisor has no means of publishing the instance metadata to","any lingering instance files for an instance. :param instance: nova.objects.instance.Instance","being recreated on a new hypervisor - all the cleanup","assigned network details are compatible with the set of MAC","_(\"Event must be an instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event","before calling this method. :param context: security context :param instance:","by which we mean 'running virtual machine' (XenAPI terminology) or","the specified instance. :param context: security context :param instance: nova.objects.instance.Instance","hypervisors. The callback will be invoked with a single parameter,",":param nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info:","nodename): \"\"\"Return whether this compute service manages a particular node.\"\"\"","\"\"\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check whether the file","NotImplementedError() def get_serial_console(self, context, instance): \"\"\"Get connection info for a","to volumes. Connector information is a dictionary representing the ip","get_vnc_console(self, context, instance): \"\"\"Get connection info for a vnc console.","returns None. This is called during spawn_instance by the compute","The suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network","for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance","instance is to have. allocate_for_instance will take this into consideration","which should be unpaused. :return: None \"\"\" # TODO(Vek): Need","it fails to quiesce by other errors (e.g. agent timeout),","of the host to add to the given `aggregate`. :param","NotImplementedError() def set_admin_password(self, instance, new_pass): \"\"\"Set the root password on","\"\"\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default the","source VM. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to","during cleanup :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError()","a network interface to a running instance. The counter action","'no constraints', a set means 'these and only these MAC","Return False here so that every hypervisor # need to","valid actions are: \"\"startup\", \"shutdown\" and \"reboot\". :return: The result","\"\"\"Register a callback to receive events. Register a callback to","list. \"\"\" # TODO(Vek): Need to pass context in for","instance. After this is called successfully, the instance's state goes","given `instance`. :param dict new_connection_info: The volume for this connection","at their # virt layer. return False def quiesce(self, context,","instance. The counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext","wr_bytes, errs], where rd indicates read, wr indicates write, req","function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param","on destination host. ( Waiting only for filtering rules to","to be passed in to methods that expect it. \"\"\"","(e.g. agent timeout), NovaException is raised. :param context: request context","to unplug. :return: None \"\"\" raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get","chosen by Nova. At the nova.virt layer, instances do not","information :param recreate: True if the instance is being recreated","auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \"\"\"Applies a","connection info for a serial console. :param context: security context","instance which should be restored from the soft-deleted data. :return:","instance: nova.objects.instance.Instance that the network ports will be associated with","After this is called successfully, the instance's state goes back","(power_state.RUNNING). If this fails, any partial instance should be completely","= nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry","The platform-specific layer is responsible for keeping track of which","**kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device mappings","get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of all nodes managed by the","\"\"\"Cleanup the instance resources . Instance should have been destroyed","is used to publish changes to the instance's metadata to","platforms, this method is unused. Note that this function takes","injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create a new instance/VM/domain on the","driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry","strings with below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'},","all assigned network details are compatible with the set of","to load the virtualization driver\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver","hypervisors (such as bare metal) cannot do freeform virtualization of","Virt drivers expect block device mapping to be presented in","\"\"\"Dispatches an event to the compute manager. Invokes the event","information :param migrate_data: a LiveMigrateData object \"\"\" pass def post_live_migration_at_source(self,","network_info, block_device_info=None): \"\"\"resume guest state when a host is booted.","records the results in the DB. :param nodename: node which","hypervisor # need to define their supported file system #","context: request context :param instance: nova.objects.instance.Instance to be quiesced :param","returns a list of nodenames managed by the service. Otherwise,","method should apply the updated rules to instances running the","to detach. :raise nova.exception.NovaException: If the detach fails. :return: None","indicates the cumulative idle CPU time (nanoseconds), wait indicates the","takes an instance ID. \"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance):","'instance name' are synonyms. Note that the instance ID or","new_pass): \"\"\"Set the root password on the specified instance. :param","rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None,","managed by the service. Otherwise, this method should return [hypervisor_hostname].","volumes attached to a specified instance. The counter action to","All counters are long integers. This method is optional. On","as bare metal) require that instances boot from the network,","nova.objects.instance.Instance to lookup Returns True if files of an instance","that the network ports will be associated with :returns: a","instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def get_vnc_console(self, context, instance): \"\"\"Get","into consideration when provisioning networking for the instance. Mapping of","to other calls into the driver. The prime example is","instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def rescue(self, context, instance, network_info,","the new VM afterwards. It may be overridden by hypervisors","CPU time (nanoseconds), wait indicates the cumulative I/O wait CPU","existence of an instance on the host. :param instance: The","dst_compute_info: Info about the receiving machine :param block_migration: if true,","instance which should be unpaused. :return: None \"\"\" # TODO(Vek):","context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of","nova.objects.instance.Instance instance: The instance which should be unpaused. :return: None","= block_device_info or {} ephemerals = block_device_info.get('ephemerals') or [] return","this layer. This layer is responsible for translating that generic","function at their # virt layer. return False def quiesce(self,","be supplied to the allocate_for_instance call by the compute manager,","def power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power off the specified instance.","will be automatically booted. The counter action for `restore` is","in the rebooting state than `timeout` seconds. :param int timeout:","groups. All security groups and their associated rules live in","a rdp console. :param context: security context :param instance: nova.objects.instance.Instance","block devices to be attached to the instance. :param preserve_ephemeral:","with a single parameter, which will be an instance of","block migration :param disk_over_commit: if true, allow disk over commit","given `instance`. :param dict block_device_info: Information about the block devices.","of the given `instance`. :param dict block_device_info: Information about the","The mountpoint in the instance where the volume for `old_connection_info`","context in for access to auth_token raise NotImplementedError() def get_host_uptime(self):","is `True`, the host will accept new instances. If it","the network, and manage their own TFTP service. This requires","when the 'VM' is actually proxied and needs to be","on, False otherwise \"\"\" raise NotImplementedError() def pause(self, instance): \"\"\"Pause","mapping to be presented in the format of a dict","instance, image_meta, root_bdm): \"\"\"Provide a default root device name for","removed from nova.compute.manager. \"\"\" # TODO(Vek): Need to pass context","an interrupted snapshot. :param context: security context :param instance: nova.objects.instance.Instance","connection info for a vnc console. :param context: security context","\"\"\" raise NotImplementedError() def get_vnc_console(self, context, instance): \"\"\"Get connection info","be retrieved directly in aggregate form, without Nova having to","in the datastore, and calling this method should apply the","the result of calling the Linux command `uptime` on this","information :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def","raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None,","admin_password: Administrator password to set in instance. :param network_info: instance","information for the resume. :param dict block_device_info: Instance volume block","NotImplementedError() def set_bootable(self, instance, is_bootable): \"\"\"Set the ability to power","since the last boot. :rtype: str \"\"\" # TODO(Vek): Need","snapshot of a volume attached to a specified instance. The","swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace the volume attached","iscsi initiator and the hostname of the machine as follows::","Depending on the underlying hypervisor technology, the guest has the","disks should be destroyed :param migrate_data: implementation specific params \"\"\"","interface from a running instance. The counter action to this","the host, False otherwise. .. note:: This implementation works for","You may obtain # a copy of the License at","the host. :param instance: nova.objects.instance.Instance to lookup Returns True if","\"\"\" raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \"\"\"resume","= logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for","should be completely cleaned up, and the virtualization platform should","the interface to attach. :raise nova.exception.NovaException: If the attach fails.","of or if the group gains or loses a rule.","'these and only these MAC addresses'. \"\"\" return None def",":param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the given","req is the total number of I/O requests made, bytes","context :param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta:","this function at their # virt layer. return False def","as the active disk after the snapshot operation has completed.","the snapshot. For example, in the case of qcow2 backed","instance): \"\"\"Cleans up any resources left after an interrupted snapshot.","instance doesn't use CPU cycles of the host anymore. The","host can accept further instances, return \"enabled\", if further instances","raise NotImplementedError() def unrescue(self, instance, network_info): \"\"\"Unrescue the specified instance.","instance: instance that is live migrating \"\"\" raise NotImplementedError() def",":param all_instances: nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self, context, aggregate, host,","host): \"\"\"Initialize anything that is necessary for the driver to","get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get the next device name based","NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None):","for rebuild :param detach_block_devices: function to detach block devices. See","`False`, the host won't accept new instances. :return: If the","managed by the compute service. This method is for multi","be migrated. :param str dest: The IP address of the","instance's metadata to the hypervisor. If the hypervisor has no","add_to_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Add a compute host to","the specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD>","volume_id, snapshot_id, delete_info): \"\"\"Deletes a snapshot of a volume attached","specified instance. :param nova.context.RequestContext context: The context for the rescue.","An error should be raised if the operation cannot complete.","host_power_action(self, action): \"\"\"Reboots, shuts down or powers up the host.","escape the connection interface. The platform-specific layer is responsible for","to the network implementation layer. For instance, with openflow switches,","object which contains information about the VIFs to unplug. :return:","device mappings for an instance to driver format. Virt drivers","Information about the block devices. :param int timeout: The time","to which platform-specific ID, and vice versa. Some methods here","**kwargs): \"\"\"Remove a compute host from an aggregate. The counter","{'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \"\"\" return None","raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information about actual","**kwargs): \"\"\"Add a compute host to an aggregate. The counter","before the end. :param nova.objects.instance.Instance instance: The instance whose disk","L2 domain, with arbitrary VLANs etc, but regular switches require","instance ID or name is not human-readable or customer-controlled --","object \"\"\" # TODO(Vek): Need to pass context in for","after check_instance_shared_storage calls :param context: security context :param data: result","has completed successfully even in cases in which the underlying","MKS console. :param context: security context :param instance: nova.objects.instance.Instance :returns","str \"\"\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host","instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \"\"\" raise NotImplementedError()","def need_legacy_block_device_info(self): \"\"\"Tell the caller if the driver requires legacy","import event as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__)",":param block_device_info: instance block device information :param network_info: instance network","a running instance in multiple phases, turning off the instance","\"\"\" raise NotImplementedError(_(\"Hypervisor driver does not support \" \"post_live_migration_at_source method\"))","nova.objects.instance.Instance instance: The instance whose volume gets replaced by another","or [] return block_device_mapping class ComputeDriver(object): \"\"\"Base class for compute","context in for access to auth_token raise NotImplementedError() def unpause(self,","nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the given `host`","a running instance. The counter action to this is :func:`detach_interface`.","for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs):","paused instance doesn't use CPU cycles of the host anymore.","function, including catching up with currently running VM's on the","All security groups and their associated rules live in the","instance. \"\"\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce","driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs)","block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network","a resize/migration. :param context: the context for the finish_revert_migration :param","nova.objects.instance.Instance \"\"\" raise NotImplementedError() def unrescue(self, instance, network_info): \"\"\"Unrescue the",":param uuid snapshot_id: The snapshot to delete. :param dict delete_info:","The instance to suspend. :return: None \"\"\" raise NotImplementedError() def","device. \"\"\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default","auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power off","root password on the specified instance. :param instance: nova.objects.instance.Instance :param","context, instance): \"\"\"Cleans up any resources left after an interrupted","should be returned. :return: Has a big overlap to the","an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF","mode or \"off_maintenance\" if maintenance mode got left. :rtype: str","given `aggregate`. :param dict kwargs: A free-form thingy... :return: None","def get_host_cpu_stats(self): \"\"\"Get the currently known host CPU stats. :returns:","dict delete_info: Volume backend technology specific data needed to be","context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about","nova.objects.ImageMeta image_meta: The metadata of the image of the instance.","be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the image","CPU cycles and memory of the host again. The counter","format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise","cumulative idle CPU time (nanoseconds), wait indicates the cumulative I/O","raise NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets the ability of this","host :param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method:","of disk information dicts in JSON format. :rtype: str \"\"\"","action for 'resume' is :func:`suspend`. Depending on the underlying hypervisor","(Beginning of) the contract that compute drivers must follow, and","identify a VM or domain. These IDs must stay internal","every hypervisor # need to define their supported file system","nova.objects.instance.Instance that the network ports will be associated with :returns:","a change to security groups. All security groups and their","\"\"\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \"\"\"Set the ability","network information :param block_device_info: instance block device information :param destroy_disks:","whether this compute service manages a particular node.\"\"\" if nodename","It's probably a good idea to log a warning in","implement this function at their # virt layer. return False","If the host can accept further instances, return \"enabled\", if","instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post operation of live migration at","image of the instance. :param nova.network.model.NetworkInfo vif: The object which","return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return","instance): \"\"\"Get connection info for a rdp console. :param context:","# a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","from the instance. :param destroy_disks: Indicates if disks should be","actually volumes. :return: json strings with below format:: \"[{'path':'disk', 'type':'raw',","instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS to","if the driver requires legacy block device info. Tell the","for filtering rules to hypervisor, since filtering rules to firewall","the specified instance to prepare for snapshots. If the specified","of usage info \"\"\" return {} def instance_on_disk(self, instance): \"\"\"Checks","files on the host. :param instance: nova.objects.instance.Instance to lookup Returns","as ex: LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"), {'event': event, 'ex':","must this instance have? Some hypervisors (such as bare metal)","to quiesce by other errors (e.g. agent timeout), NovaException is","state goes back to power_state.RUNNING. The virtualization platform should ensure","context, instance, image_meta): \"\"\"Quiesce the specified instance to prepare for","have been in rebooting state longer than the configured timeout.","timeout=0, retry_interval=0): \"\"\"Power off the specified instance. :param instance: nova.objects.instance.Instance","that is migrated :param network_info: instance network information :param block_migration:","implied. See the # License for the specific language governing","is specific to the Neutron client API. :return: None, or","instance: nova.objects.instance.Instance :returns: True if the instance was deleted from","the active disk after the snapshot operation has completed. There","\"\"\"Reboots, shuts down or powers up the host. :param str","compute service. This method is for multi compute-nodes support. If","context :param instance: nova.objects.instance.Instance \"\"\" pass def finish_migration(self, context, migration,","has the same state as before the 'pause'. :param nova.objects.instance.Instance","compute manager. Note that the format of the return value","succeed. It's probably a good idea to log a warning","nova.objects.instance.Instance instance: The instance which gets a network interface removed.","= { \"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False,","when provisioning networking for the instance. Mapping of MAC addresses","raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide a default","NotImplementedError() def get_host_uptime(self): \"\"\"Returns the result of calling the Linux","the next device name based on the block device mapping.","timeout: The time in seconds to wait for the guest","The instance to soft-delete. :return: None \"\"\" raise NotImplementedError() def","internal ID chosen by Nova. At the nova.virt layer, instances","disk_id): \"\"\"Return performance counters associated with the given disk_id on","The counter action :func:`restore` uses the database entry to create",":param context: request context :param instance: nova.objects.instance.Instance to be quiesced","instance: instance object that is migrated :param network_info: instance network","info for a rdp console. :param context: security context :param","class ComputeDriver(object): \"\"\"Base class for compute drivers. The interface to","to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None,","\"\"\"Use hotplug to add a network interface to a running","recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make this instance. A 'rebuild'","NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power off the specified","regarding an instance, and pass them into this layer. This","implementation and required for validation of access to instance shared","to the instance. :param preserve_ephemeral: True if the default ephemeral","nw_info): \"\"\"inject network info for specified instance.\"\"\" # TODO(Vek): Need","\"\"\"Suspend the specified instance. A suspended instance doesn't use CPU",":returns: a LiveMigrateData object (hypervisor-dependent) \"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self,","plugged. :param nova.network.model.NetworkInfo network_info: The object which contains information about","images which are no longer of interest. :param all_instances: nova.objects.instance.InstanceList","NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes a","Instance object as returned by DB layer. :param network_info: instance","in the memory or storage space of the host, depending","is necessary for the driver gracefully stop, including ending remote","the snapshot. \"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans","the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description","terms that are specific to the virtualization platform. \"\"\" capabilities","host, mode): \"\"\"Start/Stop host maintenance window. On start, it triggers","\"\"\"Check if instance files located on shared storage. :param context:","instance.name. The second parameter is the base64-encoded path to which","return a set of MAC addresses that the instance is","context: security context :param dest_check_data: result of check_can_live_migrate_destination \"\"\" raise","will take this into consideration when provisioning networking for the","LOG.error(_LE(\"Compute driver option required, but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute","detaches all block devices, then spins up the new VM","return True @property def need_legacy_block_device_info(self): \"\"\"Tell the caller if the","and required for validation of access to instance shared disk","that will match the actual configuration. Most hypervisors can use","of instance files on the host. :param instance: nova.objects.instance.Instance to","the host again. The counter action for 'resume' is :func:`suspend`.","has the information about the interface to detach. :raise nova.exception.NovaException:","\"\"\"Returns nodenames of all nodes managed by the compute service.","responsible for keeping track of which instance ID maps to","migration of an instance to another host. :param context: security","old state is skipped. :param block_device_info: Information about block devices","raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None):","block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about the requested","NotImplementedError() def get_num_instances(self): \"\"\"Return the total number of virtual machines.","or loses a rule. \"\"\" raise NotImplementedError() def reset_network(self, instance):","mappings that will match the actual configuration. Most hypervisors can","DHCP service. Most hypervisors can use the default implementation which","the resume. :param dict block_device_info: Instance volume block device info.","metadata of the image of the instance. :param nova.network.model.NetworkInfo vif:","new hypervisor - all the cleanup of old state is","\"\"startup\", \"shutdown\" and \"reboot\". :return: The result of the power","instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance resources","a LiveMigrateData object \"\"\" pass def post_live_migration_at_source(self, context, instance, network_info):","nova.objects.instance.Instance \"\"\" raise NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger crash dump","hypervisors can use the default implementation which returns None. Hypervisors","those platforms, this method is unused. Note that this function","a compute host from an aggregate. The counter action to","function takes an instance ID. \"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self,","access to auth_token pass def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a","compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute driver option required,","str \"\"\" # TODO(Vek): Need to pass context in for","instance to driver format. Virt drivers expect block device mapping","add a network interface to a running instance. The counter","from networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance:","2.0 (the \"License\"); you may # not use this file","during spawn_instance by the compute manager. :return: None, or a","instance): \"\"\"Abort an in-progress live migration. :param instance: instance that","nova.objects.instance.Instance object \"\"\" # TODO(Vek): Need to pass context in",":param migrate_data: implementation specific params \"\"\" raise NotImplementedError() def cleanup(self,","last boot. :rtype: str \"\"\" # TODO(Vek): Need to pass","contains information about the VIFs to plug. :return: None \"\"\"","network_info: The object which contains information about the VIFs to","specified instance to prepare for snapshots. If the specified instance","opt :returns: a ComputeDriver instance \"\"\" if not compute_driver: compute_driver","set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints',","vice versa. Some methods here take an instance of nova.compute.service.Instance.","a device name :param bdms: a nova.objects.BlockDeviceMappingList for the instance","destroy_disks: Indicates if disks should be destroyed :param migrate_data: implementation","dict block_device_info: Instance volume block device info. :return: None \"\"\"","\"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\": False, } def","\"\"\"Default the missing device names in the block device mapping.\"\"\"","context, instance, image_meta): \"\"\"Unquiesce the specified instance after snapshots. If","instance which should be paused. :return: None \"\"\" # TODO(Vek):","prepare for snapshots. If the specified instance doesn't support quiescing,","security context :param instance: instance object that was being migrated","caller whether we expect the legacy format of block device","volume block device info. :return: None \"\"\" raise NotImplementedError() def","\"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post","calling this method should apply the updated rules to instances","post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration of an instance to","\"\"\"Get console output for an instance :param context: security context","auth_token raise NotImplementedError() def get_num_instances(self): \"\"\"Return the total number of","of MAC addresses, which will be supplied to the allocate_for_instance","'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id):","machines that the hypervisor knows about. .. note:: This implementation","# All Rights Reserved. # # Licensed under the Apache","time (nanoseconds), wait indicates the cumulative I/O wait CPU time","be stored in the memory or storage space of the","implementation which returns None. Hypervisors with MAC limits should return","event): \"\"\"Dispatches an event to the compute manager. Invokes the","required, but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver)","instance volume block device info :param power_on: True if the","counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The","nova.objects.instance.Instance instance: The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo","deleted from disk, False otherwise. \"\"\" return True @property def","info for a spice console. :param context: security context :param","event. This must only be invoked from a green thread.","for access to auth_token raise NotImplementedError() def unpause(self, instance): \"\"\"Unpause","take this into consideration when provisioning networking for the instance.","performance statistics can be retrieved directly in aggregate form, without","still succeed. It's probably a good idea to log a","to complete the snapshot. For example, in the case of","`timeout` seconds. :param int timeout: The timeout (in seconds) for","security group rules Gets called when an instance gets added","are: \"\"startup\", \"shutdown\" and \"reboot\". :return: The result of the",":param block_migration: if true, prepare for block migration :param disk_over_commit:",":param nova.objects.ImageMeta image_meta: The metadata of the image of the","the instance's metadata to the hypervisor. If the hypervisor has","detached from the instance. :param destroy_disks: Indicates if disks should","but libvirt migrates those value through migrateToURI(), so , no",":returns an instance of console.type.ConsoleMKS \"\"\" raise NotImplementedError() def get_diagnostics(self,","in terms of 'instances' (Amazon EC2 and internal Nova terminology),","be triggered. :return: None \"\"\" raise NotImplementedError() def soft_delete(self, instance):",":func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return","the given disk_id on the given instance. These are returned","How often to signal guest while waiting for it to","for Resource Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get connector","to check the results. :param context: security context :param instance:","should return [hypervisor_hostname]. \"\"\" raise NotImplementedError() def node_is_available(self, nodename): \"\"\"Return","included as well that is needed for creating the snapshot.","something more efficient. \"\"\" try: return instance.uuid in self.list_instance_uuids() except","of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a","# License for the specific language governing permissions and limitations","False def register_event_listener(self, callback): \"\"\"Register a callback to receive events.","on the host. :param instance: The instance to lookup Returns","afterwards. It may be overridden by hypervisors that need to","\"\"\"Get connection info for a spice console. :param context: security","auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \"\"\"Set the root","`True`, go into maintenance mode. If `False`, leave the maintenance","action for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The context","disk_id on the given instance. These are returned as [rd_req,","drivers expect block device mapping to be presented in the","flavor: The flavor of the instance whose disk get migrated.","any resources anymore, but is still available as a database","DriverVolumeBlockDevice or any of it's more specialized subclasses. \"\"\" from","and remove images which are no longer of interest. :param","to be quiesced :param nova.objects.ImageMeta image_meta: The metadata of the",":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \"\"\" raise","up to the network implementation layer. For instance, with openflow","str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\"Exception dispatching event %(event)s:","to do the aggregation. On those platforms, this method is","nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param dict","usage info \"\"\" return {} def instance_on_disk(self, instance): \"\"\"Checks access","the guest OS reacts in details, depends on the configuration","requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList for the","instance: The instance which should be unpaused. :return: None \"\"\"","the driver gracefully stop, including ending remote sessions. This is","platform. \"\"\" capabilities = { \"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\":","to set in instance. :param network_info: instance network information :param","security context :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def get_vnc_console(self,","hostname } \"\"\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames","of the instance. :param resize_instance: True if the instance is","instance): \"\"\"Pause the given instance. A paused instance doesn't use","waiting for it to shutdown. :return: A list of disk","frequency (MHz). All values are long integers. \"\"\" raise NotImplementedError()","VM. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to pass","maintenance window. On start, it triggers the migration of all","the instance. :param preserve_ephemeral: True if the default ephemeral storage","mode): \"\"\"Start/Stop host maintenance window. On start, it triggers the","not have human-readable names at all -- such things are","JSON format. :rtype: str \"\"\" raise NotImplementedError() def snapshot(self, context,","a file on the specified instance. The first parameter is","instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created image that","instance, network_info): \"\"\"Unplug VIFs from networks at source. :param context:","the driver want networks deallocated on reschedule?\"\"\" return False def","context in for access to auth_token pass def poll_rebooting_instances(self, timeout,","overhead calculations are desired. :param instance_info: Instance/flavor to calculate overhead","instance of the nova.virt.event.Event class. \"\"\" self._compute_event_callback = callback def","track of which instance ID maps to which platform-specific ID,","a particular node.\"\"\" if nodename in self.get_available_nodes(): return True #","\"\"\"Deletes a snapshot of a volume attached to a specified","an instance of nova.compute.service.Instance, and so the instance is being","block device info. :return: None \"\"\" raise NotImplementedError() def resume_state_on_host_boot(self,","nova.objects.instance.Instance instance: The suspended instance to resume. :param nova.network.model.NetworkInfo network_info:","an instance of the nova.virt.event.Event class. \"\"\" self._compute_event_callback = callback","context, instance, network_info): \"\"\"Unplug VIFs from networks at source. :param","as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates read,","memory of the host again. The counter action for 'resume'","security context :param instance: nova.objects.instance.Instance \"\"\" pass def finish_migration(self, context,","whether the file format is supported by this driver :param","specified instance. The counter action to this is :func:`volume_snapshot_create` :param","get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information about actual disk sizes of","of console.type.ConsoleRDP \"\"\" raise NotImplementedError() def get_serial_console(self, context, instance): \"\"\"Get","def attach_interface(self, instance, image_meta, vif): \"\"\"Use hotplug to add a","optimisations, or when the 'VM' is actually proxied and needs","their associated rules live in the datastore, and calling this","be other data included as well that is needed for","\"\"\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host maintenance","nova.objects.instance.Instance instance: The instance which will get an additional network","migrate_data: a LiveMigrateData object \"\"\" pass def post_live_migration_at_source(self, context, instance,","the name of the iscsi initiator and the hostname of","switches, fixed MAC addresses can still be virtualized onto any","add the given `host` :param str host: The name of","migrate_data=None): \"\"\"Prepare an instance for live migration :param context: security","The instance which will get an additional network interface. :param","disk - ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances","of the nova.virt.event.Event class. \"\"\" self._compute_event_callback = callback def emit_event(self,","format returned by Cinder's initialize_connection() API call. In the case","in compliance with the License. You may obtain # a","Note that the instance ID or name is not human-readable","def unfilter_instance(self, instance, network_info): \"\"\"Stop filtering instance.\"\"\" # TODO(Vek): Need","space of the host, depending on the underlying hypervisor technology.","to plug. :return: None \"\"\" # TODO(Vek): Need to pass","host. ( Waiting only for filtering rules to hypervisor, since","shouldn't be scheduled to this host, return \"disabled\". :rtype: str","the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance","memory or storage space of the host, depending on the","raise NotImplementedError() def soft_delete(self, instance): \"\"\"Soft delete the specified instance.","state of the instance could be persisted on the host",":param fs_type: the file system type to be checked, the","The valid actions are: \"\"startup\", \"shutdown\" and \"reboot\". :return: The","raise NotImplementedError() def restore(self, instance): \"\"\"Restore the specified soft-deleted instance.","back to the source host to check the results. :param","during spawn_instance by the compute manager. Note that the format","context :param dest_check_data: result of check_can_live_migrate_destination \"\"\" raise NotImplementedError() def","instance for live migration :param context: security context :param instance:","of _get_instance_block_device_info :returns: a LiveMigrateData object \"\"\" raise NotImplementedError() def","raise NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger crash dump mechanism on","block_device_info=None): \"\"\"Post operation of live migration at destination host. :param","device info. Tell the caller whether we expect the legacy","default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default the missing device names in","with :returns: a string representing the host ID \"\"\" return","in the state that it was before this call began.","nova.objects.instance.Instance objects that have been in rebooting state longer than","the ability to power on/off an instance. :param instance: nova.objects.instance.Instance","host, and then calls back to the source host to","that the instance ID or name is not human-readable or","snapshots, this would include the file being merged, and the","but regular switches require pre-configured MAC->network mappings that will match","return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization","set_host_enabled(self, enabled): \"\"\"Sets the ability of this host to accept",":param instance: nova.objects.instance.Instance This function should use the data there","the default implementation which returns None. Hypervisors with MAC limits","IPTables rules and apply the rules now.\"\"\" pass def unfilter_instance(self,","nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event) except Exception as","\"\"\"inject network info for specified instance.\"\"\" # TODO(Vek): Need to","being specified as instance.name. The second parameter is the base64-encoded",":param context: security context :param instance: nova.objects.instance.Instance :returns an instance","nova.objects.instance.Instance :returns: True if the instance was deleted from disk,","detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make this","recreate: True if the instance is being recreated on a","otherwise. \"\"\" return True @property def need_legacy_block_device_info(self): \"\"\"Tell the caller","\"reboot\". :return: The result of the power action :rtype: :","type and implement this function at their # virt layer.","specified instance. A suspended instance doesn't use CPU cycles or","eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address',","such things are only known higher up the stack. Most","newly transferred disk information :param network_info: instance network information :param","clean the cache and remove images which are no longer","(Amazon EC2 and internal Nova terminology), by which we mean","instance, network_info, image_meta, rescue_password): \"\"\"Rescue the specified instance. :param nova.context.RequestContext","the guest has the same state as before the 'pause'.","have human-readable names at all -- such things are only",":param migrate_data: implementation specific params \"\"\" raise NotImplementedError() def reboot(self,","Info about the receiving machine :param block_migration: if true, prepare","instance doesn't use CPU cycles or memory of the host","method is deprecated and will be removed once it can",":param instance: nova.objects.instance.Instance :param network_info: instance network information :param reboot_type:","The counter action for 'unpause' is :func:`pause`. Depending on the","to in writing, software # distributed under the License is","of the instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info:","any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate","single parameter, which will be an instance of the nova.virt.event.Event",":func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance:","same data format returned by Cinder's initialize_connection() API call. In","function should still succeed. It's probably a good idea to","operations are not completed. :param instance: nova.objects.instance.Instance object \"\"\" #","password to set in instance. :param network_info: instance network information","device names in the block device mapping.\"\"\" raise NotImplementedError() def","storage partition must be preserved on rebuild \"\"\" raise NotImplementedError()","to attach to the volume. This is the same data","snapshot. \"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up","\"on_maintenance\" if switched to maintenance mode or \"off_maintenance\" if maintenance","format is supported by this driver :param fs_type: the file","group rules Gets called when an instance gets added to","of MAC addresses. This method allows drivers to return a","nova.compute.service.Instance. This is the data structure used by nova.compute to","or domain (Xen or libvirt terminology). An instance has an","the information about the interface to attach. :raise nova.exception.NovaException: If","operation cannot complete. \"\"\" # TODO(Vek): Need to pass context","method which is used to publish changes to the instance's","it is possible to execute live migration. This runs checks","be implemented. :param context: security context :param instance: nova.objects.instance.Instance \"\"\"","host to add to the given `aggregate`. :param dict kwargs:","block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap","License, Version 2.0 (the \"License\"); you may # not use","does not support \" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context, instance,","be called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx,","context, instance): \"\"\"Get console output for an instance :param context:","The time in seconds to wait for the guest OS","def set_bootable(self, instance, is_bootable): \"\"\"Set the ability to power on/off","the event callback registered by the compute manager to dispatch","a list. \"\"\" raise NotImplementedError() def rebuild(self, context, instance, image_meta,","instance gets unpaused and will use CPU cycles of the","is an instance of nova.compute.service.Instance, and so the instance is","Information about block devices that should be detached from the","can use the default implementation which returns None. This is","this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param","context :instance: instance object that was migrated :block_device_info: instance block","def unrescue(self, instance, network_info): \"\"\"Unrescue the specified instance. :param instance:","access to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info,","ID or name is not human-readable or customer-controlled -- it's","base-classes: (Beginning of) the contract that compute drivers must follow,","to the instance at mountpoint using info.\"\"\" raise NotImplementedError() def","means 'no constraints', a set means 'these and only these","\"\"\"Get DHCP options for this instance. Some hypervisors (such as","expect block device mapping to be presented in the format","live migration to complete :param instance: Instance being live migrated","for instances on disk. This method is an opportunity to","LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\"Exception","a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance","counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The","with something more efficient. \"\"\" try: return instance.uuid in self.list_instance_uuids()","will use CPU cycles and memory of the host again.","callback): \"\"\"Register a callback to receive events. Register a callback","IP address of the destination host. :param nova.objects.flavor.Flavor flavor: The","implementation works for all drivers, but it is not particularly","never escape the connection interface. The platform-specific layer is responsible","Stalling instances can be triggered to dump the crash data.","License is distributed on an \"AS IS\" BASIS, WITHOUT #","The object which contains information about the VIFs to unplug.","expects to be used as the active disk after the","importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError:","spins up the new VM afterwards. It may be overridden","The volume for this connection gets detached from the given","information of the given `instance`. :param dict block_device_info: Information about","disk attached to the instance.\"\"\" raise NotImplementedError() def swap_volume(self, old_connection_info,","'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap']","requires passing the appropriate options out to the DHCP service.","block_device_info or {} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals","\"\"\"Return performance counters associated with the given disk_id on the","virtualization layer, as a list. \"\"\" # TODO(Vek): Need to","ip, 'initiator': initiator, 'host': hostname } \"\"\" raise NotImplementedError() def","\"\"\"Returns the result of calling the Linux command `uptime` on","pre-configured MAC->network mappings that will match the actual configuration. Most","is the same data format returned by Cinder's initialize_connection() API",":param reboot_type: Either a HARD or SOFT reboot :param block_device_info:","nova.objects.instance.Instance object :param block_device_info: instance block device information :param network_info:","block_device_info: Optional; Can be used to filter out devices which","the instance's state goes back to power_state.RUNNING. The virtualization platform","network_info): \"\"\"Setting up filtering rules and waiting for its completion.","live migrated \"\"\" raise NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort an","instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration of an","security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial","an argument. Compute drivers constructors take a VirtAPI object as","post operation of block_migration. \"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self, context,","NotImplementedError() def attach_interface(self, instance, image_meta, vif): \"\"\"Use hotplug to add","instance): \"\"\"Get connection info for a MKS console. :param context:","'suspend'. :param nova.context.RequestContext context: The context for the resume. :param","virtual machine' (XenAPI terminology) or domain (Xen or libvirt terminology).","instance network information :param nova.objects.ImageMeta image_meta: The metadata of the","larger than the old volume, it gets resized to the","rules and apply the rules now.\"\"\" pass def unfilter_instance(self, instance,","\"\"\"Snapshots the specified instance. :param context: security context :param instance:","<NAME> # All Rights Reserved. # # Licensed under the","access to auth_token raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return the UUIDS","the instance. :param injected_files: User files to inject into instance.","CPU time (nanoseconds), since the host is booting up; freq","action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance","read, wr indicates write, req is the total number of","= block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return","and memory of the host again. The counter action for","raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get the next","raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes","The timeout (in seconds) for considering rebooting instances to be","def instance_exists(self, instance): \"\"\"Checks existence of an instance on the","network_info): \"\"\"Confirms a resize/migration, destroying the source VM. :param instance:","get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage info for volumes attached to","return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute driver module.","legacy format of block device info to be passed in","context. :param nova.objects.instance.Instance instance: The instance that has the volume","raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \"\"\"resume the","this is called successfully, the instance's state goes back to","will be associated with :returns: a string representing the host","if the instance is being recreated on a new hypervisor","host whose maintenance mode should be changed. :param bool mode:","distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR","names at all -- such things are only known higher","the virtualization platform. \"\"\" capabilities = { \"has_imagecache\": False, \"supports_recreate\":","host to remove from the given `aggregate`. :param dict kwargs:",":return: None \"\"\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info,","of estimated overhead values. \"\"\" return {'memory_mb': 0, 'disk_gb': 0}","scheduled to this host, return \"disabled\". :rtype: str \"\"\" #","ensure that the reboot action has completed successfully even in","def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up","network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance resources .","context in for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance,","instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping)","\"\"\"Get connection info for a rdp console. :param context: security","live migration can succeed, based on the results from check_can_live_migrate_destination.","network implementation layer. For instance, with openflow switches, fixed MAC","access to auth_token raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns the result","for multi compute-nodes support. If a driver supports multi compute-nodes,","-- such things are only known higher up the stack.","interface to a running instance. The counter action to this","the legacy format of block device info to be passed","calling this method. :param context: security context :param instance: Instance","about the interface to attach. :raise nova.exception.NovaException: If the attach","def soft_delete(self, instance): \"\"\"Soft delete the specified instance. A soft-deleted","If it is `False`, the host won't accept new instances.",":param nova.objects.instance.Instance instance: The instance which will get an additional","where the volume for `old_connection_info` is attached to. :param int","requested block device. device_name does not need to be set,","nova.objects.instance.Instance This function should use the data there to guide","nova.objects.instance.Instance instance: The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo","a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no","(for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have","'personalities'. This base class method shuts down the VM, detaches","usage. :param network_info: instance network information :param recreate: True if","recreated on a new hypervisor - all the cleanup of",":param destroy_disks: if true, destroy disks at destination during cleanup",":param instance: instance object that is migrated :param network_info: instance","shuts down the VM, detaches all block devices, then spins","be returned. :return: Has a big overlap to the return","The metadata of the image of the instance. :param nova.objects.BlockDeviceMapping","that call to ensure that all assigned network details are","the return value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics","\"\"\"Return the UUIDS of all the instances known to the","action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security","NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get the next device","utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to load the virtualization driver\"))","text which contains the uptime of this host since the","data. :return: None \"\"\" raise NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve","integers. This method is optional. On some platforms (e.g. XenAPI)","attached to. :param int resize_to: If the new volume is","return None def manage_image_cache(self, context, all_instances): \"\"\"Manage the driver's local","NotImplementedError() def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host maintenance window. On","being migrated :param network_info: instance network information :param block_device_info: instance","to pass context in for access to auth_token pass def","is supported by this driver :param fs_type: the file system","based on the block device mapping. :param instance: nova.objects.instance.Instance that","compliance with the License. You may obtain # a copy","context, compute_host_bdms): \"\"\"Return usage info for volumes attached to vms","of the image of the instance. \"\"\" raise NotImplementedError() def","about the sending machine :param dst_compute_info: Info about the receiving","in rebooting state longer than the configured timeout. :return: None","instance network information :param recreate: True if the instance is","gets attached to the given 'instance'. :param nova.objects.instance.Instance instance: The","# # Unless required by applicable law or agreed to","this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will","to handle any bad volumes encountered \"\"\" raise NotImplementedError() def","gracefully stop, including ending remote sessions. This is optional. \"\"\"","information dicts in JSON format. :rtype: str \"\"\" raise NotImplementedError()","instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def trigger_crash_dump(self, instance):","to store details regarding an instance, and pass them into","platform should ensure that the reboot action has completed successfully","to be able to attach to the volume. This is","to auth_token raise NotImplementedError() def get_num_instances(self): \"\"\"Return the total number","rebuild \"\"\" raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files,","rules Gets called when an instance gets added to or","delete. :param dict delete_info: Volume backend technology specific data needed","dict new_connection_info: The volume for this connection gets attached to","qcow2 backed snapshots, this would include the file being merged,","of virtual machines. Return the number of virtual machines that","do management of that cache which isn't directly related to","context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the disk","def add_to_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Add a compute host","if nodename in self.get_available_nodes(): return True # Refresh and check","post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method","dict kwargs: A free-form thingy... :return: None \"\"\" # NOTE(jogo)","preserve_ephemeral: True if the default ephemeral storage partition must be","to auth_token raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return the UUIDS of","address of the destination host. :param nova.objects.flavor.Flavor flavor: The flavor","detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage.","BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either","str host: The name of the host to remove from","raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password): \"\"\"Rescue","A free-form thingy... :return: None \"\"\" # NOTE(jogo) Currently only","{ 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list","nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine :param dst_compute_info:","host, return \"disabled\". :rtype: str \"\"\" # TODO(Vek): Need to","receive events. Register a callback to receive asynchronous event notifications","instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for","for the driver gracefully stop, including ending remote sessions. This","written on the instance; the third is the contents of","instance, image_meta): \"\"\"Quiesce the specified instance to prepare for snapshots.","detach_interface(self, instance, vif): \"\"\"Use hotunplug to remove a network interface","LiveMigrateData object (hypervisor-dependent) \"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data):","When it fails to quiesce by other errors (e.g. agent","compute_driver configuration option or, if supplied, the driver name supplied","NotImplementedError() def block_stats(self, instance, disk_id): \"\"\"Return performance counters associated with","the underlying hypervisor technology. A \"stronger\" version of `pause` is","to be freeform) is up to the network implementation layer.","etc, but regular switches require pre-configured MAC->network mappings that will","of that cache which isn't directly related to other calls","driver that manages only one node can safely ignore this","name of the host to add to the given `aggregate`.","<PASSWORD> for rescue. \"\"\" raise NotImplementedError() def set_bootable(self, instance, is_bootable):","of an instance with the supplied ID accessible on the",":func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should be paused.","needed for creating the snapshot. \"\"\" raise NotImplementedError() def volume_snapshot_delete(self,","halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network information :param","uptime of this host since the last boot. :rtype: str","usage. :param attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl","\"\"\" raise NotImplementedError() def get_serial_console(self, context, instance): \"\"\"Get connection info","type to be checked, the validate values are defined at","create_info: The data needed for nova to be able to","object instance object that is migrated. :param dest: destination host","information. This method is called when nova-compute launches, and as","list of DriverVolumeBlockDevice or any of it's more specialized subclasses.","the instance is being resized, False otherwise :param block_device_info: instance","NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The","use CPU cycles or memory of the host anymore. The","Instance being live migrated \"\"\" raise NotImplementedError() def live_migration_abort(self, instance):","given instance. A paused instance doesn't use CPU cycles of","dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the disk of","match the actual configuration. Most hypervisors can use the default","context :param instance: nova.objects.instance.Instance object :param block_device_info: instance block device","returned by Cinder's initialize_connection() API call. In the case of","The instance that has the volume attached :param uuid volume_id:","def unpause(self, instance): \"\"\"Unpause the given paused instance. The paused","get_host_cpu_stats(self): \"\"\"Get the currently known host CPU stats. :returns: a",":param nova.objects.instance.Instance instance: The instance which gets VIFs unplugged. :param","instance was deleted from disk, False otherwise. \"\"\" return True",":param int timeout: The time in seconds to wait for","the actual configuration. Most hypervisors can use the default implementation","was deleted from disk, False otherwise. \"\"\" return True @property","rules to firewall rules can be set faster). Concretely, the","0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals =","preserve_ephemeral=False): \"\"\"Destroy and re-make this instance. A 'rebuild' effectively purges","the License is distributed on an \"AS IS\" BASIS, WITHOUT","'name' elsewhere. As far as this layer is concerned, 'instance","if instance files located on shared storage. :param context: security","calls back to the source host to check the results.","network_info: instance network information :param reboot_type: Either a HARD or",":param instance: nova.objects.instance.Instance object \"\"\" # TODO(Vek): Need to pass","host: The name of the host whose maintenance mode should","preserved on rebuild \"\"\" raise NotImplementedError() def spawn(self, context, instance,","per-instance overhead calculations are desired. :param instance_info: Instance/flavor to calculate","raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \"\"\"Post operation","result of check_instance_shared_storage_local \"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data):","`instance` at instance boot time. The counter action is :func:`unplug_vifs`.","if files of an instance with the supplied ID accessible","the host anymore. The state of the VM could be",":param instance: nova.objects.instance.Instance object Returns a InstanceInfo object \"\"\" #","None \"\"\" raise NotImplementedError() def restore(self, instance): \"\"\"Restore the specified","LOG.exception(_LE(\"Unable to load the virtualization driver\")) sys.exit(1) def is_xenapi(): return","whether we expect the legacy format of block device info","from nova.compute.manager. \"\"\" # TODO(Vek): Need to pass context in","at instance boot time. The counter action is :func:`unplug_vifs`. :param",":param dest_check_data: result of check_can_live_migrate_destination \"\"\" raise NotImplementedError() def check_can_live_migrate_source(self,","directly in aggregate form, without Nova having to do the","info for specified instance.\"\"\" # TODO(Vek): Need to pass context","specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver) try: driver =","NOTE(jichenjc): Return False here so that every hypervisor # need","device name. \"\"\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check whether","instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \"\"\" raise NotImplementedError()","NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \"\"\"resume the specified","device_type=None, encryption=None): \"\"\"Attach the disk to the instance at mountpoint",":func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which should be","def suspend(self, context, instance): \"\"\"Suspend the specified instance. A suspended","the host should perform. The valid actions are: \"\"startup\", \"shutdown\"","commit :returns: a LiveMigrateData object (hypervisor-dependent) \"\"\" raise NotImplementedError() def","guest while waiting for it to shutdown. :return: A list","auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True):","group the instance is a member of or if the","the CPU stat info, eg: | {'kernel': kern, | 'idle':","on the specified instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError()","including catching up with currently running VM's on the given","\"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\": False,","machine as follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname","when an instance gets added to or removed from the","root_device_name: device name of the root disk - ephemerals: a","retrieved directly in aggregate form, without Nova having to do","if it is possible to execute live migration. This checks","Note that this function takes an instance ID. \"\"\" raise","IP address of the dom0 \"\"\" # TODO(Vek): Need to","`pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should","unplug. :return: None \"\"\" raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the","the migration/resize :param migration: the migrate/resize information :param instance: nova.objects.instance.Instance","def set_host_enabled(self, enabled): \"\"\"Sets the ability of this host to","a vnc console. :param context: security context :param instance: nova.objects.instance.Instance","underlying domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param","deallocated on reschedule?\"\"\" return False def macs_for_instance(self, instance): \"\"\"What MAC","instance which gets a network interface removed. :param nova.network.model.NetworkInfo vif:","an in-progress live migration. :param instance: instance that is live","this must be supplied. :param virtapi: a VirtAPI instance :param","or SOFT reboot :param block_device_info: Info pertaining to attached volumes","of the power action :rtype: : str \"\"\" raise NotImplementedError()","directly related to other calls into the driver. The prime","task that records the results in the DB. :param nodename:","instance, mountpoint, encryption=None): \"\"\"Detach the disk attached to the instance.\"\"\"","instance whose volume gets replaced by another one. :param str","def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options for this instance. Some","to the DHCP service. Most hypervisors can use the default","*args, **kwargs): driver_registry = dict() for driver_str in named_driver_config: driver_type,","the names of all the instances known to the virtualization","has the volume attached. :param uuid volume_id: Attached volume associated","False otherwise \"\"\" raise NotImplementedError() def pause(self, instance): \"\"\"Pause the","context: request context :param instance: nova.objects.instance.Instance to be unquiesced :param","Return the number of virtual machines that the hypervisor knows","The instance where the crash dump should be triggered. :return:","their own TFTP service. This requires passing the appropriate options","mountpoint using info.\"\"\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint,","instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for","security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is","user indicate the cumulative CPU time (nanoseconds) spent by kernel","with the snapshot :param uuid snapshot_id: The snapshot to delete.","will also have their own identity schemes, to uniquely identify","unfilter_instance(self, instance, network_info): \"\"\"Stop filtering instance.\"\"\" # TODO(Vek): Need to","block device info :param power_on: True if the instance should","and only these MAC addresses'. \"\"\" return None def dhcp_options_for_instance(self,","returned. :return: Has a big overlap to the return value","or when the 'VM' is actually proxied and needs to","disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the disk to the instance at",":instance: instance object that was migrated :block_device_info: instance block device",":param rescue_password: <PASSWORD> for rescue. \"\"\" raise NotImplementedError() def set_bootable(self,","paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network","def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a reboot on all given","form, without Nova having to do the aggregation. On those","context :param instance: Instance object as returned by DB layer.","accept new instances. :param bool enabled: If this is `True`,","information :param block_device_info: Information about block devices to be attached","unrescue(self, instance, network_info): \"\"\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance","if true, migrate VM disk. :param migrate_data: a LiveMigrateData object","guest while waiting for it to shutdown \"\"\" raise NotImplementedError()","nova.objects.instance.Instance instance: The instance to which the diagnostic data should","switches require pre-configured MAC->network mappings that will match the actual","purges all existing data from the system and remakes the","Administrator password to set in instance. :param network_info: instance network","connection, the name of the iscsi initiator and the hostname","is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should be","injected_files: User files to inject into instance. :param admin_password: <PASSWORD>","of qcow2 backed snapshots, this would include the file being","was before this call began. :param context: security context :param",":param instance: nova.objects.instance.Instance object :param block_device_info: instance block device information","addresses. This is called during spawn_instance by the compute manager.","cycles and memory of the host again. The counter action","connection interface. The platform-specific layer is responsible for keeping track","security context :param instance: nova.objects.instance.Instance object \"\"\" raise NotImplementedError() def","def emit_event(self, event): \"\"\"Dispatches an event to the compute manager.","specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it","The aggregate which should remove the given `host` :param str","at disk API module. \"\"\" # NOTE(jichenjc): Return False here","information :param network_info: instance network information :param disk_info: instance disk","to be attached to the instance. :param preserve_ephemeral: True if","def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of all nodes managed by","check_instance_shared_storage_remote(self, context, data): \"\"\"Check if instance files located on shared","\"\"\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes a","on a given host.- \"\"\" raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves","technology. A \"stronger\" version of `pause` is :func:'suspend'. The counter","also have their own identity schemes, to uniquely identify a","have been destroyed from the Hypervisor before calling this method.","on shared storage. :param context: security context :param data: result","up, and the virtualization platform should be in the state","use for rebuild :param detach_block_devices: function to detach block devices.","with given 'metadata' and 'personalities'. This base class method shuts","that was migrated :block_device_info: instance block device information :param migrate_data:","function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param","entry to create a new instance based on that. :param","option or, if supplied, the driver name supplied as an","rebooting instances to be stuck. :param list instances: A list","context: security context :param instance: Instance object as returned by","the specified security group. An error should be raised if","running instance. The counter action to this is :func:`detach_interface`. :param","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","oslo_utils import importutils import nova.conf from nova.i18n import _, _LE,","block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object \"\"\" raise","known host CPU stats. :returns: a dict containing the CPU","bdms, block_device_obj): \"\"\"Get the next device name based on the","\"\"\"Check if instance files located on shared storage. This runs","'idle': idle, | 'user': user, | 'iowait': wait, | 'frequency':","Has a big overlap to the return value of the","to use for rebuild :param detach_block_devices: function to detach block","def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices,","swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {}","block device information :param network_info: instance network information :param disk_info:","\"\"\"Rescue the specified instance. :param nova.context.RequestContext context: The context for","live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration","cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the","migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the","the IP address of the dom0 \"\"\" # TODO(Vek): Need","are long integers. \"\"\" raise NotImplementedError() def block_stats(self, instance, disk_id):","\"\"\" raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None,","datastore, and calling this method should apply the updated rules","ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info","an instance of console.type.ConsoleSerial \"\"\" raise NotImplementedError() def get_mks_console(self, context,","only these MAC addresses'. \"\"\" return None def dhcp_options_for_instance(self, instance):","on the given instance. Stalling instances can be triggered to","driver name to override the config opt :returns: a ComputeDriver",":func:`suspend`. Depending on the underlying hypervisor technology, the guest has","to driver format. Virt drivers expect block device mapping to","action the host should perform. The valid actions are: \"\"startup\",","powered on, False otherwise \"\"\" raise NotImplementedError() def confirm_migration(self, migration,","metadata of the image of the instance. \"\"\" raise NotImplementedError()","of a dict containing the following keys: - root_device_name: device","base class method shuts down the VM, detaches all block","nova.objects.instance.Instance instance: The instance where the crash dump should be","\"\"\" return None def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options for","the DHCP service. Most hypervisors can use the default implementation","of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event) except Exception","'192.168.127.12'} | ] \"\"\" return None def manage_image_cache(self, context, all_instances):","shared disk files \"\"\" return False def register_event_listener(self, callback): \"\"\"Register","migration to complete :param instance: Instance being live migrated \"\"\"","(in seconds) for considering rebooting instances to be stuck. :param","the instance ID or name is not human-readable or customer-controlled","the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the root","device name :param bdms: a nova.objects.BlockDeviceMappingList for the instance :param","the compute manager, and it is up to that call","host): \"\"\"Clean up anything that is necessary for the driver","\"\"\" # TODO(Vek): Need to pass context in for access","called during spawn_instance by the compute manager. Note that the","manager doesn't use this interface, which seems odd # since","to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for the","device mapping. :param instance: nova.objects.instance.Instance that volume is requesting a","virtapi): self.virtapi = virtapi self._compute_event_callback = None def init_host(self, host):","anymore, but is still available as a database entry. The",":param context: security context :instance: instance object that was migrated","method\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post operation","host anymore. The state of the VM could be stored","\"\"\"Return the names of all the instances known to the","'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list =","MAC limits should return a set of MAC addresses, which","The suspended instance gets resumed and will use CPU cycles","network_binding_host_id(self, context, instance): \"\"\"Get host ID to associate with network","be changed. :param bool mode: If `True`, go into maintenance","after the snapshot operation has completed. There may be other","int resize_to: If the new volume is larger than the","thing here. \"\"\"Unplug virtual interfaces (VIFs) from networks. The counter","def reset_network(self, instance): \"\"\"reset networking for specified instance.\"\"\" # TODO(Vek):","auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up filtering rules","node_is_available(self, nodename): \"\"\"Return whether this compute service manages a particular","that this function takes an instance ID. \"\"\" raise NotImplementedError()","virtualization platform should ensure that the reboot action has completed","a 'name' elsewhere. As far as this layer is concerned,","interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def get_all_bw_counters(self, instances):","action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance","compute_driver: LOG.error(_LE(\"Compute driver option required, but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading","nova.network.model.NetworkInfo network_info: The network information of the given `instance`. :param","of 'instances' (Amazon EC2 and internal Nova terminology), by which","for an instance. :param instance: nova.objects.instance.Instance :returns: True if the","when setting-up filtering rules operations are not completed. :param instance:","networking failed), this function should still succeed. It's probably a","Driver base-classes: (Beginning of) the contract that compute drivers must","resize/migration. :param context: the context for the finish_revert_migration :param instance:","post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post operation of live","translating that generic data structure into terms that are specific","law or agreed to in writing, software # distributed under","results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info:","called when nova-compute launches, and as part of a periodic","instance, disk_id): \"\"\"Return performance counters associated with the given disk_id","the block devices. :param int timeout: The time in seconds","confirm_migration(self, migration, instance, network_info): \"\"\"Confirms a resize/migration, destroying the source",":param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration_force_complete(self,",":param context: security context :param instance: instance object that is","NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide a default root","NotImplementedError() def get_vnc_console(self, context, instance): \"\"\"Get connection info for a","(for example if networking failed), this function should still succeed.","NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \"\"\"Check if it","hypervisor - all the cleanup of old state is skipped.","by other errors (e.g. agent timeout), NovaException is raised. :param","reboot action has completed successfully even in cases in which","be preserved on rebuild \"\"\" raise NotImplementedError() def spawn(self, context,","instance object that is migrated :param network_info: instance network information",":param context: security context :param instance: nova.objects.instance.Instance \"\"\" pass def","to other hosts. Consider the combination with :func:`set_host_enabled`. :param str","dest_check_data: result of check_can_live_migrate_destination \"\"\" raise NotImplementedError() def check_can_live_migrate_source(self, context,","terminology), by which we mean 'running virtual machine' (XenAPI terminology)","and manage their own TFTP service. This requires passing the","the host to add to the given `aggregate`. :param dict","returned by DB layer. :param network_info: instance network information :param","language governing permissions and limitations # under the License. \"\"\"","in self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization overhead required","this call began. :param context: security context :param instance: nova.objects.instance.Instance","on reschedule?\"\"\" return False def macs_for_instance(self, instance): \"\"\"What MAC addresses","it is possible to execute live migration. This checks if","network information :param disk_info: instance disk information :param migrate_data: a","the given `instance`. :param dict new_connection_info: The volume for this",":param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery","having to do the aggregation. On those platforms, this method","cannot complete. \"\"\" # TODO(Vek): Need to pass context in","NotImplementedError() def confirm_migration(self, migration, instance, network_info): \"\"\"Confirms a resize/migration, destroying","\" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None):","self._compute_event_callback = callback def emit_event(self, event): \"\"\"Dispatches an event to","to the given `aggregate`. :param dict kwargs: A free-form thingy...","otherwise. .. note:: Used in rebuild for HA implementation and","will be supplied to the allocate_for_instance call by the compute","def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0):","Instance should have been destroyed from the Hypervisor before calling","to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context.","get an additional network interface. :param nova.objects.ImageMeta image_meta: The metadata","as bare metal) cannot do freeform virtualization of MAC addresses.","given disk_id on the given instance. These are returned as","of the new instance. :param nova.objects.ImageMeta image_meta: The metadata of","migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance resources . Instance should have","context :param instance: nova.objects.instance.Instance that the network ports will be","this layer is concerned, 'instance ID' and 'instance name' are","wait for GuestOS to shutdown :param retry_interval: How often to","the rebooting state than `timeout` seconds. :param int timeout: The","the host, False otherwise. .. note:: Used in rebuild for","instance. A 'rebuild' effectively purges all existing data from the",":param bad_volumes_callback: Function to handle any bad volumes encountered \"\"\"","succeed, based on the results from check_can_live_migrate_destination. :param context: security","the instance metadata to the instance, then this method should","kernel and user processes respectively, idle indicates the cumulative idle","return value of the newer interface :func:`get_instance_diagnostics` :rtype: dict \"\"\"","shutdown :param retry_interval: How often to signal guest while waiting","on the underlying hypervisor technology, the guest has the same","action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets","def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if","on an \"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS","security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP","parameter is the base64-encoded path to which the file is","to an aggregate. The counter action to this is :func:`remove_from_aggregate`","the host won't accept new instances. :return: If the host","for all drivers, but it is not particularly efficient. Maintainers","an instance gets added to or removed from the security","| 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, |","interface to this class talks in terms of 'instances' (Amazon","from oslo_log import log as logging from oslo_utils import importutils","be written on the instance; the third is the contents","big overlap to the return value of the older interface",":param dict new_connection_info: The volume for this connection gets attached","instance network information :param block_migration: if true, post operation of","layer is responsible for translating that generic data structure into","for access to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass):","\"\"\"Get connection info for a vnc console. :param context: security","nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception occurs. expected","live migration. :param context: security context :param instance: instance object","to instances running the specified security group. An error should","database entry. The counter action :func:`restore` uses the database entry","migrating \"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info,","the root device. \"\"\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name,","\"stronger\" version of `pause` is :func:'suspend'. The counter action for","'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable","this function takes an instance ID. \"\"\" raise NotImplementedError() def",":param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about the","all drivers, but it is not particularly efficient. Maintainers of","not be started when setting-up filtering rules operations are not","a snapshot of a volume attached to a specified instance.","mountpoint: The mountpoint in the instance where the volume for","True if the default ephemeral storage partition must be preserved","Hypervisor. If the instance is not found (for example if","int timeout: The timeout (in seconds) for considering rebooting instances","The volume for this connection gets attached to the given","information for the resume. :param nova.objects.ImageMeta image_meta: The metadata of","a host is booted. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError()","instance with the supplied ID exists on the host, False","to the allocate_for_instance call by the compute manager, and it","the case of doing a snapshot, it is the image","instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting a resize/migration. :param context:","often to signal guest while waiting for it to shutdown","only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate,","context :param data: result of check_instance_shared_storage_local \"\"\" raise NotImplementedError() def","context, instance): \"\"\"Suspend the specified instance. A suspended instance doesn't","Instance volume block device info. :return: None \"\"\" raise NotImplementedError()","the format of a dict containing the following keys: -","name supplied as an argument. Compute drivers constructors take a","remakes the VM with given 'metadata' and 'personalities'. This base","for driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class","the instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The","migrate_data=None): \"\"\"Live migration of an instance to another host. :param","the virtualization platform. Once this successfully completes, the instance should","instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata","host, set_error=True): \"\"\"Undo for Resource Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self,","reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified instance. After this is","to attached volumes :param bad_volumes_callback: Function to handle any bad","driver module specified by the compute_driver configuration option or, if","image of the instance. \"\"\" raise NotImplementedError() def unquiesce(self, context,","allocate_for_instance call by the compute manager, and it is up","of console.type.ConsoleSpice \"\"\" raise NotImplementedError() def get_rdp_console(self, context, instance): \"\"\"Get","number of virtual machines. Return the number of virtual machines","instance, vif): \"\"\"Use hotunplug to remove a network interface from","instance. The first parameter is an instance of nova.compute.service.Instance, and","[] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {}","attached to vms on a given host.- \"\"\" raise NotImplementedError()","signal guest while waiting for it to shutdown. :return: A","the stack. Most virtualization platforms will also have their own","instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object","creating the snapshot. \"\"\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance,",":return: None \"\"\" raise NotImplementedError() def restore(self, instance): \"\"\"Restore the","unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute manager","op, aggregate, host, set_error=True): \"\"\"Undo for Resource Pools.\"\"\" raise NotImplementedError()","layer. For instance, with openflow switches, fixed MAC addresses can","running (power_state.RUNNING). If this fails, any partial instance should be","def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host maintenance window. On start,","management of that cache which isn't directly related to other","( Waiting only for filtering rules to hypervisor, since filtering","block device mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj):","SOFT reboot :param block_device_info: Info pertaining to attached volumes :param","context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \"\"\"","event as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def","CPU cycles of the host again. The counter action for","'%s'\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi)",":param instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS","to get the root device for. :param nova.objects.ImageMeta image_meta: The","be triggered to dump the crash data. How the guest","needed for nova to be able to attach to the","guest OS to shutdown. :param int retry_interval: How often to","manager should be the controlling thing here. \"\"\"Unplug virtual interfaces","instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails","macs_for_instance(self, instance): \"\"\"What MAC addresses must this instance have? Some","implementation if not set. :returns: The chosen device name. \"\"\"","this :returns: Dictionary describing resources \"\"\" raise NotImplementedError() def pre_live_migration(self,","of nova.compute.service.Instance. This is the data structure used by nova.compute","instance gets added to or removed from the security group","inevitable on destination host. ( Waiting only for filtering rules","nova.objects.BlockDeviceMapping root_bdm: The description of the root device. \"\"\" raise",":param network_info: instance network information :param block_migration: if true, post","To migrate an instance, filtering rules to hypervisors and firewalls","desired. :param instance_info: Instance/flavor to calculate overhead for. :returns: Dict","VM afterwards. It may be overridden by hypervisors that need","Neutron client API. :return: None, or a set of DHCP","NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \"\"\"Undo for","image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a resize/migration. :param context: the","context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes","through migrateToURI(), so , no need to be called. Don't","instance where the crash dump should be triggered. :return: None",":rtype: : str \"\"\" raise NotImplementedError() def host_maintenance_mode(self, host, mode):","the volume attached :param uuid volume_id: Volume to be snapshotted","volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes a snapshot of","The object which contains information about the VIFs to plug.","restored instance will be automatically booted. The counter action for","go into maintenance mode. If `False`, leave the maintenance mode.","to associate with network ports. :param context: request context :param","\"\"\"Live migration of an instance to another host. :param context:",":param context: request context :param instance: nova.objects.instance.Instance to be unquiesced","hypervisor knows about. .. note:: This implementation works for all","to pass context in for access to auth_token raise NotImplementedError()","\"\"\" pass def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta,","destination host :param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param","request context :param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta",":param virtapi: a VirtAPI instance :param compute_driver: a compute driver","\"\"\" pass def inject_network_info(self, instance, nw_info): \"\"\"inject network info for","VM's on the given host. \"\"\" # TODO(Vek): Need to","attached to the given 'instance'. :param nova.objects.instance.Instance instance: The instance","network_info): \"\"\"Stop filtering instance.\"\"\" # TODO(Vek): Need to pass context","set, and should be decided by the driver implementation if","\"\"\"Transfers the disk of a running instance in multiple phases,","name' are synonyms. Note that the instance ID or name","format. :rtype: str \"\"\" raise NotImplementedError() def snapshot(self, context, instance,","\"softer\" way of `suspend` is :func:`pause`. The counter action for","_, _LE, _LI from nova import utils from nova.virt import","block_device_info=None): \"\"\"Create a new instance/VM/domain on the virtualization platform. Once","instance. Some hypervisors (such as bare metal) require that instances","are encouraged to override this method with something more efficient.","from oslo_utils import importutils import nova.conf from nova.i18n import _,","string representing the host ID \"\"\" return instance.get('host') def load_compute_driver(virtapi,","NotImplementedError() def get_mks_console(self, context, instance): \"\"\"Get connection info for a","Some hypervisors (such as bare metal) cannot do freeform virtualization","Copyright 2011 <NAME> # All Rights Reserved. # # Licensed","that records the results in the DB. :param nodename: node","for the resume. :param nova.objects.instance.Instance instance: The suspended instance to","to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the","instance's state goes back to power_state.RUNNING. The virtualization platform should","actions are: \"\"startup\", \"shutdown\" and \"reboot\". :return: The result of","context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the","# under the License. \"\"\" Driver base-classes: (Beginning of) the","and 'personalities'. This base class method shuts down the VM,","This is optional. \"\"\" pass def get_info(self, instance): \"\"\"Get the","raise NotImplementedError() def pause(self, instance): \"\"\"Pause the given instance. A","instance metadata to the instance, then this method should not","# TODO(Vek): Need to pass context in for access to","context, op, aggregate, host, set_error=True): \"\"\"Undo for Resource Pools.\"\"\" raise","rules operations are not completed. :param instance: nova.objects.instance.Instance object \"\"\"","is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will get","destination host, and then calls back to the source host","instance: nova.objects.instance.Instance object \"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data):","# distributed under the License is distributed on an \"AS","the instance, then this method should not be implemented. :param","Resource Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self, instance): \"\"\"Get connector information","the caller if the driver requires legacy block device info.","in instance. :param network_info: instance network information :param block_device_info: Information","driver option required, but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver","the controlling thing here. \"\"\"Unplug virtual interfaces (VIFs) from networks.","nova.compute.service.Instance, and so the instance is being specified as instance.name.",":return: None \"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest,","more efficient. \"\"\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError:","is up to the network implementation layer. For instance, with","timeout: time to wait for GuestOS to shutdown :param retry_interval:",":param str host: The name of the host to remove","NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up any resources left","volume_id: Volume to be snapshotted :param create_info: The data needed",":returns: a ComputeDriver instance \"\"\" if not compute_driver: compute_driver =","raise NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve resource information. This method","driver_registry = dict() for driver_str in named_driver_config: driver_type, _sep, driver","is possible to execute live migration. This checks if the","be used to filter out devices which are actually volumes.","attach_interface(self, instance, image_meta, vif): \"\"\"Use hotplug to add a network","which seems odd # since the manager should be the","None \"\"\" # TODO(Vek): Need to pass context in for","all instances to other hosts. Consider the combination with :func:`set_host_enabled`.","or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or","nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param nova.objects.ImageMeta","configured timeout. :return: None \"\"\" # TODO(Vek): Need to pass","must be an instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\",","This is called during spawn_instance by the compute manager. Note","needs to be held across the shutdown + spin up","OS reacts in details, depends on the configuration of it.","the instance could be persisted on the host and allocate","instance. :param admin_password: <PASSWORD> to set in instance. :param bdms:","I/O wait CPU time (nanoseconds), since the host is booting","context, instance, block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare an instance for","drivers should override if per-instance overhead calculations are desired. :param","detach. :raise nova.exception.NovaException: If the detach fails. :return: None \"\"\"","an instance of console.type.ConsoleRDP \"\"\" raise NotImplementedError() def get_serial_console(self, context,","specified instance. The first parameter is an instance of nova.compute.service.Instance,","not self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event)) return if not isinstance(event,","ID, which is the identifier chosen by Nova to represent","contract that compute drivers must follow, and shared types that","\"\"\"Get connection info for a MKS console. :param context: security","representing the ip of the machine that will be making","This base class method shuts down the VM, detaches all","\"\"\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \"\"\"Get host ID","used by nova.compute to store details regarding an instance, and","fails to quiesce by other errors (e.g. agent timeout), NovaException","gets resumed and will use CPU cycles and memory of","def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str in","nova.network.model.NetworkInfo vif: The object which has the information about the","instance: nova.objects.instance.Instance object \"\"\" # TODO(Vek): Need to pass context","under the License is distributed on an \"AS IS\" BASIS,","of live migration at source host. :param context: security context","Compute drivers constructors take a VirtAPI object as their first","image of the instance. :param resize_instance: True if the instance","the rescue. :param nova.objects.instance.Instance instance: The instance being rescued. :param","security_group_id): \"\"\"This method is called after a change to security","for live migration :param context: security context :param instance: nova.objects.instance.Instance","def refresh_security_group_rules(self, security_group_id): \"\"\"This method is called after a change","governing permissions and limitations # under the License. \"\"\" Driver","to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \"\"\"Applies","to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info:","launches, and as part of a periodic task that records","is a member of or if the group gains or","this file except in compliance with the License. You may","instances on disk. This method is an opportunity to do","be checked, the validate values are defined at disk API","cleanup_host(self, host): \"\"\"Clean up anything that is necessary for the","as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config,",":param nova.objects.instance.Instance instance: The instance to get the root device","filter out devices which are actually volumes. :return: json strings","performance counters associated with the given disk_id on the given","empty) list of DriverEphemeralBlockDevice instances - swap: An instance of","stat info, eg: | {'kernel': kern, | 'idle': idle, |","to receive events. Register a callback to receive asynchronous event","In the case of doing a snapshot, it is the","file except in compliance with the License. You may obtain","instance network information \"\"\" raise NotImplementedError(_(\"Hypervisor driver does not support","this into consideration when provisioning networking for the instance. Mapping","OR CONDITIONS OF ANY KIND, either express or implied. See","def delete_instance_files(self, instance): \"\"\"Delete any lingering instance files for an",":return: Has a big overlap to the return value of","gets added to or removed from the security group the","or if the group gains or loses a rule. \"\"\"","get_serial_console(self, context, instance): \"\"\"Get connection info for a serial console.","able to attach to the volume. This is the same","that need to - e.g. for optimisations, or when the","successfully completes, the instance should be running (power_state.RUNNING). If this","the License. \"\"\" Driver base-classes: (Beginning of) the contract that","\"\"\"resume guest state when a host is booted. :param instance:","destination host. :param context: security context :param instance: instance object","virtual machines that the hypervisor knows about. .. note:: This","is concerned, 'instance ID' and 'instance name' are synonyms. Note","number of I/O requests made, bytes is the total number",":param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration(self,","If a driver supports multi compute-nodes, this method returns a","Register a callback to receive asynchronous event notifications from hypervisors.","boot time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance:","live migration. :param instance: instance that is live migrating \"\"\"","timeout=0, retry_interval=0): \"\"\"Transfers the disk of a running instance in","raise NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return diagnostics data about the","it's an internal ID chosen by Nova. At the nova.virt","method should not be implemented. :param context: security context :param","located on shared storage. :param context: security context :param data:","for access to auth_token raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns the","the instance is being recreated on a new hypervisor -","to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \"\"\"Plug virtual","of old state is skipped. :param block_device_info: Information about block","containing the following keys: - root_device_name: device name of the","NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password): \"\"\"Rescue the","this class talks in terms of 'instances' (Amazon EC2 and","block devices, then spins up the new VM afterwards. It","thingy... :return: None \"\"\" # NOTE(jogo) Currently only used for","int retry_interval: How often to signal guest while waiting for","access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up","a given host.- \"\"\" raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the","set of MAC addresses. This is called during spawn_instance by","The instance to get the root device for. :param nova.objects.ImageMeta","The first parameter is an instance of nova.compute.service.Instance, and so","on/off an instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def","User files to inject into instance. :param admin_password: Administrator password","instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the disk","driver's local image cache. Some drivers chose to cache images","instance. The paused instance gets unpaused and will use CPU","def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None,","host.- \"\"\" raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the IP address","as well that is needed for creating the snapshot. \"\"\"","up; freq indicates the current CPU frequency (MHz). All values","\"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver want","from the network, and manage their own TFTP service. This","None \"\"\" raise NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve resource information.","The counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance:","auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \"\"\"Plug virtual interfaces","in for access to auth_token raise NotImplementedError() def change_instance_metadata(self, context,","use this file except in compliance with the License. You","list of disk information dicts in JSON format. :rtype: str","if further instances shouldn't be scheduled to this host, return","to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power","access to auth_token raise NotImplementedError() def unpause(self, instance): \"\"\"Unpause the","def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap') or","interest. :param all_instances: nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self, context, aggregate,","The compute manager doesn't use this interface, which seems odd","network, and manage their own TFTP service. This requires passing","as before the 'suspend'. :param nova.context.RequestContext context: The context for","of the instance. \"\"\" raise NotImplementedError() def network_binding_host_id(self, context, instance):","image of the instance. :param rescue_password: <PASSWORD> for rescue. \"\"\"","service. This requires passing the appropriate options out to the","It may be overridden by hypervisors that need to -","client API. :return: None, or a set of DHCP options,",":param data: result of check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self, context,","since filtering rules to firewall rules can be set faster).","VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains information","the Apache License, Version 2.0 (the \"License\"); you may #","network_info: Necessary network information for the resume. :param dict block_device_info:","reboot on all given 'instances'. Reboots the given `instances` which","re-make this instance. A 'rebuild' effectively purges all existing data","nova.objects.instance.Instance instance: The instance to suspend. :return: None \"\"\" raise","instance: nova.objects.instance.Instance This function should use the data there to","the given `instance`. :param dict old_connection_info: The volume for this","it to shutdown \"\"\" raise NotImplementedError() def power_on(self, context, instance,","compute drivers must follow, and shared types that support that","callback will be invoked with a single parameter, which will","rules to hypervisors and firewalls are inevitable on destination host.","metadata of the image of the instance. :param rescue_password: <PASSWORD>","the instance. \"\"\" raise NotImplementedError() def destroy(self, context, instance, network_info,","file system # type and implement this function at their","drivers must follow, and shared types that support that contract","to the instance. \"\"\" raise NotImplementedError() def destroy(self, context, instance,","\"\"\"Start/Stop host maintenance window. On start, it triggers the migration","if true, post operation of block_migration. \"\"\" raise NotImplementedError() def","group gains or loses a rule. \"\"\" raise NotImplementedError() def","Consider the combination with :func:`set_host_enabled`. :param str host: The name","to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which","or libvirt terminology). An instance has an ID, which is","disk. :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def","the crash dump should be triggered. :return: None \"\"\" raise","been in rebooting state longer than the configured timeout. :return:","dict of nova uuid => dict of usage info \"\"\"","LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration(self, context, instance, dest,","'host': hostname } \"\"\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns","\"\"\" raise NotImplementedError() def pause(self, instance): \"\"\"Pause the given instance.","instance. \"\"\" raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None,","to prepare for snapshots. If the specified instance doesn't support","host to an aggregate. The counter action to this is","that generic data structure into terms that are specific to","is responsible for keeping track of which instance ID maps","idle indicates the cumulative idle CPU time (nanoseconds), wait indicates","nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk. :param migrate_data:","for the guest OS to shutdown. :param int retry_interval: How","action for 'unpause' is :func:`pause`. Depending on the underlying hypervisor","are not completed. :param instance: nova.objects.instance.Instance object \"\"\" # TODO(Vek):","info.\"\"\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \"\"\"Detach","format. Virt drivers expect block device mapping to be presented","module specified by the compute_driver configuration option or, if supplied,","instance: The instance to soft-delete. :return: None \"\"\" raise NotImplementedError()",":param context: security context :param dest_check_data: result of check_can_live_migrate_destination \"\"\"","\"\"\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \"\"\"Return the","virtapi: a VirtAPI instance :param compute_driver: a compute driver name","skipped. :param block_device_info: Information about block devices to be attached","get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data about the given instance. :param","to set in instance. :param bdms: block-device-mappings to use for","True def volume_snapshot_create(self, context, instance, volume_id, create_info): \"\"\"Snapshots volumes attached","be stuck. :param list instances: A list of nova.objects.instance.Instance objects","mode: If `True`, go into maintenance mode. If `False`, leave","with currently running VM's on the given host. \"\"\" #","terminology). An instance has an ID, which is the identifier",":param migrate_data: a LiveMigrateData object \"\"\" pass def post_live_migration_at_source(self, context,","bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make","instance, network_info): \"\"\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \"\"\"","return value is specific to the Neutron client API. :return:","is called when nova-compute launches, and as part of a",":param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info:","up destination node after a failed live migration. :param context:","def volume_snapshot_create(self, context, instance, volume_id, create_info): \"\"\"Snapshots volumes attached to","that will hold the snapshot. \"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self,","compute drivers. The interface to this class talks in terms","instance): \"\"\"Get connector information for the instance for attaching to","off the specified instance. :param instance: nova.objects.instance.Instance :param timeout: time","migration. :param context: security context :param instance: instance object that","snapshot(self, context, instance, image_id, update_task_state): \"\"\"Snapshots the specified instance. :param","counter action for 'resume' is :func:`suspend`. Depending on the underlying","true, prepare for block migration :param disk_over_commit: if true, allow","phases, turning off the instance before the end. :param nova.objects.instance.Instance","swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info):","to a pre-created image that will hold the snapshot. \"\"\"","def block_stats(self, instance, disk_id): \"\"\"Return performance counters associated with the","format of the return value is specific to the Neutron","calls into the driver. The prime example is to clean","on the configuration of it. :param nova.objects.instance.Instance instance: The instance","def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data about the given instance.","instance of the given flavor. Defaults to zero, drivers should","machine :param block_migration: if true, prepare for block migration :param","if not self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event)) return if not","constructors take a VirtAPI object as their first object and","network ports. :param context: request context :param instance: nova.objects.instance.Instance that","\"\"\"Replace the volume attached to the given `instance`. :param dict","device name for the driver. :param nova.objects.instance.Instance instance: The instance","returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates","info :param power_on: True if the instance should be powered","dest: The IP address of the destination host. :param nova.objects.flavor.Flavor","but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver) try:","volume_id, create_info): \"\"\"Snapshots volumes attached to a specified instance. The","older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def get_all_bw_counters(self,","\"supports_attach_interface\": False, \"supports_device_tagging\": False, } def __init__(self, virtapi): self.virtapi =","\"\"\"Prepare an instance for live migration :param context: security context","an instance, and pass them into this layer. This layer","up due to a full pipeline. All counters are long","filter_defer_apply_on(self): \"\"\"Defer application of IPTables rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn","be called. Don't use thread for this method since migration","dicts in JSON format. :rtype: str \"\"\" raise NotImplementedError() def","indicates the current CPU frequency (MHz). All values are long","not be implemented. :param context: security context :param instance: nova.objects.instance.Instance","get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context in for","Invokes the event callback registered by the compute manager to","for snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported","the # License for the specific language governing permissions and","= block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info =","return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances()","try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in","processes respectively, idle indicates the cumulative idle CPU time (nanoseconds),","decided by the driver implementation if not set. :returns: The","follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname } \"\"\"","\"\"\"reset networking for specified instance.\"\"\" # TODO(Vek): Need to pass","The counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context:","to be called since it defines PROJNET, PROJMASK. but libvirt","the compute driver module specified by the compute_driver configuration option","of the machine as follows:: { 'ip': ip, 'initiator': initiator,","Waiting only for filtering rules to hypervisor, since filtering rules","mapping. :param instance: nova.objects.instance.Instance that volume is requesting a device","Some hypervisors (such as bare metal) require that instances boot","the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When","flavor. Defaults to zero, drivers should override if per-instance overhead",":rtype: str \"\"\" raise NotImplementedError() def snapshot(self, context, instance, image_id,","instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the root device.","and the file being merged into (if appropriate). :return: None","See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information :param","cache which isn't directly related to other calls into the","\"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True,","context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified instance.","set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means 'these and","context, instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes a snapshot of a","from the security group the instance is a member of","not support \" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context, instance, network_info,","None def manage_image_cache(self, context, all_instances): \"\"\"Manage the driver's local image","\"\"\" raise NotImplementedError() def block_stats(self, instance, disk_id): \"\"\"Return performance counters","about the receiving machine :param block_migration: if true, prepare for","object and this must be supplied. :param virtapi: a VirtAPI","which has the information about the interface to attach. :raise","action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets","cycles of the host anymore. The state of the VM","class talks in terms of 'instances' (Amazon EC2 and internal","far as this layer is concerned, 'instance ID' and 'instance","is booting up; freq indicates the current CPU frequency (MHz).","block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info):","return [hypervisor_hostname]. \"\"\" raise NotImplementedError() def node_is_available(self, nodename): \"\"\"Return whether","about block devices to be attached to the instance. :param","a MKS console. :param context: security context :param instance: nova.objects.instance.Instance","an instance of console.type.ConsoleMKS \"\"\" raise NotImplementedError() def get_diagnostics(self, instance):","Connector information is a dictionary representing the ip of the","is :func:`pause`. Depending on the underlying hypervisor technology, the guest","'/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server',","any bad volumes encountered \"\"\" raise NotImplementedError() def get_console_pool_info(self, console_type):","for a spice console. :param context: security context :param instance:","def filter_defer_apply_on(self): \"\"\"Defer application of IPTables rules.\"\"\" pass def filter_defer_apply_off(self):","True if the instance is being resized, False otherwise :param","this connection gets detached from the given `instance`. :param dict","interface to attach. :raise nova.exception.NovaException: If the attach fails. :return:","instance): \"\"\"Refresh security group rules Gets called when an instance","destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified","drivers, but it is not particularly efficient. Maintainers of the","compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return","otherwise :param block_device_info: instance volume block device info :param power_on:","bool enabled: If this is `True`, the host will accept","of `suspend` is :func:`pause`. The counter action for `suspend` is","for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting","to the compute manager. Invokes the event callback registered by","virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping':","context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance","freq indicates the current CPU frequency (MHz). All values are","raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None,","# NOTE(markus_z): 2015-08-18 # The compute manager doesn't use this","in for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer","instance. The restored instance will be automatically booted. The counter","current CPU frequency (MHz). All values are long integers. \"\"\"","removed. :param nova.network.model.NetworkInfo vif: The object which has the information","the underlying domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance","you may # not use this file except in compliance","= block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info =","may be overridden by hypervisors that need to - e.g.","to the platform-specific layer, and never escape the connection interface.","state that it was before this call began. :param context:","of doing a snapshot, it is the image file Cinder","The metadata of the image of the instance. :param injected_files:","There may be other data included as well that is","replaced by another one. :param str mountpoint: The mountpoint in","the same state as before the 'suspend'. :param nova.context.RequestContext context:",":param nova.objects.instance.Instance instance: The instance whose disk should be migrated.","rules to hypervisor, since filtering rules to firewall rules can","specified by the compute_driver configuration option or, if supplied, the","IPTables rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn off deferral of IPTables",":param instance: nova.objects.instance.Instance object \"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context,","instance, block_device_info=None): \"\"\"Retrieve information about actual disk sizes of an","to auth_token raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns the result of","block_device_info=None, power_on=True): \"\"\"Completes a resize/migration. :param context: the context for","is the number of requests held up due to a","host again. The counter action for 'resume' is :func:`suspend`. Depending","should add the given `host` :param str host: The name","will get an additional network interface. :param nova.objects.ImageMeta image_meta: The","host, **kwargs): \"\"\"Add a compute host to an aggregate. The","_sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args,","the state that it was before this call began. :param","attached. :param uuid volume_id: Attached volume associated with the snapshot","be the controlling thing here. \"\"\"Unplug virtual interfaces (VIFs) from","(nanoseconds) spent by kernel and user processes respectively, idle indicates","shutdown + spin up steps. :param context: security context :param","the specified instance. The first parameter is an instance of","return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return","set faster). Concretely, the below method must be called. -","snapshot. \"\"\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id,","remove the given `host` :param str host: The name of","\"\"\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage info","instance): \"\"\"Force live migration to complete :param instance: Instance being","of the instance could be persisted on the host and","method shuts down the VM, detaches all block devices, then","`instance`. :param dict new_connection_info: The volume for this connection gets","context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending","than `timeout` seconds. :param int timeout: The timeout (in seconds)","instance that has the volume attached. :param uuid volume_id: Attached","of calling the Linux command `uptime` on this host. :return:","snapshot to delete. :param dict delete_info: Volume backend technology specific","is called successfully, the instance's state goes back to power_state.RUNNING.",":param nova.objects.instance.Instance instance: The soft-deleted instance which should be restored","for the resume. :param nova.objects.ImageMeta image_meta: The metadata of the","host ID to associate with network ports. :param context: request","\"\"\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \"\"\"Check","to zero, drivers should override if per-instance overhead calculations are","the new instance. :param nova.objects.ImageMeta image_meta: The metadata of the","this method returns a list of nodenames managed by the","\"\"\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True):","a ComputeDriver instance \"\"\" if not compute_driver: compute_driver = CONF.compute_driver","NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy","to dump the crash data. How the guest OS reacts",":func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate:","also base64-encoded. NOTE(russellb) This method is deprecated and will be","an additional network interface. :param nova.objects.ImageMeta image_meta: The metadata of","result of the power action :rtype: : str \"\"\" raise",":func:`pause`. The counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext","to signal guest while waiting for it to shutdown \"\"\"","API. :return: None, or a set of DHCP options, eg:","instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event) except","supplied ID exists on the host, False otherwise. .. note::","or a set of DHCP options, eg: | [{'opt_name': 'bootfile-name',","raise NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check whether the file format","network_info, block_migration=False, block_device_info=None): \"\"\"Post operation of live migration at destination","to cache images for instances on disk. This method is","to methods that expect it. \"\"\" return True def volume_snapshot_create(self,","def detach_interface(self, instance, vif): \"\"\"Use hotunplug to remove a network","the resume. :param nova.objects.instance.Instance instance: The suspended instance to resume.","specified soft-deleted instance. The restored instance will be automatically booted.","block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name')","of the host again. The counter action for 'resume' is","instance: The instance whose disk should be migrated. :param str","of this host to accept new instances. :param bool enabled:","own TFTP service. This requires passing the appropriate options out","there to guide the creation of the new instance. :param","pass def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a reboot on all","rebuild for HA implementation and required for validation of access","of the host anymore. The state of the instance could","are defined at disk API module. \"\"\" # NOTE(jichenjc): Return",":param nova.objects.instance.Instance instance: The suspended instance to resume. :param nova.network.model.NetworkInfo","path to which the file is to be written on","} \"\"\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of","usage counters for each interface on each running VM. :param","optional driver method which is used to publish changes to","powers up the host. :param str action: The action the","in for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance):","related to other calls into the driver. The prime example","attach fails. :return: None \"\"\" raise NotImplementedError() def detach_interface(self, instance,","can be set faster). Concretely, the below method must be","nova.i18n import _, _LE, _LI from nova import utils from","this method should return [hypervisor_hostname]. \"\"\" raise NotImplementedError() def node_is_available(self,","ValueError( _(\"Event must be an instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting","Some methods here take an instance of nova.compute.service.Instance. This is","it. \"\"\" return True def volume_snapshot_create(self, context, instance, volume_id, create_info):","big overlap to the return value of the newer interface","the UUIDS of all the instances known to the virtualization","resources from a driver that manages only one node can","compute manager doesn't use this interface, which seems odd #","a compute driver name to override the config opt :returns:","an instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event)","given 'metadata' and 'personalities'. This base class method shuts down","removed from the security group the instance is a member","block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals') or","guest has the same state as before the 'pause'. :param","that was being migrated :param network_info: instance network information :param","software # distributed under the License is distributed on an","block_device_info=None): \"\"\"Check if it is possible to execute live migration.","it is not particularly efficient. Maintainers of the virt drivers","None def init_host(self, host): \"\"\"Initialize anything that is necessary for","an instance with the supplied ID accessible on the host,","dict kwargs: A free-form thingy... :return: None \"\"\" raise NotImplementedError()","on shared storage. This runs check on the destination host,","be destroyed :param migrate_data: implementation specific params \"\"\" raise NotImplementedError()","| ] \"\"\" return None def manage_image_cache(self, context, all_instances): \"\"\"Manage","from nova.virt import block_device as virt_block_device block_device_info = { 'root_device_name':","NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting","to be stuck. :param list instances: A list of nova.objects.instance.Instance","rebooting state longer than the configured timeout. :return: None \"\"\"","called. Don't use thread for this method since migration should","the instance. \"\"\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \"\"\"Get","to the instance metadata. This is an optional driver method","instance object that was being migrated :param network_info: instance network","be freeform) is up to the network implementation layer. For","nova-instance-instance-xxx, etc.) to_xml may have to be called since it","nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry =","at destination during cleanup :param migrate_data: a LiveMigrateData object \"\"\"","initiator, 'host': hostname } \"\"\" raise NotImplementedError() def get_available_nodes(self, refresh=False):","raised if the operation cannot complete. \"\"\" # TODO(Vek): Need","nova.virt import event as virtevent CONF = nova.conf.CONF LOG =","not need to be set, and should be decided by","of the iscsi initiator and the hostname of the machine","the results from check_can_live_migrate_destination. :param context: security context :param instance:","device name based on the block device mapping. :param instance:","instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create a new instance/VM/domain","perform. The valid actions are: \"\"startup\", \"shutdown\" and \"reboot\". :return:","The context for the suspend. :param nova.objects.instance.Instance instance: The instance","instance, network_info, block_device_info=None): \"\"\"resume guest state when a host is","_LE, _LI from nova import utils from nova.virt import event","additional network interface. :param nova.objects.ImageMeta image_meta: The metadata of the","context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False,","volumes attached to vms on a given host.- \"\"\" raise","supplied ID accessible on the host, False otherwise. .. note::","which should be paused. :return: None \"\"\" # TODO(Vek): Need","console.type.ConsoleVNC \"\"\" raise NotImplementedError() def get_spice_console(self, context, instance): \"\"\"Get connection",":param disk_info: the newly transferred disk information :param network_info: instance","str(event)) return if not isinstance(event, virtevent.Event): raise ValueError( _(\"Event must","| {'kernel': kern, | 'idle': idle, | 'user': user, |","instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \"\"\" raise NotImplementedError()","being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for the","disk sizes of an instance. :param instance: nova.objects.Instance :param block_device_info:","The result of the power action :rtype: : str \"\"\"","build an instance of the given flavor. Defaults to zero,","is called during spawn_instance by the compute manager. :return: None,","or None - block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice","False otherwise. .. note:: Used in rebuild for HA implementation","or {} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def","# Licensed under the Apache License, Version 2.0 (the \"License\");","return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping","information :param network_info: instance network information :param nova.objects.ImageMeta image_meta: The","virtualization overhead required to build an instance of the given","IDs must stay internal to the platform-specific layer, and never","this method. :param context: security context :param instance: Instance object","representing the host ID \"\"\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None):","instance. :param nova.objects.instance.Instance instance: The instance to which the diagnostic","parameter, which will be an instance of the nova.virt.event.Event class.","dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns:","bad_volumes_callback: Function to handle any bad volumes encountered \"\"\" raise",":func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will get an",":param nova.network.model.NetworkInfo network_info: The network information of the given `instance`.","a dict containing the following keys: - root_device_name: device name","# Refresh and check again. return nodename in self.get_available_nodes(refresh=True) def","raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This method is called after","shared types that support that contract \"\"\" import sys from","KIND, either express or implied. See the # License for","\"\"\" # NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError()","instance of console.type.ConsoleSpice \"\"\" raise NotImplementedError() def get_rdp_console(self, context, instance):","argument. Compute drivers constructors take a VirtAPI object as their","can still be virtualized onto any L2 domain, with arbitrary","rebuild :param detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl","volume attached to a specified instance. The counter action to","def cleanup_host(self, host): \"\"\"Clean up anything that is necessary for","data from the system and remakes the VM with given","of requests held up due to a full pipeline. All","another one. :param str mountpoint: The mountpoint in the instance","specified instance. After this is called successfully, the instance's state","all the instances known to the virtualization layer, as a","instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute manager doesn't","is a dictionary representing the ip of the machine that","LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"), {'event': event, 'ex': ex}) def","destroyed from the Hypervisor before calling this method. :param context:","name for the driver. :param nova.objects.instance.Instance instance: The instance to","instance. :param preserve_ephemeral: True if the default ephemeral storage partition","NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage counters for each","\"\"\"Power off the specified instance. :param instance: nova.objects.instance.Instance :param timeout:","be set faster). Concretely, the below method must be called.","the compute manager to dispatch the event. This must only","attaching to volumes. Connector information is a dictionary representing the","def manage_image_cache(self, context, all_instances): \"\"\"Manage the driver's local image cache.","reboot_type: Either a HARD or SOFT reboot :param block_device_info: Info","all_instances: nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs):","This is the same data format returned by Cinder's initialize_connection()","module. \"\"\" # NOTE(jichenjc): Return False here so that every",":param admin_password: Administrator password to set in instance. :param network_info:","an instance of console.type.ConsoleSpice \"\"\" raise NotImplementedError() def get_rdp_console(self, context,","before the 'suspend'. :param nova.context.RequestContext context: The context for the","to be presented in the format of a dict containing","def get_info(self, instance): \"\"\"Get the current status of an instance,","kern, | 'idle': idle, | 'user': user, | 'iowait': wait,","disk_info: instance disk information :param migrate_data: a LiveMigrateData object \"\"\"","return value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\"","instance: nova.objects.instance.Instance object Returns a InstanceInfo object \"\"\" # TODO(Vek):","which instance ID maps to which platform-specific ID, and vice","be detached from the instance. :param destroy_disks: Indicates if disks","access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics","instance block device information :param destroy_disks: if true, destroy disks","with the supplied ID accessible on the host, False otherwise.","self._compute_event_callback = None def init_host(self, host): \"\"\"Initialize anything that is","up the stack. This is unfortunately also called a 'name'","Need to pass context in for access to auth_token raise","are compatible with the set of MAC addresses. This is","from the Hypervisor before calling this method. :param context: security","`resize_to`. :return: None \"\"\" raise NotImplementedError() def attach_interface(self, instance, image_meta,",":param data: result of check_instance_shared_storage_local \"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self,","\"\"\"Pause the given instance. A paused instance doesn't use CPU","security groups and their associated rules live in the datastore,","options out to the DHCP service. Most hypervisors can use","compatible with the set of MAC addresses. This is called","with arbitrary VLANs etc, but regular switches require pre-configured MAC->network","instance): \"\"\"Suspend the specified instance. A suspended instance doesn't use","vif): \"\"\"Use hotunplug to remove a network interface from a","one node can safely ignore this :returns: Dictionary describing resources","\"\"\"Delete any lingering instance files for an instance. :param instance:","the given `instance`. :param dict block_device_info: Information about the block","instance, image_meta): \"\"\"Unquiesce the specified instance after snapshots. If the","the virtualization driver\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver == 'xenapi.XenAPIDriver'",":param dest: destination host :param post_method: post operation method. expected","gets a network interface removed. :param nova.network.model.NetworkInfo vif: The object","access to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0):","nova.objects.instance.Instance instance: The instance which should be paused. :return: None","paused. :return: None \"\"\" # TODO(Vek): Need to pass context","= importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except","a resize/migration. :param context: the context for the migration/resize :param","true, allow disk over commit :returns: a LiveMigrateData object (hypervisor-dependent)",":param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info","considering rebooting instances to be stuck. :param list instances: A","instance :param context: security context :param instance: nova.objects.instance.Instance \"\"\" raise","'unpause' is :func:`pause`. Depending on the underlying hypervisor technology, the","a failed live migration. :param context: security context :param instance:","to which the file is to be written on the","no longer of interest. :param all_instances: nova.objects.instance.InstanceList \"\"\" pass def","Refresh and check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self):","obtain # a copy of the License at # #","VM. :param instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def get_all_volume_usage(self, context,",":param dict kwargs: A free-form thingy... :return: None \"\"\" raise","The IP address of the destination host. :param nova.objects.flavor.Flavor flavor:","\"\"\"Get connection info for a serial console. :param context: security","host will accept new instances. If it is `False`, the","for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The context for","object \"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \"\"\"Check if","block_device_info=None): \"\"\"Retrieve information about actual disk sizes of an instance.","need_legacy_block_device_info(self): \"\"\"Tell the caller if the driver requires legacy block","block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified instance from the Hypervisor.","to delete. :param dict delete_info: Volume backend technology specific data","their supported file system # type and implement this function","instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \"\"\" raise NotImplementedError() def","not compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute driver","attached to the instance. \"\"\" raise NotImplementedError() def destroy(self, context,","\"\"\"Return usage info for volumes attached to vms on a","instance: The suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary","security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the","calling the Linux command `uptime` on this host. :return: A","technology specific data needed to be able to complete the","given `instance` at instance boot time. The counter action is","own identity schemes, to uniquely identify a VM or domain.","volume is larger than the old volume, it gets resized","long integers. \"\"\" raise NotImplementedError() def block_stats(self, instance, disk_id): \"\"\"Return","internal Nova terminology), by which we mean 'running virtual machine'","for translating that generic data structure into terms that are","hypervisor technology, the guest has the same state as before","LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s'","must stay internal to the platform-specific layer, and never escape","possible to execute live migration. This checks if the live","successfully, the instance's state goes back to power_state.RUNNING. The virtualization","nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the given `host`","will be removed once it can be removed from nova.compute.manager.","Note that the format of the return value is specific","access to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): #","appropriate options out to the DHCP service. Most hypervisors can","counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which","instance/VM/domain on the virtualization platform. Once this successfully completes, the","the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def",":param timeout: time to wait for GuestOS to shutdown :param","or name is not human-readable or customer-controlled -- it's an","instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def unrescue(self, instance,","NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information about actual disk","pass def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up filtering rules and","host. :param nova.objects.flavor.Flavor flavor: The flavor of the instance whose","is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which should","the root device for. :param nova.objects.ImageMeta image_meta: The metadata of","admin_password: <PASSWORD> to set in instance. :param bdms: block-device-mappings to","isn't directly related to other calls into the driver. The","None \"\"\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \"\"\"Use","instance to soft-delete. :return: None \"\"\" raise NotImplementedError() def restore(self,","@property def need_legacy_block_device_info(self): \"\"\"Tell the caller if the driver requires","it to shutdown. :return: A list of disk information dicts","LiveMigrateData object \"\"\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info,","access to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \"\"\"Plug","respectively, idle indicates the cumulative idle CPU time (nanoseconds), wait","source host. :param context: security context :instance: instance object that","at destination host. :param context: security context :param instance: instance","A \"softer\" way of `suspend` is :func:`pause`. The counter action","should not be started when setting-up filtering rules operations are","network details are compatible with the set of MAC addresses.","NotImplementedError() def unpause(self, instance): \"\"\"Unpause the given paused instance. The","context in for access to auth_token raise NotImplementedError() def get_console_output(self,","the volume attached to the given `instance`. :param dict old_connection_info:","raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup on host","rdp console. :param context: security context :param instance: nova.objects.instance.Instance :returns","\"\"\" raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the currently known host","uuid snapshot_id: The snapshot to delete. :param dict delete_info: Volume","instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object","action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security","won't accept new instances. :return: If the host can accept","the suspend. :param nova.objects.instance.Instance instance: The instance to suspend. :return:","the host can accept further instances, return \"enabled\", if further","False otherwise \"\"\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info):","rescue. :param nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo","for compute drivers. The interface to this class talks in","the 'VM' is actually proxied and needs to be held","vif: The object which has the information about the interface","_get_instance_block_device_info :returns: a LiveMigrateData object \"\"\" raise NotImplementedError() def get_instance_disk_info(self,","should be destroyed :param migrate_data: implementation specific params \"\"\" raise","soft-deleted instance doesn't allocate any resources anymore, but is still","disk, False otherwise. \"\"\" return True @property def need_legacy_block_device_info(self): \"\"\"Tell","is unused. Note that this function takes an instance ID.",":returns: dict of nova uuid => dict of usage info","networking for the instance. Mapping of MAC addresses to actual","to maintenance mode or \"off_maintenance\" if maintenance mode got left.","that support that contract \"\"\" import sys from oslo_log import","API module. \"\"\" # NOTE(jichenjc): Return False here so that","NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger crash dump mechanism on the","cumulative I/O wait CPU time (nanoseconds), since the host is","entry. The counter action :func:`restore` uses the database entry to","WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express","permissions and limitations # under the License. \"\"\" Driver base-classes:","encryption=None): \"\"\"Attach the disk to the instance at mountpoint using","seems odd # since the manager should be the controlling","of the instance. :param injected_files: User files to inject into","| 'iowait': wait, | 'frequency': freq}, where kern and user","into instance. :param admin_password: <PASSWORD> to set in instance. :param","rescue(self, context, instance, network_info, image_meta, rescue_password): \"\"\"Rescue the specified instance.","\"\"\"Base class for compute drivers. The interface to this class","HARD or SOFT reboot :param block_device_info: Info pertaining to attached","block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the disk of a running instance","compute_host_bdms): \"\"\"Return usage info for volumes attached to vms on","information \"\"\" raise NotImplementedError(_(\"Hypervisor driver does not support \" \"post_live_migration_at_source","None - block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or","volume attached. :param uuid volume_id: Attached volume associated with the","free-form thingy... :return: None \"\"\" # NOTE(jogo) Currently only used","of IPTables rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn off deferral of","before the 'pause'. :param nova.objects.instance.Instance instance: The instance which should","use CPU cycles and memory of the host again. The","def live_migration_force_complete(self, instance): \"\"\"Force live migration to complete :param instance:","total number of I/O requests made, bytes is the total","instance to lookup Returns True if an instance with the","about block devices that should be detached from the instance.","new instance/VM/domain on the virtualization platform. Once this successfully completes,","enabled: If this is `True`, the host will accept new","nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach block devices.","names of all the instances known to the virtualization layer,","def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute driver module. Load the","On some platforms (e.g. XenAPI) performance statistics can be retrieved","(if appropriate). :return: None \"\"\" raise NotImplementedError() def default_root_device_name(self, instance,","long integers. This method is optional. On some platforms (e.g.","network information for the resume. :param nova.objects.ImageMeta image_meta: The metadata","auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None,","or domain. These IDs must stay internal to the platform-specific","virt drivers are encouraged to override this method with something","automatically booted. The counter action for `restore` is :func:`soft_delete`. :param","\"\"\"Stop filtering instance.\"\"\" # TODO(Vek): Need to pass context in","calculate overhead for. :returns: Dict of estimated overhead values. \"\"\"","information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly","on disk. This method is an opportunity to do management","counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The","prime example is to clean the cache and remove images","\"\"\" Driver base-classes: (Beginning of) the contract that compute drivers","set_error=True): \"\"\"Undo for Resource Pools.\"\"\" raise NotImplementedError() def get_volume_connector(self, instance):","than the configured timeout. :return: None \"\"\" # TODO(Vek): Need","the appropriate options out to the DHCP service. Most hypervisors","instance. A suspended instance doesn't use CPU cycles or memory","rd_bytes, wr_req, wr_bytes, errs], where rd indicates read, wr indicates","cycles of the host again. The counter action for 'unpause'","it is the image file Cinder expects to be used","of a volume attached to a specified instance. The counter","time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The","addresses to actual networks (or permitting them to be freeform)","be persisted on the host and allocate storage space this","service. Otherwise, this method should return [hypervisor_hostname]. \"\"\" raise NotImplementedError()","instance at mountpoint using info.\"\"\" raise NotImplementedError() def detach_volume(self, connection_info,","them into this layer. This layer is responsible for translating","given instance. Stalling instances can be triggered to dump the","which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which","to signal guest while waiting for it to shutdown. :return:","stack. Most virtualization platforms will also have their own identity","the host whose maintenance mode should be changed. :param bool","return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return","instance will be automatically booted. The counter action for `restore`","The security context. :param nova.objects.instance.Instance instance: The instance that has","console_type): # TODO(Vek): Need to pass context in for access","\"\"\"Post operation of live migration at source host. :param context:","or implied. See the # License for the specific language","maintenance mode got left. :rtype: str \"\"\" raise NotImplementedError() def","configuration option or, if supplied, the driver name supplied as","drivers constructors take a VirtAPI object as their first object","be powered on, False otherwise \"\"\" raise NotImplementedError() def confirm_migration(self,",":param str dest: The IP address of the destination host.","down the VM, detaches all block devices, then spins up","a new hypervisor - all the cleanup of old state","instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \"\"\" raise","errs], where rd indicates read, wr indicates write, req is","associated with the given disk_id on the given instance. These","migration :param context: security context :param instance: nova.objects.instance.Instance object :param","errors (e.g. agent timeout), NovaException is raised. :param context: request","destination host. ( Waiting only for filtering rules to hypervisor,","The instance to lookup Returns True if an instance with","network_info: Necessary network information for the resume. :param nova.objects.ImageMeta image_meta:","limits should return a set of MAC addresses, which will","compute driver module. Load the compute driver module specified by","not human-readable or customer-controlled -- it's an internal ID chosen","as part of a periodic task that records the results","nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage","sending machine :param dst_compute_info: Info about the receiving machine :param","if not compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute","instance, image_meta, vif): \"\"\"Use hotplug to add a network interface","complete. \"\"\" # TODO(Vek): Need to pass context in for","def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting a","drivers to return a set of MAC addresses that the","get_volume_connector(self, instance): \"\"\"Get connector information for the instance for attaching",":return: None \"\"\" raise NotImplementedError() def detach_interface(self, instance, vif): \"\"\"Use","this host to accept new instances. :param bool enabled: If","gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains",":returns: Dict of estimated overhead values. \"\"\" return {'memory_mb': 0,","spice console. :param context: security context :param instance: nova.objects.instance.Instance :returns","dict block_device_info: Information about the block devices. :param int timeout:","raise NotImplementedError() def reset_network(self, instance): \"\"\"reset networking for specified instance.\"\"\"","containing the CPU stat info, eg: | {'kernel': kern, |","devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information","- e.g. for optimisations, or when the 'VM' is actually","of an instance to another host. :param context: security context","addresses, which will be supplied to the allocate_for_instance call by","the format of the return value is specific to the","the specified instance. :param nova.context.RequestContext context: The context for the","\"\"\"Return the total number of virtual machines. Return the number","The name of the host whose maintenance mode should be","{'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \"\"\"Return the names of","signal guest while waiting for it to shutdown \"\"\" raise","return swap and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info):","attached to the given `instance`. :param dict old_connection_info: The volume","\"\"\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute driver","NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \"\"\"resume guest state","method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception","(MHz). All values are long integers. \"\"\" raise NotImplementedError() def","NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Remove a compute","and user processes respectively, idle indicates the cumulative idle CPU","Reserved. # # Licensed under the Apache License, Version 2.0","anymore. The state of the instance could be persisted on","vnc console. :param context: security context :param instance: nova.objects.instance.Instance :returns","merged, and the file being merged into (if appropriate). :return:","security group. An error should be raised if the operation","that will be making the connection, the name of the","to override the config opt :returns: a ComputeDriver instance \"\"\"","False here so that every hypervisor # need to define","NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the IP address of the dom0","maintenance mode. If `False`, leave the maintenance mode. :return: \"on_maintenance\"","the same data format returned by Cinder's initialize_connection() API call.","if true, prepare for block migration :param disk_over_commit: if true,","instance could be persisted on the host and allocate storage","\"\"\" return True def volume_snapshot_create(self, context, instance, volume_id, create_info): \"\"\"Snapshots","given `host` :param str host: The name of the host","pass def post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug VIFs from networks","implementation specific params \"\"\" raise NotImplementedError() def cleanup(self, context, instance,","migrate_data: implementation specific params \"\"\" raise NotImplementedError() def cleanup(self, context,","an opportunity to do management of that cache which isn't","here so that every hypervisor # need to define their","virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to load the","context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified instance","\"\"\" raise NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method,","instance. :param admin_password: Administrator password to set in instance. :param","and firewalls are inevitable on destination host. ( Waiting only","default ephemeral storage partition must be preserved on rebuild \"\"\"","object \"\"\" raise NotImplementedError() def live_migration(self, context, instance, dest, post_method,","context: security context :param instance: nova.objects.instance.Instance :param image_id: Reference to","sys from oslo_log import log as logging from oslo_utils import","suspend(self, context, instance): \"\"\"Suspend the specified instance. A suspended instance","completed. There may be other data included as well that","context in for access to auth_token raise NotImplementedError() def attach_volume(self,",":param block_device_info: instance volume block device info :param power_on: True","instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute driver module. Load",":param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine","auth_token raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return the UUIDS of all","block device mapping to be presented in the format of","to be called. Don't use thread for this method since","the block device mapping. :param instance: nova.objects.instance.Instance that volume is","in for access to auth_token raise NotImplementedError() def suspend(self, context,","recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration of an instance to another","'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError() def","of a running instance in multiple phases, turning off the","block_migration=False, migrate_data=None): \"\"\"Live migration of an instance to another host.","Gigabyte) of `resize_to`. :return: None \"\"\" raise NotImplementedError() def attach_interface(self,","None. This is called during spawn_instance by the compute manager.","unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains information about","a list. \"\"\" # TODO(Vek): Need to pass context in","reverting a resize/migration. :param context: the context for the finish_revert_migration","object which contains information about the VIFs to plug. :return:","A 'rebuild' effectively purges all existing data from the system","instance: Instance object as returned by DB layer. :param network_info:","resume(self, context, instance, network_info, block_device_info=None): \"\"\"resume the specified suspended instance.","on the block device mapping. :param instance: nova.objects.instance.Instance that volume","security context :instance: instance object that was migrated :block_device_info: instance","or agreed to in writing, software # distributed under the","or storage space of the host, depending on the underlying","fails. :return: None \"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance,","to do management of that cache which isn't directly related","on the host, False otherwise. .. note:: This implementation works","instances. :return: If the host can accept further instances, return","execute live migration. This runs checks on the destination host,",":return: None \"\"\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate,","pass context in for access to auth_token pass def ensure_filtering_rules_for_instance(self,","Can be used to filter out devices which are actually","optional. \"\"\" pass def get_info(self, instance): \"\"\"Get the current status","specified instance after snapshots. If the specified instance doesn't support","object reference :param network_info: instance network information \"\"\" raise NotImplementedError(_(\"Hypervisor","virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info =","interface :func:`get_instance_diagnostics` :rtype: dict \"\"\" # TODO(Vek): Need to pass","reference :param network_info: instance network information \"\"\" raise NotImplementedError(_(\"Hypervisor driver","used as the active disk after the snapshot operation has","the disk of a running instance in multiple phases, turning","context :param instance: instance object that is migrated :param network_info:","ex}) def delete_instance_files(self, instance): \"\"\"Delete any lingering instance files for","instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \"\"\" raise NotImplementedError()","instance. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to pass","nova.objects.instance.Instance being migrated/resized :param network_info: instance network information :param block_device_info:","can use the default implementation which returns None. Hypervisors with","indicate the cumulative CPU time (nanoseconds) spent by kernel and","| 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} |","driver does not support \" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self, context,","Need to pass context in for access to auth_token pass","network_info=None, block_device_info=None): \"\"\"Create a new instance/VM/domain on the virtualization platform.","change to security groups. All security groups and their associated","NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return diagnostics data about the given","not set. :returns: The chosen device name. \"\"\" raise NotImplementedError()","The instance which should be paused. :return: None \"\"\" #","def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver want networks deallocated on","MAC addresses'. \"\"\" return None def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP","def snapshot(self, context, instance, image_id, update_task_state): \"\"\"Snapshots the specified instance.","is to have. allocate_for_instance will take this into consideration when","for the suspend. :param nova.objects.instance.Instance instance: The instance to suspend.","ports. :param context: request context :param instance: nova.objects.instance.Instance that the","<filename>nova/virt/driver.py # Copyright 2011 <NAME> # All Rights Reserved. #","otherwise \"\"\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \"\"\"Confirms","the disk to the instance at mountpoint using info.\"\"\" raise","eg: | {'kernel': kern, | 'idle': idle, | 'user': user,","nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information about instance resource",":param network_info: instance network information :param block_device_info: Information about block","block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping","in for access to auth_token raise NotImplementedError() def attach_volume(self, context,","used to publish changes to the instance's metadata to the","def get_host_ip_addr(self): \"\"\"Retrieves the IP address of the dom0 \"\"\"","is optional. On some platforms (e.g. XenAPI) performance statistics can","is being resized, False otherwise :param block_device_info: instance volume block","to be used as the active disk after the snapshot","Mapping of MAC addresses to actual networks (or permitting them","str host: The name of the host to add to","Gets called when an instance gets added to or removed","instance): \"\"\"Does the driver want networks deallocated on reschedule?\"\"\" return","pass def inject_network_info(self, instance, nw_info): \"\"\"inject network info for specified","the platform-specific layer, and never escape the connection interface. The",":param retry_interval: How often to signal guest while waiting for",":param power_on: True if the instance should be powered on,","provisioning networking for the instance. Mapping of MAC addresses to","host: The name of the host to remove from the","False def macs_for_instance(self, instance): \"\"\"What MAC addresses must this instance","and remakes the VM with given 'metadata' and 'personalities'. This","dict old_connection_info: The volume for this connection gets detached from","network_info, block_device_info=None): \"\"\"resume the specified suspended instance. The suspended instance","nodename): \"\"\"Retrieve resource information. This method is called when nova-compute","power on/off an instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError()","from a green thread. \"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding event","interfaces (VIFs) into the given `instance` at instance boot time.","a VM or domain. These IDs must stay internal to","the contract that compute drivers must follow, and shared types","src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if it is possible to","quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce by","completion. To migrate an instance, filtering rules to hypervisors and","NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver want networks deallocated","effectively purges all existing data from the system and remakes","event notifications from hypervisors. The callback will be invoked with","driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str in named_driver_config:","we mean 'running virtual machine' (XenAPI terminology) or domain (Xen","name of the root disk - ephemerals: a (potentially empty)","access to auth_token raise NotImplementedError() def cleanup_host(self, host): \"\"\"Clean up","expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk. :param","shared storage. :param context: security context :param data: result of","host maintenance window. On start, it triggers the migration of","across the shutdown + spin up steps. :param context: security","set_admin_password(self, instance, new_pass): \"\"\"Set the root password on the specified","a network interface removed. :param nova.network.model.NetworkInfo vif: The object which","the driver name supplied as an argument. Compute drivers constructors","self.get_available_nodes(): return True # Refresh and check again. return nodename","libvirt terminology). An instance has an ID, which is the","of the instance. :param nova.network.model.NetworkInfo vif: The object which has","host. \"\"\" # TODO(Vek): Need to pass context in for","def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required cleanup on dest host","instance is a member of or if the group gains","dest: destination host :param post_method: post operation method. expected nova.compute.manager._post_live_migration.","up any resources left after an interrupted snapshot. :param context:","Optional; Can be used to filter out devices which are","VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which contains information","(XenAPI terminology) or domain (Xen or libvirt terminology). An instance","has the information about the interface to attach. :raise nova.exception.NovaException:","context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \"\"\"","raise NotImplementedError() def detach_interface(self, instance, vif): \"\"\"Use hotunplug to remove","object that was being migrated :param network_info: instance network information","is being specified as instance.name. The second parameter is the","network_info: instance network information \"\"\" raise NotImplementedError(_(\"Hypervisor driver does not","bare metal) cannot do freeform virtualization of MAC addresses. This","to be attached to the instance. \"\"\" raise NotImplementedError() def","encouraged to override this method with something more efficient. \"\"\"","def restore(self, instance): \"\"\"Restore the specified soft-deleted instance. The restored","\"\"\"Quiesce the specified instance to prepare for snapshots. If the","raise NotImplementedError() def node_is_available(self, nodename): \"\"\"Return whether this compute service","set means 'these and only these MAC addresses'. \"\"\" return","instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about","the driver implementation if not set. :returns: The chosen device","raise NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force live migration to complete","log a warning in that case. :param context: security context","This runs check on the destination host, and then calls","inject_network_info(self, instance, nw_info): \"\"\"inject network info for specified instance.\"\"\" #","fs_type): \"\"\"Check whether the file format is supported by this","resource information. This method is called when nova-compute launches, and","`uptime` on this host. :return: A text which contains the","NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of all nodes managed","context: The context for the rescue. :param nova.objects.instance.Instance instance: The","on the host, False otherwise. .. note:: Used in rebuild","power_on: True if the instance should be powered on, False","or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network information","is requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList for","instance, dest_check_data, block_device_info=None): \"\"\"Check if it is possible to execute","action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security","given 'instance'. :param nova.objects.instance.Instance instance: The instance whose volume gets","undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \"\"\"Undo for Resource Pools.\"\"\"",":param nova.objects.BlockDeviceMapping root_bdm: The description of the root device. \"\"\"","trigger_crash_dump(self, instance): \"\"\"Trigger crash dump mechanism on the given instance.","diagnostic data should be returned. :return: Has a big overlap","raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required cleanup on","compute_driver=None): \"\"\"Load a compute driver module. Load the compute driver","concerned, 'instance ID' and 'instance name' are synonyms. Note that","def node_is_available(self, nodename): \"\"\"Return whether this compute service manages a","the file being merged into (if appropriate). :return: None \"\"\"","the virtualization platform should be in the state that it","pass def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance,","VM or domain. These IDs must stay internal to the","get_host_ip_addr(self): \"\"\"Retrieves the IP address of the dom0 \"\"\" #","the instance is to have. allocate_for_instance will take this into","instance, timeout=0, retry_interval=0): \"\"\"Power off the specified instance. :param instance:","occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk.","filtering rules to firewall rules can be set faster). Concretely,","an instance :param context: security context :param instance: nova.objects.instance.Instance \"\"\"","None \"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor,","chosen device name. \"\"\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check","get the root device for. :param nova.objects.ImageMeta image_meta: The metadata",":param instance: nova.objects.instance.Instance \"\"\" pass def finish_migration(self, context, migration, instance,","number of virtual machines that the hypervisor knows about. ..","the sending machine :param dst_compute_info: Info about the receiving machine","support. If a driver supports multi compute-nodes, this method returns","the volume for `old_connection_info` is attached to. :param int resize_to:","to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context.","DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, |","in for access to auth_token raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns","post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up any resources left after an","'user': user, | 'iowait': wait, | 'frequency': freq}, where kern","necessary for the driver gracefully stop, including ending remote sessions.","method must be called. - setup_basic_filtering (for nova-basic, etc.) -","which platform-specific ID, and vice versa. Some methods here take","data structure into terms that are specific to the virtualization","volume for `old_connection_info` is attached to. :param int resize_to: If",":return: A text which contains the uptime of this host","an instance, by name (not ID!) :param instance: nova.objects.instance.Instance object","all_instances): \"\"\"Manage the driver's local image cache. Some drivers chose","These IDs must stay internal to the platform-specific layer, and","an instance to driver format. Virt drivers expect block device","The context for the rescue. :param nova.objects.instance.Instance instance: The instance","nova.objects.instance.Instance object Returns a InstanceInfo object \"\"\" # TODO(Vek): Need","driver to function, including catching up with currently running VM's","which will be an instance of the nova.virt.event.Event class. \"\"\"","\"\"\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This method is called","on the host. :param instance: nova.objects.instance.Instance to lookup Returns True","utils from nova.virt import event as virtevent CONF = nova.conf.CONF","true, post operation of block_migration. \"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self,","cleanup :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def","which gets a network interface removed. :param nova.network.model.NetworkInfo vif: The","\"\"\"Clean up destination node after a failed live migration. :param","NotImplementedError() def unrescue(self, instance, network_info): \"\"\"Unrescue the specified instance. :param","be able to complete the snapshot. For example, in the","or customer-controlled -- it's an internal ID chosen by Nova.","# NOTE(jichenjc): Return False here so that every hypervisor #","virtualization platform. \"\"\" capabilities = { \"has_imagecache\": False, \"supports_recreate\": False,","the 'suspend'. :param nova.context.RequestContext context: The context for the resume.","could be persisted on the host and allocate storage space","Unless required by applicable law or agreed to in writing,","implementation layer. For instance, with openflow switches, fixed MAC addresses","\"\"\"Writes a file on the specified instance. The first parameter","indicates write, req is the total number of I/O requests","Nova having to do the aggregation. On those platforms, this","instance for attaching to volumes. Connector information is a dictionary","for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return","diff): \"\"\"Applies a diff to the instance metadata. This is","The counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context:","list instances: A list of nova.objects.instance.Instance objects that have been","up anything that is necessary for the driver gracefully stop,","the disk attached to the instance.\"\"\" raise NotImplementedError() def swap_volume(self,","lookup Returns True if files of an instance with the","def power_on(self, context, instance, network_info, block_device_info=None): \"\"\"Power on the specified","counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The","def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \"\"\"Deletes a snapshot","operation of live migration at source host. :param context: security","console.type.ConsoleSerial \"\"\" raise NotImplementedError() def get_mks_console(self, context, instance): \"\"\"Get connection","integers. \"\"\" raise NotImplementedError() def block_stats(self, instance, disk_id): \"\"\"Return performance","remove from the given `aggregate`. :param dict kwargs: A free-form","ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means","nova.objects.instance.Instance instance: The instance that has the volume attached. :param","waiting for it to shutdown \"\"\" raise NotImplementedError() def power_on(self,","either express or implied. See the # License for the","of) the contract that compute drivers must follow, and shared","host, False otherwise. .. note:: Used in rebuild for HA","the machine as follows:: { 'ip': ip, 'initiator': initiator, 'host':","User files to inject into instance. :param admin_password: <PASSWORD> to","None \"\"\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host,","must be called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for","def get_volume_connector(self, instance): \"\"\"Get connector information for the instance for","a snapshot, it is the image file Cinder expects to","\"\"\" raise NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage counters","defined at disk API module. \"\"\" # NOTE(jichenjc): Return False","a spice console. :param context: security context :param instance: nova.objects.instance.Instance","necessary for the driver to function, including catching up with","raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver want networks","migration should not be started when setting-up filtering rules operations","timeout. :return: None \"\"\" # TODO(Vek): Need to pass context","without Nova having to do the aggregation. On those platforms,","raise NotImplementedError() def get_host_uptime(self): \"\"\"Returns the result of calling the","at all -- such things are only known higher up","ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping =","the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info","to be checked, the validate values are defined at disk","goes back to power_state.RUNNING. The virtualization platform should ensure that","the destination host, and then calls back to the source","third is the contents of the file, also base64-encoded. NOTE(russellb)","\"\"\" return {} def instance_on_disk(self, instance): \"\"\"Checks access of instance","including ending remote sessions. This is optional. \"\"\" pass def","the specified suspended instance. The suspended instance gets resumed and","if the operation cannot complete. \"\"\" # TODO(Vek): Need to","'initiator': initiator, 'host': hostname } \"\"\" raise NotImplementedError() def get_available_nodes(self,","instance metadata. This is an optional driver method which is","set_bootable(self, instance, is_bootable): \"\"\"Set the ability to power on/off an","as logging from oslo_utils import importutils import nova.conf from nova.i18n","image_meta: The metadata of the image of the instance. :param","file system type to be checked, the validate values are","to that call to ensure that all assigned network details","instance block device information :param migrate_data: a LiveMigrateData object \"\"\"","CPU time (nanoseconds) spent by kernel and user processes respectively,","context :param instance: nova.objects.instance.Instance \"\"\" pass def inject_network_info(self, instance, nw_info):","block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance resources . Instance","depends on the configuration of it. :param nova.objects.instance.Instance instance: The","Returns a InstanceInfo object \"\"\" # TODO(Vek): Need to pass","could be stored in the memory or storage space of","multi compute-nodes, this method returns a list of nodenames managed","addresses can still be virtualized onto any L2 domain, with",":param str mountpoint: The mountpoint in the instance where the","finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting a resize/migration.","this instance. Some hypervisors (such as bare metal) require that","for its completion. To migrate an instance, filtering rules to","nova.context.RequestContext context: The context for the rescue. :param nova.objects.instance.Instance instance:","the virtualization layer, as a list. \"\"\" # TODO(Vek): Need","This implementation works for all drivers, but it is not","security context :param instance: instance object reference :param network_info: instance","controlling thing here. \"\"\"Unplug virtual interfaces (VIFs) from networks. The","the combination with :func:`set_host_enabled`. :param str host: The name of","the cumulative CPU time (nanoseconds) spent by kernel and user","a dict containing the CPU stat info, eg: | {'kernel':",":param disk_info: instance disk information :param migrate_data: a LiveMigrateData object","(VIFs) from networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance","raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Remove a","inject into instance. :param admin_password: <PASSWORD> to set in instance.","ID maps to which platform-specific ID, and vice versa. Some",":param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object","instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network","NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0,","paused instance. The paused instance gets unpaused and will use","wait indicates the cumulative I/O wait CPU time (nanoseconds), since","The counter action for 'resume' is :func:`suspend`. Depending on the","'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return","Cinder's initialize_connection() API call. In the case of doing a","soft-deleted instance which should be restored from the soft-deleted data.","`instances` which are longer in the rebooting state than `timeout`","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","state than `timeout` seconds. :param int timeout: The timeout (in","the host is booting up; freq indicates the current CPU","NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \"\"\"Detach the disk","destroy disks at destination during cleanup :param migrate_data: a LiveMigrateData","auth_token raise NotImplementedError() def get_console_output(self, context, instance): \"\"\"Get console output","the connection interface. The platform-specific layer is responsible for keeping","refresh_security_group_rules(self, security_group_id): \"\"\"This method is called after a change to",":param instance: instance object reference :param network_info: instance network information","should be triggered. :return: None \"\"\" raise NotImplementedError() def soft_delete(self,","create a new instance based on that. :param nova.objects.instance.Instance instance:","will accept new instances. If it is `False`, the host","instance, network_info): \"\"\"Plug virtual interfaces (VIFs) into the given `instance`","the instance should be powered on, False otherwise \"\"\" raise","raised. :param context: request context :param instance: nova.objects.instance.Instance to be","class for compute drivers. The interface to this class talks","of check_can_live_migrate_destination \"\"\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data,","def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping')","migration at source host. :param context: security context :instance: instance","volume for this connection gets attached to the given 'instance'.","the host to remove from the given `aggregate`. :param dict","compute host from an aggregate. The counter action to this","can safely ignore this :returns: Dictionary describing resources \"\"\" raise","started when setting-up filtering rules operations are not completed. :param","pipeline. All counters are long integers. This method is optional.","information :param block_device_info: instance volume block device info :param power_on:","security context :param data: result of check_instance_shared_storage_local \"\"\" pass def","The counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance:","GuestOS to shutdown :param retry_interval: How often to signal guest","raise NotImplementedError() def get_vnc_console(self, context, instance): \"\"\"Get connection info for",":param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def unrescue(self, instance, network_info):","\"\"\" return True @property def need_legacy_block_device_info(self): \"\"\"Tell the caller if","a (potentially empty) list of DriverVolumeBlockDevice or any of it's","even in cases in which the underlying domain/vm is paused","but is still available as a database entry. The counter","% compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\"Unable to","the live migration can succeed, based on the results from","fixed MAC addresses can still be virtualized onto any L2","block_device_info: instance volume block device info :param power_on: True if","on, False otherwise \"\"\" raise NotImplementedError() def confirm_migration(self, migration, instance,","instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified instance. After",":func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs plugged.","nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate","to security groups. All security groups and their associated rules","those value through migrateToURI(), so , no need to be","be an instance of nova.virt.event.Event\")) try: LOG.debug(\"Emitting event %s\", str(event))","bad_volumes_callback=None): \"\"\"Reboot the specified instance. After this is called successfully,","if networking failed), this function should still succeed. It's probably","\"\"\"What MAC addresses must this instance have? Some hypervisors (such","counters are long integers. This method is optional. On some","the default ephemeral storage partition must be preserved on rebuild","their # virt layer. return False def quiesce(self, context, instance,","data needed to be able to complete the snapshot. For","None \"\"\" # NOTE(jogo) Currently only used for XenAPI-Pool raise","otherwise \"\"\" raise NotImplementedError() def pause(self, instance): \"\"\"Pause the given","instance, is_bootable): \"\"\"Set the ability to power on/off an instance.","and should be decided by the driver implementation if not","for access to auth_token raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return the","to auth_token raise NotImplementedError() def cleanup_host(self, host): \"\"\"Clean up anything","context in for access to auth_token raise NotImplementedError() def power_off(self,","if true, destroy disks at destination during cleanup :param migrate_data:","raise NotImplementedError() def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop host maintenance window.","ports will be associated with :returns: a string representing the","instance: The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info:","detach fails. :return: None \"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self, context,","disk information dicts in JSON format. :rtype: str \"\"\" raise","resources left after an interrupted snapshot. :param context: security context","include the file being merged, and the file being merged",":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \"\"\" raise","seconds) for considering rebooting instances to be stuck. :param list","quiesced :param nova.objects.ImageMeta image_meta: The metadata of the image of","interface to detach. :raise nova.exception.NovaException: If the detach fails. :return:","block device information :param destroy_disks: if true, destroy disks at",":param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info","an internal ID chosen by Nova. At the nova.virt layer,","making the connection, the name of the iscsi initiator and","the receiving machine :param block_migration: if true, prepare for block","instance object that is migrated. :param dest: destination host :param","(in Gigabyte) of `resize_to`. :return: None \"\"\" raise NotImplementedError() def","that the instance is to have. allocate_for_instance will take this","The snapshot to delete. :param dict delete_info: Volume backend technology","being live migrated \"\"\" raise NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort","into this layer. This layer is responsible for translating that","called successfully, the instance's state goes back to power_state.RUNNING. The","setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may","device mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get","cleaned up, and the virtualization platform should be in the","change_instance_metadata(self, context, instance, diff): \"\"\"Applies a diff to the instance","info, eg: | {'kernel': kern, | 'idle': idle, | 'user':","return \"disabled\". :rtype: str \"\"\" # TODO(Vek): Need to pass","data should be returned. :return: Has a big overlap to","None means 'no constraints', a set means 'these and only","into (if appropriate). :return: None \"\"\" raise NotImplementedError() def default_root_device_name(self,","distributed under the License is distributed on an \"AS IS\"","(nanoseconds), wait indicates the cumulative I/O wait CPU time (nanoseconds),","This must only be invoked from a green thread. \"\"\"","to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z):","failed), this function should still succeed. It's probably a good","instance, network_info): \"\"\"Setting up filtering rules and waiting for its","instance: The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info:","raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \"\"\"Power on","network ports will be associated with :returns: a string representing","contract \"\"\" import sys from oslo_log import log as logging","method is called after a change to security groups. All","Reboots the given `instances` which are longer in the rebooting","for access to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info):","inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes a file on the specified","The context for the resume. :param nova.objects.instance.Instance instance: The suspended","hypervisor, since filtering rules to firewall rules can be set","an instance to another host. :param context: security context :param","the given paused instance. The paused instance gets unpaused and","wr indicates write, req is the total number of I/O","are no longer of interest. :param all_instances: nova.objects.instance.InstanceList \"\"\" pass","delete_info): \"\"\"Deletes a snapshot of a volume attached to a","if supplied, the driver name supplied as an argument. Compute","a network interface from a running instance. The counter action","auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh security group rules","str \"\"\" raise NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets the ability","the compute service. This method is for multi compute-nodes support.","resize_instance: True if the instance is being resized, False otherwise","and the virtualization platform should be in the state that","the host again. The counter action for 'unpause' is :func:`pause`.","for access to auth_token raise NotImplementedError() def host_power_action(self, action): \"\"\"Reboots,","root_bdm): \"\"\"Provide a default root device name for the driver.","# The compute manager doesn't use this interface, which seems","still be virtualized onto any L2 domain, with arbitrary VLANs","the given instance. A paused instance doesn't use CPU cycles","other calls into the driver. The prime example is to","of the root device. \"\"\" raise NotImplementedError() def default_device_names_for_instance(self, instance,","block device information :param migrate_data: a LiveMigrateData object \"\"\" pass",":param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def post_live_migration(self,","remove images which are no longer of interest. :param all_instances:","import sys from oslo_log import log as logging from oslo_utils","def remove_from_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Remove a compute host","failed live migration. :param context: security context :param instance: instance","metal) require that instances boot from the network, and manage","instance, block_device_info, migrate_data=None): \"\"\"Post operation of live migration at source","soft_delete(self, instance): \"\"\"Soft delete the specified instance. A soft-deleted instance","`False`, leave the maintenance mode. :return: \"on_maintenance\" if switched to","name of the host whose maintenance mode should be changed.","- setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml","See the # License for the specific language governing permissions","supplied to the allocate_for_instance call by the compute manager, and","The state of the VM could be stored in the","should be the controlling thing here. \"\"\"Unplug virtual interfaces (VIFs)","raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \"\"\"Check if instance files","the base64-encoded path to which the file is to be","context for the suspend. :param nova.objects.instance.Instance instance: The instance to","dest_check_data): \"\"\"Do required cleanup on dest host after check_can_live_migrate calls","block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info","instance: The instance to suspend. :return: None \"\"\" raise NotImplementedError()","should override if per-instance overhead calculations are desired. :param instance_info:","changes to the instance's metadata to the hypervisor. If the","down or powers up the host. :param str action: The",":param nodename: node which the caller want to get resources","context, instance, block_device_info, migrate_data=None): \"\"\"Post operation of live migration at","option required, but not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"),","return False def macs_for_instance(self, instance): \"\"\"What MAC addresses must this","If `True`, go into maintenance mode. If `False`, leave the","network info for specified instance.\"\"\" # TODO(Vek): Need to pass","longer of interest. :param all_instances: nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self,","{} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap):","whose maintenance mode should be changed. :param bool mode: If","context, instance, network_info, block_device_info=None): \"\"\"Power on the specified instance. :param","block_device_info=None): \"\"\"resume the specified suspended instance. The suspended instance gets","instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def unrescue(self, instance, network_info): \"\"\"Unrescue","self.virtapi = virtapi self._compute_event_callback = None def init_host(self, host): \"\"\"Initialize","return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \"\"\"Return the names","is raised. When it fails to quiesce by other errors","block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info","on the instance; the third is the contents of the","requests made, bytes is the total number of bytes transferred,","of block device info to be passed in to methods","the data there to guide the creation of the new","a resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance \"\"\"",".. note:: This implementation works for all drivers, but it","invoked from a green thread. \"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding","of all the instances known to the virtualization layer, as","permitting them to be freeform) is up to the network","compute-nodes support. If a driver supports multi compute-nodes, this method","will hold the snapshot. \"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context,","by Nova to represent the instance further up the stack.","a compute driver module. Load the compute driver module specified","a driver that manages only one node can safely ignore","in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver)","def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] > 0","def get_mks_console(self, context, instance): \"\"\"Get connection info for a MKS","the given `instance` at instance boot time. The counter action","raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None,","nova.compute.manager. \"\"\" # TODO(Vek): Need to pass context in for","the image of the instance. :param resize_instance: True if the","nodes managed by the compute service. This method is for","receive asynchronous event notifications from hypervisors. The callback will be","method with something more efficient. \"\"\" try: return instance.uuid in","import log as logging from oslo_utils import importutils import nova.conf","ImportError: LOG.exception(_LE(\"Unable to load the virtualization driver\")) sys.exit(1) def is_xenapi():","into instance. :param admin_password: Administrator password to set in instance.","instance: The instance which will get an additional network interface.","image_meta, vif): \"\"\"Use hotplug to add a network interface to","vif): \"\"\"Use hotplug to add a network interface to a","which isn't directly related to other calls into the driver.","def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device mappings for an instance","any L2 domain, with arbitrary VLANs etc, but regular switches","instance boot time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance","running instance. The counter action to this is :func:`attach_interface`. :param","manages only one node can safely ignore this :returns: Dictionary","raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \"\"\"Detach the",":param block_device_info: instance block device information :param destroy_disks: if true,","host to check the results. :param context: security context :param","again. The counter action for 'unpause' is :func:`pause`. Depending on","for this instance. Some hypervisors (such as bare metal) require","\"\"\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide a","and will use CPU cycles of the host again. The","to the source host to check the results. :param context:","\"\"\"Confirms a resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance","would include the file being merged, and the file being","other errors (e.g. agent timeout), NovaException is raised. :param context:","NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \"\"\"Check if instance files located","console output for an instance :param context: security context :param","\"\"\"Return diagnostics data about the given instance. :param nova.objects.instance.Instance instance:","persisted on the host and allocate storage space this way.","instance before the end. :param nova.objects.instance.Instance instance: The instance whose","instance. :param instance: nova.objects.instance.Instance :param timeout: time to wait for","\"\"\" raise NotImplementedError() def soft_delete(self, instance): \"\"\"Soft delete the specified","errs is the number of requests held up due to","instance, mountpoint, resize_to): \"\"\"Replace the volume attached to the given","NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes a file on","the guest has the same state as before the 'suspend'.","on the specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the","further up the stack. This is unfortunately also called a",":return: \"on_maintenance\" if switched to maintenance mode or \"off_maintenance\" if","known to the virtualization layer, as a list. \"\"\" raise","to shutdown :param retry_interval: How often to signal guest while","options for this instance. Some hypervisors (such as bare metal)","supplied, the driver name supplied as an argument. Compute drivers","Hypervisor before calling this method. :param context: security context :param","instance network information :param block_device_info: instance block device information :param",":param instance: nova.objects.instance.Instance to lookup Returns True if files of","hypervisors (such as bare metal) require that instances boot from","volumes :param bad_volumes_callback: Function to handle any bad volumes encountered","resize/migration. :param context: the context for the migration/resize :param migration:","and needs to be held across the shutdown + spin","of the image of the instance. :param nova.network.model.NetworkInfo vif: The","into the driver. The prime example is to clean the","block_device_info=None, power_on=True): \"\"\"Finish reverting a resize/migration. :param context: the context","here. \"\"\"Unplug virtual interfaces (VIFs) from networks. The counter action","be removed from nova.compute.manager. \"\"\" # TODO(Vek): Need to pass","by the compute manager. :return: None, or a set of","given instance. :param nova.objects.instance.Instance instance: The instance to which the","to actual networks (or permitting them to be freeform) is","should be restored from the soft-deleted data. :return: None \"\"\"","load the virtualization driver\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver ==","the newly transferred disk information :param network_info: instance network information","to firewall rules can be set faster). Concretely, the below","False, \"supports_attach_interface\": False, \"supports_device_tagging\": False, } def __init__(self, virtapi): self.virtapi","for the migration/resize :param migration: the migrate/resize information :param instance:",":param block_device_info: Information about block devices to be attached to","it. :param nova.objects.instance.Instance instance: The instance where the crash dump","data structure used by nova.compute to store details regarding an","value through migrateToURI(), so , no need to be called.","def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live","shared storage. This runs check on the destination host, and","block_device_info, migrate_data=None): \"\"\"Post operation of live migration at source host.","the rules now.\"\"\" pass def unfilter_instance(self, instance, network_info): \"\"\"Stop filtering","instance: nova.objects.instance.Instance :param network_info: instance network information :param reboot_type: Either","name to override the config opt :returns: a ComputeDriver instance","LiveMigrateData object \"\"\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve","layer. :param network_info: instance network information :param block_device_info: Information about","def get_available_resource(self, nodename): \"\"\"Retrieve resource information. This method is called","same state as before the 'suspend'. :param nova.context.RequestContext context: The","rescue_password: <PASSWORD> for rescue. \"\"\" raise NotImplementedError() def set_bootable(self, instance,","bdms: block-device-mappings to use for rebuild :param detach_block_devices: function to","host, **kwargs): \"\"\"Remove a compute host from an aggregate. The","instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def","migrated \"\"\" raise NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort an in-progress","None. Hypervisors with MAC limits should return a set of","is :func:'suspend'. The counter action for `pause` is :func:`unpause`. :param","on the given instance. These are returned as [rd_req, rd_bytes,","cycles or memory of the host anymore. The state of","use the data there to guide the creation of the","raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application of IPTables rules.\"\"\" pass","and will use CPU cycles and memory of the host","operation of block_migration. \"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance):","the interface to detach. :raise nova.exception.NovaException: If the detach fails.","and vice versa. Some methods here take an instance of","of all nodes managed by the compute service. This method","retry_interval: How often to signal guest while waiting for it","old_connection_info: The volume for this connection gets detached from the","context: security context :param data: result of check_instance_shared_storage_local \"\"\" pass","cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required cleanup on dest host after","source. :param context: security context :param instance: instance object reference","instance: instance object reference :param network_info: instance network information \"\"\"","\"\"\"Set the root password on the specified instance. :param instance:","instance. :param rescue_password: <PASSWORD> for rescue. \"\"\" raise NotImplementedError() def","event %(event)s: %(ex)s\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance):","auth_token raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application of IPTables rules.\"\"\"","take an instance of nova.compute.service.Instance. This is the data structure","The object which has the information about the interface to","\"\"\" raise NotImplementedError() def node_is_available(self, nodename): \"\"\"Return whether this compute","any resources left after an interrupted snapshot. :param context: security","which should remove the given `host` :param str host: The","devices to be attached to the instance. \"\"\" raise NotImplementedError()",":param context: security context :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError()","addresses that the instance is to have. allocate_for_instance will take","left after an interrupted snapshot. :param context: security context :param","that all assigned network details are compatible with the set","required cleanup on dest host after check_can_live_migrate calls :param context:","being migrated/resized :param disk_info: the newly transferred disk information :param","\"\"\"Post operation of live migration at destination host. :param context:","info for a MKS console. :param context: security context :param",":returns: The chosen device name. \"\"\" raise NotImplementedError() def is_supported_fs_format(self,","hold the snapshot. \"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance):","merged into (if appropriate). :return: None \"\"\" raise NotImplementedError() def","publishing the instance metadata to the instance, then this method","OF ANY KIND, either express or implied. See the #","'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError() def refresh_security_group_rules(self,","to add to the given `aggregate`. :param dict kwargs: A","def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals')","new instances. :param bool enabled: If this is `True`, the","data format returned by Cinder's initialize_connection() API call. In the","a set means 'these and only these MAC addresses'. \"\"\"","the machine that will be making the connection, the name","specified instance.\"\"\" # TODO(Vek): Need to pass context in for","off deferral of IPTables rules and apply the rules now.\"\"\"","'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size']","def plug_vifs(self, instance, network_info): \"\"\"Plug virtual interfaces (VIFs) into the","of DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty) list","new instances. :return: If the host can accept further instances,","from nova import utils from nova.virt import event as virtevent","driver name supplied as an argument. Compute drivers constructors take","further instances, return \"enabled\", if further instances shouldn't be scheduled","here take an instance of nova.compute.service.Instance. This is the data","NotImplementedError() def network_binding_host_id(self, context, instance): \"\"\"Get host ID to associate","with the License. You may obtain # a copy of","agent timeout), NovaException is raised. :param context: request context :param","raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up any resources","a periodic task that records the results in the DB.","is responsible for translating that generic data structure into terms","\"\"\"resume the specified suspended instance. The suspended instance gets resumed","platforms will also have their own identity schemes, to uniquely","these MAC addresses'. \"\"\" return None def dhcp_options_for_instance(self, instance): \"\"\"Get","nova-compute launches, and as part of a periodic task that","start, it triggers the migration of all instances to other","from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param","shutdown. :param int retry_interval: How often to signal guest while","The aggregate which should add the given `host` :param str",":param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The","object Returns a InstanceInfo object \"\"\" # TODO(Vek): Need to","the Hypervisor. If the instance is not found (for example","which we mean 'running virtual machine' (XenAPI terminology) or domain","auth_token raise NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts down or","return True # Refresh and check again. return nodename in","security context. :param nova.objects.instance.Instance instance: The instance that has the","with the supplied ID exists on the host, False otherwise.","configuration. Most hypervisors can use the default implementation which returns","The instance whose disk should be migrated. :param str dest:","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","to execute live migration. This checks if the live migration","only one node can safely ignore this :returns: Dictionary describing","cleanup on dest host after check_can_live_migrate calls :param context: security","context in for access to auth_token raise NotImplementedError() def set_admin_password(self,","nova.objects.instance.Instance :param timeout: time to wait for GuestOS to shutdown","hotunplug to remove a network interface from a running instance.","firewalls are inevitable on destination host. ( Waiting only for","being resized, False otherwise :param block_device_info: instance volume block device",":param src_compute_info: Info about the sending machine :param dst_compute_info: Info","{} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info):","diagnostics data about the given instance. :param nova.objects.instance.Instance instance: The","images for instances on disk. This method is an opportunity","network_info): \"\"\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \"\"\" #","\"\"\" raise NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger crash dump mechanism","versa. Some methods here take an instance of nova.compute.service.Instance. This","migration/resize :param migration: the migrate/resize information :param instance: nova.objects.instance.Instance being","keys: - root_device_name: device name of the root disk -","remove a network interface from a running instance. The counter","def list_instances(self): \"\"\"Return the names of all the instances known","from nova.virt import event as virtevent CONF = nova.conf.CONF LOG","instance; the third is the contents of the file, also","node which the caller want to get resources from a","params \"\"\" raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type,","migrate_data=None): \"\"\"Clean up destination node after a failed live migration.","CPU cycles or memory of the host anymore. The state","expect it. \"\"\" return True def volume_snapshot_create(self, context, instance, volume_id,","to hypervisor, since filtering rules to firewall rules can be","be called since it defines PROJNET, PROJMASK. but libvirt migrates","context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which","check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if it","for validation of access to instance shared disk files \"\"\"","for volumes attached to vms on a given host.- \"\"\"","volume gets replaced by another one. :param str mountpoint: The","'ip': ip, 'initiator': initiator, 'host': hostname } \"\"\" raise NotImplementedError()","object (hypervisor-dependent) \"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do","A text which contains the uptime of this host since","as their first object and this must be supplied. :param","have to be called since it defines PROJNET, PROJMASK. but","instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The metadata","end. :param nova.objects.instance.Instance instance: The instance whose disk should be","named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type]","instance files located on shared storage. This runs check on","context, instance, dest_check_data, block_device_info=None): \"\"\"Check if it is possible to",":param uuid volume_id: Attached volume associated with the snapshot :param","instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None,","= virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info","be scheduled to this host, return \"disabled\". :rtype: str \"\"\"","\"\"\" raise NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets the ability of","The instance whose volume gets replaced by another one. :param","\"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required cleanup","for the instance for attaching to volumes. Connector information is","True if the instance should be powered on, False otherwise","types that support that contract \"\"\" import sys from oslo_log","%s\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\"Exception dispatching event","{ \"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\":","0, 'disk_gb': 0} def list_instances(self): \"\"\"Return the names of all","or powers up the host. :param str action: The action","HA implementation and required for validation of access to instance","raise NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort an in-progress live migration.","must be supplied. :param virtapi: a VirtAPI instance :param compute_driver:","address of the dom0 \"\"\" # TODO(Vek): Need to pass","list of DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice","raise NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts down or powers","importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping):","counter action :func:`restore` uses the database entry to create a","maps to which platform-specific ID, and vice versa. Some methods","about the block devices. :param int timeout: The time in","disk_info, migrate_data=None): \"\"\"Prepare an instance for live migration :param context:","The name of the host to remove from the given","compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute driver option","the caller whether we expect the legacy format of block","live migration :param context: security context :param instance: nova.objects.instance.Instance object","instance, b64_path, b64_contents): \"\"\"Writes a file on the specified instance.","an instance for live migration :param context: security context :param","from the given `aggregate`. :param dict kwargs: A free-form thingy...","active disk after the snapshot operation has completed. There may",":returns: a dict containing the CPU stat info, eg: |","of `resize_to`. :return: None \"\"\" raise NotImplementedError() def attach_interface(self, instance,","for 'unpause' is :func:`pause`. Depending on the underlying hypervisor technology,","recover_method: recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param","for specified instance.\"\"\" # TODO(Vek): Need to pass context in","instance): \"\"\"Soft delete the specified instance. A soft-deleted instance doesn't","instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine :param","Nova. At the nova.virt layer, instances do not have human-readable","instances): \"\"\"Perform a reboot on all given 'instances'. Reboots the","on dest host after check_can_live_migrate calls :param context: security context","image_meta): \"\"\"Quiesce the specified instance to prepare for snapshots. If","odd # since the manager should be the controlling thing","instance on the host. :param instance: The instance to lookup","destination during cleanup :param migrate_data: a LiveMigrateData object \"\"\" raise","driver. The prime example is to clean the cache and","this method with something more efficient. \"\"\" return len(self.list_instances()) def","volume attached :param uuid volume_id: Volume to be snapshotted :param","0} def list_instances(self): \"\"\"Return the names of all the instances","this interface, which seems odd # since the manager should","restore(self, instance): \"\"\"Restore the specified soft-deleted instance. The restored instance","of it's more specialized subclasses. \"\"\" from nova.virt import block_device","If the hypervisor has no means of publishing the instance","Volume backend technology specific data needed to be able to","driver '%s'\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver,","migrated :param network_info: instance network information :param block_device_info: instance block","raise NotImplementedError() def network_binding_host_id(self, context, instance): \"\"\"Get host ID to","[] return block_device_mapping class ComputeDriver(object): \"\"\"Base class for compute drivers.","whose volume gets replaced by another one. :param str mountpoint:","use thread for this method since migration should not be","for the driver to function, including catching up with currently","dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options for this instance. Some hypervisors","has an ID, which is the identifier chosen by Nova","destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup the instance resources . Instance should","should apply the updated rules to instances running the specified","Either a HARD or SOFT reboot :param block_device_info: Info pertaining","of the newer interface :func:`get_instance_diagnostics` :rtype: dict \"\"\" # TODO(Vek):","cumulative CPU time (nanoseconds) spent by kernel and user processes","If the new volume is larger than the old volume,","disk get migrated. :param nova.network.model.NetworkInfo network_info: The network information of","to dispatch the event. This must only be invoked from","check the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance","diff to the instance metadata. This is an optional driver","of which instance ID maps to which platform-specific ID, and",":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \"\"\" raise","method allows drivers to return a set of MAC addresses","returns None. Hypervisors with MAC limits should return a set","off the instance before the end. :param nova.objects.instance.Instance instance: The","resized, False otherwise :param block_device_info: instance volume block device info","with something more efficient. \"\"\" return len(self.list_instances()) def instance_exists(self, instance):","migration :param disk_over_commit: if true, allow disk over commit :returns:","context: The context for the suspend. :param nova.objects.instance.Instance instance: The","represent the instance further up the stack. This is unfortunately","DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty) list of","by the compute manager. Note that the format of the","associated with the snapshot :param uuid snapshot_id: The snapshot to","context: security context :param instance: instance object that is migrated","an instance, filtering rules to hypervisors and firewalls are inevitable","the file is to be written on the instance; the","NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot","need to define their supported file system # type and","specific data needed to be able to complete the snapshot.","if disks should be destroyed :param migrate_data: implementation specific params","\"\"\"Check whether the file format is supported by this driver","must follow, and shared types that support that contract \"\"\"","ensure that all assigned network details are compatible with the",":param context: the context for the migration/resize :param migration: the","def rescue(self, context, instance, network_info, image_meta, rescue_password): \"\"\"Rescue the specified","suspended instance doesn't use CPU cycles or memory of the","try: LOG.debug(\"Emitting event %s\", str(event)) self._compute_event_callback(event) except Exception as ex:","to guide the creation of the new instance. :param nova.objects.ImageMeta","expect the legacy format of block device info to be","stuck. :param list instances: A list of nova.objects.instance.Instance objects that","of `pause` is :func:'suspend'. The counter action for `pause` is","the file being merged, and the file being merged into","of nova.objects.instance.Instance objects that have been in rebooting state longer","context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if it is","network_info: instance network information :param block_device_info: instance block device information","\"\"\"Return whether this compute service manages a particular node.\"\"\" if","resources anymore, but is still available as a database entry.","context :param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta:","of the instance. :param rescue_password: <PASSWORD> for rescue. \"\"\" raise","disk over commit :returns: a LiveMigrateData object (hypervisor-dependent) \"\"\" raise","VIFs to unplug. :return: None \"\"\" raise NotImplementedError() def get_host_cpu_stats(self):","block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or","NotImplementedError() def list_instance_uuids(self): \"\"\"Return the UUIDS of all the instances","the host, depending on the underlying hypervisor technology. A \"stronger\"","live migration. This runs checks on the destination host, and","the specified instance after snapshots. If the specified instance doesn't","def get_console_output(self, context, instance): \"\"\"Get console output for an instance","VIFs to plug. :return: None \"\"\" # TODO(Vek): Need to","of bytes transferred, and errs is the number of requests",".. note:: Used in rebuild for HA implementation and required","express or implied. See the # License for the specific","= importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance,","the instance further up the stack. This is unfortunately also","results from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance","def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up any resources left after","member of or if the group gains or loses a","`restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which","reboot :param block_device_info: Info pertaining to attached volumes :param bad_volumes_callback:","info. Tell the caller whether we expect the legacy format","to or removed from the security group the instance is","instances: A list of nova.objects.instance.Instance objects that have been in","\"\"\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info,","instance files for an instance. :param instance: nova.objects.instance.Instance :returns: True","vms on a given host.- \"\"\" raise NotImplementedError() def get_host_ip_addr(self):","that it was before this call began. :param context: security","after check_can_live_migrate calls :param context: security context :param dest_check_data: result","proxied and needs to be held across the shutdown +","given `instance`. :param dict old_connection_info: The volume for this connection","node after a failed live migration. :param context: security context","to add a network interface to a running instance. The","instance, block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare an instance for live",":rtype: str \"\"\" raise NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets the","`pause` is :func:'suspend'. The counter action for `pause` is :func:`unpause`.","a running instance. The counter action to this is :func:`attach_interface`.","values. \"\"\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \"\"\"Return","instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The network","after an interrupted snapshot. :param context: security context :param instance:","receiving machine :param block_migration: if true, prepare for block migration","root disk - ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice","interface on each running VM. :param instances: nova.objects.instance.InstanceList \"\"\" raise","information for the instance for attaching to volumes. Connector information","The instance to which the diagnostic data should be returned.","platform. Once this successfully completes, the instance should be running","kwargs: A free-form thingy... :return: None \"\"\" # NOTE(jogo) Currently","network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up destination node after a","context, instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting a resize/migration. :param","image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of",":func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate:","timeout: The timeout (in seconds) for considering rebooting instances to","in seconds to wait for the guest OS to shutdown.","network information \"\"\" raise NotImplementedError(_(\"Hypervisor driver does not support \"","to - e.g. for optimisations, or when the 'VM' is","LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force live","method with something more efficient. \"\"\" return len(self.list_instances()) def instance_exists(self,","the driver's local image cache. Some drivers chose to cache","context, instance, network_info, block_device_info=None): \"\"\"resume the specified suspended instance. The","def unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce the specified instance after","context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up destination node","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","is larger than the old volume, it gets resized to","space this way. A \"softer\" way of `suspend` is :func:`pause`.","value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise","manager to dispatch the event. This must only be invoked","state of the VM could be stored in the memory","keeping track of which instance ID maps to which platform-specific","block_migration: if true, post operation of block_migration. \"\"\" raise NotImplementedError()","objects that have been in rebooting state longer than the","network_info: instance network information :param recreate: True if the instance","def filter_defer_apply_off(self): \"\"\"Turn off deferral of IPTables rules and apply","NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required cleanup on dest","another host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object","filtering rules operations are not completed. :param instance: nova.objects.instance.Instance object","completed. :param instance: nova.objects.instance.Instance object \"\"\" # TODO(Vek): Need to","Information about block devices to be attached to the instance.","this instance. A 'rebuild' effectively purges all existing data from","spawn_instance by the compute manager. :return: None, or a set","virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {}","instance. The suspended instance gets resumed and will use CPU","be invoked with a single parameter, which will be an","mode. If `False`, leave the maintenance mode. :return: \"on_maintenance\" if","instance: nova.objects.instance.Instance \"\"\" pass def finish_migration(self, context, migration, instance, disk_info,","in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information about instance resource usage.","a volume attached to a specified instance. The counter action","instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the disk to the","context, instance): \"\"\"Get connection info for a vnc console. :param","driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class =",":func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets a network","an aggregate. The counter action to this is :func:`remove_from_aggregate` :param","instance in multiple phases, turning off the instance before the","def get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get the next device name","host should perform. The valid actions are: \"\"startup\", \"shutdown\" and","storage space of the host, depending on the underlying hypervisor","to publish changes to the instance's metadata to the hypervisor.","context: security context :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def","or memory of the host anymore. The state of the","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","this method with something more efficient. \"\"\" try: return instance.uuid","service. Most hypervisors can use the default implementation which returns","drivers chose to cache images for instances on disk. This","instance to which the diagnostic data should be returned. :return:","shutdown. :return: A list of disk information dicts in JSON","spin up steps. :param context: security context :param instance: nova.objects.instance.Instance","while waiting for it to shutdown. :return: A list of","in for access to auth_token raise NotImplementedError() def cleanup_host(self, host):","etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be","virtual interfaces (VIFs) into the given `instance` at instance boot","a single parameter, which will be an instance of the","logging from oslo_utils import importutils import nova.conf from nova.i18n import","the return value of the newer interface :func:`get_instance_diagnostics` :rtype: dict","def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare an","\"\"\"Unpause the given paused instance. The paused instance gets unpaused","for optimisations, or when the 'VM' is actually proxied and","dict containing the following keys: - root_device_name: device name of","should be in the state that it was before this",":param instance: nova.objects.Instance :param block_device_info: Optional; Can be used to","admin_password, network_info=None, block_device_info=None): \"\"\"Create a new instance/VM/domain on the virtualization","a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration(self, context, instance,","nova.network.model.NetworkInfo network_info: The object which contains information about the VIFs","which are longer in the rebooting state than `timeout` seconds.","calls :param context: security context :param dest_check_data: result of check_can_live_migrate_destination","of interest. :param all_instances: nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self, context,","raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None,","a rule. \"\"\" raise NotImplementedError() def reset_network(self, instance): \"\"\"reset networking","faster). Concretely, the below method must be called. - setup_basic_filtering","name of the host to remove from the given `aggregate`.","reset_network(self, instance): \"\"\"reset networking for specified instance.\"\"\" # TODO(Vek): Need","using info.\"\"\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None):","context: security context :param instance: nova.objects.instance.Instance This function should use","instance_on_disk(self, instance): \"\"\"Checks access of instance files on the host.","\"\"\" from nova.virt import block_device as virt_block_device block_device_info = {","network_info, image_meta, rescue_password): \"\"\"Rescue the specified instance. :param nova.context.RequestContext context:","instance. The counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance","require pre-configured MAC->network mappings that will match the actual configuration.","The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information","network_info, block_device_info=None, power_on=True): \"\"\"Finish reverting a resize/migration. :param context: the","of the machine that will be making the connection, the","%(event)s: %(ex)s\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance): \"\"\"Delete","def post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug VIFs from networks at","nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \"\"\" raise NotImplementedError() def","to the return value of the older interface :func:`get_diagnostics` :rtype:","info. :return: None \"\"\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance,","block device mapping. :param instance: nova.objects.instance.Instance that volume is requesting","also called a 'name' elsewhere. As far as this layer",":param block_device_info: Optional; Can be used to filter out devices",":param recover_method: recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration.","instance: The instance to lookup Returns True if an instance","| 'idle': idle, | 'user': user, | 'iowait': wait, |","The metadata of the image of the instance. :param rescue_password:","return len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks existence of an instance","or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means","rebooting state than `timeout` seconds. :param int timeout: The timeout","pass def unfilter_instance(self, instance, network_info): \"\"\"Stop filtering instance.\"\"\" # TODO(Vek):","transferred disk information :param network_info: instance network information :param nova.objects.ImageMeta","rescue_password): \"\"\"Rescue the specified instance. :param nova.context.RequestContext context: The context","new volume is larger than the old volume, it gets","knows about. .. note:: This implementation works for all drivers,","restored from the soft-deleted data. :return: None \"\"\" raise NotImplementedError()","instance should be completely cleaned up, and the virtualization platform","`suspend` is :func:`pause`. The counter action for `suspend` is :func:`resume`.","system type to be checked, the validate values are defined","data there to guide the creation of the new instance.","instance that has the volume attached :param uuid volume_id: Volume","runs checks on the destination host, and then calls back","except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate","and calling this method should apply the updated rules to","{'event': event, 'ex': ex}) def delete_instance_files(self, instance): \"\"\"Delete any lingering","drivers. The interface to this class talks in terms of","DHCP options for this instance. Some hypervisors (such as bare","should be decided by the driver implementation if not set.","resources \"\"\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info,","to receive asynchronous event notifications from hypervisors. The callback will","the detach fails. :return: None \"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self,","= driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block","migrated/resized :param disk_info: the newly transferred disk information :param network_info:","mountpoint in the instance where the volume for `old_connection_info` is","disk API module. \"\"\" # NOTE(jichenjc): Return False here so","should perform. The valid actions are: \"\"startup\", \"shutdown\" and \"reboot\".",": str \"\"\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \"\"\"Start/Stop","about actual disk sizes of an instance. :param instance: nova.objects.Instance",":param nova.context.RequestContext context: The context for the resume. :param nova.objects.instance.Instance","to be held across the shutdown + spin up steps.","network_info: instance network information :param nova.objects.ImageMeta image_meta: The metadata of","this instance have? Some hypervisors (such as bare metal) cannot","is not particularly efficient. Maintainers of the virt drivers are","\"\"\"Get the next device name based on the block device","'192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \"\"\"","instance): \"\"\"Unpause the given paused instance. The paused instance gets","as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping),","attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the","\"\"\" raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password,","the diagnostic data should be returned. :return: Has a big","if the instance was deleted from disk, False otherwise. \"\"\"","specified instance. A soft-deleted instance doesn't allocate any resources anymore,","means of publishing the instance metadata to the instance, then","access to instance shared disk files \"\"\" return False def","for an instance to driver format. Virt drivers expect block","to power on/off an instance. :param instance: nova.objects.instance.Instance \"\"\" raise","description of the root device. \"\"\" raise NotImplementedError() def default_device_names_for_instance(self,","def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up filtering rules and waiting","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","is unfortunately also called a 'name' elsewhere. As far as","be paused. :return: None \"\"\" # TODO(Vek): Need to pass","to auth_token raise NotImplementedError() def unpause(self, instance): \"\"\"Unpause the given","wait, | 'frequency': freq}, where kern and user indicate the","None def dhcp_options_for_instance(self, instance): \"\"\"Get DHCP options for this instance.","of the instance. \"\"\" raise NotImplementedError() def unquiesce(self, context, instance,","detach_volume(self, connection_info, instance, mountpoint, encryption=None): \"\"\"Detach the disk attached to","block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or []","\"\"\"Get connector information for the instance for attaching to volumes.","the file format is supported by this driver :param fs_type:","NOTE(russellb) This method is deprecated and will be removed once","instance of console.type.ConsoleVNC \"\"\" raise NotImplementedError() def get_spice_console(self, context, instance):","the given instance. :param nova.objects.instance.Instance instance: The instance to which","not compute_driver: LOG.error(_LE(\"Compute driver option required, but not specified\")) sys.exit(1)",":param nova.objects.instance.Instance instance: The instance to suspend. :return: None \"\"\"","soft-delete. :return: None \"\"\" raise NotImplementedError() def restore(self, instance): \"\"\"Restore","state as before the 'suspend'. :param nova.context.RequestContext context: The context","have? Some hypervisors (such as bare metal) cannot do freeform","try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver,","This layer is responsible for translating that generic data structure","for the specific language governing permissions and limitations # under","when a host is booted. :param instance: nova.objects.instance.Instance \"\"\" raise","def get_vnc_console(self, context, instance): \"\"\"Get connection info for a vnc","host. :param instance: nova.objects.instance.Instance to lookup Returns True if files","for 'resume' is :func:`suspend`. Depending on the underlying hypervisor technology,",":param dict block_device_info: Information about the block devices. :param int","virtualized onto any L2 domain, with arbitrary VLANs etc, but","\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\" raise NotImplementedError()","checks on the destination host, and then calls back to","in self.get_available_nodes(): return True # Refresh and check again. return","support that contract \"\"\" import sys from oslo_log import log","file format is supported by this driver :param fs_type: the","files of an instance with the supplied ID accessible on","this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param","Linux command `uptime` on this host. :return: A text which","remote sessions. This is optional. \"\"\" pass def get_info(self, instance):","partial instance should be completely cleaned up, and the virtualization","the below method must be called. - setup_basic_filtering (for nova-basic,","\"\"\" raise NotImplementedError() def get_mks_console(self, context, instance): \"\"\"Get connection info","for. :param nova.objects.ImageMeta image_meta: The metadata of the image of","pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check","How the guest OS reacts in details, depends on the","VM disk. :param migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError()","as an argument. Compute drivers constructors take a VirtAPI object","as a list. \"\"\" raise NotImplementedError() def rebuild(self, context, instance,","want networks deallocated on reschedule?\"\"\" return False def macs_for_instance(self, instance):","MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set","resources . Instance should have been destroyed from the Hypervisor","disk information :param network_info: instance network information :param nova.objects.ImageMeta image_meta:","in for access to auth_token raise NotImplementedError() def power_off(self, instance,","This function should use the data there to guide the","resize_to): \"\"\"Replace the volume attached to the given `instance`. :param","about instance resource usage. :returns: dict of nova uuid =>","allow disk over commit :returns: a LiveMigrateData object (hypervisor-dependent) \"\"\"","\"\"\" pass def get_info(self, instance): \"\"\"Get the current status of","the instance is being specified as instance.name. The second parameter","found (for example if networking failed), this function should still","info for volumes attached to vms on a given host.-","instance, with openflow switches, fixed MAC addresses can still be","should have been destroyed from the Hypervisor before calling this","%(ex)s\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance): \"\"\"Delete any","instance of nova.compute.service.Instance. This is the data structure used by","block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info",":param context: security context :param data: result of check_instance_shared_storage_local \"\"\"","of virtual machines that the hypervisor knows about. .. note::","nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The metadata of","to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \"\"\"Set the","wait for the guest OS to shutdown. :param int retry_interval:","the requested block device. device_name does not need to be","set in instance. :param network_info: instance network information :param block_device_info:","regular switches require pre-configured MAC->network mappings that will match the","instance. A soft-deleted instance doesn't allocate any resources anymore, but","and then calls back to the source host to check","block_stats(self, instance, disk_id): \"\"\"Return performance counters associated with the given","dest_check_data, block_device_info=None): \"\"\"Check if it is possible to execute live","block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals(","instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified instance from","networking for specified instance.\"\"\" # TODO(Vek): Need to pass context","support quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce","counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The","possible to execute live migration. This runs checks on the","second parameter is the base64-encoded path to which the file","of the host, depending on the underlying hypervisor technology. A","be held across the shutdown + spin up steps. :param","counter action for 'unpause' is :func:`pause`. Depending on the underlying","customer-controlled -- it's an internal ID chosen by Nova. At","resource usage. :returns: dict of nova uuid => dict of","get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device mappings for an instance to","raise NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data about the",":param context: security context :param instance: nova.objects.instance.Instance This function should","information :param nova.objects.ImageMeta image_meta: The metadata of the image of","associate with network ports. :param context: request context :param instance:","associated with :returns: a string representing the host ID \"\"\"","instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can be used","schemes, to uniquely identify a VM or domain. These IDs","or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class","on each running VM. :param instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError()","method returns a list of nodenames managed by the service.","information is a dictionary representing the ip of the machine","\"\"\"Get information about instance resource usage. :returns: dict of nova","more efficient. \"\"\" return len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks existence","(the \"License\"); you may # not use this file except","information about actual disk sizes of an instance. :param instance:","attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage.","the instance. :param nova.network.model.NetworkInfo vif: The object which has the","raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \"\"\"Snapshots the","raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the currently known host CPU","information about the VIFs to unplug. :return: None \"\"\" raise","ID \"\"\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute","this would include the file being merged, and the file","and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info =","notifications from hypervisors. The callback will be invoked with a","the group gains or loses a rule. \"\"\" raise NotImplementedError()","connection_info, instance, mountpoint, encryption=None): \"\"\"Detach the disk attached to the","database entry to create a new instance based on that.","host is booted. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def","networks deallocated on reschedule?\"\"\" return False def macs_for_instance(self, instance): \"\"\"What","into terms that are specific to the virtualization platform. \"\"\"","uuid volume_id: Attached volume associated with the snapshot :param uuid","dump should be triggered. :return: None \"\"\" raise NotImplementedError() def","block_device_mapping): \"\"\"Converts block device mappings for an instance to driver","(potentially empty) list of DriverEphemeralBlockDevice instances - swap: An instance","crash dump mechanism on the given instance. Stalling instances can","for `old_connection_info` is attached to. :param int resize_to: If the","the hypervisor. If the hypervisor has no means of publishing","cache and remove images which are no longer of interest.","action): \"\"\"Reboots, shuts down or powers up the host. :param","is possible to execute live migration. This runs checks on","began. :param context: security context :param instance: nova.objects.instance.Instance This function","At the nova.virt layer, instances do not have human-readable names","migrate VM disk. :param migrate_data: a LiveMigrateData object \"\"\" raise","the VM, detaches all block devices, then spins up the","auth_token pass def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a reboot on","given instance. These are returned as [rd_req, rd_bytes, wr_req, wr_bytes,","result of check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info,","it triggers the migration of all instances to other hosts.","talks in terms of 'instances' (Amazon EC2 and internal Nova","to wait for GuestOS to shutdown :param retry_interval: How often","the image of the instance. \"\"\" raise NotImplementedError() def unquiesce(self,","def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \"\"\"Check if it is","MAC addresses to actual networks (or permitting them to be","steps. :param context: security context :param instance: nova.objects.instance.Instance This function","the validate values are defined at disk API module. \"\"\"","networks (or permitting them to be freeform) is up to","network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the disk of a running","so the instance is being specified as instance.name. The second","is to clean the cache and remove images which are","instance: The instance where the crash dump should be triggered.","block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up destination node after a failed","method. :param context: security context :param instance: Instance object as","network_info: instance network information :param block_device_info: Information about block devices","old_connection_info, new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace the volume attached to","instance shared disk files \"\"\" return False def register_event_listener(self, callback):","be virtualized onto any L2 domain, with arbitrary VLANs etc,","MAC addresses. This is called during spawn_instance by the compute","otherwise. .. note:: This implementation works for all drivers, but","use the default implementation which returns None. This is called","- all the cleanup of old state is skipped. :param","located on shared storage. This runs check on the destination","the virtualization overhead required to build an instance of the","\"\"\" return None def manage_image_cache(self, context, all_instances): \"\"\"Manage the driver's","{} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {}","register_event_listener(self, callback): \"\"\"Register a callback to receive events. Register a","...]\" \"\"\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \"\"\"This method is","the instance metadata. This is an optional driver method which","pertaining to attached volumes :param bad_volumes_callback: Function to handle any","of nova.compute.service.Instance, and so the instance is being specified as","bandwidth usage counters for each interface on each running VM.","instance): \"\"\"What MAC addresses must this instance have? Some hypervisors",":param network_info: instance network information \"\"\" raise NotImplementedError(_(\"Hypervisor driver does","context in for access to auth_token raise NotImplementedError() def suspend(self,","The description of the root device. \"\"\" raise NotImplementedError() def","Otherwise, this method should return [hypervisor_hostname]. \"\"\" raise NotImplementedError() def","estimated overhead values. \"\"\" return {'memory_mb': 0, 'disk_gb': 0} def","of MAC addresses to actual networks (or permitting them to","for the resume. :param dict block_device_info: Instance volume block device","ID exists on the host, False otherwise. .. note:: This","with below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\"","migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers","0} def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] >",":param create_info: The data needed for nova to be able","name (not ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo",":param nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param","get_rdp_console(self, context, instance): \"\"\"Get connection info for a rdp console.","InstanceInfo object \"\"\" # TODO(Vek): Need to pass context in","the specified instance. :param instance: nova.objects.instance.Instance :param timeout: time to","the given host. \"\"\" # TODO(Vek): Need to pass context","an instance of the given flavor. Defaults to zero, drivers","context for the rescue. :param nova.objects.instance.Instance instance: The instance being","the supplied ID accessible on the host, False otherwise. ..","The metadata of the image of the instance. \"\"\" raise","to shutdown. :param int retry_interval: How often to signal guest","return block_device_mapping class ComputeDriver(object): \"\"\"Base class for compute drivers. The","refresh_instance_security_rules(self, instance): \"\"\"Refresh security group rules Gets called when an","consideration when provisioning networking for the instance. Mapping of MAC","\"\"\"Add a compute host to an aggregate. The counter action","Dict of estimated overhead values. \"\"\" return {'memory_mb': 0, 'disk_gb':","NotImplementedError() def change_instance_metadata(self, context, instance, diff): \"\"\"Applies a diff to","the instance before the end. :param nova.objects.instance.Instance instance: The instance","LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict()","synonyms. Note that the instance ID or name is not","should be running (power_state.RUNNING). If this fails, any partial instance","the current status of an instance, by name (not ID!)","that is necessary for the driver gracefully stop, including ending","terminology) or domain (Xen or libvirt terminology). An instance has","instances, return \"enabled\", if further instances shouldn't be scheduled to",":param bool mode: If `True`, go into maintenance mode. If",":param instance_info: Instance/flavor to calculate overhead for. :returns: Dict of","on the host and allocate storage space this way. A","raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \"\"\"Applies a diff","if it is possible to execute live migration. This runs","part of a periodic task that records the results in","actually proxied and needs to be held across the shutdown","the cumulative idle CPU time (nanoseconds), wait indicates the cumulative","to the virtualization platform. \"\"\" capabilities = { \"has_imagecache\": False,",":returns: a LiveMigrateData object \"\"\" raise NotImplementedError() def get_instance_disk_info(self, instance,","then calls back to the source host to check the","aggregation. On those platforms, this method is unused. Note that","network information :param reboot_type: Either a HARD or SOFT reboot","connector information for the instance for attaching to volumes. Connector","follow, and shared types that support that contract \"\"\" import","def get_num_instances(self): \"\"\"Return the total number of virtual machines. Return","periodic task that records the results in the DB. :param","in for access to auth_token raise NotImplementedError() def get_num_instances(self): \"\"\"Return","or removed from the security group the instance is a","import _, _LE, _LI from nova import utils from nova.virt","a string representing the host ID \"\"\" return instance.get('host') def","of the given flavor. Defaults to zero, drivers should override","context for the resume. :param nova.objects.instance.Instance instance: The suspended instance","the instance where the volume for `old_connection_info` is attached to.","cleanup of old state is skipped. :param block_device_info: Information about",":param list instances: A list of nova.objects.instance.Instance objects that have","\"\"\" return False def register_event_listener(self, callback): \"\"\"Register a callback to","a specified instance. The counter action to this is :func:`volume_snapshot_create`","underlying hypervisor technology. A \"stronger\" version of `pause` is :func:'suspend'.","in for access to auth_token raise NotImplementedError() def finish_revert_migration(self, context,","user processes respectively, idle indicates the cumulative idle CPU time","of DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice or","ability to power on/off an instance. :param instance: nova.objects.instance.Instance \"\"\"","network information :param block_device_info: instance volume block device info :param","be restored from the soft-deleted data. :return: None \"\"\" raise","and re-make this instance. A 'rebuild' effectively purges all existing","that case. :param context: security context :param instance: Instance object","gets detached from the given `instance`. :param dict new_connection_info: The","its completion. To migrate an instance, filtering rules to hypervisors","optional. On some platforms (e.g. XenAPI) performance statistics can be","if not isinstance(event, virtevent.Event): raise ValueError( _(\"Event must be an","the driver. The prime example is to clean the cache","compute driver '%s'\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' %","still available as a database entry. The counter action :func:`restore`","from nova.i18n import _, _LE, _LI from nova import utils","aggregate which should remove the given `host` :param str host:","following keys: - root_device_name: device name of the root disk","depending on the underlying hypervisor technology. A \"stronger\" version of","idle CPU time (nanoseconds), wait indicates the cumulative I/O wait","all the cleanup of old state is skipped. :param block_device_info:","up with currently running VM's on the given host. \"\"\"","for it to shutdown \"\"\" raise NotImplementedError() def power_on(self, context,","to which the diagnostic data should be returned. :return: Has","update_task_state): \"\"\"Snapshots the specified instance. :param context: security context :param","exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM","check_can_live_migrate calls :param context: security context :param dest_check_data: result of","remove_from_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Remove a compute host from","NotImplementedError() def soft_delete(self, instance): \"\"\"Soft delete the specified instance. A","\"\"\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \"\"\"Cleans up any","instance_info: Instance/flavor to calculate overhead for. :returns: Dict of estimated","context :param instance: nova.objects.instance.Instance object \"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self,","instance, bdms, block_device_obj): \"\"\"Get the next device name based on","from the soft-deleted data. :return: None \"\"\" raise NotImplementedError() def","machine that will be making the connection, the name of","instance. Stalling instances can be triggered to dump the crash","instance disk information :param migrate_data: a LiveMigrateData object \"\"\" raise","image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False):","well that is needed for creating the snapshot. \"\"\" raise","def instance_on_disk(self, instance): \"\"\"Checks access of instance files on the","and never escape the connection interface. The platform-specific layer is","return \"enabled\", if further instances shouldn't be scheduled to this","will be making the connection, the name of the iscsi","import block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals':","context in for access to auth_token raise NotImplementedError() def host_power_action(self,","list of nodenames managed by the service. Otherwise, this method","a VirtAPI object as their first object and this must","format of block device info to be passed in to","can be retrieved directly in aggregate form, without Nova having","called since it defines PROJNET, PROJMASK. but libvirt migrates those","if instance files located on shared storage. This runs check","but it is not particularly efficient. Maintainers of the virt","auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data about","in for access to auth_token pass def poll_rebooting_instances(self, timeout, instances):","are longer in the rebooting state than `timeout` seconds. :param","spent by kernel and user processes respectively, idle indicates the","False def quiesce(self, context, instance, image_meta): \"\"\"Quiesce the specified instance","NotImplementedError() def set_host_enabled(self, enabled): \"\"\"Sets the ability of this host","by applicable law or agreed to in writing, software #","Indicates if disks should be destroyed :param migrate_data: implementation specific","of the host again. The counter action for 'unpause' is","instance, and pass them into this layer. This layer is","is skipped. :param block_device_info: Information about block devices to be","while waiting for it to shutdown \"\"\" raise NotImplementedError() def","which returns None. Hypervisors with MAC limits should return a","window. On start, it triggers the migration of all instances","names in the block device mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self,","boot from the network, and manage their own TFTP service.","instances boot from the network, and manage their own TFTP","NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context","LiveMigrateData object \"\"\" pass def post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug",":param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result","migrated. :param str dest: The IP address of the destination","and limitations # under the License. \"\"\" Driver base-classes: (Beginning","block_device_info: Information about the block devices. :param int timeout: The","Defaults to zero, drivers should override if per-instance overhead calculations","cleanup on host after check_instance_shared_storage calls :param context: security context","platforms (e.g. XenAPI) performance statistics can be retrieved directly in","virtualization platform should be in the state that it was","def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \"\"\"Post operation of live","applicable law or agreed to in writing, software # distributed","the supplied ID exists on the host, False otherwise. ..","based on the results from check_can_live_migrate_destination. :param context: security context","the instance. Mapping of MAC addresses to actual networks (or",":return: A list of disk information dicts in JSON format.","NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the currently known host CPU stats.","instance whose disk should be migrated. :param str dest: The","of the file, also base64-encoded. NOTE(russellb) This method is deprecated","resume. :param dict block_device_info: Instance volume block device info. :return:","operation has completed. There may be other data included as","migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a","the specified instance. A soft-deleted instance doesn't allocate any resources","when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true,","def quiesce(self, context, instance, image_meta): \"\"\"Quiesce the specified instance to",":returns: Dictionary describing resources \"\"\" raise NotImplementedError() def pre_live_migration(self, context,","root_device_name, *block_device_lists): \"\"\"Default the missing device names in the block","action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted","of MAC addresses. This is called during spawn_instance by the","instance have? Some hypervisors (such as bare metal) cannot do",":param instance: instance that is live migrating \"\"\" raise NotImplementedError()","\"\"\"Get the current status of an instance, by name (not","handle any bad volumes encountered \"\"\" raise NotImplementedError() def get_console_pool_info(self,","may have to be called since it defines PROJNET, PROJMASK.","any partial instance should be completely cleaned up, and the","networks at source. :param context: security context :param instance: instance","return if not isinstance(event, virtevent.Event): raise ValueError( _(\"Event must be","instance. Mapping of MAC addresses to actual networks (or permitting","metadata to the hypervisor. If the hypervisor has no means","| [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', |","NotImplementedError() def get_rdp_console(self, context, instance): \"\"\"Get connection info for a","event %s\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\"Exception dispatching","their first object and this must be supplied. :param virtapi:","(VIFs) into the given `instance` at instance boot time. The","context in for access to auth_token raise NotImplementedError() def plug_vifs(self,","Dictionary describing resources \"\"\" raise NotImplementedError() def pre_live_migration(self, context, instance,","flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the disk of a","instance to suspend. :return: None \"\"\" raise NotImplementedError() def resume(self,","given host. \"\"\" # TODO(Vek): Need to pass context in","nova.conf from nova.i18n import _, _LE, _LI from nova import","longer than the configured timeout. :return: None \"\"\" # TODO(Vek):","passed in to methods that expect it. \"\"\" return True","device information :param destroy_disks: if true, destroy disks at destination","check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup on host after check_instance_shared_storage calls",":param admin_password: <PASSWORD> to set in instance. :param bdms: block-device-mappings","Returns True if an instance with the supplied ID exists","nova import utils from nova.virt import event as virtevent CONF","be completely cleaned up, and the virtualization platform should be","name is not human-readable or customer-controlled -- it's an internal","context: the context for the finish_revert_migration :param instance: nova.objects.instance.Instance being","swap: An instance of DriverSwapBlockDevice or None - block_device_mapping: a","NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True):","for an instance :param context: security context :param instance: nova.objects.instance.Instance","MAC addresses, which will be supplied to the allocate_for_instance call","overlap to the return value of the older interface :func:`get_diagnostics`","security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice","in the case of qcow2 backed snapshots, this would include","security groups. All security groups and their associated rules live","attached volumes :param bad_volumes_callback: Function to handle any bad volumes","sizes of an instance. :param instance: nova.objects.Instance :param block_device_info: Optional;","\"shutdown\" and \"reboot\". :return: The result of the power action","given flavor. Defaults to zero, drivers should override if per-instance","that is needed for creating the snapshot. \"\"\" raise NotImplementedError()","generic data structure into terms that are specific to the",":return: None \"\"\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif):","host, False otherwise. .. note:: This implementation works for all","gets resized to the given size (in Gigabyte) of `resize_to`.","block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap') or {'device_name':","PROJMASK. but libvirt migrates those value through migrateToURI(), so ,","MAC addresses. This method allows drivers to return a set","on the underlying hypervisor technology. A \"stronger\" version of `pause`","NotImplementedError() def get_console_output(self, context, instance): \"\"\"Get console output for an","block devices that should be detached from the instance. :param","Most hypervisors can use the default implementation which returns None.","the compute manager. Note that the format of the return","triggered. :return: None \"\"\" raise NotImplementedError() def soft_delete(self, instance): \"\"\"Soft","name of the iscsi initiator and the hostname of the","human-readable or customer-controlled -- it's an internal ID chosen by","driver want networks deallocated on reschedule?\"\"\" return False def macs_for_instance(self,","injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy","security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC","files located on shared storage. This runs check on the","VIFs from networks at source. :param context: security context :param","doesn't use CPU cycles of the host anymore. The state","instance): \"\"\"Check if instance files located on shared storage. This","instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param dest:","instance network information :param block_device_info: Information about block devices to","MAC->network mappings that will match the actual configuration. Most hypervisors","volume. This is the same data format returned by Cinder's","a LiveMigrateData object \"\"\" raise NotImplementedError() def post_live_migration(self, context, instance,","methods here take an instance of nova.compute.service.Instance. This is the","instance resource usage. :returns: dict of nova uuid => dict","is called during spawn_instance by the compute manager. Note that","except ImportError: LOG.exception(_LE(\"Unable to load the virtualization driver\")) sys.exit(1) def","object which has the information about the interface to detach.","instances can be triggered to dump the crash data. How","\"\"\"This method is called after a change to security groups.","is deprecated and will be removed once it can be","higher up the stack. Most virtualization platforms will also have","if maintenance mode got left. :rtype: str \"\"\" raise NotImplementedError()","prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be called since","gets unpaused and will use CPU cycles of the host","\"\"\"Retrieve information about actual disk sizes of an instance. :param","manages a particular node.\"\"\" if nodename in self.get_available_nodes(): return True","NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare","unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the image of","checked, the validate values are defined at disk API module.","def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post operation of","more specialized subclasses. \"\"\" from nova.virt import block_device as virt_block_device","to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application of IPTables","network information :param nova.objects.ImageMeta image_meta: The metadata of the image",":param network_info: instance network information :param reboot_type: Either a HARD","arbitrary VLANs etc, but regular switches require pre-configured MAC->network mappings","NotImplementedError() def suspend(self, context, instance): \"\"\"Suspend the specified instance. A","was migrated :block_device_info: instance block device information :param migrate_data: a","is the base64-encoded path to which the file is to","next device name based on the block device mapping. :param","security context :param instance: Instance object as returned by DB",":param nova.context.RequestContext context: The context for the suspend. :param nova.objects.instance.Instance","object \"\"\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information","volumes. :return: json strings with below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240',","\"\"\" import sys from oslo_log import log as logging from","driver implementation if not set. :returns: The chosen device name.","technology, the guest has the same state as before the",":param int retry_interval: How often to signal guest while waiting","swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info","instance: nova.objects.Instance :param block_device_info: Optional; Can be used to filter","time in seconds to wait for the guest OS to","def inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes a file on the",":return: None \"\"\" raise NotImplementedError() def soft_delete(self, instance): \"\"\"Soft delete","cache images for instances on disk. This method is an","is migrated. :param dest: destination host :param post_method: post operation","get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage counters for each interface on","nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of","a set of MAC addresses that the instance is to","b64_path, b64_contents): \"\"\"Writes a file on the specified instance. The","event to the compute manager. Invokes the event callback registered","\"\"\" raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \"\"\"Power","all given 'instances'. Reboots the given `instances` which are longer","block_migration: if true, prepare for block migration :param disk_over_commit: if","it defines PROJNET, PROJMASK. but libvirt migrates those value through","deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver want networks deallocated on reschedule?\"\"\"","volume_id: Attached volume associated with the snapshot :param uuid snapshot_id:","doesn't use this interface, which seems odd # since the","disk should be migrated. :param str dest: The IP address","instances known to the virtualization layer, as a list. \"\"\"","nodenames managed by the service. Otherwise, this method should return","the operation cannot complete. \"\"\" # TODO(Vek): Need to pass","must be preserved on rebuild \"\"\" raise NotImplementedError() def spawn(self,","probably a good idea to log a warning in that","raise NotImplementedError() def plug_vifs(self, instance, network_info): \"\"\"Plug virtual interfaces (VIFs)","being merged, and the file being merged into (if appropriate).","complete :param instance: Instance being live migrated \"\"\" raise NotImplementedError()","aggregate which should add the given `host` :param str host:","use CPU cycles of the host again. The counter action","filtering rules to hypervisors and firewalls are inevitable on destination","in rebuild for HA implementation and required for validation of","(e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means 'these",":param destroy_disks: Indicates if disks should be destroyed :param migrate_data:","a callback to receive events. Register a callback to receive","NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None):","nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance: The instance","the instance. :param rescue_password: <PASSWORD> for rescue. \"\"\" raise NotImplementedError()","get migrated. :param nova.network.model.NetworkInfo network_info: The network information of the",":param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to pass context","NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application of IPTables rules.\"\"\" pass def","context, instance): \"\"\"Get connection info for a serial console. :param","host ID \"\"\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a","given host.- \"\"\" raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the IP","NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices,","instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization overhead",":returns an instance of console.type.ConsoleRDP \"\"\" raise NotImplementedError() def get_serial_console(self,",":param resize_instance: True if the instance is being resized, False","raise NotImplementedError() def set_admin_password(self, instance, new_pass): \"\"\"Set the root password","\"\"\"Force live migration to complete :param instance: Instance being live","access to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff):","str mountpoint: The mountpoint in the instance where the volume","by this driver :param fs_type: the file system type to","= driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return","booted. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def rescue(self, context,","case of qcow2 backed snapshots, this would include the file","destroy_vifs=True): \"\"\"Cleanup the instance resources . Instance should have been","(hypervisor-dependent) \"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \"\"\"Do required","`instance`. :param dict block_device_info: Information about the block devices. :param","so that every hypervisor # need to define their supported","Info pertaining to attached volumes :param bad_volumes_callback: Function to handle","which the caller want to get resources from a driver",":return: None \"\"\" # TODO(Vek): Need to pass context in","call by the compute manager, and it is up to","called when an instance gets added to or removed from",":param compute_driver: a compute driver name to override the config","inject into instance. :param admin_password: Administrator password to set in","instance of console.type.ConsoleRDP \"\"\" raise NotImplementedError() def get_serial_console(self, context, instance):",":rtype: dict \"\"\" # TODO(Vek): Need to pass context in","security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination","raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \"\"\"Finish","context: security context :param data: result of check_instance_shared_storage_local \"\"\" raise","migrated :block_device_info: instance block device information :param migrate_data: a LiveMigrateData","VM, detaches all block devices, then spins up the new","as follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname }","will be an instance of the nova.virt.event.Event class. \"\"\" self._compute_event_callback","constraints', a set means 'these and only these MAC addresses'.","one. :param str mountpoint: The mountpoint in the instance where","seconds to wait for the guest OS to shutdown. :param","a database entry. The counter action :func:`restore` uses the database","file being merged, and the file being merged into (if","instance further up the stack. This is unfortunately also called","is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance","def get_all_bw_counters(self, instances): \"\"\"Return bandwidth usage counters for each interface","for a MKS console. :param context: security context :param instance:","Attached volume associated with the snapshot :param uuid snapshot_id: The","information :param block_device_info: Information about block devices that should be","The instance which gets a network interface removed. :param nova.network.model.NetworkInfo","reschedule?\"\"\" return False def macs_for_instance(self, instance): \"\"\"What MAC addresses must","suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information","of the image of the instance. :param resize_instance: True if","importutils import nova.conf from nova.i18n import _, _LE, _LI from","base64-encoded. NOTE(russellb) This method is deprecated and will be removed","instance: The instance whose volume gets replaced by another one.","accept new instances. If it is `False`, the host won't","source host to check the results. :param context: security context","need to - e.g. for optimisations, or when the 'VM'","host. :param context: security context :param instance: instance object that","image_meta): \"\"\"Unquiesce the specified instance after snapshots. If the specified",":return: None, or a set of DHCP options, eg: |","whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The network information","nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to pass context in for","triggers the migration of all instances to other hosts. Consider","to override this method with something more efficient. \"\"\" try:","addresses must this instance have? Some hypervisors (such as bare","virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args,","\"\"\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \"\"\"Use hotplug","override this method with something more efficient. \"\"\" try: return","instance block device information :param network_info: instance network information :param","be removed once it can be removed from nova.compute.manager. \"\"\"","for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application","about the given instance. :param nova.objects.instance.Instance instance: The instance to","crash dump should be triggered. :return: None \"\"\" raise NotImplementedError()","currently running VM's on the given host. \"\"\" # TODO(Vek):",":param instance: The instance to lookup Returns True if an","disk_over_commit: if true, allow disk over commit :returns: a LiveMigrateData","DB. :param nodename: node which the caller want to get","instance is being resized, False otherwise :param block_device_info: instance volume",":func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance:","plug_vifs(self, instance, network_info): \"\"\"Plug virtual interfaces (VIFs) into the given","is optional. \"\"\" pass def get_info(self, instance): \"\"\"Get the current","on the specified instance. The first parameter is an instance","volume attached to the given `instance`. :param dict old_connection_info: The","recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration:","'tftp-server', | 'opt_value': '192.168.127.12'} | ] \"\"\" return None def","the file system type to be checked, the validate values","limitations # under the License. \"\"\" Driver base-classes: (Beginning of)","def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def","and implement this function at their # virt layer. return","The counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext context:","once it can be removed from nova.compute.manager. \"\"\" # TODO(Vek):",":param uuid volume_id: Volume to be snapshotted :param create_info: The","to hypervisors and firewalls are inevitable on destination host. (","instance network information :param block_device_info: Information about block devices that","instance. A paused instance doesn't use CPU cycles of the","stay internal to the platform-specific layer, and never escape the","network_info): # NOTE(markus_z): 2015-08-18 # The compute manager doesn't use","instance of nova.compute.service.Instance, and so the instance is being specified","allocate any resources anymore, but is still available as a","powered on, False otherwise \"\"\" raise NotImplementedError() def pause(self, instance):","number of bytes transferred, and errs is the number of","to inject into instance. :param admin_password: Administrator password to set","subclasses. \"\"\" from nova.virt import block_device as virt_block_device block_device_info =","in the block device mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self, instance,","details, depends on the configuration of it. :param nova.objects.instance.Instance instance:","the snapshot operation has completed. There may be other data","idea to log a warning in that case. :param context:","by Cinder's initialize_connection() API call. In the case of doing","in for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance):","migrate_data: implementation specific params \"\"\" raise NotImplementedError() def reboot(self, context,","to the virtualization layer, as a list. \"\"\" raise NotImplementedError()","due to a full pipeline. All counters are long integers.","instance ID. \"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the","overlap to the return value of the newer interface :func:`get_instance_diagnostics`","uuid volume_id: Volume to be snapshotted :param create_info: The data","context in for access to auth_token raise NotImplementedError() def finish_revert_migration(self,","resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a resize/migration. :param context: the context","that volume is requesting a device name :param bdms: a","Version 2.0 (the \"License\"); you may # not use this","and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or","NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh security group rules Gets called","instance): \"\"\"reset networking for specified instance.\"\"\" # TODO(Vek): Need to","import utils from nova.virt import event as virtevent CONF =","A free-form thingy... :return: None \"\"\" raise NotImplementedError() def undo_aggregate_operation(self,","maintenance mode should be changed. :param bool mode: If `True`,","context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param","rules now.\"\"\" pass def unfilter_instance(self, instance, network_info): \"\"\"Stop filtering instance.\"\"\"","connection info for a MKS console. :param context: security context","block_device_info: instance block device information :param destroy_disks: if true, destroy","for this connection gets detached from the given `instance`. :param","access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh security","pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Add a compute","to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up filtering","instance, new_pass): \"\"\"Set the root password on the specified instance.","format of a dict containing the following keys: - root_device_name:","NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \"\"\"Post operation of","block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make this instance. A 'rebuild' effectively","<PASSWORD> \"\"\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes","context, instance, volume_id, create_info): \"\"\"Snapshots volumes attached to a specified","it is `False`, the host won't accept new instances. :return:","to be able to complete the snapshot. For example, in","The second parameter is the base64-encoded path to which the","compute driver module specified by the compute_driver configuration option or,","host anymore. The state of the instance could be persisted","The counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance:","dispatch the event. This must only be invoked from a","\"\"\" if not compute_driver: compute_driver = CONF.compute_driver if not compute_driver:","migration at destination host. :param context: security context :param instance:","volume associated with the snapshot :param uuid snapshot_id: The snapshot","not completed. :param instance: nova.objects.instance.Instance object \"\"\" # TODO(Vek): Need","anything that is necessary for the driver to function, including","over commit :returns: a LiveMigrateData object (hypervisor-dependent) \"\"\" raise NotImplementedError()","metadata to the instance, then this method should not be","output for an instance :param context: security context :param instance:","devices which are actually volumes. :return: json strings with below","\"\"\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info,","instance. :param resize_instance: True if the instance is being resized,","{'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name']","to auth_token raise NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts down","be supplied. :param virtapi: a VirtAPI instance :param compute_driver: a","counters associated with the given disk_id on the given instance.","destroy_disks=True, migrate_data=None): \"\"\"Clean up destination node after a failed live","to another host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance","up to that call to ensure that all assigned network","# NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError() def","On those platforms, this method is unused. Note that this","None, or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None","The virtualization platform should ensure that the reboot action has","=> dict of usage info \"\"\" return {} def instance_on_disk(self,","instance to another host. :param context: security context :param instance:","of console.type.ConsoleVNC \"\"\" raise NotImplementedError() def get_spice_console(self, context, instance): \"\"\"Get","instance based on that. :param nova.objects.instance.Instance instance: The instance to","def get_per_instance_usage(self): \"\"\"Get information about instance resource usage. :returns: dict","def inject_network_info(self, instance, nw_info): \"\"\"inject network info for specified instance.\"\"\"","soft-deleted data. :return: None \"\"\" raise NotImplementedError() def get_available_resource(self, nodename):","instances running the specified security group. An error should be","aggregate: The aggregate which should remove the given `host` :param","information about the interface to attach. :raise nova.exception.NovaException: If the","calls :param context: security context :param data: result of check_instance_shared_storage_local","TODO(Vek): Need to pass context in for access to auth_token","to this host, return \"disabled\". :rtype: str \"\"\" # TODO(Vek):","by DB layer. :param network_info: instance network information :param block_device_info:","detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function","True @property def need_legacy_block_device_info(self): \"\"\"Tell the caller if the driver","the following keys: - root_device_name: device name of the root",":return: The result of the power action :rtype: : str","to complete :param instance: Instance being live migrated \"\"\" raise","nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information :param recreate:","instance of DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty)","the event. This must only be invoked from a green","are specific to the virtualization platform. \"\"\" capabilities = {","should be changed. :param bool mode: If `True`, go into","if the instance is being resized, False otherwise :param block_device_info:","for access to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info,","These are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where","which are actually volumes. :return: json strings with below format::","- swap: An instance of DriverSwapBlockDevice or None - block_device_mapping:","running instance in multiple phases, turning off the instance before","is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs","implementation which returns None. This is called during spawn_instance by","to the volume. This is the same data format returned","overhead values. \"\"\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self):","'instances' (Amazon EC2 and internal Nova terminology), by which we","lookup Returns True if an instance with the supplied ID","on all given 'instances'. Reboots the given `instances` which are","some platforms (e.g. XenAPI) performance statistics can be retrieved directly","False, \"supports_device_tagging\": False, } def __init__(self, virtapi): self.virtapi = virtapi","disk of a running instance in multiple phases, turning off","virtualization platforms will also have their own identity schemes, to","kern and user indicate the cumulative CPU time (nanoseconds) spent","| 'frequency': freq}, where kern and user indicate the cumulative","multiple phases, turning off the instance before the end. :param","instance): \"\"\"Get connection info for a spice console. :param context:","'VM' is actually proxied and needs to be held across","def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information about actual disk sizes","known to the virtualization layer, as a list. \"\"\" #",":param detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for","booting up; freq indicates the current CPU frequency (MHz). All","is needed for creating the snapshot. \"\"\" raise NotImplementedError() def","This requires passing the appropriate options out to the DHCP","\"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\": False, } def __init__(self, virtapi):","object as their first object and this must be supplied.","finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True):","a default root device name for the driver. :param nova.objects.instance.Instance","use this interface, which seems odd # since the manager","data): \"\"\"Do cleanup on host after check_instance_shared_storage calls :param context:","that cache which isn't directly related to other calls into","NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the","want to get resources from a driver that manages only","is the total number of bytes transferred, and errs is",":param bdms: block-device-mappings to use for rebuild :param detach_block_devices: function","the datastore, and calling this method should apply the updated","setting-up filtering rules operations are not completed. :param instance: nova.objects.instance.Instance","up the host. :param str action: The action the host","rule. \"\"\" raise NotImplementedError() def reset_network(self, instance): \"\"\"reset networking for","ignore this :returns: Dictionary describing resources \"\"\" raise NotImplementedError() def","get_info(self, instance): \"\"\"Get the current status of an instance, by","NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean","| {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \"\"\" return","object which has the information about the interface to attach.","can be triggered to dump the crash data. How the","image_id: Reference to a pre-created image that will hold the","specified instance. :param instance: nova.objects.instance.Instance :param timeout: time to wait","instance after snapshots. If the specified instance doesn't support quiescing,","or, if supplied, the driver name supplied as an argument.","\"\"\" raise NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force live migration to","\"\"\"Return bandwidth usage counters for each interface on each running","is still available as a database entry. The counter action","manage_image_cache(self, context, all_instances): \"\"\"Manage the driver's local image cache. Some","device info to be passed in to methods that expect","virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info","of the root disk - ephemerals: a (potentially empty) list","about the requested block device. device_name does not need to","counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The",":param nova.objects.instance.Instance instance: The instance which gets VIFs plugged. :param","migrateToURI(), so , no need to be called. Don't use","boot. :rtype: str \"\"\" # TODO(Vek): Need to pass context","to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices:","`aggregate`. :param dict kwargs: A free-form thingy... :return: None \"\"\"","post_live_migration(self, context, instance, block_device_info, migrate_data=None): \"\"\"Post operation of live migration","devices, then spins up the new VM afterwards. It may","\"\"\" raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password,","and it is up to that call to ensure that","instance. :param network_info: instance network information :param block_device_info: Information about","access to auth_token raise NotImplementedError() def get_num_instances(self): \"\"\"Return the total","migrate_data=None): \"\"\"Post operation of live migration at source host. :param","combination with :func:`set_host_enabled`. :param str host: The name of the","NotImplementedError() def is_supported_fs_format(self, fs_type): \"\"\"Check whether the file format is","partition must be preserved on rebuild \"\"\" raise NotImplementedError() def","from disk, False otherwise. \"\"\" return True @property def need_legacy_block_device_info(self):",". Instance should have been destroyed from the Hypervisor before","of the host whose maintenance mode should be changed. :param","migration. :param instance: instance that is live migrating \"\"\" raise","structure into terms that are specific to the virtualization platform.","a serial console. :param context: security context :param instance: nova.objects.instance.Instance","wr_req, wr_bytes, errs], where rd indicates read, wr indicates write,","live migration at source host. :param context: security context :instance:","raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the IP address of the","example if networking failed), this function should still succeed. It's","bytes transferred, and errs is the number of requests held","base64-encoded path to which the file is to be written","an event to the compute manager. Invokes the event callback","instance. :param destroy_disks: Indicates if disks should be destroyed :param","unused. Note that this function takes an instance ID. \"\"\"","instance): \"\"\"Get the current status of an instance, by name","def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach","'instance ID' and 'instance name' are synonyms. Note that the","def estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization overhead required to build","are actually volumes. :return: json strings with below format:: \"[{'path':'disk',","green thread. \"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event))","with openflow switches, fixed MAC addresses can still be virtualized","to define their supported file system # type and implement","As far as this layer is concerned, 'instance ID' and","freeform virtualization of MAC addresses. This method allows drivers to","results in the DB. :param nodename: node which the caller","usage info for volumes attached to vms on a given","emit_event(self, event): \"\"\"Dispatches an event to the compute manager. Invokes",":raise nova.exception.NovaException: If the attach fails. :return: None \"\"\" raise","instance: nova.objects.instance.Instance \"\"\" pass def inject_network_info(self, instance, nw_info): \"\"\"inject network","object that is migrated. :param dest: destination host :param post_method:","be snapshotted :param create_info: The data needed for nova to","**kwargs): driver_registry = dict() for driver_str in named_driver_config: driver_type, _sep,","as instance.name. The second parameter is the base64-encoded path to","unfortunately also called a 'name' elsewhere. As far as this","where the crash dump should be triggered. :return: None \"\"\"","zero, drivers should override if per-instance overhead calculations are desired.","to remove from the given `aggregate`. :param dict kwargs: A","network_info, disk_info, migrate_data=None): \"\"\"Prepare an instance for live migration :param","store details regarding an instance, and pass them into this","instance is not found (for example if networking failed), this","root device for. :param nova.objects.ImageMeta image_meta: The metadata of the","to the hypervisor. If the hypervisor has no means of","by name (not ID!) :param instance: nova.objects.instance.Instance object Returns a","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","live in the datastore, and calling this method should apply","def resume(self, context, instance, network_info, block_device_info=None): \"\"\"resume the specified suspended","callback registered by the compute manager to dispatch the event.","[rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates read, wr","unpaused. :return: None \"\"\" # TODO(Vek): Need to pass context","quiesce by other errors (e.g. agent timeout), NovaException is raised.","None \"\"\" raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None):","This method is optional. On some platforms (e.g. XenAPI) performance","internal to the platform-specific layer, and never escape the connection",":param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The","device information :param migrate_data: a LiveMigrateData object \"\"\" pass def","pass def get_info(self, instance): \"\"\"Get the current status of an","def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default the missing device names",":rtype: str \"\"\" # TODO(Vek): Need to pass context in","method is called when nova-compute launches, and as part of","compute driver name to override the config opt :returns: a","specific params \"\"\" raise NotImplementedError() def cleanup(self, context, instance, network_info,","the host anymore. The state of the instance could be","NovaException is raised. :param context: request context :param instance: nova.objects.instance.Instance","context in for access to auth_token raise NotImplementedError() def cleanup_host(self,","action :rtype: : str \"\"\" raise NotImplementedError() def host_maintenance_mode(self, host,","layer. return False def quiesce(self, context, instance, image_meta): \"\"\"Quiesce the","whose disk should be migrated. :param str dest: The IP","running the specified security group. An error should be raised","the guest OS to shutdown. :param int retry_interval: How often","NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \"\"\"Check if instance files located","object \"\"\" pass def post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug VIFs","raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce the specified","snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is","block_migration=False, disk_over_commit=False): \"\"\"Check if it is possible to execute live","name based on the block device mapping. :param instance: nova.objects.instance.Instance","bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping","instance \"\"\" if not compute_driver: compute_driver = CONF.compute_driver if not","running VM. :param instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def get_all_volume_usage(self,","is the data structure used by nova.compute to store details","than the old volume, it gets resized to the given","rules can be set faster). Concretely, the below method must","NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \"\"\"Default the missing device","migration: the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param","for a serial console. :param context: security context :param instance:","storage. This runs check on the destination host, and then","under the Apache License, Version 2.0 (the \"License\"); you may","block_device_info=None): \"\"\"resume guest state when a host is booted. :param",":param instance: Instance object as returned by DB layer. :param","This method is deprecated and will be removed once it","can succeed, based on the results from check_can_live_migrate_destination. :param context:","resized to the given size (in Gigabyte) of `resize_to`. :return:","pre-created image that will hold the snapshot. \"\"\" raise NotImplementedError()","good idea to log a warning in that case. :param","nova.exception.NovaException: If the detach fails. :return: None \"\"\" raise NotImplementedError()","True # Refresh and check again. return nodename in self.get_available_nodes(refresh=True)","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","for this connection gets attached to the given 'instance'. :param","context: security context :param instance: nova.objects.instance.Instance object :param block_device_info: instance","the instance. \"\"\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta):","needed to be able to complete the snapshot. For example,","image_meta: The metadata of the image of the instance. \"\"\"","'rebuild' effectively purges all existing data from the system and","for the rescue. :param nova.objects.instance.Instance instance: The instance being rescued.","network information of the given `instance`. :param dict block_device_info: Information",":param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def get_vnc_console(self, context, instance):","check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False,","required by applicable law or agreed to in writing, software","e.g. for optimisations, or when the 'VM' is actually proxied","to auth_token raise NotImplementedError() def get_console_output(self, context, instance): \"\"\"Get console","to inject into instance. :param admin_password: <PASSWORD> to set in","estimate_instance_overhead(self, instance_info): \"\"\"Estimate the virtualization overhead required to build an","resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for the resume.",":return: None, or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])).","instance files located on shared storage. :param context: security context","domain (Xen or libvirt terminology). An instance has an ID,","check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data:","loses a rule. \"\"\" raise NotImplementedError() def reset_network(self, instance): \"\"\"reset","action: The action the host should perform. The valid actions","agreed to in writing, software # distributed under the License","the Hypervisor before calling this method. :param context: security context","gets replaced by another one. :param str mountpoint: The mountpoint","deprecated and will be removed once it can be removed","values are long integers. \"\"\" raise NotImplementedError() def block_stats(self, instance,","this driver :param fs_type: the file system type to be","data. How the guest OS reacts in details, depends on","the context for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized","of the destination host. :param nova.objects.flavor.Flavor flavor: The flavor of","instance_exists(self, instance): \"\"\"Checks existence of an instance on the host.","access to auth_token raise NotImplementedError() def suspend(self, context, instance): \"\"\"Suspend",":param instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms):","to have. allocate_for_instance will take this into consideration when provisioning","NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort an in-progress live migration. :param","event, 'ex': ex}) def delete_instance_files(self, instance): \"\"\"Delete any lingering instance","if per-instance overhead calculations are desired. :param instance_info: Instance/flavor to","instance network information :param disk_info: instance disk information :param migrate_data:","service. This method is for multi compute-nodes support. If a",":param str host: The name of the host whose maintenance","in for access to auth_token raise NotImplementedError() def get_console_output(self, context,","in for access to auth_token raise NotImplementedError() def unpause(self, instance):","Most virtualization platforms will also have their own identity schemes,",":param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the given","context :param instance: instance object that was being migrated :param","driver gracefully stop, including ending remote sessions. This is optional.","set in instance. :param bdms: block-device-mappings to use for rebuild","This checks if the live migration can succeed, based on","\"\"\"Abort an in-progress live migration. :param instance: instance that is","on that. :param nova.objects.instance.Instance instance: The instance to soft-delete. :return:","The data needed for nova to be able to attach","the specified instance. After this is called successfully, the instance's","the given flavor. Defaults to zero, drivers should override if","to lookup Returns True if files of an instance with","to be written on the instance; the third is the","The counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context:","to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh security group","the file, also base64-encoded. NOTE(russellb) This method is deprecated and","The counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance:","instance. :param injected_files: User files to inject into instance. :param","of the dom0 \"\"\" # TODO(Vek): Need to pass context","publish changes to the instance's metadata to the hypervisor. If","with all info about the requested block device. device_name does","the identifier chosen by Nova to represent the instance further","the aggregation. On those platforms, this method is unused. Note","block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap')","runs check on the destination host, and then calls back","migrated/resized :param network_info: instance network information :param block_device_info: instance volume","nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \"\"\" raise NotImplementedError() def","\"\"\" raise NotImplementedError() def live_migration_abort(self, instance): \"\"\"Abort an in-progress live","cache. Some drivers chose to cache images for instances on","which should add the given `host` :param str host: The","hostname of the machine as follows:: { 'ip': ip, 'initiator':","sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver) try: driver = importutils.import_object(","instance that is live migrating \"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self,","raise NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh security group rules Gets","out devices which are actually volumes. :return: json strings with","instance): \"\"\"Return diagnostics data about the given instance. :param nova.objects.instance.Instance","in aggregate form, without Nova having to do the aggregation.","on the virtualization platform. Once this successfully completes, the instance","configuration of it. :param nova.objects.instance.Instance instance: The instance where the","a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force","is_supported_fs_format(self, fs_type): \"\"\"Check whether the file format is supported by","the instance. :param destroy_disks: Indicates if disks should be destroyed","the third is the contents of the file, also base64-encoded.","an ID, which is the identifier chosen by Nova to","disk files \"\"\" return False def register_event_listener(self, callback): \"\"\"Register a","False otherwise. \"\"\" return True @property def need_legacy_block_device_info(self): \"\"\"Tell the","self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\"Exception dispatching event %(event)s: %(ex)s\"),","to calculate overhead for. :returns: Dict of estimated overhead values.","a reboot on all given 'instances'. Reboots the given `instances`","nova.virt import block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name,","action has completed successfully even in cases in which the","- ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances -","nodenames of all nodes managed by the compute service. This","an optional driver method which is used to publish changes","\"\"\" capabilities = { \"has_imagecache\": False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False,","a LiveMigrateData object \"\"\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None):","context in for access to auth_token raise NotImplementedError() def change_instance_metadata(self,","for access to auth_token pass def poll_rebooting_instances(self, timeout, instances): \"\"\"Perform","\"\"\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek):","nova.compute to store details regarding an instance, and pass them","particularly efficient. Maintainers of the virt drivers are encouraged to","operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any","information :param reboot_type: Either a HARD or SOFT reboot :param","to clean the cache and remove images which are no","new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace the volume attached to the","context: security context :param instance: nova.objects.instance.Instance \"\"\" pass def finish_migration(self,",":param block_device_info: Info pertaining to attached volumes :param bad_volumes_callback: Function","about the VIFs to unplug. :return: None \"\"\" raise NotImplementedError()","info \"\"\" return {} def instance_on_disk(self, instance): \"\"\"Checks access of","\"\"\"Tell the caller if the driver requires legacy block device","InstanceQuiesceNotSupported is raised. When it fails to quiesce by other","suspended instance. The suspended instance gets resumed and will use","devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach","to soft-delete. :return: None \"\"\" raise NotImplementedError() def restore(self, instance):","and waiting for its completion. To migrate an instance, filtering","stop, including ending remote sessions. This is optional. \"\"\" pass","network interface removed. :param nova.network.model.NetworkInfo vif: The object which has","# # Licensed under the Apache License, Version 2.0 (the","\"\"\" self._compute_event_callback = callback def emit_event(self, event): \"\"\"Dispatches an event",":param nova.network.model.NetworkInfo vif: The object which has the information about","connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \"\"\"Attach the disk to","def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create","destination host. :param nova.objects.flavor.Flavor flavor: The flavor of the instance","will be invoked with a single parameter, which will be","instance doesn't allocate any resources anymore, but is still available",":param instance: instance object that was being migrated :param network_info:","Concretely, the below method must be called. - setup_basic_filtering (for","to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \"\"\"Return diagnostics data","object \"\"\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None):","number of requests held up due to a full pipeline.","seconds. :param int timeout: The timeout (in seconds) for considering","total number of virtual machines. Return the number of virtual","works for all drivers, but it is not particularly efficient.","example, in the case of qcow2 backed snapshots, this would","the contents of the file, also base64-encoded. NOTE(russellb) This method","interface. The platform-specific layer is responsible for keeping track of","raise NotImplementedError() def cleanup_host(self, host): \"\"\"Clean up anything that is","For example, in the case of qcow2 backed snapshots, this","which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which","volume for this connection gets detached from the given `instance`.","devices that should be detached from the instance. :param destroy_disks:","raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 #","the service. Otherwise, this method should return [hypervisor_hostname]. \"\"\" raise","by the compute manager to dispatch the event. This must","block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list)","it is up to that call to ensure that all","before this call began. :param context: security context :param instance:",":param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def trigger_crash_dump(self, instance): \"\"\"Trigger","If the instance is not found (for example if networking","we expect the legacy format of block device info to","since the manager should be the controlling thing here. \"\"\"Unplug","platform-specific layer is responsible for keeping track of which instance","'ex': ex}) def delete_instance_files(self, instance): \"\"\"Delete any lingering instance files","virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] =","def register_event_listener(self, callback): \"\"\"Register a callback to receive events. Register","the attach fails. :return: None \"\"\" raise NotImplementedError() def detach_interface(self,","mode got left. :rtype: str \"\"\" raise NotImplementedError() def set_host_enabled(self,","device information :param network_info: instance network information :param disk_info: instance","Licensed under the Apache License, Version 2.0 (the \"License\"); you","a list of nodenames managed by the service. Otherwise, this","to wait for the guest OS to shutdown. :param int","\"\"\"Completes a resize/migration. :param context: the context for the migration/resize","completes, the instance should be running (power_state.RUNNING). If this fails,","with the given disk_id on the given instance. These are","\"\"\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False):","instance: The instance to get the root device for. :param","nova.virt.event.Event class. \"\"\" self._compute_event_callback = callback def emit_event(self, event): \"\"\"Dispatches","same state as before the 'pause'. :param nova.objects.instance.Instance instance: The","being migrated/resized :param network_info: instance network information :param block_device_info: instance","driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device","note:: Used in rebuild for HA implementation and required for","\"\"\" pass def post_live_migration_at_source(self, context, instance, network_info): \"\"\"Unplug VIFs from","for access to auth_token raise NotImplementedError() def cleanup_host(self, host): \"\"\"Clean","retry_interval=0): \"\"\"Power off the specified instance. :param instance: nova.objects.instance.Instance :param","check on the destination host, and then calls back to",":func:`restore` uses the database entry to create a new instance","new instance. :param nova.objects.ImageMeta image_meta: The metadata of the image","an instance. :param instance: nova.objects.instance.Instance :returns: True if the instance","The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance","def host_power_action(self, action): \"\"\"Reboots, shuts down or powers up the","instance, diff): \"\"\"Applies a diff to the instance metadata. This","\"\"\" raise NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return diagnostics data about","interrupted snapshot. :param context: security context :param instance: nova.objects.instance.Instance \"\"\"","version of `pause` is :func:'suspend'. The counter action for `pause`","the total number of virtual machines. Return the number of","raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \"\"\"Writes a file","is necessary for the driver to function, including catching up","def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \"\"\"Cleanup","context, instance, diff): \"\"\"Applies a diff to the instance metadata.","- block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or any","a diff to the instance metadata. This is an optional",":return: json strings with below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file',","efficient. \"\"\" return len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks existence of","memory of the host anymore. The state of the instance","\"\"\"Create a new instance/VM/domain on the virtualization platform. Once this","def get_spice_console(self, context, instance): \"\"\"Get connection info for a spice","\"\"\"Retrieve resource information. This method is called when nova-compute launches,","files \"\"\" return False def register_event_listener(self, callback): \"\"\"Register a callback","instance network information :param block_device_info: instance volume block device info","An instance has an ID, which is the identifier chosen","all block devices, then spins up the new VM afterwards.","on this host. :return: A text which contains the uptime","to filter out devices which are actually volumes. :return: json",":param instance: nova.objects.instance.Instance \"\"\" pass def inject_network_info(self, instance, nw_info): \"\"\"inject","stack. This is unfortunately also called a 'name' elsewhere. As","and 'instance name' are synonyms. Note that the instance ID","compute manager to dispatch the event. This must only be","OS to shutdown. :param int retry_interval: How often to signal","by the compute_driver configuration option or, if supplied, the driver","data needed for nova to be able to attach to","hypervisor. If the hypervisor has no means of publishing the",":param network_info: instance network information :param disk_info: instance disk information","+ spin up steps. :param context: security context :param instance:","image that will hold the snapshot. \"\"\" raise NotImplementedError() def","driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def","status of an instance, by name (not ID!) :param instance:","NotImplementedError() def unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce the specified instance","str host: The name of the host whose maintenance mode","# virt layer. return False def quiesce(self, context, instance, image_meta):","the host. :param str action: The action the host should","are inevitable on destination host. ( Waiting only for filtering","to function, including catching up with currently running VM's on","the root password on the specified instance. :param instance: nova.objects.instance.Instance","If `False`, leave the maintenance mode. :return: \"on_maintenance\" if switched","post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when","specified as instance.name. The second parameter is the base64-encoded path","context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \"\"\"","the number of virtual machines that the hypervisor knows about.","instance: The instance that has the volume attached. :param uuid",":returns: a string representing the host ID \"\"\" return instance.get('host')","delete the specified instance. A soft-deleted instance doesn't allocate any","the default implementation which returns None. This is called during","driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] =","of check_instance_shared_storage_local \"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do","in the instance where the volume for `old_connection_info` is attached","in writing, software # distributed under the License is distributed","requests held up due to a full pipeline. All counters","fs_type: the file system type to be checked, the validate","to return a set of MAC addresses that the instance","console.type.ConsoleMKS \"\"\" raise NotImplementedError() def get_diagnostics(self, instance): \"\"\"Return diagnostics data","NotImplementedError() def get_spice_console(self, context, instance): \"\"\"Get connection info for a","triggered to dump the crash data. How the guest OS","instance: The instance that has the volume attached :param uuid","complete the snapshot. For example, in the case of qcow2","for attaching to volumes. Connector information is a dictionary representing","instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred disk","block devices. :param int timeout: The time in seconds to","overhead for. :returns: Dict of estimated overhead values. \"\"\" return","as before the 'pause'. :param nova.objects.instance.Instance instance: The instance which","device info. :return: None \"\"\" raise NotImplementedError() def resume_state_on_host_boot(self, context,","\"\"\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \"\"\"resume","it was before this call began. :param context: security context",":param instance: Instance being live migrated \"\"\" raise NotImplementedError() def","True if an instance with the supplied ID exists on","block_device_info: Instance volume block device info. :return: None \"\"\" raise","filtering rules and waiting for its completion. To migrate an","in instance. :param bdms: block-device-mappings to use for rebuild :param","information about the VIFs to plug. :return: None \"\"\" #","are synonyms. Note that the instance ID or name is","be overridden by hypervisors that need to - e.g. for","reacts in details, depends on the configuration of it. :param","for this method since migration should not be started when","counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which","this host. :return: A text which contains the uptime of","\"\"\"Remove a compute host from an aggregate. The counter action","the compute manager. Invokes the event callback registered by the","flavor of the instance whose disk get migrated. :param nova.network.model.NetworkInfo","\"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \"\"\"Check if instance","apply the updated rules to instances running the specified security",":param nova.objects.instance.Instance instance: The instance that has the volume attached.","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised.","file is to be written on the instance; the third","file on the specified instance. The first parameter is an","context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the","snapshot. For example, in the case of qcow2 backed snapshots,","network_info): \"\"\"Plug virtual interfaces (VIFs) into the given `instance` at","and check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get","which will get an additional network interface. :param nova.objects.ImageMeta image_meta:","instance. :param nova.network.model.NetworkInfo vif: The object which has the information","power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power off the specified instance. :param","resize_to: If the new volume is larger than the old","check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \"\"\"Check if it is possible","only known higher up the stack. Most virtualization platforms will",":return: None \"\"\" raise NotImplementedError() def get_host_cpu_stats(self): \"\"\"Get the currently","return False def register_event_listener(self, callback): \"\"\"Register a callback to receive","context in for access to auth_token raise NotImplementedError() def get_num_instances(self):","image file Cinder expects to be used as the active","the driver. :param nova.objects.instance.Instance instance: The instance to get the","for usage. :param attach_block_devices: function to attach block devices. See","root_bdm: The description of the root device. \"\"\" raise NotImplementedError()","This method is for multi compute-nodes support. If a driver","raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \"\"\"Replace","specified instance. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need to","volume is requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList","to a full pipeline. All counters are long integers. This","files for an instance. :param instance: nova.objects.instance.Instance :returns: True if","# Unless required by applicable law or agreed to in","is distributed on an \"AS IS\" BASIS, WITHOUT # WARRANTIES",":return: None \"\"\" raise NotImplementedError() def resume(self, context, instance, network_info,","\"\"\"Power on the specified instance. :param instance: nova.objects.instance.Instance \"\"\" raise","Necessary network information for the resume. :param nova.objects.ImageMeta image_meta: The","disks at destination during cleanup :param migrate_data: a LiveMigrateData object","the creation of the new instance. :param nova.objects.ImageMeta image_meta: The","do the aggregation. On those platforms, this method is unused.","layer is concerned, 'instance ID' and 'instance name' are synonyms.","attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \"\"\"Destroy and re-make this instance.","to the given 'instance'. :param nova.objects.instance.Instance instance: The instance whose","true, migrate VM disk. :param migrate_data: a LiveMigrateData object \"\"\"","\"\"\" raise NotImplementedError() def get_spice_console(self, context, instance): \"\"\"Get connection info","by hypervisors that need to - e.g. for optimisations, or","this function should still succeed. It's probably a good idea","nova.objects.instance.Instance :param network_info: instance network information :param reboot_type: Either a","snapshot :param uuid snapshot_id: The snapshot to delete. :param dict","access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \"\"\"Defer application of","lingering instance files for an instance. :param instance: nova.objects.instance.Instance :returns:","instance_info): \"\"\"Estimate the virtualization overhead required to build an instance","connection info for a spice console. :param context: security context","[{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value':","block_migration. \"\"\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \"\"\"Check if","ending remote sessions. This is optional. \"\"\" pass def get_info(self,","\"\"\" raise NotImplementedError() def detach_interface(self, instance, vif): \"\"\"Use hotunplug to","not specified\")) sys.exit(1) LOG.info(_LI(\"Loading compute driver '%s'\"), compute_driver) try: driver","to shutdown. :return: A list of disk information dicts in","which the underlying domain/vm is paused or halted/stopped. :param instance:","state is skipped. :param block_device_info: Information about block devices to","of I/O requests made, bytes is the total number of","security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add","gains or loses a rule. \"\"\" raise NotImplementedError() def reset_network(self,","the reboot action has completed successfully even in cases in","compute_driver: a compute driver name to override the config opt","block_device_info, network_info, disk_info, migrate_data=None): \"\"\"Prepare an instance for live migration","pass context in for access to auth_token raise NotImplementedError() def","is for multi compute-nodes support. If a driver supports multi","Hypervisors with MAC limits should return a set of MAC","elsewhere. As far as this layer is concerned, 'instance ID'","= virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or","(such as bare metal) require that instances boot from the","dispatching event %(event)s: %(ex)s\"), {'event': event, 'ex': ex}) def delete_instance_files(self,","driver requires legacy block device info. Tell the caller whether","new_connection_info: The volume for this connection gets attached to the","guide the creation of the new instance. :param nova.objects.ImageMeta image_meta:","The name of the host to add to the given","driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts","destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified instance from the Hypervisor. If","'resume' is :func:`suspend`. Depending on the underlying hypervisor technology, the","suspend. :param nova.objects.instance.Instance instance: The instance to suspend. :return: None","state longer than the configured timeout. :return: None \"\"\" #","and pass them into this layer. This layer is responsible","swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] > 0 def","files to inject into instance. :param admin_password: Administrator password to","NotImplementedError() def reset_network(self, instance): \"\"\"reset networking for specified instance.\"\"\" #","the image file Cinder expects to be used as the","network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \"\"\"Destroy the specified instance from the","full pipeline. All counters are long integers. This method is","A list of disk information dicts in JSON format. :rtype:","\"\"\"Do required cleanup on dest host after check_can_live_migrate calls :param","live migrating \"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info,","write, req is the total number of I/O requests made,","rules live in the datastore, and calling this method should","crash data. How the guest OS reacts in details, depends","instances do not have human-readable names at all -- such","free-form thingy... :return: None \"\"\" raise NotImplementedError() def undo_aggregate_operation(self, context,","dictionary representing the ip of the machine that will be","any of it's more specialized subclasses. \"\"\" from nova.virt import","resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance \"\"\" #","be running (power_state.RUNNING). If this fails, any partial instance should","None \"\"\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None):",":param nova.objects.instance.Instance instance: The instance to which the diagnostic data","a pre-created image that will hold the snapshot. \"\"\" raise","sessions. This is optional. \"\"\" pass def get_info(self, instance): \"\"\"Get","gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which contains",":func:`get_instance_diagnostics` :rtype: dict \"\"\" # TODO(Vek): Need to pass context","time (nanoseconds) spent by kernel and user processes respectively, idle","to this class talks in terms of 'instances' (Amazon EC2","method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if","Used in rebuild for HA implementation and required for validation","nova.objects.instance.Instance instance: The instance to soft-delete. :return: None \"\"\" raise","instances. If it is `False`, the host won't accept new","for access to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0,","instance. :param nova.objects.ImageMeta image_meta: The metadata of the image of","{'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value':","network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified instance. After this","as a database entry. The counter action :func:`restore` uses the","mountpoint, resize_to): \"\"\"Replace the volume attached to the given `instance`.","The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The","to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint,","hypervisor technology. A \"stronger\" version of `pause` is :func:'suspend'. The","the DB. :param nodename: node which the caller want to","the root disk - ephemerals: a (potentially empty) list of","machine' (XenAPI terminology) or domain (Xen or libvirt terminology). An","of access to instance shared disk files \"\"\" return False","raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass","context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \"\"\"Transfers the","be presented in the format of a dict containing the","events. Register a callback to receive asynchronous event notifications from","None, or a set of DHCP options, eg: | [{'opt_name':","auth_token raise NotImplementedError() def unpause(self, instance): \"\"\"Unpause the given paused","check the results. :param context: security context :param instance: nova.objects.instance.Instance","volumes. Connector information is a dictionary representing the ip of","the case of qcow2 backed snapshots, this would include the","freeform) is up to the network implementation layer. For instance,","in for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info):","'opt_value': '192.168.127.12'} | ] \"\"\" return None def manage_image_cache(self, context,","is the identifier chosen by Nova to represent the instance","host. :param instance: The instance to lookup Returns True if","pass def filter_defer_apply_off(self): \"\"\"Turn off deferral of IPTables rules and","with network ports. :param context: request context :param instance: nova.objects.instance.Instance","parameter is an instance of nova.compute.service.Instance, and so the instance","by Nova. At the nova.virt layer, instances do not have","return {} def instance_on_disk(self, instance): \"\"\"Checks access of instance files","instance to get the root device for. :param nova.objects.ImageMeta image_meta:","instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network information :param","VM with given 'metadata' and 'personalities'. This base class method","which are no longer of interest. :param all_instances: nova.objects.instance.InstanceList \"\"\"","to get resources from a driver that manages only one","the License. You may obtain # a copy of the","counters for each interface on each running VM. :param instances:","attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance","= { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) }","NotImplementedError() def pause(self, instance): \"\"\"Pause the given instance. A paused","nova.objects.Instance :param block_device_info: Optional; Can be used to filter out","mode. :return: \"on_maintenance\" if switched to maintenance mode or \"off_maintenance\"","override the config opt :returns: a ComputeDriver instance \"\"\" if","hosts. Consider the combination with :func:`set_host_enabled`. :param str host: The","in to methods that expect it. \"\"\" return True def","guest has the same state as before the 'suspend'. :param","should ensure that the reboot action has completed successfully even","context :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def get_vnc_console(self, context,","isinstance(event, virtevent.Event): raise ValueError( _(\"Event must be an instance of","\"\"\"Trigger crash dump mechanism on the given instance. Stalling instances","{} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object):","NotImplementedError() def cleanup_host(self, host): \"\"\"Clean up anything that is necessary","raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \"\"\"Power off the","\"\"\"Estimate the virtualization overhead required to build an instance of","should be migrated. :param str dest: The IP address of","raise NotImplementedError() def unpause(self, instance): \"\"\"Unpause the given paused instance.","of the image of the instance. :param injected_files: User files","raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return the UUIDS of all the","The metadata of the image of the instance. :param nova.network.model.NetworkInfo","image_meta, root_bdm): \"\"\"Provide a default root device name for the","\"\"\"Reboot the specified instance. After this is called successfully, the","shutdown \"\"\" raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None):","| {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', |","migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def live_migration(self, context,","Some drivers chose to cache images for instances on disk.","driver :param fs_type: the file system type to be checked,","into maintenance mode. If `False`, leave the maintenance mode. :return:","ComputeDriver instance \"\"\" if not compute_driver: compute_driver = CONF.compute_driver if","poll_rebooting_instances(self, timeout, instances): \"\"\"Perform a reboot on all given 'instances'.","oslo_log import log as logging from oslo_utils import importutils import","calculations are desired. :param instance_info: Instance/flavor to calculate overhead for.","is raised. :param context: request context :param instance: nova.objects.instance.Instance to","is an opportunity to do management of that cache which","This method is an opportunity to do management of that","instance, network_info): \"\"\"Stop filtering instance.\"\"\" # TODO(Vek): Need to pass","info for a serial console. :param context: security context :param","the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param","able to complete the snapshot. For example, in the case","freq}, where kern and user indicate the cumulative CPU time","The soft-deleted instance which should be restored from the soft-deleted","\"disabled\". :rtype: str \"\"\" # TODO(Vek): Need to pass context","function should use the data there to guide the creation","host to accept new instances. :param bool enabled: If this","\"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding event %s\", str(event)) return if","interface. :param nova.objects.ImageMeta image_meta: The metadata of the image of","rd indicates read, wr indicates write, req is the total","PROJNET, PROJMASK. but libvirt migrates those value through migrateToURI(), so","def pause(self, instance): \"\"\"Pause the given instance. A paused instance","specified suspended instance. The suspended instance gets resumed and will","event callback registered by the compute manager to dispatch the","currently known host CPU stats. :returns: a dict containing the","context, instance, network_info, block_migration=False, block_device_info=None): \"\"\"Post operation of live migration","of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError()","instance. :param instance: nova.objects.instance.Instance :returns: True if the instance was","A soft-deleted instance doesn't allocate any resources anymore, but is","expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception occurs.",":param network_info: instance network information :param block_device_info: instance block device","take a VirtAPI object as their first object and this","requires legacy block device info. Tell the caller whether we","info for a vnc console. :param context: security context :param","\"\"\" raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True,","accept further instances, return \"enabled\", if further instances shouldn't be","def default_root_device_name(self, instance, image_meta, root_bdm): \"\"\"Provide a default root device","the given instance. Stalling instances can be triggered to dump","context, instance): \"\"\"Get host ID to associate with network ports.",":param context: request context :param instance: nova.objects.instance.Instance that the network","\"\"\"Clean up anything that is necessary for the driver gracefully","mapping.\"\"\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \"\"\"Get the","actual networks (or permitting them to be freeform) is up","the instance; the third is the contents of the file,","asynchronous event notifications from hypervisors. The callback will be invoked","attach. :raise nova.exception.NovaException: If the attach fails. :return: None \"\"\"","up the stack. Most virtualization platforms will also have their","the configured timeout. :return: None \"\"\" # TODO(Vek): Need to","and internal Nova terminology), by which we mean 'running virtual","have. allocate_for_instance will take this into consideration when provisioning networking","instance: instance object that was being migrated :param network_info: instance","data: result of check_instance_shared_storage_local \"\"\" pass def check_can_live_migrate_destination(self, context, instance,","virtualization platform. Once this successfully completes, the instance should be","thread for this method since migration should not be started",":param network_info: instance network information :param recreate: True if the",":rtype: nova.virt.diagnostics.Diagnostics \"\"\" raise NotImplementedError() def get_all_bw_counters(self, instances): \"\"\"Return bandwidth","context, data): \"\"\"Check if instance files located on shared storage.","to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which","that has the volume attached :param uuid volume_id: Volume to","in for access to auth_token raise NotImplementedError() def plug_vifs(self, instance,","instance files on the host. :param instance: nova.objects.instance.Instance to lookup","list of nova.objects.instance.Instance objects that have been in rebooting state","If the detach fails. :return: None \"\"\" raise NotImplementedError() def","to the given size (in Gigabyte) of `resize_to`. :return: None","context, dest_check_data): \"\"\"Do required cleanup on dest host after check_can_live_migrate","security context :param dest_check_data: result of check_can_live_migrate_destination \"\"\" raise NotImplementedError()","\"\"\"Cleans up any resources left after an interrupted snapshot. :param","is_bootable): \"\"\"Set the ability to power on/off an instance. :param","check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object","License for the specific language governing permissions and limitations #","be attached to the instance. :param preserve_ephemeral: True if the","that expect it. \"\"\" return True def volume_snapshot_create(self, context, instance,","DB layer. :param network_info: instance network information :param block_device_info: Information","at source. :param context: security context :param instance: instance object","migrate an instance, filtering rules to hypervisors and firewalls are","accept new instances. :return: If the host can accept further","The chosen device name. \"\"\" raise NotImplementedError() def is_supported_fs_format(self, fs_type):","attached to the instance.\"\"\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info,","instance. :param nova.context.RequestContext context: The context for the rescue. :param","NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve resource information. This method is","resume. :param nova.objects.instance.Instance instance: The suspended instance to resume. :param","virtevent.Event): raise ValueError( _(\"Event must be an instance of nova.virt.event.Event\"))","\"\"\" raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True,","migrate_data=None): \"\"\"Destroy the specified instance from the Hypervisor. If the","`host` :param str host: The name of the host to","which returns None. This is called during spawn_instance by the","migration. This checks if the live migration can succeed, based","network_info): \"\"\"Unplug VIFs from networks at source. :param context: security","the number of requests held up due to a full","'running virtual machine' (XenAPI terminology) or domain (Xen or libvirt","def refresh_instance_security_rules(self, instance): \"\"\"Refresh security group rules Gets called when","return driver_registry def get_block_device_info(instance, block_device_mapping): \"\"\"Converts block device mappings for","the missing device names in the block device mapping.\"\"\" raise","NotImplementedError(_(\"Hypervisor driver does not support \" \"post_live_migration_at_source method\")) def post_live_migration_at_destination(self,","of DriverVolumeBlockDevice or any of it's more specialized subclasses. \"\"\"","instance gets resumed and will use CPU cycles and memory","of all instances to other hosts. Consider the combination with","image of the instance. \"\"\" raise NotImplementedError() def network_binding_host_id(self, context,","drivers are encouraged to override this method with something more","block_migration: if true, migrate VM disk. :param migrate_data: a LiveMigrateData","the instance is not found (for example if networking failed),","2015-08-18 # The compute manager doesn't use this interface, which","`True`, the host will accept new instances. If it is","DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice or None","nova.objects.instance.Instance object \"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \"\"\"Check","should be powered on, False otherwise \"\"\" raise NotImplementedError() def","instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up destination node after","action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance","a member of or if the group gains or loses",":param dst_compute_info: Info about the receiving machine :param block_migration: if","in that case. :param context: security context :param instance: Instance","raise NotImplementedError() def set_bootable(self, instance, is_bootable): \"\"\"Set the ability to","context :param instance: nova.objects.instance.Instance This function should use the data","'instances'. Reboots the given `instances` which are longer in the","instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result","Nova to represent the instance further up the stack. This","override this method with something more efficient. \"\"\" return len(self.list_instances())","CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs):","the dom0 \"\"\" # TODO(Vek): Need to pass context in",":param image_id: Reference to a pre-created image that will hold","\"\"\"Soft delete the specified instance. A soft-deleted instance doesn't allocate","the host. :param instance: The instance to lookup Returns True","that the hypervisor knows about. .. note:: This implementation works","that the format of the return value is specific to","def macs_for_instance(self, instance): \"\"\"What MAC addresses must this instance have?","ensure_filtering_rules_for_instance(self, instance, network_info): \"\"\"Setting up filtering rules and waiting for","log as logging from oslo_utils import importutils import nova.conf from","Load the compute driver module specified by the compute_driver configuration",":param preserve_ephemeral: True if the default ephemeral storage partition must","local image cache. Some drivers chose to cache images for","the driver to function, including catching up with currently running","\"\"\"Initialize anything that is necessary for the driver to function,","the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info:","for nova to be able to attach to the volume.","retry_interval=0): \"\"\"Transfers the disk of a running instance in multiple","the shutdown + spin up steps. :param context: security context","openflow switches, fixed MAC addresses can still be virtualized onto","VLANs etc, but regular switches require pre-configured MAC->network mappings that","disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \"\"\"Completes a resize/migration. :param","API call. In the case of doing a snapshot, it","def init_host(self, host): \"\"\"Initialize anything that is necessary for the",":param recreate: True if the instance is being recreated on",":param context: security context :param instance: nova.objects.instance.Instance object \"\"\" raise","name :param bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj:","for it to shutdown. :return: A list of disk information","used to filter out devices which are actually volumes. :return:","manager. Invokes the event callback registered by the compute manager","class method shuts down the VM, detaches all block devices,","from the system and remakes the VM with given 'metadata'","of nova uuid => dict of usage info \"\"\" return","structure used by nova.compute to store details regarding an instance,","that contract \"\"\" import sys from oslo_log import log as","below method must be called. - setup_basic_filtering (for nova-basic, etc.)","attached :param uuid volume_id: Volume to be snapshotted :param create_info:","or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def","it gets resized to the given size (in Gigabyte) of","size (in Gigabyte) of `resize_to`. :return: None \"\"\" raise NotImplementedError()","is the contents of the file, also base64-encoded. NOTE(russellb) This","is live migrating \"\"\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance,","} swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def","def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup on host after check_instance_shared_storage","host, depending on the underlying hypervisor technology. A \"stronger\" version","the given `instances` which are longer in the rebooting state","= callback def emit_event(self, event): \"\"\"Dispatches an event to the","after a change to security groups. All security groups and","This method allows drivers to return a set of MAC","device. device_name does not need to be set, and should","Returns True if files of an instance with the supplied","encryption=None): \"\"\"Detach the disk attached to the instance.\"\"\" raise NotImplementedError()","security context :param instance: nova.objects.instance.Instance object :param block_device_info: instance block","which contains information about the VIFs to plug. :return: None","hotplug to add a network interface to a running instance.","the return value is specific to the Neutron client API.","context, data): \"\"\"Do cleanup on host after check_instance_shared_storage calls :param","registered by the compute manager to dispatch the event. This","instance): \"\"\"Get DHCP options for this instance. Some hypervisors (such","\"\"\"Provide a default root device name for the driver. :param","the specified instance. A suspended instance doesn't use CPU cycles","raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None):","to log a warning in that case. :param context: security","mountpoint, encryption=None): \"\"\"Detach the disk attached to the instance.\"\"\" raise","A list of nova.objects.instance.Instance objects that have been in rebooting","host since the last boot. :rtype: str \"\"\" # TODO(Vek):","cases in which the underlying domain/vm is paused or halted/stopped.",":return: None \"\"\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm):","compute manager. :return: None, or a set of MAC ids","object that is migrated :param network_info: instance network information :param","nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with","context, instance): \"\"\"Get connection info for a spice console. :param","for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info:","instance: nova.objects.instance.Instance to lookup Returns True if files of an","method is unused. Note that this function takes an instance","by the compute service. This method is for multi compute-nodes","config opt :returns: a ComputeDriver instance \"\"\" if not compute_driver:",":param disk_over_commit: if true, allow disk over commit :returns: a","= CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute driver option required, but","# WARRANTIES OR CONDITIONS OF ANY KIND, either express or","layer, instances do not have human-readable names at all --","\"\"\"Applies a diff to the instance metadata. This is an","instance, volume_id, create_info): \"\"\"Snapshots volumes attached to a specified instance.","the source host to check the results. :param context: security","\"\"\"Do cleanup on host after check_instance_shared_storage calls :param context: security","TFTP service. This requires passing the appropriate options out to","must only be invoked from a green thread. \"\"\" if","to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context.","each running VM. :param instances: nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def","firewall rules can be set faster). Concretely, the below method","The instance which should be unpaused. :return: None \"\"\" #","interface removed. :param nova.network.model.NetworkInfo vif: The object which has the","specified instance. :param instance: nova.objects.instance.Instance \"\"\" raise NotImplementedError() def trigger_crash_dump(self,","nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of","migration can succeed, based on the results from check_can_live_migrate_destination. :param","completed successfully even in cases in which the underlying domain/vm","specified security group. An error should be raised if the","the specified soft-deleted instance. The restored instance will be automatically","to power_state.RUNNING. The virtualization platform should ensure that the reboot","which is used to publish changes to the instance's metadata","block_device_info = block_device_info or {} return block_device_info.get('swap') or {'device_name': None,","plug. :return: None \"\"\" # TODO(Vek): Need to pass context","raise NotImplementedError(_(\"Hypervisor driver does not support \" \"post_live_migration_at_source method\")) def","the compute manager. :return: None, or a set of MAC","instance with the supplied ID accessible on the host, False","which the file is to be written on the instance;","aggregate, host, set_error=True): \"\"\"Undo for Resource Pools.\"\"\" raise NotImplementedError() def","instance.\"\"\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to):","\"\"\"Use hotunplug to remove a network interface from a running","if an instance with the supplied ID exists on the","caller want to get resources from a driver that manages","is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs","dump mechanism on the given instance. Stalling instances can be","the information about the interface to detach. :raise nova.exception.NovaException: If","instance object reference :param network_info: instance network information \"\"\" raise","If this fails, any partial instance should be completely cleaned","\"\"\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name","passing the appropriate options out to the DHCP service. Most","compute host to an aggregate. The counter action to this","a (potentially empty) list of DriverEphemeralBlockDevice instances - swap: An","instance should be running (power_state.RUNNING). If this fails, any partial","\"\"\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \"\"\"Retrieve information about",":param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred","for considering rebooting instances to be stuck. :param list instances:","ComputeDriver(object): \"\"\"Base class for compute drivers. The interface to this","layer, as a list. \"\"\" # TODO(Vek): Need to pass",":func:`pause`. Depending on the underlying hypervisor technology, the guest has","about. .. note:: This implementation works for all drivers, but","return False def quiesce(self, context, instance, image_meta): \"\"\"Quiesce the specified","be in the state that it was before this call","CPU stat info, eg: | {'kernel': kern, | 'idle': idle,","add to the given `aggregate`. :param dict kwargs: A free-form","None, 'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name'] and","used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host,","- prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be called","NotImplementedError() def restore(self, instance): \"\"\"Restore the specified soft-deleted instance. The","NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \"\"\"Snapshots the specified","timeout (in seconds) for considering rebooting instances to be stuck.",":param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network information","block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info","for access to auth_token raise NotImplementedError() def get_num_instances(self): \"\"\"Return the","of the host anymore. The state of the VM could","anymore. The state of the VM could be stored in","class. \"\"\" self._compute_event_callback = callback def emit_event(self, event): \"\"\"Dispatches an",":param network_info: instance network information :param nova.objects.ImageMeta image_meta: The metadata","callback to receive asynchronous event notifications from hypervisors. The callback","opportunity to do management of that cache which isn't directly","reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the specified","disk_info: the newly transferred disk information :param network_info: instance network","manager. :return: None, or a set of MAC ids (e.g.","nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \"\"\" raise NotImplementedError() def","group. An error should be raised if the operation cannot","called during spawn_instance by the compute manager. :return: None, or",":func:`resume`. :param nova.context.RequestContext context: The context for the suspend. :param","CONF.compute_driver if not compute_driver: LOG.error(_LE(\"Compute driver option required, but not","override if per-instance overhead calculations are desired. :param instance_info: Instance/flavor","counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The","Once this successfully completes, the instance should be running (power_state.RUNNING).","initiator and the hostname of the machine as follows:: {","\"\"\"Destroy the specified instance from the Hypervisor. If the instance","get_per_instance_usage(self): \"\"\"Get information about instance resource usage. :returns: dict of","is being recreated on a new hypervisor - all the","the snapshot :param uuid snapshot_id: The snapshot to delete. :param",":param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The","for the instance. Mapping of MAC addresses to actual networks","An instance of DriverSwapBlockDevice or None - block_device_mapping: a (potentially","hypervisors can use the default implementation which returns None. This","the VM with given 'metadata' and 'personalities'. This base class","instance of console.type.ConsoleSerial \"\"\" raise NotImplementedError() def get_mks_console(self, context, instance):","below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\" \"\"\"","since it defines PROJNET, PROJMASK. but libvirt migrates those value","do not have human-readable names at all -- such things","nova.objects.instance.InstanceList \"\"\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage","to uniquely identify a VM or domain. These IDs must","made, bytes is the total number of bytes transferred, and","in the format of a dict containing the following keys:","from hypervisors. The callback will be invoked with a single","can be removed from nova.compute.manager. \"\"\" # TODO(Vek): Need to","instance: The instance which should be paused. :return: None \"\"\"","context, aggregate, host, **kwargs): \"\"\"Add a compute host to an","for block migration :param disk_over_commit: if true, allow disk over","for access to auth_token raise NotImplementedError() def suspend(self, context, instance):","image cache. Some drivers chose to cache images for instances","2011 <NAME> # All Rights Reserved. # # Licensed under","instance): \"\"\"Get host ID to associate with network ports. :param","security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove","nova.objects.instance.Instance \"\"\" raise NotImplementedError() def get_vnc_console(self, context, instance): \"\"\"Get connection","and apply the rules now.\"\"\" pass def unfilter_instance(self, instance, network_info):","be able to attach to the volume. This is the","set of MAC addresses, which will be supplied to the","\"AS IS\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY","'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'},","live_migration_abort(self, instance): \"\"\"Abort an in-progress live migration. :param instance: instance","= virtapi self._compute_event_callback = None def init_host(self, host): \"\"\"Initialize anything","block-device-mappings to use for rebuild :param detach_block_devices: function to detach","len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks existence of an instance on","The prime example is to clean the cache and remove","mechanism on the given instance. Stalling instances can be triggered","removed once it can be removed from nova.compute.manager. \"\"\" #","maintenance mode or \"off_maintenance\" if maintenance mode got left. :rtype:","contains the uptime of this host since the last boot.","instances to be stuck. :param list instances: A list of","and will be removed once it can be removed from","to. :param int resize_to: If the new volume is larger","(potentially empty) list of DriverVolumeBlockDevice or any of it's more","live migration at destination host. :param context: security context :param","def list_instance_uuids(self): \"\"\"Return the UUIDS of all the instances known","dst_compute_info, block_migration=False, disk_over_commit=False): \"\"\"Check if it is possible to execute","to the instance, then this method should not be implemented.","the instance was deleted from disk, False otherwise. \"\"\" return","timeout), NovaException is raised. :param context: request context :param instance:","no means of publishing the instance metadata to the instance,","instance from the Hypervisor. If the instance is not found","data about the given instance. :param nova.objects.instance.Instance instance: The instance","host again. The counter action for 'unpause' is :func:`pause`. Depending","in JSON format. :rtype: str \"\"\" raise NotImplementedError() def snapshot(self,","be powered on, False otherwise \"\"\" raise NotImplementedError() def pause(self,","an instance of console.type.ConsoleVNC \"\"\" raise NotImplementedError() def get_spice_console(self, context,",":param nova.objects.instance.Instance instance: The instance which should be paused. :return:","attached to a specified instance. The counter action to this","\"\"\" raise NotImplementedError() def get_host_ip_addr(self): \"\"\"Retrieves the IP address of","str dest: The IP address of the destination host. :param","context: the context for the migration/resize :param migration: the migrate/resize","the ip of the machine that will be making the","metadata of the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm:","defines PROJNET, PROJMASK. but libvirt migrates those value through migrateToURI(),","to the given `instance`. :param dict old_connection_info: The volume for","the total number of bytes transferred, and errs is the","raised. When it fails to quiesce by other errors (e.g.","unquiesce(self, context, instance, image_meta): \"\"\"Unquiesce the specified instance after snapshots.","host CPU stats. :returns: a dict containing the CPU stat","virtapi self._compute_event_callback = None def init_host(self, host): \"\"\"Initialize anything that","interface, which seems odd # since the manager should be","the nova.virt.event.Event class. \"\"\" self._compute_event_callback = callback def emit_event(self, event):","info about the requested block device. device_name does not need","context, aggregate, host, **kwargs): \"\"\"Remove a compute host from an","time (nanoseconds), since the host is booting up; freq indicates",":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \"\"\" raise","console.type.ConsoleRDP \"\"\" raise NotImplementedError() def get_serial_console(self, context, instance): \"\"\"Get connection","The paused instance gets unpaused and will use CPU cycles","from a running instance. The counter action to this is","things are only known higher up the stack. Most virtualization","of an instance on the host. :param instance: The instance","fails, any partial instance should be completely cleaned up, and","get_num_instances(self): \"\"\"Return the total number of virtual machines. Return the","\"\"\"Restore the specified soft-deleted instance. The restored instance will be","default root device name for the driver. :param nova.objects.instance.Instance instance:","rules.\"\"\" pass def filter_defer_apply_off(self): \"\"\"Turn off deferral of IPTables rules","ID accessible on the host, False otherwise. .. note:: Used","appropriate). :return: None \"\"\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta,","NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup on host after","in for access to auth_token raise NotImplementedError() def unplug_vifs(self, instance,","describing resources \"\"\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info,","raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \"\"\"Undo","the hypervisor knows about. .. note:: This implementation works for","use the default implementation which returns None. Hypervisors with MAC","def change_instance_metadata(self, context, instance, diff): \"\"\"Applies a diff to the","of the VM could be stored in the memory or","device name of the root disk - ephemerals: a (potentially","virt layer. return False def quiesce(self, context, instance, image_meta): \"\"\"Quiesce","a driver supports multi compute-nodes, this method returns a list","to lookup Returns True if an instance with the supplied","of MAC addresses that the instance is to have. allocate_for_instance","is up to that call to ensure that all assigned","return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \"\"\"Get information about instance","access to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \"\"\"Set","\"\"\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Add a","host and allocate storage space this way. A \"softer\" way","under the License. \"\"\" Driver base-classes: (Beginning of) the contract","a set of MAC addresses, which will be supplied to","swap and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info","'iowait': wait, | 'frequency': freq}, where kern and user indicate","to execute live migration. This runs checks on the destination","the snapshot. \"\"\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id,",":param migration: the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized","which should be restored from the soft-deleted data. :return: None","for a vnc console. :param context: security context :param instance:","instance, filtering rules to hypervisors and firewalls are inevitable on","aggregate, host, **kwargs): \"\"\"Remove a compute host from an aggregate.","allows drivers to return a set of MAC addresses that","On start, it triggers the migration of all instances to","a dictionary representing the ip of the machine that will","in details, depends on the configuration of it. :param nova.objects.instance.Instance","the currently known host CPU stats. :returns: a dict containing","\"\"\" raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password):","file, also base64-encoded. NOTE(russellb) This method is deprecated and will","def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \"\"\"Reboot the","system # type and implement this function at their #","NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage info for volumes","refresh=False): \"\"\"Returns nodenames of all nodes managed by the compute","to be snapshotted :param create_info: The data needed for nova","disk after the snapshot operation has completed. There may be","that have been in rebooting state longer than the configured","then spins up the new VM afterwards. It may be","raise NotImplementedError() def get_available_nodes(self, refresh=False): \"\"\"Returns nodenames of all nodes","\"\"\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \"\"\"Do cleanup on","context in for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self):","UUIDS of all the instances known to the virtualization layer,","for access to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance,","device for. :param nova.objects.ImageMeta image_meta: The metadata of the image","this method since migration should not be started when setting-up","For instance, with openflow switches, fixed MAC addresses can still","nova.objects.BlockDeviceMapping instance with all info about the requested block device.","raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \"\"\"Use hotplug to","nova.virt layer, instances do not have human-readable names at all","should return a set of MAC addresses, which will be","nodename in self.get_available_nodes(): return True # Refresh and check again.","unpaused and will use CPU cycles of the host again.","or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap and","all existing data from the system and remakes the VM","stored in the memory or storage space of the host,",":param bool enabled: If this is `True`, the host will","context, instance, image_id, update_task_state): \"\"\"Snapshots the specified instance. :param context:","the cleanup of old state is skipped. :param block_device_info: Information","A suspended instance doesn't use CPU cycles or memory of","information :param disk_info: instance disk information :param migrate_data: a LiveMigrateData","by the compute manager, and it is up to that","XenAPI) performance statistics can be retrieved directly in aggregate form,","logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str","overridden by hypervisors that need to - e.g. for optimisations,","has completed. There may be other data included as well","host. :param str action: The action the host should perform.","detached from the given `instance`. :param dict new_connection_info: The volume","instance. :param bdms: block-device-mappings to use for rebuild :param detach_block_devices:","list. \"\"\" raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files,","\"\"\" # NOTE(jichenjc): Return False here so that every hypervisor","if the group gains or loses a rule. \"\"\" raise","None \"\"\" raise NotImplementedError() def detach_interface(self, instance, vif): \"\"\"Use hotunplug","the network implementation layer. For instance, with openflow switches, fixed","domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info:","as this layer is concerned, 'instance ID' and 'instance name'","auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18","instance): \"\"\"Trigger crash dump mechanism on the given instance. Stalling","\"\"\"Converts block device mappings for an instance to driver format.",":param context: security context :param instance: instance object reference :param","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","\"\"\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \"\"\"Check if instance","rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \"\"\"Clean up destination","that. :param nova.objects.instance.Instance instance: The instance to soft-delete. :return: None","block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to","metadata. This is an optional driver method which is used","snapshotted :param create_info: The data needed for nova to be","required to build an instance of the given flavor. Defaults","in for access to auth_token raise NotImplementedError() def host_power_action(self, action):","compute-nodes, this method returns a list of nodenames managed by","the specified instance from the Hypervisor. If the instance is","instances - swap: An instance of DriverSwapBlockDevice or None -","network_info: instance network information :param disk_info: instance disk information :param","\"\"\"Does the driver want networks deallocated on reschedule?\"\"\" return False","default implementation which returns None. This is called during spawn_instance","raise NotImplementedError() def get_mks_console(self, context, instance): \"\"\"Get connection info for","require that instances boot from the network, and manage their","the instance for attaching to volumes. Connector information is a",":param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object \"\"\"","state as before the 'pause'. :param nova.objects.instance.Instance instance: The instance","for rescue. \"\"\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \"\"\"Set","completely cleaned up, and the virtualization platform should be in","metal) cannot do freeform virtualization of MAC addresses. This method",":returns an instance of console.type.ConsoleSerial \"\"\" raise NotImplementedError() def get_mks_console(self,","in for access to auth_token raise NotImplementedError() def list_instance_uuids(self): \"\"\"Return","None \"\"\" raise NotImplementedError() def soft_delete(self, instance): \"\"\"Soft delete the","instance, network_info, block_device_info=None): \"\"\"resume the specified suspended instance. The suspended","context: security context :param instance: instance object that was being","ID. \"\"\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \"\"\"Does the driver","idle, | 'user': user, | 'iowait': wait, | 'frequency': freq},","this connection gets attached to the given 'instance'. :param nova.objects.instance.Instance","virtual interfaces (VIFs) from networks. The counter action is :func:`plug_vifs`.","the given instance. These are returned as [rd_req, rd_bytes, wr_req,","self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info):","and as part of a periodic task that records the","context: The security context. :param nova.objects.instance.Instance instance: The instance that","added to or removed from the security group the instance","that is migrated. :param dest: destination host :param post_method: post","for HA implementation and required for validation of access to","should not be implemented. :param context: security context :param instance:","nova.objects.instance.Instance instance: The instance to get the root device for.","resume. :param nova.objects.ImageMeta image_meta: The metadata of the image of","a good idea to log a warning in that case.","instance network information :param reboot_type: Either a HARD or SOFT","the host ID \"\"\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \"\"\"Load","context: The context for the resume. :param nova.objects.instance.Instance instance: The","counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The","instance: nova.objects.instance.Instance that volume is requesting a device name :param","a LiveMigrateData object (hypervisor-dependent) \"\"\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context,","the results. :param context: security context :param instance: nova.objects.instance.Instance object","the source VM. :param instance: nova.objects.instance.Instance \"\"\" # TODO(Vek): Need","callback def emit_event(self, event): \"\"\"Dispatches an event to the compute","power action :rtype: : str \"\"\" raise NotImplementedError() def host_maintenance_mode(self,","platform should be in the state that it was before","context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \"\"\"Live migration of","instance should be powered on, False otherwise \"\"\" raise NotImplementedError()","NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None):","shuts down or powers up the host. :param str action:","Info about the sending machine :param dst_compute_info: Info about the","instances): \"\"\"Return bandwidth usage counters for each interface on each","to instance shared disk files \"\"\" return False def register_event_listener(self,","False, \"supports_recreate\": False, \"supports_migrate_to_same_host\": False, \"supports_attach_interface\": False, \"supports_device_tagging\": False, }","efficient. \"\"\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return","in for access to auth_token raise NotImplementedError() def set_admin_password(self, instance,","then this method should not be implemented. :param context: security","the last boot. :rtype: str \"\"\" # TODO(Vek): Need to","it can be removed from nova.compute.manager. \"\"\" # TODO(Vek): Need","file Cinder expects to be used as the active disk","raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \"\"\"Return usage info for",":param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param","the <PASSWORD> \"\"\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents):","host. :param context: security context :instance: instance object that was","access to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance,","machines. Return the number of virtual machines that the hypervisor","ephemeral storage partition must be preserved on rebuild \"\"\" raise",":param dict old_connection_info: The volume for this connection gets detached","is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance","instance. The counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance","switched to maintenance mode or \"off_maintenance\" if maintenance mode got",":param context: security context :param instance: instance object that was","is :func:`suspend`. Depending on the underlying hypervisor technology, the guest","(not ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo object","allocate storage space this way. A \"softer\" way of `suspend`","being merged into (if appropriate). :return: None \"\"\" raise NotImplementedError()","block_device_info=None): \"\"\"Power on the specified instance. :param instance: nova.objects.instance.Instance \"\"\"","if not compute_driver: LOG.error(_LE(\"Compute driver option required, but not specified\"))","of IPTables rules and apply the rules now.\"\"\" pass def","the end. :param nova.objects.instance.Instance instance: The instance whose disk should","connection gets attached to the given 'instance'. :param nova.objects.instance.Instance instance:","called after a change to security groups. All security groups","block devices to be attached to the instance. \"\"\" raise","instance has an ID, which is the identifier chosen by","other hosts. Consider the combination with :func:`set_host_enabled`. :param str host:","an instance of nova.compute.service.Instance. This is the data structure used","image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \"\"\"Create a new instance/VM/domain on","the underlying hypervisor technology, the guest has the same state","specified instance. The counter action to this is :func:`volume_snapshot_delete` :param","nova.objects.instance.InstanceList \"\"\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Add","identity schemes, to uniquely identify a VM or domain. These","of nodenames managed by the service. Otherwise, this method should","\"\"\"Plug virtual interfaces (VIFs) into the given `instance` at instance","migrate_data: a LiveMigrateData object \"\"\" raise NotImplementedError() def post_live_migration(self, context,","\"\"\"Set the ability to power on/off an instance. :param instance:","\"\"\"Unplug virtual interfaces (VIFs) from networks. The counter action is","get resources from a driver that manages only one node","be quiesced :param nova.objects.ImageMeta image_meta: The metadata of the image","aggregate. The counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext","XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \"\"\"Remove","volume_snapshot_create(self, context, instance, volume_id, create_info): \"\"\"Snapshots volumes attached to a","something more efficient. \"\"\" return len(self.list_instances()) def instance_exists(self, instance): \"\"\"Checks","dom0 \"\"\" # TODO(Vek): Need to pass context in for","raise NotImplementedError() def get_console_output(self, context, instance): \"\"\"Get console output for","is to be written on the instance; the third is","manager. Note that the format of the return value is","a warning in that case. :param context: security context :param","them to be freeform) is up to the network implementation","load_compute_driver(virtapi, compute_driver=None): \"\"\"Load a compute driver module. Load the compute","context: security context :param instance: nova.objects.instance.Instance object \"\"\" raise NotImplementedError()","The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The","encountered \"\"\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need","dict() for driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=')","underlying hypervisor technology, the guest has the same state as",":param nova.objects.flavor.Flavor flavor: The flavor of the instance whose disk","context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \"\"\"","if the instance should be powered on, False otherwise \"\"\"","True if files of an instance with the supplied ID","block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping)","from an aggregate. The counter action to this is :func:`add_to_aggregate`","raise NotImplementedError() def get_num_instances(self): \"\"\"Return the total number of virtual","doesn't allocate any resources anymore, but is still available as","that has the volume attached. :param uuid volume_id: Attached volume","finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network","for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \"\"\"Refresh","CPU frequency (MHz). All values are long integers. \"\"\" raise","LOG.debug(\"Discarding event %s\", str(event)) return if not isinstance(event, virtevent.Event): raise","If this is `True`, the host will accept new instances.","# since the manager should be the controlling thing here.","onto any L2 domain, with arbitrary VLANs etc, but regular","License. You may obtain # a copy of the License","params \"\"\" raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None,","system and remakes the VM with given 'metadata' and 'personalities'.","get_console_output(self, context, instance): \"\"\"Get console output for an instance :param","ANY KIND, either express or implied. See the # License","cannot do freeform virtualization of MAC addresses. This method allows","each interface on each running VM. :param instances: nova.objects.instance.InstanceList \"\"\"","result of calling the Linux command `uptime` on this host.",":param nova.network.model.NetworkInfo network_info: The object which contains information about the","apply the rules now.\"\"\" pass def unfilter_instance(self, instance, network_info): \"\"\"Stop","instance where the volume for `old_connection_info` is attached to. :param","\"\"\" raise NotImplementedError() def get_rdp_console(self, context, instance): \"\"\"Get connection info","delete_instance_files(self, instance): \"\"\"Delete any lingering instance files for an instance.","is actually proxied and needs to be held across the","spawn_instance by the compute manager. Note that the format of","a green thread. \"\"\" if not self._compute_event_callback: LOG.debug(\"Discarding event %s\",","nova.objects.instance.Instance instance: The soft-deleted instance which should be restored from","-- it's an internal ID chosen by Nova. At the","This method is called when nova-compute launches, and as part","ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances - swap:","way of `suspend` is :func:`pause`. The counter action for `suspend`","live_migration_force_complete(self, instance): \"\"\"Force live migration to complete :param instance: Instance","json strings with below format:: \"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080'","further instances shouldn't be scheduled to this host, return \"disabled\".","nova.objects.instance.Instance :param image_id: Reference to a pre-created image that will","object \"\"\" raise NotImplementedError() def live_migration_force_complete(self, instance): \"\"\"Force live migration","to vms on a given host.- \"\"\" raise NotImplementedError() def","\"\"\" raise NotImplementedError() def reset_network(self, instance): \"\"\"reset networking for specified","\"\"\" raise NotImplementedError() def get_available_resource(self, nodename): \"\"\"Retrieve resource information. This"],"string":"[\n \"\\\"\\\"\\\"Checks access of instance files on the host. :param instance:\",\n \"NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down or powers up\",\n \"context: security context :instance: instance object that was migrated :block_device_info:\",\n \"%s\\\", str(event)) return if not isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event\",\n \":param int resize_to: If the new volume is larger than\",\n \"(or permitting them to be freeform) is up to the\",\n \"actual configuration. Most hypervisors can use the default implementation which\",\n \"in multiple phases, turning off the instance before the end.\",\n \"an instance with the supplied ID exists on the host,\",\n \":param str action: The action the host should perform. The\",\n \"contains information about the VIFs to unplug. :return: None \\\"\\\"\\\"\",\n \"particular node.\\\"\\\"\\\" if nodename in self.get_available_nodes(): return True # Refresh\",\n \"network interface from a running instance. The counter action to\",\n \"the 'pause'. :param nova.objects.instance.Instance instance: The instance which should be\",\n \"to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context.\",\n \"the migration of all instances to other hosts. Consider the\",\n \"callback to receive events. Register a callback to receive asynchronous\",\n \"raise NotImplementedError() def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance counters associated\",\n \"def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest state when\",\n \"the cumulative I/O wait CPU time (nanoseconds), since the host\",\n \"# need to define their supported file system # type\",\n \"newer interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"where kern and user indicate the cumulative CPU time (nanoseconds)\",\n \"that are specific to the virtualization platform. \\\"\\\"\\\" capabilities =\",\n \"block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals') or []\",\n \"with MAC limits should return a set of MAC addresses,\",\n \"be decided by the driver implementation if not set. :returns:\",\n \"def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk attached\",\n \"the new volume is larger than the old volume, it\",\n \"migrated :param network_info: instance network information :param block_migration: if true,\",\n \"is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate\",\n \"the database entry to create a new instance based on\",\n \"object as returned by DB layer. :param network_info: instance network\",\n \"instance: Instance being live migrated \\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self,\",\n \"addresses'. \\\"\\\"\\\" return None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options\",\n \"driver format. Virt drivers expect block device mapping to be\",\n \":param nova.context.RequestContext context: The context for the rescue. :param nova.objects.instance.Instance\",\n \"left. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets\",\n \"context in for access to auth_token raise NotImplementedError() def unplug_vifs(self,\",\n \"instance. :param context: security context :param instance: nova.objects.instance.Instance :param image_id:\",\n \"\\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info):\",\n \"with :func:`set_host_enabled`. :param str host: The name of the host\",\n \"info to be passed in to methods that expect it.\",\n \"of the image of the instance. :param rescue_password: <PASSWORD> for\",\n \"security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self, instance,\",\n \"def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the root password on the\",\n \"storage. :param context: security context :param data: result of check_instance_shared_storage_local\",\n \"raise NotImplementedError() def suspend(self, context, instance): \\\"\\\"\\\"Suspend the specified instance.\",\n \"specified instance. :param context: security context :param instance: nova.objects.instance.Instance :param\",\n \"if switched to maintenance mode or \\\"off_maintenance\\\" if maintenance mode\",\n \"of the virt drivers are encouraged to override this method\",\n \"and user indicate the cumulative CPU time (nanoseconds) spent by\",\n \"\\\"\\\"\\\"Sets the ability of this host to accept new instances.\",\n \"self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance resource usage. :returns:\",\n \"`instance`. :param dict old_connection_info: The volume for this connection gets\",\n \"application of IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral\",\n \"raise NotImplementedError() def get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection info for\",\n \"execute live migration. This checks if the live migration can\",\n \"in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self,\",\n \"console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection\",\n \"all nodes managed by the compute service. This method is\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def rescue(self, context, instance,\",\n \"context, instance): \\\"\\\"\\\"Get connection info for a rdp console. :param\",\n \"\\\"\\\"\\\" raise NotImplementedError() def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the specified\",\n \"the stack. This is unfortunately also called a 'name' elsewhere.\",\n \"virtual machines. Return the number of virtual machines that the\",\n \"MAC addresses can still be virtualized onto any L2 domain,\",\n \":func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs unplugged.\",\n \"a VirtAPI instance :param compute_driver: a compute driver name to\",\n \"been destroyed from the Hypervisor before calling this method. :param\",\n \"on host after check_instance_shared_storage calls :param context: security context :param\",\n \"nova.context.RequestContext context: The context for the suspend. :param nova.objects.instance.Instance instance:\",\n \"platform-specific layer, and never escape the connection interface. The platform-specific\",\n \":param context: security context :param instance: nova.objects.instance.Instance object :param block_device_info:\",\n \"{ 'ip': ip, 'initiator': initiator, 'host': hostname } \\\"\\\"\\\" raise\",\n \"raise ValueError( _(\\\"Event must be an instance of nova.virt.event.Event\\\")) try:\",\n \"got left. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self, enabled):\",\n \"the caller want to get resources from a driver that\",\n \"} def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None\",\n \"get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given instance. :param\",\n \"in the DB. :param nodename: node which the caller want\",\n \"auth_token raise NotImplementedError() def suspend(self, context, instance): \\\"\\\"\\\"Suspend the specified\",\n \"with the set of MAC addresses. This is called during\",\n \"stats. :returns: a dict containing the CPU stat info, eg:\",\n \"usage. :returns: dict of nova uuid => dict of usage\",\n \"a InstanceInfo object \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"image of the instance. :param injected_files: User files to inject\",\n \"be automatically booted. The counter action for `restore` is :func:`soft_delete`.\",\n \"all info about the requested block device. device_name does not\",\n \"MAC addresses that the instance is to have. allocate_for_instance will\",\n \"to a specified instance. The counter action to this is\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); you may # not\",\n \"for access to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance,\",\n \"are long integers. This method is optional. On some platforms\",\n \"raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing\",\n \"def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage info for volumes attached\",\n \"called a 'name' elsewhere. As far as this layer is\",\n \"running VM's on the given host. \\\"\\\"\\\" # TODO(Vek): Need\",\n \"All Rights Reserved. # # Licensed under the Apache License,\",\n \"migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self, instance):\",\n \"mode should be changed. :param bool mode: If `True`, go\",\n \"addresses. This method allows drivers to return a set of\",\n \"call to ensure that all assigned network details are compatible\",\n \"should remove the given `host` :param str host: The name\",\n \"the hostname of the machine as follows:: { 'ip': ip,\",\n \"of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the\",\n \"`suspend` is :func:`resume`. :param nova.context.RequestContext context: The context for the\",\n \"to shutdown \\\"\\\"\\\" raise NotImplementedError() def power_on(self, context, instance, network_info,\",\n \"the Neutron client API. :return: None, or a set of\",\n \"doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails to\",\n \"can accept further instances, return \\\"enabled\\\", if further instances shouldn't\",\n \"def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if instance files located on\",\n \"the virt drivers are encouraged to override this method with\",\n \"nova to be able to attach to the volume. This\",\n \"not use this file except in compliance with the License.\",\n \"efficient. Maintainers of the virt drivers are encouraged to override\",\n \":param block_migration: if true, migrate VM disk. :param migrate_data: a\",\n \"have their own identity schemes, to uniquely identify a VM\",\n \"a big overlap to the return value of the newer\",\n \"the security group the instance is a member of or\",\n \"etc.) to_xml may have to be called since it defines\",\n \"this method should not be implemented. :param context: security context\",\n \"on the given host. \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute\",\n \"The flavor of the instance whose disk get migrated. :param\",\n \"again. The counter action for 'resume' is :func:`suspend`. Depending on\",\n \"instance is being recreated on a new hypervisor - all\",\n \"warning in that case. :param context: security context :param instance:\",\n \"NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on the\",\n \"driver supports multi compute-nodes, this method returns a list of\",\n \"See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach block\",\n \"nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary\",\n \"be used as the active disk after the snapshot operation\",\n \"Tell the caller whether we expect the legacy format of\",\n \"waiting for its completion. To migrate an instance, filtering rules\",\n \"def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the\",\n \"from a driver that manages only one node can safely\",\n \"def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if instance files located on\",\n \"list_instances(self): \\\"\\\"\\\"Return the names of all the instances known to\",\n \"\\\"License\\\"); you may # not use this file except in\",\n \"associated rules live in the datastore, and calling this method\",\n \"raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms,\",\n \"initialize_connection() API call. In the case of doing a snapshot,\",\n \"[hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return whether this\",\n \"to create a new instance based on that. :param nova.objects.instance.Instance\",\n \"network_info: The network information of the given `instance`. :param dict\",\n \"filtering instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"driver module. Load the compute driver module specified by the\",\n \"The callback will be invoked with a single parameter, which\",\n \"implementation specific params \\\"\\\"\\\" raise NotImplementedError() def reboot(self, context, instance,\",\n \"wait CPU time (nanoseconds), since the host is booting up;\",\n \"resumed and will use CPU cycles and memory of the\",\n \"rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for the resume.\",\n \"NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is called after a\",\n \"snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot of a volume attached to\",\n \"held across the shutdown + spin up steps. :param context:\",\n \"access to auth_token raise NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts\",\n \":raise nova.exception.NovaException: If the detach fails. :return: None \\\"\\\"\\\" raise\",\n \"return a set of MAC addresses, which will be supplied\",\n \"information :param destroy_disks: if true, destroy disks at destination during\",\n \"are only known higher up the stack. Most virtualization platforms\",\n \"maintenance mode. :return: \\\"on_maintenance\\\" if switched to maintenance mode or\",\n \"presented in the format of a dict containing the following\",\n \"int timeout: The time in seconds to wait for the\",\n \"need to be called. Don't use thread for this method\",\n \"true, destroy disks at destination during cleanup :param migrate_data: a\",\n \"allocate_for_instance will take this into consideration when provisioning networking for\",\n \"to build an instance of the given flavor. Defaults to\",\n \"snapshot operation has completed. There may be other data included\",\n \"def confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying the\",\n \"def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None def\",\n \"by the driver implementation if not set. :returns: The chosen\",\n \"the connection, the name of the iscsi initiator and the\",\n \"command `uptime` on this host. :return: A text which contains\",\n \"changed. :param bool mode: If `True`, go into maintenance mode.\",\n \"False, } def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback =\",\n \"instance with all info about the requested block device. device_name\",\n \"for creating the snapshot. \\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self, context,\",\n \"block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class for\",\n \":returns an instance of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self,\",\n \"as returned by DB layer. :param network_info: instance network information\",\n \"caller if the driver requires legacy block device info. Tell\",\n \"disk to the instance at mountpoint using info.\\\"\\\"\\\" raise NotImplementedError()\",\n \"it's more specialized subclasses. \\\"\\\"\\\" from nova.virt import block_device as\",\n \"platform-specific ID, and vice versa. Some methods here take an\",\n \"\\\"\\\"\\\" raise NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore the specified soft-deleted\",\n \"validation of access to instance shared disk files \\\"\\\"\\\" return\",\n \"check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None):\",\n \"\\\"\\\"\\\"Setting up filtering rules and waiting for its completion. To\",\n \"ip of the machine that will be making the connection,\",\n \"of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self, context, instance): \\\"\\\"\\\"Get\",\n \"thingy... :return: None \\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self, context, op,\",\n \"the current CPU frequency (MHz). All values are long integers.\",\n \"an instance on the host. :param instance: The instance to\",\n \"\\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off\",\n \"Exception as ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"), {'event': event,\",\n \"ID' and 'instance name' are synonyms. Note that the instance\",\n \"from the Hypervisor. If the instance is not found (for\",\n \"object :param block_device_info: instance block device information :param network_info: instance\",\n \"\\\"off_maintenance\\\" if maintenance mode got left. :rtype: str \\\"\\\"\\\" raise\",\n \"def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context in\",\n \"node can safely ignore this :returns: Dictionary describing resources \\\"\\\"\\\"\",\n \"raise NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information for the\",\n \"of an instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can\",\n \"return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to load the virtualization\",\n \"an aggregate. The counter action to this is :func:`add_to_aggregate` :param\",\n \"the given size (in Gigabyte) of `resize_to`. :return: None \\\"\\\"\\\"\",\n \"timeout, instances): \\\"\\\"\\\"Perform a reboot on all given 'instances'. Reboots\",\n \"context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self,\",\n \"] \\\"\\\"\\\" return None def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the\",\n \"new VM afterwards. It may be overridden by hypervisors that\",\n \"ability of this host to accept new instances. :param bool\",\n \"This is unfortunately also called a 'name' elsewhere. As far\",\n \"files to inject into instance. :param admin_password: <PASSWORD> to set\",\n \"instances to other hosts. Consider the combination with :func:`set_host_enabled`. :param\",\n \"str action: The action the host should perform. The valid\",\n \"domain. These IDs must stay internal to the platform-specific layer,\",\n \"the cache and remove images which are no longer of\",\n \":param nova.objects.instance.Instance instance: The instance to soft-delete. :return: None \\\"\\\"\\\"\",\n \"EC2 and internal Nova terminology), by which we mean 'running\",\n \"new instances. If it is `False`, the host won't accept\",\n \"<PASSWORD> to set in instance. :param bdms: block-device-mappings to use\",\n \"\\\"\\\"\\\"Manage the driver's local image cache. Some drivers chose to\",\n \"destroying the source VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek):\",\n \"NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information for the instance\",\n \"for the driver. :param nova.objects.instance.Instance instance: The instance to get\",\n \":returns: True if the instance was deleted from disk, False\",\n \"on rebuild \\\"\\\"\\\" raise NotImplementedError() def spawn(self, context, instance, image_meta,\",\n \"information about the interface to detach. :raise nova.exception.NovaException: If the\",\n \"time to wait for GuestOS to shutdown :param retry_interval: How\",\n \"check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup\",\n \"that instances boot from the network, and manage their own\",\n \"context in for access to auth_token raise NotImplementedError() def list_instance_uuids(self):\",\n \"this compute service manages a particular node.\\\"\\\"\\\" if nodename in\",\n \"set. :returns: The chosen device name. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"except Exception as ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"), {'event':\",\n \"A nova.objects.BlockDeviceMapping instance with all info about the requested block\",\n \"support \\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False,\",\n \"details are compatible with the set of MAC addresses. This\",\n \"instance. The counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext\",\n \"the host will accept new instances. If it is `False`,\",\n \"block_device_info: instance block device information :param network_info: instance network information\",\n \"get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection info for a MKS console.\",\n \"should be detached from the instance. :param destroy_disks: Indicates if\",\n \"supplied as an argument. Compute drivers constructors take a VirtAPI\",\n \"manage their own TFTP service. This requires passing the appropriate\",\n \"True if the instance is being recreated on a new\",\n \"pause(self, instance): \\\"\\\"\\\"Pause the given instance. A paused instance doesn't\",\n \"the ability of this host to accept new instances. :param\",\n \"based on that. :param nova.objects.instance.Instance instance: The instance to soft-delete.\",\n \"instance: The instance which gets a network interface removed. :param\",\n \"validate values are defined at disk API module. \\\"\\\"\\\" #\",\n \"is `False`, the host won't accept new instances. :return: If\",\n \"the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need\",\n \"result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a\",\n \"of an instance, by name (not ID!) :param instance: nova.objects.instance.Instance\",\n \"request context :param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta\",\n \"attached to the instance. :param preserve_ephemeral: True if the default\",\n \"an instance ID. \\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does\",\n \"given `instances` which are longer in the rebooting state than\",\n \"will match the actual configuration. Most hypervisors can use the\",\n \"called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.)\",\n \"Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information for\",\n \"or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or\",\n \"supplied. :param virtapi: a VirtAPI instance :param compute_driver: a compute\",\n \"access to auth_token raise NotImplementedError() def get_console_output(self, context, instance): \\\"\\\"\\\"Get\",\n \"guest state when a host is booted. :param instance: nova.objects.instance.Instance\",\n \"soft-deleted instance. The restored instance will be automatically booted. The\",\n \"network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this instance. A\",\n \"creation of the new instance. :param nova.objects.ImageMeta image_meta: The metadata\",\n \":param instance: nova.objects.instance.Instance that the network ports will be associated\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"\\\"\\\"\\\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce the\",\n \"NotImplementedError() def detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug to remove a\",\n \"Necessary network information for the resume. :param dict block_device_info: Instance\",\n \"identifier chosen by Nova to represent the instance further up\",\n \"driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver)\",\n \"snapshot. :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass\",\n \"from networks at source. :param context: security context :param instance:\",\n \"context in for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self,\",\n \"this method is unused. Note that this function takes an\",\n \"backed snapshots, this would include the file being merged, and\",\n \"layer is responsible for keeping track of which instance ID\",\n \"has the same state as before the 'suspend'. :param nova.context.RequestContext\",\n \"instance, by name (not ID!) :param instance: nova.objects.instance.Instance object Returns\",\n \"doesn't use CPU cycles or memory of the host anymore.\",\n \"Function to handle any bad volumes encountered \\\"\\\"\\\" raise NotImplementedError()\",\n \"metadata of the image of the instance. :param resize_instance: True\",\n \"and \\\"reboot\\\". :return: The result of the power action :rtype:\",\n \"A \\\"stronger\\\" version of `pause` is :func:'suspend'. The counter action\",\n \"instance: The instance to which the diagnostic data should be\",\n \"may obtain # a copy of the License at #\",\n \"was being migrated :param network_info: instance network information :param block_device_info:\",\n \"nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param dest: destination\",\n \"longer in the rebooting state than `timeout` seconds. :param int\",\n \"Volume to be snapshotted :param create_info: The data needed for\",\n \"a HARD or SOFT reboot :param block_device_info: Info pertaining to\",\n \"supported by this driver :param fs_type: the file system type\",\n \"disk_over_commit=False): \\\"\\\"\\\"Check if it is possible to execute live migration.\",\n \"filtering rules to hypervisor, since filtering rules to firewall rules\",\n \"the configuration of it. :param nova.objects.instance.Instance instance: The instance where\",\n \"The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance\",\n \"{} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of instance files on\",\n \"and this must be supplied. :param virtapi: a VirtAPI instance\",\n \"the memory or storage space of the host, depending on\",\n \"thread. \\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event)) return\",\n \"def get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection info for a serial\",\n \"given paused instance. The paused instance gets unpaused and will\",\n \"is the total number of I/O requests made, bytes is\",\n \"root device. \\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists):\",\n \"counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The\",\n \"hypervisors that need to - e.g. for optimisations, or when\",\n \":func:`set_host_enabled`. :param str host: The name of the host whose\",\n \"for GuestOS to shutdown :param retry_interval: How often to signal\",\n \"successfully even in cases in which the underlying domain/vm is\",\n \"layer, and never escape the connection interface. The platform-specific layer\",\n \"Don't use thread for this method since migration should not\",\n \"This is the data structure used by nova.compute to store\",\n \"methods that expect it. \\\"\\\"\\\" return True def volume_snapshot_create(self, context,\",\n \"b64_contents): \\\"\\\"\\\"Writes a file on the specified instance. The first\",\n \"may # not use this file except in compliance with\",\n \"self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization overhead required to\",\n \"current status of an instance, by name (not ID!) :param\",\n \"list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of all the instances known to\",\n \":param nova.objects.instance.Instance instance: The instance where the crash dump should\",\n \"compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to load\",\n \"the total number of I/O requests made, bytes is the\",\n \"default implementation which returns None. Hypervisors with MAC limits should\",\n \"dump the crash data. How the guest OS reacts in\",\n \"and allocate storage space this way. A \\\"softer\\\" way of\",\n \"case. :param context: security context :param instance: Instance object as\",\n \"user, | 'iowait': wait, | 'frequency': freq}, where kern and\",\n \"first parameter is an instance of nova.compute.service.Instance, and so the\",\n \"that should be detached from the instance. :param destroy_disks: Indicates\",\n \"be raised if the operation cannot complete. \\\"\\\"\\\" # TODO(Vek):\",\n \"'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ]\",\n \"which the diagnostic data should be returned. :return: Has a\",\n \"state when a host is booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"service manages a particular node.\\\"\\\"\\\" if nodename in self.get_available_nodes(): return\",\n \"dest, post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of an instance\",\n \"This is called during spawn_instance by the compute manager. :return:\",\n \"NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self,\",\n \"self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event)) return if not isinstance(event, virtevent.Event):\",\n \"node.\\\"\\\"\\\" if nodename in self.get_available_nodes(): return True # Refresh and\",\n \"known higher up the stack. Most virtualization platforms will also\",\n \"booted. The counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource\",\n \"multi compute-nodes support. If a driver supports multi compute-nodes, this\",\n \":param nova.objects.instance.Instance instance: The instance which should be unpaused. :return:\",\n \"the image of the instance. \\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self,\",\n \"interfaces (VIFs) from networks. The counter action is :func:`plug_vifs`. :param\",\n \"where rd indicates read, wr indicates write, req is the\",\n \"compute service manages a particular node.\\\"\\\"\\\" if nodename in self.get_available_nodes():\",\n \"Instance/flavor to calculate overhead for. :returns: Dict of estimated overhead\",\n \"no need to be called. Don't use thread for this\",\n \"for access to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info):\",\n \"check_instance_shared_storage calls :param context: security context :param data: result of\",\n \"\\\"\\\"\\\"Load a compute driver module. Load the compute driver module\",\n \"Currently only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context,\",\n \"snapshot, it is the image file Cinder expects to be\",\n \"does not need to be set, and should be decided\",\n \"action :func:`restore` uses the database entry to create a new\",\n \"import importutils import nova.conf from nova.i18n import _, _LE, _LI\",\n \":param instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created image\",\n \"example is to clean the cache and remove images which\",\n \"the network ports will be associated with :returns: a string\",\n \"about the VIFs to plug. :return: None \\\"\\\"\\\" # TODO(Vek):\",\n \"of DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'},\",\n \"which contains the uptime of this host since the last\",\n \"context: security context :param instance: instance object reference :param network_info:\",\n \"data): \\\"\\\"\\\"Check if instance files located on shared storage. :param\",\n \"networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The\",\n \"mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk to the instance\",\n \"access of instance files on the host. :param instance: nova.objects.instance.Instance\",\n \"may be other data included as well that is needed\",\n \"and their associated rules live in the datastore, and calling\",\n \"the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def\",\n \"the instance should be running (power_state.RUNNING). If this fails, any\",\n \"power_state.RUNNING. The virtualization platform should ensure that the reboot action\",\n \"use CPU cycles of the host anymore. The state of\",\n \"create_info): \\\"\\\"\\\"Snapshots volumes attached to a specified instance. The counter\",\n \"in which the underlying domain/vm is paused or halted/stopped. :param\",\n \"kwargs: A free-form thingy... :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified instance. :param context: security context\",\n \"VM could be stored in the memory or storage space\",\n \"aggregate. The counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext\",\n \"nova.objects.instance.Instance instance: The instance whose disk should be migrated. :param\",\n \"the context for the migration/resize :param migration: the migrate/resize information\",\n \"- root_device_name: device name of the root disk - ephemerals:\",\n \"block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance. After this is called\",\n \"uniquely identify a VM or domain. These IDs must stay\",\n \"instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context in for access\",\n \"The counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context:\",\n \"root device name for the driver. :param nova.objects.instance.Instance instance: The\",\n \"VirtAPI instance :param compute_driver: a compute driver name to override\",\n \"information :param block_migration: if true, post operation of block_migration. \\\"\\\"\\\"\",\n \"(Xen or libvirt terminology). An instance has an ID, which\",\n \"check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if instance files located on shared\",\n \"implemented. :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass\",\n \"the results in the DB. :param nodename: node which the\",\n \":param attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for\",\n \"the given `host` :param str host: The name of the\",\n \"host is booting up; freq indicates the current CPU frequency\",\n \":returns an instance of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self,\",\n \"the instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint,\",\n \"context: request context :param instance: nova.objects.instance.Instance that the network ports\",\n \"devices to be attached to the instance. :param preserve_ephemeral: True\",\n \"host after check_can_live_migrate calls :param context: security context :param dest_check_data:\",\n \"of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData\",\n \"network information :param block_migration: if true, post operation of block_migration.\",\n \"be attached to the instance. \\\"\\\"\\\" raise NotImplementedError() def destroy(self,\",\n \"of live migration at destination host. :param context: security context\",\n \"device mapping to be presented in the format of a\",\n \":param nova.objects.instance.Instance instance: The instance which gets a network interface\",\n \"when nova-compute launches, and as part of a periodic task\",\n \"ID to associate with network ports. :param context: request context\",\n \"information about instance resource usage. :returns: dict of nova uuid\",\n \"actual disk sizes of an instance. :param instance: nova.objects.Instance :param\",\n \"the resume. :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"block_device_info: Information about block devices that should be detached from\",\n \"specific params \\\"\\\"\\\" raise NotImplementedError() def reboot(self, context, instance, network_info,\",\n \"Maintainers of the virt drivers are encouraged to override this\",\n \"often to signal guest while waiting for it to shutdown.\",\n \"instance :param compute_driver: a compute driver name to override the\",\n \"The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should\",\n \"Nova terminology), by which we mean 'running virtual machine' (XenAPI\",\n \"raise NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False,\",\n \"disk information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError()\",\n \"filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of IPTables rules and apply the\",\n \"mappings for an instance to driver format. Virt drivers expect\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash\",\n \"again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information about\",\n \":param dict kwargs: A free-form thingy... :return: None \\\"\\\"\\\" #\",\n \":param nova.objects.instance.Instance instance: The instance whose volume gets replaced by\",\n \"def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump mechanism on the given\",\n \"NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration to complete :param\",\n \"instances. :param bool enabled: If this is `True`, the host\",\n \"to accept new instances. :param bool enabled: If this is\",\n \"instance. \\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host\",\n \"NotImplementedError() def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs) into\",\n \"migration. This runs checks on the destination host, and then\",\n \"\\\"\\\"\\\"Attach the disk to the instance at mountpoint using info.\\\"\\\"\\\"\",\n \"\\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks existence of an\",\n \"the allocate_for_instance call by the compute manager, and it is\",\n \"\\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms a\",\n \"should be paused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"contents of the file, also base64-encoded. NOTE(russellb) This method is\",\n \"of this host since the last boot. :rtype: str \\\"\\\"\\\"\",\n \"the specific language governing permissions and limitations # under the\",\n \"action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security\",\n \"on the results from check_can_live_migrate_destination. :param context: security context :param\",\n \"instance is being specified as instance.name. The second parameter is\",\n \"The restored instance will be automatically booted. The counter action\",\n \"if the live migration can succeed, based on the results\",\n \"network_info: instance network information :param block_device_info: instance volume block device\",\n \"the destination host. :param nova.objects.flavor.Flavor flavor: The flavor of the\",\n \"migration of all instances to other hosts. Consider the combination\",\n \"The interface to this class talks in terms of 'instances'\",\n \"method is an opportunity to do management of that cache\",\n \"\\\"\\\"\\\"Perform a reboot on all given 'instances'. Reboots the given\",\n \"connection info for a rdp console. :param context: security context\",\n \"the updated rules to instances running the specified security group.\",\n \"def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection info for a rdp\",\n \"instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified instance. :param context: security\",\n \"given 'instances'. Reboots the given `instances` which are longer in\",\n \"set of DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value':\",\n \"NOTE(markus_z): 2015-08-18 # The compute manager doesn't use this interface,\",\n \"this host since the last boot. :rtype: str \\\"\\\"\\\" #\",\n \"migrated. :param nova.network.model.NetworkInfo network_info: The network information of the given\",\n \"instance. These are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs],\",\n \"aggregate, host, **kwargs): \\\"\\\"\\\"Add a compute host to an aggregate.\",\n \"nova uuid => dict of usage info \\\"\\\"\\\" return {}\",\n \":param new_pass: the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def inject_file(self, instance,\",\n \"fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def detach_interface(self, instance, vif):\",\n \"\\\"\\\"\\\"Refresh security group rules Gets called when an instance gets\",\n \"driver method which is used to publish changes to the\",\n \"{'kernel': kern, | 'idle': idle, | 'user': user, | 'iowait':\",\n \"rules to instances running the specified security group. An error\",\n \"backend technology specific data needed to be able to complete\",\n \"volume block device info :param power_on: True if the instance\",\n \"to_xml may have to be called since it defines PROJNET,\",\n \"the instance. :param resize_instance: True if the instance is being\",\n \"value is specific to the Neutron client API. :return: None,\",\n \"is not human-readable or customer-controlled -- it's an internal ID\",\n \"should still succeed. It's probably a good idea to log\",\n \"CONDITIONS OF ANY KIND, either express or implied. See the\",\n \"which is the identifier chosen by Nova to represent the\",\n \"instance): \\\"\\\"\\\"Get connection info for a serial console. :param context:\",\n \"event %s\\\", str(event)) return if not isinstance(event, virtevent.Event): raise ValueError(\",\n \"missing device names in the block device mapping.\\\"\\\"\\\" raise NotImplementedError()\",\n \"nova.context.RequestContext context: The context for the resume. :param nova.objects.instance.Instance instance:\",\n \"this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param\",\n \"\\\"supports_device_tagging\\\": False, } def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback\",\n \":return: If the host can accept further instances, return \\\"enabled\\\",\",\n \"this method should apply the updated rules to instances running\",\n \"to auth_token raise NotImplementedError() def suspend(self, context, instance): \\\"\\\"\\\"Suspend the\",\n \"the system and remakes the VM with given 'metadata' and\",\n \"live migration. This checks if the live migration can succeed,\",\n \"now.\\\"\\\"\\\" pass def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" #\",\n \"the image of the instance. :param rescue_password: <PASSWORD> for rescue.\",\n \"network_info, block_device_info=None): \\\"\\\"\\\"Power on the specified instance. :param instance: nova.objects.instance.Instance\",\n \"be unpaused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"the virtualization layer, as a list. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"is called after a change to security groups. All security\",\n \"def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the volume\",\n \"\\\"\\\"\\\"Destroy and re-make this instance. A 'rebuild' effectively purges all\",\n \"'metadata' and 'personalities'. This base class method shuts down the\",\n \"post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug VIFs from networks at source.\",\n \"to remove a network interface from a running instance. The\",\n \"is the image file Cinder expects to be used as\",\n \"specific to the virtualization platform. \\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\":\",\n \"the config opt :returns: a ComputeDriver instance \\\"\\\"\\\" if not\",\n \"data: result of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context,\",\n \"block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or any of\",\n \"block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of live migration at destination host.\",\n \"case of doing a snapshot, it is the image file\",\n \"IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"into the given `instance` at instance boot time. The counter\",\n \"and errs is the number of requests held up due\",\n \"compute manager. Invokes the event callback registered by the compute\",\n \"\\\"\\\"\\\"Retrieves the IP address of the dom0 \\\"\\\"\\\" # TODO(Vek):\",\n \"raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if instance files\",\n \"by the service. Otherwise, this method should return [hypervisor_hostname]. \\\"\\\"\\\"\",\n \"held up due to a full pipeline. All counters are\",\n \"context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest state when a host\",\n \"bare metal) require that instances boot from the network, and\",\n \"network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param context:\",\n \"invoked with a single parameter, which will be an instance\",\n \"to attach. :raise nova.exception.NovaException: If the attach fails. :return: None\",\n \":param context: the context for the finish_revert_migration :param instance: nova.objects.instance.Instance\",\n \"an instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can be\",\n \"new instance based on that. :param nova.objects.instance.Instance instance: The instance\",\n \"to the virtualization layer, as a list. \\\"\\\"\\\" # TODO(Vek):\",\n \"ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo object \\\"\\\"\\\"\",\n \"layer. This layer is responsible for translating that generic data\",\n \"migrates those value through migrateToURI(), so , no need to\",\n \"be invoked from a green thread. \\\"\\\"\\\" if not self._compute_event_callback:\",\n \"\\\"\\\"\\\"Turn off deferral of IPTables rules and apply the rules\",\n \"is not found (for example if networking failed), this function\",\n \"chosen by Nova to represent the instance further up the\",\n \"doing a snapshot, it is the image file Cinder expects\",\n \"I/O requests made, bytes is the total number of bytes\",\n \"be associated with :returns: a string representing the host ID\",\n \"security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS\",\n \"spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a\",\n \"instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot of a volume\",\n \"'pause'. :param nova.objects.instance.Instance instance: The instance which should be unpaused.\",\n \"All values are long integers. \\\"\\\"\\\" raise NotImplementedError() def block_stats(self,\",\n \"a callback to receive asynchronous event notifications from hypervisors. The\",\n \"init_host(self, host): \\\"\\\"\\\"Initialize anything that is necessary for the driver\",\n \"The state of the instance could be persisted on the\",\n \"host. :return: A text which contains the uptime of this\",\n \"that the reboot action has completed successfully even in cases\",\n \"supported file system # type and implement this function at\",\n \"by another one. :param str mountpoint: The mountpoint in the\",\n \"_LI from nova import utils from nova.virt import event as\",\n \"host from an aggregate. The counter action to this is\",\n \"devices. :param int timeout: The time in seconds to wait\",\n \"auth_token raise NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean up anything that\",\n \"be set, and should be decided by the driver implementation\",\n \"the crash data. How the guest OS reacts in details,\",\n \":param instance: nova.objects.instance.Instance :returns: True if the instance was deleted\",\n \":func:'suspend'. The counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance\",\n \"nova.exception.NovaException: If the attach fails. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to\",\n \"of a periodic task that records the results in the\",\n \"context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated.\",\n \"instance, then this method should not be implemented. :param context:\",\n \"attach to the volume. This is the same data format\",\n \"dest host after check_can_live_migrate calls :param context: security context :param\",\n \"def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the file format is supported\",\n \":param network_info: instance network information :param block_device_info: instance volume block\",\n \"to ensure that all assigned network details are compatible with\",\n \"prepare for block migration :param disk_over_commit: if true, allow disk\",\n \"The metadata of the image of the instance. :param resize_instance:\",\n \"for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all\",\n \"return True def volume_snapshot_create(self, context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes\",\n \"the nova.virt layer, instances do not have human-readable names at\",\n \"should use the data there to guide the creation of\",\n \"migrated. :param dest: destination host :param post_method: post operation method.\",\n \"method since migration should not be started when setting-up filtering\",\n \"False otherwise. .. note:: This implementation works for all drivers,\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self, context, migration, instance, disk_info, network_info,\",\n \"the host and allocate storage space this way. A \\\"softer\\\"\",\n \"| 'user': user, | 'iowait': wait, | 'frequency': freq}, where\",\n \"instance object that was migrated :block_device_info: instance block device information\",\n \"context in for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self,\",\n \"the VM could be stored in the memory or storage\",\n \"domain, with arbitrary VLANs etc, but regular switches require pre-configured\",\n \"delete_info: Volume backend technology specific data needed to be able\",\n \"context, all_instances): \\\"\\\"\\\"Manage the driver's local image cache. Some drivers\",\n \"to the instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance,\",\n \"\\\"\\\"\\\"Unquiesce the specified instance after snapshots. If the specified instance\",\n \"by nova.compute to store details regarding an instance, and pass\",\n \"only for filtering rules to hypervisor, since filtering rules to\",\n \"mean 'running virtual machine' (XenAPI terminology) or domain (Xen or\",\n \"context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that\",\n \"to a running instance. The counter action to this is\",\n \"host: The name of the host to add to the\",\n \"up the new VM afterwards. It may be overridden by\",\n \"first object and this must be supplied. :param virtapi: a\",\n \"resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest state when a\",\n \"to the instance's metadata to the hypervisor. If the hypervisor\",\n \"power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration. :param context: the context for\",\n \"from the given `instance`. :param dict new_connection_info: The volume for\",\n \"of check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info,\",\n \"destroyed :param migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError() def\",\n \"context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \\\"\\\"\\\"\",\n \"of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics\",\n \"and so the instance is being specified as instance.name. The\",\n \"power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param context: the context for the\",\n \"| 'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\" return None def manage_image_cache(self,\",\n \"should be unpaused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide\",\n \"method is optional. On some platforms (e.g. XenAPI) performance statistics\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network info\",\n \"the volume attached. :param uuid volume_id: Attached volume associated with\",\n \"is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate\",\n \"of publishing the instance metadata to the instance, then this\",\n \"\\\"\\\"\\\"Get the currently known host CPU stats. :returns: a dict\",\n \"about block devices to be attached to the instance. \\\"\\\"\\\"\",\n \"migration, instance, network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying the source VM.\",\n \"storage space this way. A \\\"softer\\\" way of `suspend` is\",\n \"security context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError()\",\n \":param dict block_device_info: Instance volume block device info. :return: None\",\n \"responsible for translating that generic data structure into terms that\",\n \"turning off the instance before the end. :param nova.objects.instance.Instance instance:\",\n \"VirtAPI object as their first object and this must be\",\n \"only be invoked from a green thread. \\\"\\\"\\\" if not\",\n \"security context :param instance: nova.objects.instance.Instance :param image_id: Reference to a\",\n \"that compute drivers must follow, and shared types that support\",\n \"call. In the case of doing a snapshot, it is\",\n \"host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host maintenance window. On start, it\",\n \"not isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event must be an instance\",\n \"is :func:`resume`. :param nova.context.RequestContext context: The context for the suspend.\",\n \"or \\\"off_maintenance\\\" if maintenance mode got left. :rtype: str \\\"\\\"\\\"\",\n \"block_device_info: Information about block devices to be attached to the\",\n \"options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name':\",\n \"False otherwise :param block_device_info: instance volume block device info :param\",\n \"statistics can be retrieved directly in aggregate form, without Nova\",\n \"host won't accept new instances. :return: If the host can\",\n \"'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'}\",\n \"The instance that has the volume attached. :param uuid volume_id:\",\n \"result of _get_instance_block_device_info :returns: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError()\",\n \"(such as bare metal) cannot do freeform virtualization of MAC\",\n \":param block_migration: if true, post operation of block_migration. \\\"\\\"\\\" raise\",\n \":param dict delete_info: Volume backend technology specific data needed to\",\n \"instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it is possible\",\n \"block_device_obj): \\\"\\\"\\\"Get the next device name based on the block\",\n \"block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base\",\n \"by kernel and user processes respectively, idle indicates the cumulative\",\n \"exists on the host, False otherwise. .. note:: This implementation\",\n \"raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms a resize/migration,\",\n \"transferred, and errs is the number of requests held up\",\n \"specified instance from the Hypervisor. If the instance is not\",\n \"be an instance of the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback =\",\n \"has the volume attached :param uuid volume_id: Volume to be\",\n \"injected_files: User files to inject into instance. :param admin_password: Administrator\",\n \"in cases in which the underlying domain/vm is paused or\",\n \"and the hostname of the machine as follows:: { 'ip':\",\n \"file being merged into (if appropriate). :return: None \\\"\\\"\\\" raise\",\n \"for usage. :param network_info: instance network information :param recreate: True\",\n \"This is an optional driver method which is used to\",\n \"means 'these and only these MAC addresses'. \\\"\\\"\\\" return None\",\n \"for a rdp console. :param context: security context :param instance:\",\n \"in-progress live migration. :param instance: instance that is live migrating\",\n \"network information :param block_device_info: Information about block devices to be\",\n \"virtualization layer, as a list. \\\"\\\"\\\" raise NotImplementedError() def rebuild(self,\",\n \"aggregate form, without Nova having to do the aggregation. On\",\n \"nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def\",\n \"a compute host to an aggregate. The counter action to\",\n \"= block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class\",\n \"deferral of IPTables rules and apply the rules now.\\\"\\\"\\\" pass\",\n \"\\\"\\\"\\\"Check if it is possible to execute live migration. This\",\n \"\\\"\\\"\\\"Unplug VIFs from networks at source. :param context: security context\",\n \"machine :param dst_compute_info: Info about the receiving machine :param block_migration:\",\n \"block_device_info or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0}\",\n \"= dict() for driver_str in named_driver_config: driver_type, _sep, driver =\",\n \"The network information of the given `instance`. :param dict block_device_info:\",\n \"str \\\"\\\"\\\" raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state):\",\n \"the power action :rtype: : str \\\"\\\"\\\" raise NotImplementedError() def\",\n \"get_host_uptime(self): \\\"\\\"\\\"Returns the result of calling the Linux command `uptime`\",\n \"instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return\",\n \"ID chosen by Nova. At the nova.virt layer, instances do\",\n \"instance ID maps to which platform-specific ID, and vice versa.\",\n \"connection gets detached from the given `instance`. :param dict new_connection_info:\",\n \"NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the\",\n \"> 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals\",\n \"at mountpoint using info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self, connection_info, instance,\",\n \"the same state as before the 'pause'. :param nova.objects.instance.Instance instance:\",\n \"context, instance): \\\"\\\"\\\"Check if instance files located on shared storage.\",\n \"destroy_disks: if true, destroy disks at destination during cleanup :param\",\n \":param int timeout: The timeout (in seconds) for considering rebooting\",\n \"required for validation of access to instance shared disk files\",\n \"unpause(self, instance): \\\"\\\"\\\"Unpause the given paused instance. The paused instance\",\n \"the uptime of this host since the last boot. :rtype:\",\n \"their own identity schemes, to uniquely identify a VM or\",\n \"hypervisors and firewalls are inevitable on destination host. ( Waiting\",\n \"is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets a\",\n \"for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which\",\n \"= block_device_info or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size':\",\n \"manager, and it is up to that call to ensure\",\n \"'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name':\",\n \"data included as well that is needed for creating the\",\n \"is an optional driver method which is used to publish\",\n \"\\\"\\\"\\\"Finish reverting a resize/migration. :param context: the context for the\",\n \"the given 'instance'. :param nova.objects.instance.Instance instance: The instance whose volume\",\n \"out to the DHCP service. Most hypervisors can use the\",\n \"all -- such things are only known higher up the\",\n \"instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on the specified instance. :param instance:\",\n \"to override this method with something more efficient. \\\"\\\"\\\" return\",\n \"this fails, any partial instance should be completely cleaned up,\",\n \"context: security context :param instance: nova.objects.instance.Instance :returns an instance of\",\n \"instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError()\",\n \"If the attach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"groups and their associated rules live in the datastore, and\",\n \"libvirt migrates those value through migrateToURI(), so , no need\",\n \"for each interface on each running VM. :param instances: nova.objects.instance.InstanceList\",\n \"to suspend. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume(self, context,\",\n \"since the host is booting up; freq indicates the current\",\n \"disk. This method is an opportunity to do management of\",\n \"given size (in Gigabyte) of `resize_to`. :return: None \\\"\\\"\\\" raise\",\n \"pass them into this layer. This layer is responsible for\",\n \"as a list. \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None):\",\n \"\\\"\\\"\\\"Snapshots volumes attached to a specified instance. The counter action\",\n \"\\\"\\\"\\\"Get host ID to associate with network ports. :param context:\",\n \"the data structure used by nova.compute to store details regarding\",\n \"suspend. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume(self, context, instance,\",\n \"virtualization of MAC addresses. This method allows drivers to return\",\n \"console. :param context: security context :param instance: nova.objects.instance.Instance :returns an\",\n \":block_device_info: instance block device information :param migrate_data: a LiveMigrateData object\",\n \"safely ignore this :returns: Dictionary describing resources \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing device names in the\",\n \"uses the database entry to create a new instance based\",\n \"destination node after a failed live migration. :param context: security\",\n \"= None def init_host(self, host): \\\"\\\"\\\"Initialize anything that is necessary\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of all\",\n \"except in compliance with the License. You may obtain #\",\n \"note:: This implementation works for all drivers, but it is\",\n \"'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This\",\n \"instance): \\\"\\\"\\\"Get console output for an instance :param context: security\",\n \"the Linux command `uptime` on this host. :return: A text\",\n \"'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return the names of all the\",\n \"raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None):\",\n \"object that was migrated :block_device_info: instance block device information :param\",\n \"NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return whether this compute service manages\",\n \"a specified instance. The counter action to this is :func:`volume_snapshot_delete`\",\n \"this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param\",\n \"volume, it gets resized to the given size (in Gigabyte)\",\n \"if not set. :returns: The chosen device name. \\\"\\\"\\\" raise\",\n \"back to power_state.RUNNING. The virtualization platform should ensure that the\",\n \"dict of usage info \\\"\\\"\\\" return {} def instance_on_disk(self, instance):\",\n \"def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo for Resource\",\n \":param context: security context :param instance: nova.objects.instance.Instance :param image_id: Reference\",\n \"src_compute_info: Info about the sending machine :param dst_compute_info: Info about\",\n \"the maintenance mode. :return: \\\"on_maintenance\\\" if switched to maintenance mode\",\n \"context, instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified instance. :param\",\n \"available as a database entry. The counter action :func:`restore` uses\",\n \"the block device mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms,\",\n \"indicates the cumulative I/O wait CPU time (nanoseconds), since the\",\n \"*block_device_lists): \\\"\\\"\\\"Default the missing device names in the block device\",\n \"network interface. :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"the driver requires legacy block device info. Tell the caller\",\n \"call began. :param context: security context :param instance: nova.objects.instance.Instance This\",\n \"# type and implement this function at their # virt\",\n \"get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection info for a spice console.\",\n \"serial console. :param context: security context :param instance: nova.objects.instance.Instance :returns\",\n \":param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance: The\",\n \"nova.objects.instance.Instance instance: The instance that has the volume attached :param\",\n \"to the Neutron client API. :return: None, or a set\",\n \"return None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options for this\",\n \":return: None \\\"\\\"\\\" # NOTE(jogo) Currently only used for XenAPI-Pool\",\n \"be started when setting-up filtering rules operations are not completed.\",\n \"indicates read, wr indicates write, req is the total number\",\n \"do freeform virtualization of MAC addresses. This method allows drivers\",\n \"specific language governing permissions and limitations # under the License.\",\n \"# not use this file except in compliance with the\",\n \"compute manager, and it is up to that call to\",\n \"network information :param recreate: True if the instance is being\",\n \"the image of the instance. :param injected_files: User files to\",\n \"is migrated :param network_info: instance network information :param block_migration: if\",\n \"on a new hypervisor - all the cleanup of old\",\n \"CPU cycles of the host anymore. The state of the\",\n \"the manager should be the controlling thing here. \\\"\\\"\\\"Unplug virtual\",\n \"network information for the resume. :param dict block_device_info: Instance volume\",\n \"of the host to remove from the given `aggregate`. :param\",\n \"about the interface to detach. :raise nova.exception.NovaException: If the detach\",\n \"specific to the Neutron client API. :return: None, or a\",\n \"context for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param\",\n \"security group the instance is a member of or if\",\n \"capabilities = { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\":\",\n \"password on the specified instance. :param instance: nova.objects.instance.Instance :param new_pass:\",\n \"after a failed live migration. :param context: security context :param\",\n \"the old volume, it gets resized to the given size\",\n \"an instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def unrescue(self,\",\n \"nova.objects.flavor.Flavor flavor: The flavor of the instance whose disk get\",\n \":param nova.objects.instance.Instance instance: The instance that has the volume attached\",\n \"that every hypervisor # need to define their supported file\",\n \"NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta,\",\n \"security context :param instance: instance object that is migrated :param\",\n \"host after check_instance_shared_storage calls :param context: security context :param data:\",\n \"method should return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def node_is_available(self, nodename):\",\n \"be passed in to methods that expect it. \\\"\\\"\\\" return\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known\",\n \"the compute_driver configuration option or, if supplied, the driver name\",\n \"are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd\",\n \":param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError()\",\n \"that is necessary for the driver to function, including catching\",\n \"Reference to a pre-created image that will hold the snapshot.\",\n \"block device. device_name does not need to be set, and\",\n \"updated rules to instances running the specified security group. An\",\n \"supports multi compute-nodes, this method returns a list of nodenames\",\n \"that manages only one node can safely ignore this :returns:\",\n \"nova.objects.instance.Instance that volume is requesting a device name :param bdms:\",\n \"information :param block_device_info: instance block device information :param destroy_disks: if\",\n \"check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information\",\n \"block_device_info: Info pertaining to attached volumes :param bad_volumes_callback: Function to\",\n \"the instances known to the virtualization layer, as a list.\",\n \"'instance'. :param nova.objects.instance.Instance instance: The instance whose volume gets replaced\",\n \"instance resources . Instance should have been destroyed from the\",\n \"block device mappings for an instance to driver format. Virt\",\n \"need to be set, and should be decided by the\",\n \"block device info to be passed in to methods that\",\n \"name. \\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the\",\n \"paused instance gets unpaused and will use CPU cycles of\",\n \"has no means of publishing the instance metadata to the\",\n \"the set of MAC addresses. This is called during spawn_instance\",\n \"the image of the instance. :param nova.network.model.NetworkInfo vif: The object\",\n \"snapshot_id: The snapshot to delete. :param dict delete_info: Volume backend\",\n \"the instance is a member of or if the group\",\n \"of the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The\",\n \"image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified instance. :param nova.context.RequestContext context: The\",\n \"chose to cache images for instances on disk. This method\",\n \"legacy block device info. Tell the caller whether we expect\",\n \"specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \\\"\\\"\\\"\",\n \"This runs checks on the destination host, and then calls\",\n \"specialized subclasses. \\\"\\\"\\\" from nova.virt import block_device as virt_block_device block_device_info\",\n \"Cinder expects to be used as the active disk after\",\n \"the iscsi initiator and the hostname of the machine as\",\n \"nova.objects.instance.Instance :param new_pass: the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def inject_file(self,\",\n \"raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest\",\n \"network_info: instance network information :param block_migration: if true, post operation\",\n \"or any of it's more specialized subclasses. \\\"\\\"\\\" from nova.virt\",\n \"\\\"\\\"\\\"Detach the disk attached to the instance.\\\"\\\"\\\" raise NotImplementedError() def\",\n \"to auth_token pass def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a reboot\",\n \"driver. :param nova.objects.instance.Instance instance: The instance to get the root\",\n \"# Copyright 2011 <NAME> # All Rights Reserved. # #\",\n \"this host, return \\\"disabled\\\". :rtype: str \\\"\\\"\\\" # TODO(Vek): Need\",\n \"\\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get\",\n \"old volume, it gets resized to the given size (in\",\n \"way. A \\\"softer\\\" way of `suspend` is :func:`pause`. The counter\",\n \"auth_token raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the result of calling\",\n \"CPU stats. :returns: a dict containing the CPU stat info,\",\n \"ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"), {'event': event, 'ex': ex})\",\n \"values are defined at disk API module. \\\"\\\"\\\" # NOTE(jichenjc):\",\n \"not found (for example if networking failed), this function should\",\n \"bad volumes encountered \\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self, console_type): #\",\n \"this is `True`, the host will accept new instances. If\",\n \"human-readable names at all -- such things are only known\",\n \"will use CPU cycles of the host again. The counter\",\n \"power_on(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on the specified instance.\",\n \"instance): \\\"\\\"\\\"Checks existence of an instance on the host. :param\",\n \"result of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance,\",\n \"instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration.\",\n \"def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress live migration. :param instance:\",\n \"a big overlap to the return value of the older\",\n \"and shared types that support that contract \\\"\\\"\\\" import sys\",\n \":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \\\"\\\"\\\" raise\",\n \"the newer interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" # TODO(Vek): Need\",\n \"to be set, and should be decided by the driver\",\n \":param block_device_info: Information about block devices that should be detached\",\n \"instance of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return\",\n \"results. :param context: security context :param instance: nova.objects.instance.Instance object \\\"\\\"\\\"\",\n \"rescue. \\\"\\\"\\\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the\",\n \"writing, software # distributed under the License is distributed on\",\n \"a new instance based on that. :param nova.objects.instance.Instance instance: The\",\n \", no need to be called. Don't use thread for\",\n \"up steps. :param context: security context :param instance: nova.objects.instance.Instance This\",\n \"def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given instance.\",\n \"instance): \\\"\\\"\\\"Get connection info for a vnc console. :param context:\",\n \"if true, allow disk over commit :returns: a LiveMigrateData object\",\n \"driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device mappings for an\",\n \"context :param instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created\",\n \"instance to prepare for snapshots. If the specified instance doesn't\",\n \"module. Load the compute driver module specified by the compute_driver\",\n \"the VIFs to unplug. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"dict containing the CPU stat info, eg: | {'kernel': kern,\",\n \"block device info. Tell the caller whether we expect the\",\n \"not particularly efficient. Maintainers of the virt drivers are encouraged\",\n \"the hypervisor has no means of publishing the instance metadata\",\n \"total number of bytes transferred, and errs is the number\",\n \"def get_host_uptime(self): \\\"\\\"\\\"Returns the result of calling the Linux command\",\n \"context :param instance: instance object reference :param network_info: instance network\",\n \"context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new\",\n \"is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance\",\n \"quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce the specified instance to prepare\",\n \"a full pipeline. All counters are long integers. This method\",\n \"raise NotImplementedError() def get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection info for\",\n \"on the destination host, and then calls back to the\",\n \"details regarding an instance, and pass them into this layer.\",\n \"operation of live migration at destination host. :param context: security\",\n \"\\\"\\\"\\\" raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots\",\n \"that is live migrating \\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context,\",\n \"suspended instance gets resumed and will use CPU cycles and\",\n \"(e.g. XenAPI) performance statistics can be retrieved directly in aggregate\",\n \"define their supported file system # type and implement this\",\n \"up filtering rules and waiting for its completion. To migrate\",\n \"the instance resources . Instance should have been destroyed from\",\n \":param injected_files: User files to inject into instance. :param admin_password:\",\n \"aggregate: The aggregate which should add the given `host` :param\",\n \"get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information. This method is called when\",\n \"instance): \\\"\\\"\\\"Delete any lingering instance files for an instance. :param\",\n \"\\\"\\\"\\\"Checks existence of an instance on the host. :param instance:\",\n \"raise NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage counters for\",\n \"request context :param instance: nova.objects.instance.Instance that the network ports will\",\n \"files located on shared storage. :param context: security context :param\",\n \"terms of 'instances' (Amazon EC2 and internal Nova terminology), by\",\n \"enabled): \\\"\\\"\\\"Sets the ability of this host to accept new\",\n \"for. :returns: Dict of estimated overhead values. \\\"\\\"\\\" return {'memory_mb':\",\n \"which will be supplied to the allocate_for_instance call by the\",\n \"method is for multi compute-nodes support. If a driver supports\",\n \"which contains information about the VIFs to unplug. :return: None\",\n \"error should be raised if the operation cannot complete. \\\"\\\"\\\"\",\n \"block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class for compute drivers. The interface\",\n \"bool mode: If `True`, go into maintenance mode. If `False`,\",\n \"import nova.conf from nova.i18n import _, _LE, _LI from nova\",\n \"default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a default root device name\",\n \"this way. A \\\"softer\\\" way of `suspend` is :func:`pause`. The\",\n \"guest OS reacts in details, depends on the configuration of\",\n \"False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, } def __init__(self,\",\n \"\\\"enabled\\\", if further instances shouldn't be scheduled to this host,\",\n \"instance, network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying the source VM. :param\",\n \"at source host. :param context: security context :instance: instance object\",\n \"are desired. :param instance_info: Instance/flavor to calculate overhead for. :returns:\",\n \"instance: The soft-deleted instance which should be restored from the\",\n \"instance which will get an additional network interface. :param nova.objects.ImageMeta\",\n \"dict \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"is attached to. :param int resize_to: If the new volume\",\n \"`old_connection_info` is attached to. :param int resize_to: If the new\",\n \"The action the host should perform. The valid actions are:\",\n \"MAC addresses must this instance have? Some hypervisors (such as\",\n \"__init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None def init_host(self,\",\n \"which has the information about the interface to detach. :raise\",\n \"a new instance/VM/domain on the virtualization platform. Once this successfully\",\n \"if the default ephemeral storage partition must be preserved on\",\n \"License. \\\"\\\"\\\" Driver base-classes: (Beginning of) the contract that compute\",\n \"for access to auth_token raise NotImplementedError() def get_console_output(self, context, instance):\",\n \":param context: security context :param instance: Instance object as returned\",\n \"of it. :param nova.objects.instance.Instance instance: The instance where the crash\",\n \"new_pass: the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def inject_file(self, instance, b64_path,\",\n \"to represent the instance further up the stack. This is\",\n \"admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and\",\n \"is :func:`pause`. The counter action for `suspend` is :func:`resume`. :param\",\n \"is booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def rescue(self,\",\n \"accessible on the host, False otherwise. .. note:: Used in\",\n \"should be raised if the operation cannot complete. \\\"\\\"\\\" #\",\n \"device info :param power_on: True if the instance should be\",\n \"(nanoseconds), since the host is booting up; freq indicates the\",\n \"after snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported\",\n \"since migration should not be started when setting-up filtering rules\",\n \"rules and waiting for its completion. To migrate an instance,\",\n \"of block_migration. \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check\",\n \"raise NotImplementedError() def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection info for\",\n \"catching up with currently running VM's on the given host.\",\n \"instance): \\\"\\\"\\\"Checks access of instance files on the host. :param\",\n \":param instance: nova.objects.instance.Instance that volume is requesting a device name\",\n \"bytes is the total number of bytes transferred, and errs\",\n \"instance: nova.objects.instance.Instance object :param block_device_info: instance block device information :param\",\n \"a set of DHCP options, eg: | [{'opt_name': 'bootfile-name', |\",\n \"context, instance): \\\"\\\"\\\"Get connection info for a MKS console. :param\",\n \"\\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post\",\n \"the volume. This is the same data format returned by\",\n \"A paused instance doesn't use CPU cycles of the host\",\n \"set of MAC addresses that the instance is to have.\",\n \":param str host: The name of the host to add\",\n \"so , no need to be called. Don't use thread\",\n \"value of the newer interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" #\",\n \"pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance\",\n \"empty) list of DriverVolumeBlockDevice or any of it's more specialized\",\n \"leave the maintenance mode. :return: \\\"on_maintenance\\\" if switched to maintenance\",\n \"True if the instance was deleted from disk, False otherwise.\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to\",\n \"Rights Reserved. # # Licensed under the Apache License, Version\",\n \"to the return value of the newer interface :func:`get_instance_diagnostics` :rtype:\",\n \"instance): \\\"\\\"\\\"Restore the specified soft-deleted instance. The restored instance will\",\n \"this successfully completes, the instance should be running (power_state.RUNNING). If\",\n \"layer, as a list. \\\"\\\"\\\" raise NotImplementedError() def rebuild(self, context,\",\n \"nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def\",\n \"the VIFs to plug. :return: None \\\"\\\"\\\" # TODO(Vek): Need\",\n \"for keeping track of which instance ID maps to which\",\n \"this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets\",\n \"existing data from the system and remakes the VM with\",\n \"device_name does not need to be set, and should be\",\n \"overhead required to build an instance of the given flavor.\",\n \"metadata of the image of the instance. :param injected_files: User\",\n \"network interface to a running instance. The counter action to\",\n \"pass context in for access to auth_token pass def poll_rebooting_instances(self,\",\n \"of the return value is specific to the Neutron client\",\n \"anything that is necessary for the driver gracefully stop, including\",\n \"host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance\",\n \"other data included as well that is needed for creating\",\n \"'frequency': freq}, where kern and user indicate the cumulative CPU\",\n \"the instance at mountpoint using info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self,\",\n \"ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to load the virtualization driver\\\")) sys.exit(1)\",\n \"def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host ID to associate with\",\n \"checks if the live migration can succeed, based on the\",\n \"ID, and vice versa. Some methods here take an instance\",\n \":param bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A\",\n \"instances shouldn't be scheduled to this host, return \\\"disabled\\\". :rtype:\",\n \"context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self,\",\n \"be making the connection, the name of the iscsi initiator\",\n \"context for the migration/resize :param migration: the migrate/resize information :param\",\n \"network information :param block_device_info: Information about block devices that should\",\n \"nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred disk information\",\n \"raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if\",\n \"instance, nw_info): \\\"\\\"\\\"inject network info for specified instance.\\\"\\\"\\\" # TODO(Vek):\",\n \"'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method\",\n \"uuid => dict of usage info \\\"\\\"\\\" return {} def\",\n \"the soft-deleted data. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self,\",\n \"volumes encountered \\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek):\",\n \"nodename: node which the caller want to get resources from\",\n \"the given `aggregate`. :param dict kwargs: A free-form thingy... :return:\",\n \"security context :param instance: nova.objects.instance.Instance This function should use the\",\n \"hypervisor has no means of publishing the instance metadata to\",\n \"any lingering instance files for an instance. :param instance: nova.objects.instance.Instance\",\n \"being recreated on a new hypervisor - all the cleanup\",\n \"assigned network details are compatible with the set of MAC\",\n \"_(\\\"Event must be an instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event\",\n \"before calling this method. :param context: security context :param instance:\",\n \"by which we mean 'running virtual machine' (XenAPI terminology) or\",\n \"the specified instance. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"hypervisors. The callback will be invoked with a single parameter,\",\n \":param nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info:\",\n \"nodename): \\\"\\\"\\\"Return whether this compute service manages a particular node.\\\"\\\"\\\"\",\n \"\\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the file\",\n \"NotImplementedError() def get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection info for a\",\n \"to volumes. Connector information is a dictionary representing the ip\",\n \"get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection info for a vnc console.\",\n \"returns None. This is called during spawn_instance by the compute\",\n \"The suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network\",\n \"for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance\",\n \"instance is to have. allocate_for_instance will take this into consideration\",\n \"which should be unpaused. :return: None \\\"\\\"\\\" # TODO(Vek): Need\",\n \"it fails to quiesce by other errors (e.g. agent timeout),\",\n \"of the host to add to the given `aggregate`. :param\",\n \"NotImplementedError() def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the root password on\",\n \"\\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the\",\n \"source VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"during cleanup :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError()\",\n \"a network interface to a running instance. The counter action\",\n \"'no constraints', a set means 'these and only these MAC\",\n \"Return False here so that every hypervisor # need to\",\n \"valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return: The result\",\n \"\\\"\\\"\\\"Register a callback to receive events. Register a callback to\",\n \"list. \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"instance. After this is called successfully, the instance's state goes\",\n \"given `instance`. :param dict new_connection_info: The volume for this connection\",\n \"at their # virt layer. return False def quiesce(self, context,\",\n \"instance. The counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext\",\n \"wr_bytes, errs], where rd indicates read, wr indicates write, req\",\n \"function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param\",\n \"on destination host. ( Waiting only for filtering rules to\",\n \"to be passed in to methods that expect it. \\\"\\\"\\\"\",\n \"(e.g. agent timeout), NovaException is raised. :param context: request context\",\n \"to unplug. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get\",\n \"chosen by Nova. At the nova.virt layer, instances do not\",\n \"information :param recreate: True if the instance is being recreated\",\n \"auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies a\",\n \"connection info for a serial console. :param context: security context\",\n \"instance which should be restored from the soft-deleted data. :return:\",\n \"instance: nova.objects.instance.Instance that the network ports will be associated with\",\n \"After this is called successfully, the instance's state goes back\",\n \"(power_state.RUNNING). If this fails, any partial instance should be completely\",\n \"= nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry\",\n \"The platform-specific layer is responsible for keeping track of which\",\n \"**kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device mappings\",\n \"get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of all nodes managed by the\",\n \"\\\"\\\"\\\"Cleanup the instance resources . Instance should have been destroyed\",\n \"is used to publish changes to the instance's metadata to\",\n \"platforms, this method is unused. Note that this function takes\",\n \"injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain on the\",\n \"driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry\",\n \"strings with below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'},\",\n \"all assigned network details are compatible with the set of\",\n \"to load the virtualization driver\\\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver\",\n \"hypervisors (such as bare metal) cannot do freeform virtualization of\",\n \"Virt drivers expect block device mapping to be presented in\",\n \"\\\"\\\"\\\"Dispatches an event to the compute manager. Invokes the event\",\n \"information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" pass def post_live_migration_at_source(self,\",\n \"network_info, block_device_info=None): \\\"\\\"\\\"resume guest state when a host is booted.\",\n \"records the results in the DB. :param nodename: node which\",\n \"hypervisor # need to define their supported file system #\",\n \"context: request context :param instance: nova.objects.instance.Instance to be quiesced :param\",\n \"returns a list of nodenames managed by the service. Otherwise,\",\n \"method should apply the updated rules to instances running the\",\n \"to detach. :raise nova.exception.NovaException: If the detach fails. :return: None\",\n \"indicates the cumulative idle CPU time (nanoseconds), wait indicates the\",\n \"takes an instance ID. \\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance):\",\n \"'instance name' are synonyms. Note that the instance ID or\",\n \"new_pass): \\\"\\\"\\\"Set the root password on the specified instance. :param\",\n \"rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None,\",\n \"managed by the service. Otherwise, this method should return [hypervisor_hostname].\",\n \"volumes attached to a specified instance. The counter action to\",\n \"All counters are long integers. This method is optional. On\",\n \"as bare metal) require that instances boot from the network,\",\n \"nova.objects.instance.Instance to lookup Returns True if files of an instance\",\n \"that the network ports will be associated with :returns: a\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get\",\n \"into consideration when provisioning networking for the instance. Mapping of\",\n \"to other calls into the driver. The prime example is\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def rescue(self, context, instance, network_info,\",\n \"the new VM afterwards. It may be overridden by hypervisors\",\n \"CPU time (nanoseconds), wait indicates the cumulative I/O wait CPU\",\n \"existence of an instance on the host. :param instance: The\",\n \"dst_compute_info: Info about the receiving machine :param block_migration: if true,\",\n \"instance which should be unpaused. :return: None \\\"\\\"\\\" # TODO(Vek):\",\n \"context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of\",\n \"nova.objects.instance.Instance instance: The instance which should be unpaused. :return: None\",\n \"= block_device_info or {} ephemerals = block_device_info.get('ephemerals') or [] return\",\n \"this layer. This layer is responsible for translating that generic\",\n \"function at their # virt layer. return False def quiesce(self,\",\n \"be supplied to the allocate_for_instance call by the compute manager,\",\n \"def power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the specified instance.\",\n \"will be automatically booted. The counter action for `restore` is\",\n \"in the rebooting state than `timeout` seconds. :param int timeout:\",\n \"groups. All security groups and their associated rules live in\",\n \"a rdp console. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"block devices to be attached to the instance. :param preserve_ephemeral:\",\n \"with a single parameter, which will be an instance of\",\n \"block migration :param disk_over_commit: if true, allow disk over commit\",\n \"given `instance`. :param dict block_device_info: Information about the block devices.\",\n \"of the given `instance`. :param dict block_device_info: Information about the\",\n \"The mountpoint in the instance where the volume for `old_connection_info`\",\n \"context in for access to auth_token raise NotImplementedError() def get_host_uptime(self):\",\n \"is `True`, the host will accept new instances. If it\",\n \"the network, and manage their own TFTP service. This requires\",\n \"when the 'VM' is actually proxied and needs to be\",\n \"on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause\",\n \"mapping to be presented in the format of a dict\",\n \"instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a default root device name for\",\n \"removed from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"an interrupted snapshot. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"connection info for a vnc console. :param context: security context\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection info\",\n \"be retrieved directly in aggregate form, without Nova having to\",\n \"in the datastore, and calling this method should apply the\",\n \"the result of calling the Linux command `uptime` on this\",\n \"information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def\",\n \"raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None,\",\n \"admin_password: Administrator password to set in instance. :param network_info: instance\",\n \"information for the resume. :param dict block_device_info: Instance volume block\",\n \"NotImplementedError() def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the ability to power\",\n \"since the last boot. :rtype: str \\\"\\\"\\\" # TODO(Vek): Need\",\n \"snapshot of a volume attached to a specified instance. The\",\n \"swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the volume attached\",\n \"iscsi initiator and the hostname of the machine as follows::\",\n \"Depending on the underlying hypervisor technology, the guest has the\",\n \"disks should be destroyed :param migrate_data: implementation specific params \\\"\\\"\\\"\",\n \"interface from a running instance. The counter action to this\",\n \"the host, False otherwise. .. note:: This implementation works for\",\n \"You may obtain # a copy of the License at\",\n \"the host. :param instance: nova.objects.instance.Instance to lookup Returns True if\",\n \"\\\"\\\"\\\" raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume\",\n \"= logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for\",\n \"should be completely cleaned up, and the virtualization platform should\",\n \"the interface to attach. :raise nova.exception.NovaException: If the attach fails.\",\n \"of or if the group gains or loses a rule.\",\n \"'these and only these MAC addresses'. \\\"\\\"\\\" return None def\",\n \":param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the given\",\n \"req is the total number of I/O requests made, bytes\",\n \"context :param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta:\",\n \"this function at their # virt layer. return False def\",\n \"as the active disk after the snapshot operation has completed.\",\n \"the snapshot. For example, in the case of qcow2 backed\",\n \"instance): \\\"\\\"\\\"Cleans up any resources left after an interrupted snapshot.\",\n \"instance doesn't use CPU cycles of the host anymore. The\",\n \"host can accept further instances, return \\\"enabled\\\", if further instances\",\n \"raise NotImplementedError() def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the specified instance.\",\n \"instance: instance that is live migrating \\\"\\\"\\\" raise NotImplementedError() def\",\n \":param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self, context, aggregate, host,\",\n \"host): \\\"\\\"\\\"Initialize anything that is necessary for the driver to\",\n \"get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get the next device name based\",\n \"NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None):\",\n \"for rebuild :param detach_block_devices: function to detach block devices. See\",\n \"`False`, the host won't accept new instances. :return: If the\",\n \"managed by the compute service. This method is for multi\",\n \"be migrated. :param str dest: The IP address of the\",\n \"instance's metadata to the hypervisor. If the hypervisor has no\",\n \"add_to_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Add a compute host to\",\n \"the specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD>\",\n \"volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot of a volume attached\",\n \"specified instance. :param nova.context.RequestContext context: The context for the rescue.\",\n \"An error should be raised if the operation cannot complete.\",\n \"host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down or powers up the host.\",\n \"escape the connection interface. The platform-specific layer is responsible for\",\n \"to the network implementation layer. For instance, with openflow switches,\",\n \"object which contains information about the VIFs to unplug. :return:\",\n \"device mappings for an instance to driver format. Virt drivers\",\n \"Information about the block devices. :param int timeout: The time\",\n \"to which platform-specific ID, and vice versa. Some methods here\",\n \"**kwargs): \\\"\\\"\\\"Remove a compute host from an aggregate. The counter\",\n \"{'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\" return None\",\n \"raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about actual\",\n \"**kwargs): \\\"\\\"\\\"Add a compute host to an aggregate. The counter\",\n \"before the end. :param nova.objects.instance.Instance instance: The instance whose disk\",\n \"L2 domain, with arbitrary VLANs etc, but regular switches require\",\n \"instance ID or name is not human-readable or customer-controlled --\",\n \"object \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"after check_instance_shared_storage calls :param context: security context :param data: result\",\n \"has completed successfully even in cases in which the underlying\",\n \"MKS console. :param context: security context :param instance: nova.objects.instance.Instance :returns\",\n \"str \\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host\",\n \"instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError()\",\n \"def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if the driver requires legacy\",\n \"import event as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__)\",\n \":param block_device_info: instance block device information :param network_info: instance network\",\n \"a running instance in multiple phases, turning off the instance\",\n \"\\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does not support \\\" \\\"post_live_migration_at_source method\\\"))\",\n \"nova.objects.instance.Instance instance: The instance whose volume gets replaced by another\",\n \"or [] return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class for compute\",\n \"context in for access to auth_token raise NotImplementedError() def unpause(self,\",\n \"nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the given `host`\",\n \"a running instance. The counter action to this is :func:`detach_interface`.\",\n \"for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs):\",\n \"paused instance doesn't use CPU cycles of the host anymore.\",\n \"function, including catching up with currently running VM's on the\",\n \"All security groups and their associated rules live in the\",\n \"instance. \\\"\\\"\\\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce\",\n \"driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs)\",\n \"block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network\",\n \"a resize/migration. :param context: the context for the finish_revert_migration :param\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the\",\n \":param uuid snapshot_id: The snapshot to delete. :param dict delete_info:\",\n \"The instance to suspend. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"device. \\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default\",\n \"auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off\",\n \"root password on the specified instance. :param instance: nova.objects.instance.Instance :param\",\n \"context, instance): \\\"\\\"\\\"Cleans up any resources left after an interrupted\",\n \"should be returned. :return: Has a big overlap to the\",\n \"an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF\",\n \"mode or \\\"off_maintenance\\\" if maintenance mode got left. :rtype: str\",\n \"given `aggregate`. :param dict kwargs: A free-form thingy... :return: None\",\n \"def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known host CPU stats. :returns:\",\n \"dict delete_info: Volume backend technology specific data needed to be\",\n \"context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about\",\n \"nova.objects.ImageMeta image_meta: The metadata of the image of the instance.\",\n \"be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"CPU cycles and memory of the host again. The counter\",\n \"format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise\",\n \"cumulative idle CPU time (nanoseconds), wait indicates the cumulative I/O\",\n \"raise NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability of this\",\n \"host :param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method:\",\n \"of disk information dicts in JSON format. :rtype: str \\\"\\\"\\\"\",\n \"action for 'resume' is :func:`suspend`. Depending on the underlying hypervisor\",\n \"(Beginning of) the contract that compute drivers must follow, and\",\n \"identify a VM or domain. These IDs must stay internal\",\n \"every hypervisor # need to define their supported file system\",\n \"nova.objects.instance.Instance that the network ports will be associated with :returns:\",\n \"a change to security groups. All security groups and their\",\n \"\\\"\\\"\\\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the ability\",\n \"network information :param block_device_info: instance block device information :param destroy_disks:\",\n \"whether this compute service manages a particular node.\\\"\\\"\\\" if nodename\",\n \"It's probably a good idea to log a warning in\",\n \"implement this function at their # virt layer. return False\",\n \"If the host can accept further instances, return \\\"enabled\\\", if\",\n \"instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of live migration at\",\n \"image of the instance. :param nova.network.model.NetworkInfo vif: The object which\",\n \"return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return\",\n \"instance): \\\"\\\"\\\"Get connection info for a rdp console. :param context:\",\n \"# a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"from the instance. :param destroy_disks: Indicates if disks should be\",\n \"actually volumes. :return: json strings with below format:: \\\"[{'path':'disk', 'type':'raw',\",\n \"instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS to\",\n \"if the driver requires legacy block device info. Tell the\",\n \"for filtering rules to hypervisor, since filtering rules to firewall\",\n \"the specified instance to prepare for snapshots. If the specified\",\n \"of usage info \\\"\\\"\\\" return {} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks\",\n \"files on the host. :param instance: nova.objects.instance.Instance to lookup Returns\",\n \"as ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"), {'event': event, 'ex':\",\n \"must this instance have? Some hypervisors (such as bare metal)\",\n \"to quiesce by other errors (e.g. agent timeout), NovaException is\",\n \"state goes back to power_state.RUNNING. The virtualization platform should ensure\",\n \"context, instance, image_meta): \\\"\\\"\\\"Quiesce the specified instance to prepare for\",\n \"have been in rebooting state longer than the configured timeout.\",\n \"timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the specified instance. :param instance: nova.objects.instance.Instance\",\n \"that is migrated :param network_info: instance network information :param block_migration:\",\n \"implied. See the # License for the specific language governing\",\n \"is specific to the Neutron client API. :return: None, or\",\n \"instance: nova.objects.instance.Instance :returns: True if the instance was deleted from\",\n \"the active disk after the snapshot operation has completed. There\",\n \"\\\"\\\"\\\"Reboots, shuts down or powers up the host. :param str\",\n \"compute service. This method is for multi compute-nodes support. If\",\n \"context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self, context, migration,\",\n \"has the same state as before the 'pause'. :param nova.objects.instance.Instance\",\n \"compute manager. Note that the format of the return value\",\n \"succeed. It's probably a good idea to log a warning\",\n \"nova.objects.instance.Instance instance: The instance which gets a network interface removed.\",\n \"= { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False,\",\n \"when provisioning networking for the instance. Mapping of MAC addresses\",\n \"raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a default\",\n \"NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the result of calling the Linux\",\n \"the next device name based on the block device mapping.\",\n \"timeout: The time in seconds to wait for the guest\",\n \"The instance to soft-delete. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"internal ID chosen by Nova. At the nova.virt layer, instances\",\n \"disk_id): \\\"\\\"\\\"Return performance counters associated with the given disk_id on\",\n \"The counter action :func:`restore` uses the database entry to create\",\n \":param context: request context :param instance: nova.objects.instance.Instance to be quiesced\",\n \"instance: instance object that is migrated :param network_info: instance network\",\n \"info for a rdp console. :param context: security context :param\",\n \"class ComputeDriver(object): \\\"\\\"\\\"Base class for compute drivers. The interface to\",\n \"to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None,\",\n \"\\\"\\\"\\\"Use hotplug to add a network interface to a running\",\n \"recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this instance. A 'rebuild'\",\n \"NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the specified\",\n \"regarding an instance, and pass them into this layer. This\",\n \"implementation and required for validation of access to instance shared\",\n \"to the instance. :param preserve_ephemeral: True if the default ephemeral\",\n \"nw_info): \\\"\\\"\\\"inject network info for specified instance.\\\"\\\"\\\" # TODO(Vek): Need\",\n \"\\\"\\\"\\\"Suspend the specified instance. A suspended instance doesn't use CPU\",\n \":returns: a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self,\",\n \"plugged. :param nova.network.model.NetworkInfo network_info: The object which contains information about\",\n \"images which are no longer of interest. :param all_instances: nova.objects.instance.InstanceList\",\n \"NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a\",\n \"Instance object as returned by DB layer. :param network_info: instance\",\n \"in the memory or storage space of the host, depending\",\n \"is necessary for the driver gracefully stop, including ending remote\",\n \"the snapshot. \\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans\",\n \"the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description\",\n \"terms that are specific to the virtualization platform. \\\"\\\"\\\" capabilities\",\n \"host, mode): \\\"\\\"\\\"Start/Stop host maintenance window. On start, it triggers\",\n \"\\\"\\\"\\\"Check if instance files located on shared storage. :param context:\",\n \"instance.name. The second parameter is the base64-encoded path to which\",\n \"return a set of MAC addresses that the instance is\",\n \"context: security context :param dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\" raise\",\n \"will take this into consideration when provisioning networking for the\",\n \"LOG.error(_LE(\\\"Compute driver option required, but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute\",\n \"detaches all block devices, then spins up the new VM\",\n \"return True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if the\",\n \"and required for validation of access to instance shared disk\",\n \"that will match the actual configuration. Most hypervisors can use\",\n \"of instance files on the host. :param instance: nova.objects.instance.Instance to\",\n \"the host again. The counter action for 'resume' is :func:`suspend`.\",\n \"has the information about the interface to detach. :raise nova.exception.NovaException:\",\n \"\\\"\\\"\\\"Returns nodenames of all nodes managed by the compute service.\",\n \"responsible for keeping track of which instance ID maps to\",\n \"migration of an instance to another host. :param context: security\",\n \"old state is skipped. :param block_device_info: Information about block devices\",\n \"raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None):\",\n \"block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about the requested\",\n \"NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the total number of virtual machines.\",\n \"or loses a rule. \\\"\\\"\\\" raise NotImplementedError() def reset_network(self, instance):\",\n \"mappings that will match the actual configuration. Most hypervisors can\",\n \"DHCP service. Most hypervisors can use the default implementation which\",\n \"the resume. :param dict block_device_info: Instance volume block device info.\",\n \"metadata of the image of the instance. :param nova.network.model.NetworkInfo vif:\",\n \"new hypervisor - all the cleanup of old state is\",\n \"\\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return: The result of the power\",\n \"instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources\",\n \"a LiveMigrateData object \\\"\\\"\\\" pass def post_live_migration_at_source(self, context, instance, network_info):\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump\",\n \"hypervisors can use the default implementation which returns None. Hypervisors\",\n \"those platforms, this method is unused. Note that this function\",\n \"a compute host from an aggregate. The counter action to\",\n \"function takes an instance ID. \\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self,\",\n \"access to auth_token pass def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a\",\n \"compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute driver option required,\",\n \"str \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"instance to driver format. Virt drivers expect block device mapping\",\n \"add a network interface to a running instance. The counter\",\n \"from networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance:\",\n \"2.0 (the \\\"License\\\"); you may # not use this file\",\n \"during spawn_instance by the compute manager. :return: None, or a\",\n \"instance): \\\"\\\"\\\"Abort an in-progress live migration. :param instance: instance that\",\n \"nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \":param migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError() def cleanup(self,\",\n \"last boot. :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"contains information about the VIFs to plug. :return: None \\\"\\\"\\\"\",\n \"network_info: The object which contains information about the VIFs to\",\n \"specified instance to prepare for snapshots. If the specified instance\",\n \"opt :returns: a ComputeDriver instance \\\"\\\"\\\" if not compute_driver: compute_driver\",\n \"set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints',\",\n \"vice versa. Some methods here take an instance of nova.compute.service.Instance.\",\n \"a device name :param bdms: a nova.objects.BlockDeviceMappingList for the instance\",\n \"destroy_disks: Indicates if disks should be destroyed :param migrate_data: implementation\",\n \"dict block_device_info: Instance volume block device info. :return: None \\\"\\\"\\\"\",\n \"\\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, } def\",\n \"\\\"\\\"\\\"Default the missing device names in the block device mapping.\\\"\\\"\\\"\",\n \"context, instance, image_meta): \\\"\\\"\\\"Unquiesce the specified instance after snapshots. If\",\n \"instance which should be paused. :return: None \\\"\\\"\\\" # TODO(Vek):\",\n \"prepare for snapshots. If the specified instance doesn't support quiescing,\",\n \"security context :param instance: instance object that was being migrated\",\n \"caller whether we expect the legacy format of block device\",\n \"volume block device info. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"\\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post\",\n \"calling this method should apply the updated rules to instances\",\n \"post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of an instance to\",\n \"\\\"\\\"\\\"Get console output for an instance :param context: security context\",\n \"auth_token raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the total number of\",\n \"of MAC addresses, which will be supplied to the allocate_for_instance\",\n \"'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id):\",\n \"machines that the hypervisor knows about. .. note:: This implementation\",\n \"# All Rights Reserved. # # Licensed under the Apache\",\n \"time (nanoseconds), wait indicates the cumulative I/O wait CPU time\",\n \"be stored in the memory or storage space of the\",\n \"implementation which returns None. Hypervisors with MAC limits should return\",\n \"event): \\\"\\\"\\\"Dispatches an event to the compute manager. Invokes the\",\n \"required, but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver)\",\n \"instance volume block device info :param power_on: True if the\",\n \"counter action to this is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The\",\n \"nova.objects.instance.Instance instance: The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo\",\n \"deleted from disk, False otherwise. \\\"\\\"\\\" return True @property def\",\n \"info for a spice console. :param context: security context :param\",\n \"event. This must only be invoked from a green thread.\",\n \"for access to auth_token raise NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause\",\n \"take this into consideration when provisioning networking for the instance.\",\n \"performance statistics can be retrieved directly in aggregate form, without\",\n \"still succeed. It's probably a good idea to log a\",\n \"to complete the snapshot. For example, in the case of\",\n \"`timeout` seconds. :param int timeout: The timeout (in seconds) for\",\n \"security group rules Gets called when an instance gets added\",\n \"are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return: The result of the\",\n \":param block_migration: if true, prepare for block migration :param disk_over_commit:\",\n \":param nova.objects.ImageMeta image_meta: The metadata of the image of the\",\n \"the instance's metadata to the hypervisor. If the hypervisor has\",\n \"detached from the instance. :param destroy_disks: Indicates if disks should\",\n \"but libvirt migrates those value through migrateToURI(), so , no\",\n \":returns an instance of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self,\",\n \"in terms of 'instances' (Amazon EC2 and internal Nova terminology),\",\n \"be triggered. :return: None \\\"\\\"\\\" raise NotImplementedError() def soft_delete(self, instance):\",\n \":func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return\",\n \"the given disk_id on the given instance. These are returned\",\n \"How often to signal guest while waiting for it to\",\n \"for Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector\",\n \"to check the results. :param context: security context :param instance:\",\n \"should return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return\",\n \"included as well that is needed for creating the snapshot.\",\n \"something more efficient. \\\"\\\"\\\" try: return instance.uuid in self.list_instance_uuids() except\",\n \"of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a\",\n \"# License for the specific language governing permissions and limitations\",\n \"False def register_event_listener(self, callback): \\\"\\\"\\\"Register a callback to receive events.\",\n \"on the host. :param instance: The instance to lookup Returns\",\n \"afterwards. It may be overridden by hypervisors that need to\",\n \"\\\"\\\"\\\"Get connection info for a spice console. :param context: security\",\n \"auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the root\",\n \"`True`, go into maintenance mode. If `False`, leave the maintenance\",\n \"action for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The context\",\n \"disk_id on the given instance. These are returned as [rd_req,\",\n \"drivers expect block device mapping to be presented in the\",\n \"flavor: The flavor of the instance whose disk get migrated.\",\n \"any resources anymore, but is still available as a database\",\n \"DriverVolumeBlockDevice or any of it's more specialized subclasses. \\\"\\\"\\\" from\",\n \"and remove images which are no longer of interest. :param\",\n \"to be quiesced :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \\\"\\\"\\\" raise\",\n \"up to the network implementation layer. For instance, with openflow\",\n \"str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s:\",\n \"to do the aggregation. On those platforms, this method is\",\n \"nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param dict\",\n \"usage info \\\"\\\"\\\" return {} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access\",\n \"the guest OS reacts in details, depends on the configuration\",\n \"requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList for the\",\n \"instance: The instance which should be unpaused. :return: None \\\"\\\"\\\"\",\n \"the driver gracefully stop, including ending remote sessions. This is\",\n \"platform. \\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\":\",\n \"to set in instance. :param network_info: instance network information :param\",\n \"security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self,\",\n \"hostname } \\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames\",\n \"of the instance. :param resize_instance: True if the instance is\",\n \"instance): \\\"\\\"\\\"Pause the given instance. A paused instance doesn't use\",\n \"waiting for it to shutdown. :return: A list of disk\",\n \"frequency (MHz). All values are long integers. \\\"\\\"\\\" raise NotImplementedError()\",\n \"VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"maintenance window. On start, it triggers the migration of all\",\n \"the instance. :param preserve_ephemeral: True if the default ephemeral storage\",\n \"mode): \\\"\\\"\\\"Start/Stop host maintenance window. On start, it triggers the\",\n \"not have human-readable names at all -- such things are\",\n \"JSON format. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def snapshot(self, context,\",\n \"a file on the specified instance. The first parameter is\",\n \"instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created image that\",\n \"instance, network_info): \\\"\\\"\\\"Unplug VIFs from networks at source. :param context:\",\n \"the driver want networks deallocated on reschedule?\\\"\\\"\\\" return False def\",\n \"context in for access to auth_token pass def poll_rebooting_instances(self, timeout,\",\n \"overhead calculations are desired. :param instance_info: Instance/flavor to calculate overhead\",\n \"instance of the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback = callback def\",\n \"track of which instance ID maps to which platform-specific ID,\",\n \"a particular node.\\\"\\\"\\\" if nodename in self.get_available_nodes(): return True #\",\n \"\\\"\\\"\\\"Deletes a snapshot of a volume attached to a specified\",\n \"an instance of nova.compute.service.Instance, and so the instance is being\",\n \"block device info. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self,\",\n \"nova.objects.instance.Instance instance: The suspended instance to resume. :param nova.network.model.NetworkInfo network_info:\",\n \"an instance of the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback = callback\",\n \"context, instance, network_info): \\\"\\\"\\\"Unplug VIFs from networks at source. :param\",\n \"as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates read,\",\n \"memory of the host again. The counter action for 'resume'\",\n \"security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self, context,\",\n \"whether the file format is supported by this driver :param\",\n \"specified instance. The counter action to this is :func:`volume_snapshot_create` :param\",\n \"get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about actual disk sizes of\",\n \"of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self, context, instance): \\\"\\\"\\\"Get\",\n \"def attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use hotplug to add a\",\n \"optimisations, or when the 'VM' is actually proxied and needs\",\n \"their associated rules live in the datastore, and calling this\",\n \"be other data included as well that is needed for\",\n \"\\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host maintenance\",\n \"nova.objects.instance.Instance instance: The instance which will get an additional network\",\n \"migrate_data: a LiveMigrateData object \\\"\\\"\\\" pass def post_live_migration_at_source(self, context, instance,\",\n \"the name of the iscsi initiator and the hostname of\",\n \"switches, fixed MAC addresses can still be virtualized onto any\",\n \"add the given `host` :param str host: The name of\",\n \"migrate_data=None): \\\"\\\"\\\"Prepare an instance for live migration :param context: security\",\n \"The instance which will get an additional network interface. :param\",\n \"disk - ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances\",\n \"of the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback = callback def emit_event(self,\",\n \"format returned by Cinder's initialize_connection() API call. In the case\",\n \"in compliance with the License. You may obtain # a\",\n \"Note that the instance ID or name is not human-readable\",\n \"def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek): Need\",\n \"space of the host, depending on the underlying hypervisor technology.\",\n \"to plug. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"host. ( Waiting only for filtering rules to hypervisor, since\",\n \"shouldn't be scheduled to this host, return \\\"disabled\\\". :rtype: str\",\n \"the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance\",\n \"memory or storage space of the host, depending on the\",\n \"raise NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft delete the specified instance.\",\n \"state of the instance could be persisted on the host\",\n \":param fs_type: the file system type to be checked, the\",\n \"The valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return: The\",\n \"raise NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore the specified soft-deleted instance.\",\n \"back to the source host to check the results. :param\",\n \"during spawn_instance by the compute manager. Note that the format\",\n \"context :param dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def\",\n \"instance for live migration :param context: security context :param instance:\",\n \"of _get_instance_block_device_info :returns: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def\",\n \"raise NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump mechanism on\",\n \"block_device_info=None): \\\"\\\"\\\"Post operation of live migration at destination host. :param\",\n \"device info. Tell the caller whether we expect the legacy\",\n \"default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing device names in\",\n \"with :returns: a string representing the host ID \\\"\\\"\\\" return\",\n \"in the state that it was before this call began.\",\n \"nova.objects.instance.Instance objects that have been in rebooting state longer than\",\n \"the ability to power on/off an instance. :param instance: nova.objects.instance.Instance\",\n \"host, and then calls back to the source host to\",\n \"that the instance ID or name is not human-readable or\",\n \"snapshots, this would include the file being merged, and the\",\n \"but regular switches require pre-configured MAC->network mappings that will match\",\n \"return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization\",\n \"set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability of this host to accept\",\n \":param instance: nova.objects.instance.Instance This function should use the data there\",\n \"the default implementation which returns None. Hypervisors with MAC limits\",\n \"IPTables rules and apply the rules now.\\\"\\\"\\\" pass def unfilter_instance(self,\",\n \"nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event) except Exception as\",\n \"\\\"\\\"\\\"inject network info for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to\",\n \"being specified as instance.name. The second parameter is the base64-encoded\",\n \":param context: security context :param instance: nova.objects.instance.Instance :returns an instance\",\n \"nova.objects.instance.Instance :returns: True if the instance was deleted from disk,\",\n \"detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this\",\n \"recreate: True if the instance is being recreated on a\",\n \"otherwise. \\\"\\\"\\\" return True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller\",\n \"\\\"reboot\\\". :return: The result of the power action :rtype: :\",\n \"type and implement this function at their # virt layer.\",\n \"specified instance. A suspended instance doesn't use CPU cycles or\",\n \"eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address',\",\n \"such things are only known higher up the stack. Most\",\n \"newly transferred disk information :param network_info: instance network information :param\",\n \"clean the cache and remove images which are no longer\",\n \"(Amazon EC2 and internal Nova terminology), by which we mean\",\n \"instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified instance. :param nova.context.RequestContext\",\n \"the guest has the same state as before the 'pause'.\",\n \"have human-readable names at all -- such things are only\",\n \":param migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError() def reboot(self,\",\n \"Info about the receiving machine :param block_migration: if true, prepare\",\n \"instance doesn't use CPU cycles or memory of the host\",\n \"method is deprecated and will be removed once it can\",\n \":param instance: nova.objects.instance.Instance :param network_info: instance network information :param reboot_type:\",\n \"The counter action for 'unpause' is :func:`pause`. Depending on the\",\n \"to in writing, software # distributed under the License is\",\n \"of the instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info:\",\n \"any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate\",\n \"single parameter, which will be an instance of the nova.virt.event.Event\",\n \":func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance:\",\n \"same data format returned by Cinder's initialize_connection() API call. In\",\n \"function should still succeed. It's probably a good idea to\",\n \"operations are not completed. :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" #\",\n \"password to set in instance. :param network_info: instance network information\",\n \"device names in the block device mapping.\\\"\\\"\\\" raise NotImplementedError() def\",\n \"storage partition must be preserved on rebuild \\\"\\\"\\\" raise NotImplementedError()\",\n \"to attach to the volume. This is the same data\",\n \"snapshot. \\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up\",\n \"\\\"on_maintenance\\\" if switched to maintenance mode or \\\"off_maintenance\\\" if maintenance\",\n \"format is supported by this driver :param fs_type: the file\",\n \"group rules Gets called when an instance gets added to\",\n \"of MAC addresses. This method allows drivers to return a\",\n \"nova.compute.service.Instance. This is the data structure used by nova.compute to\",\n \"or domain (Xen or libvirt terminology). An instance has an\",\n \"the information about the interface to attach. :raise nova.exception.NovaException: If\",\n \"operation cannot complete. \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"method which is used to publish changes to the instance's\",\n \"it is possible to execute live migration. This runs checks\",\n \"be implemented. :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"host to add to the given `aggregate`. :param dict kwargs:\",\n \"block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap\",\n \"License, Version 2.0 (the \\\"License\\\"); you may # not use\",\n \"does not support \\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context, instance,\",\n \"be called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx,\",\n \"context, instance): \\\"\\\"\\\"Get console output for an instance :param context:\",\n \"The time in seconds to wait for the guest OS\",\n \"def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the ability to power on/off\",\n \"the event callback registered by the compute manager to dispatch\",\n \"a list. \\\"\\\"\\\" raise NotImplementedError() def rebuild(self, context, instance, image_meta,\",\n \"instance gets unpaused and will use CPU cycles of the\",\n \"is an instance of nova.compute.service.Instance, and so the instance is\",\n \"Information about block devices that should be detached from the\",\n \"can use the default implementation which returns None. This is\",\n \"this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param\",\n \"context :instance: instance object that was migrated :block_device_info: instance block\",\n \"def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the specified instance. :param instance:\",\n \"access to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info,\",\n \"ID or name is not human-readable or customer-controlled -- it's\",\n \"base-classes: (Beginning of) the contract that compute drivers must follow,\",\n \"to the instance at mountpoint using info.\\\"\\\"\\\" raise NotImplementedError() def\",\n \"means 'no constraints', a set means 'these and only these\",\n \"\\\"\\\"\\\"Get DHCP options for this instance. Some hypervisors (such as\",\n \"expect block device mapping to be presented in the format\",\n \"live migration to complete :param instance: Instance being live migrated\",\n \"for instances on disk. This method is an opportunity to\",\n \"LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\\\"Exception\",\n \"a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance\",\n \"counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The\",\n \"with something more efficient. \\\"\\\"\\\" try: return instance.uuid in self.list_instance_uuids()\",\n \"will use CPU cycles and memory of the host again.\",\n \"callback): \\\"\\\"\\\"Register a callback to receive events. Register a callback\",\n \"IP address of the destination host. :param nova.objects.flavor.Flavor flavor: The\",\n \"implementation works for all drivers, but it is not particularly\",\n \"never escape the connection interface. The platform-specific layer is responsible\",\n \"Stalling instances can be triggered to dump the crash data.\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT #\",\n \"The object which contains information about the VIFs to unplug.\",\n \"expects to be used as the active disk after the\",\n \"importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError:\",\n \"spins up the new VM afterwards. It may be overridden\",\n \"The volume for this connection gets detached from the given\",\n \"information of the given `instance`. :param dict block_device_info: Information about\",\n \"disk attached to the instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self, old_connection_info,\",\n \"'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap']\",\n \"requires passing the appropriate options out to the DHCP service.\",\n \"block_device_info or {} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals\",\n \"\\\"\\\"\\\"Return performance counters associated with the given disk_id on the\",\n \"virtualization layer, as a list. \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"ip, 'initiator': initiator, 'host': hostname } \\\"\\\"\\\" raise NotImplementedError() def\",\n \"\\\"\\\"\\\"Returns the result of calling the Linux command `uptime` on\",\n \"pre-configured MAC->network mappings that will match the actual configuration. Most\",\n \"is the same data format returned by Cinder's initialize_connection() API\",\n \":param reboot_type: Either a HARD or SOFT reboot :param block_device_info:\",\n \"nova.objects.instance.Instance object :param block_device_info: instance block device information :param network_info:\",\n \"block_device_info: Optional; Can be used to filter out devices which\",\n \"the instance's state goes back to power_state.RUNNING. The virtualization platform\",\n \"network_info): \\\"\\\"\\\"Setting up filtering rules and waiting for its completion.\",\n \"live migrated \\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an\",\n \"instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of an\",\n \"security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial\",\n \"an argument. Compute drivers constructors take a VirtAPI object as\",\n \"post operation of block_migration. \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self, context,\",\n \"NotImplementedError() def attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use hotplug to add\",\n \"instance): \\\"\\\"\\\"Get connection info for a MKS console. :param context:\",\n \"'suspend'. :param nova.context.RequestContext context: The context for the resume. :param\",\n \"virtual machine' (XenAPI terminology) or domain (Xen or libvirt terminology).\",\n \"instance network information :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"larger than the old volume, it gets resized to the\",\n \"rules and apply the rules now.\\\"\\\"\\\" pass def unfilter_instance(self, instance,\",\n \"\\\"\\\"\\\"Snapshots the specified instance. :param context: security context :param instance:\",\n \"<NAME> # All Rights Reserved. # # Licensed under the\",\n \"access to auth_token raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS\",\n \"the instance. :param injected_files: User files to inject into instance.\",\n \"CPU time (nanoseconds), since the host is booting up; freq\",\n \"action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance\",\n \"read, wr indicates write, req is the total number of\",\n \"= block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return\",\n \"and memory of the host again. The counter action for\",\n \"raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get the next\",\n \"raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes\",\n \"The timeout (in seconds) for considering rebooting instances to be\",\n \"def instance_exists(self, instance): \\\"\\\"\\\"Checks existence of an instance on the\",\n \"network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying the source VM. :param instance:\",\n \"get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage info for volumes attached to\",\n \"return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute driver module.\",\n \"legacy format of block device info to be passed in\",\n \"context. :param nova.objects.instance.Instance instance: The instance that has the volume\",\n \"raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the\",\n \"this is called successfully, the instance's state goes back to\",\n \"will be associated with :returns: a string representing the host\",\n \"if the instance is being recreated on a new hypervisor\",\n \"host whose maintenance mode should be changed. :param bool mode:\",\n \"distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR\",\n \"names at all -- such things are only known higher\",\n \"the virtualization platform. \\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\":\",\n \"host to remove from the given `aggregate`. :param dict kwargs:\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info,\",\n \"of estimated overhead values. \\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb': 0}\",\n \"scheduled to this host, return \\\"disabled\\\". :rtype: str \\\"\\\"\\\" #\",\n \"ensure that the reboot action has completed successfully even in\",\n \"def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up\",\n \"network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources .\",\n \"context in for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance,\",\n \"instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping)\",\n \"\\\"\\\"\\\"Get connection info for a rdp console. :param context: security\",\n \"live migration can succeed, based on the results from check_can_live_migrate_destination.\",\n \"network implementation layer. For instance, with openflow switches, fixed MAC\",\n \"access to auth_token raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the result\",\n \"for multi compute-nodes support. If a driver supports multi compute-nodes,\",\n \"-- such things are only known higher up the stack.\",\n \"interface to a running instance. The counter action to this\",\n \"the legacy format of block device info to be passed\",\n \"calling this method. :param context: security context :param instance: Instance\",\n \"about the interface to attach. :raise nova.exception.NovaException: If the attach\",\n \"def soft_delete(self, instance): \\\"\\\"\\\"Soft delete the specified instance. A soft-deleted\",\n \"If it is `False`, the host won't accept new instances.\",\n \":param nova.objects.instance.Instance instance: The instance which will get an additional\",\n \"where the volume for `old_connection_info` is attached to. :param int\",\n \"requested block device. device_name does not need to be set,\",\n \"nova.objects.instance.Instance This function should use the data there to guide\",\n \"nova.objects.instance.Instance instance: The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo\",\n \"a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no\",\n \"(for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have\",\n \"'personalities'. This base class method shuts down the VM, detaches\",\n \"usage. :param network_info: instance network information :param recreate: True if\",\n \"recreated on a new hypervisor - all the cleanup of\",\n \":param destroy_disks: if true, destroy disks at destination during cleanup\",\n \":param instance: instance object that is migrated :param network_info: instance\",\n \"shuts down the VM, detaches all block devices, then spins\",\n \"be returned. :return: Has a big overlap to the return\",\n \"The metadata of the image of the instance. :param nova.objects.BlockDeviceMapping\",\n \"that call to ensure that all assigned network details are\",\n \"the return value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics\",\n \"\\\"\\\"\\\"Return the UUIDS of all the instances known to the\",\n \"action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security\",\n \"NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get the next device\",\n \"utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to load the virtualization driver\\\"))\",\n \"text which contains the uptime of this host since the\",\n \"data. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve\",\n \"integers. This method is optional. On some platforms (e.g. XenAPI)\",\n \"attached to. :param int resize_to: If the new volume is\",\n \"return None def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the driver's local\",\n \"NotImplementedError() def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host maintenance window. On\",\n \"being migrated :param network_info: instance network information :param block_device_info: instance\",\n \"to pass context in for access to auth_token pass def\",\n \"is supported by this driver :param fs_type: the file system\",\n \"based on the block device mapping. :param instance: nova.objects.instance.Instance that\",\n \"compliance with the License. You may obtain # a copy\",\n \"context, compute_host_bdms): \\\"\\\"\\\"Return usage info for volumes attached to vms\",\n \"of the image of the instance. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"about the sending machine :param dst_compute_info: Info about the receiving\",\n \"in rebooting state longer than the configured timeout. :return: None\",\n \"instance network information :param recreate: True if the instance is\",\n \"gets attached to the given 'instance'. :param nova.objects.instance.Instance instance: The\",\n \"# # Unless required by applicable law or agreed to\",\n \"this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will\",\n \"to handle any bad volumes encountered \\\"\\\"\\\" raise NotImplementedError() def\",\n \"gracefully stop, including ending remote sessions. This is optional. \\\"\\\"\\\"\",\n \"information dicts in JSON format. :rtype: str \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self, instance):\",\n \"to store details regarding an instance, and pass them into\",\n \"platform should ensure that the reboot action has completed successfully\",\n \"to be able to attach to the volume. This is\",\n \"to auth_token raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the total number\",\n \"rebuild \\\"\\\"\\\" raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files,\",\n \"rules Gets called when an instance gets added to or\",\n \"delete. :param dict delete_info: Volume backend technology specific data needed\",\n \"dict new_connection_info: The volume for this connection gets attached to\",\n \"qcow2 backed snapshots, this would include the file being merged,\",\n \"of virtual machines. Return the number of virtual machines that\",\n \"do management of that cache which isn't directly related to\",\n \"context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk\",\n \"def add_to_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Add a compute host\",\n \"if nodename in self.get_available_nodes(): return True # Refresh and check\",\n \"post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method\",\n \"dict kwargs: A free-form thingy... :return: None \\\"\\\"\\\" # NOTE(jogo)\",\n \"preserve_ephemeral: True if the default ephemeral storage partition must be\",\n \"to auth_token raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of\",\n \"address of the destination host. :param nova.objects.flavor.Flavor flavor: The flavor\",\n \"detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage.\",\n \"BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"str host: The name of the host to remove from\",\n \"raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue\",\n \"A free-form thingy... :return: None \\\"\\\"\\\" # NOTE(jogo) Currently only\",\n \"{ 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list\",\n \"nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine :param dst_compute_info:\",\n \"host, return \\\"disabled\\\". :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"receive events. Register a callback to receive asynchronous event notifications\",\n \"instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for\",\n \"for the driver gracefully stop, including ending remote sessions. This\",\n \"written on the instance; the third is the contents of\",\n \"instance, image_meta): \\\"\\\"\\\"Quiesce the specified instance to prepare for snapshots.\",\n \"detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug to remove a network interface\",\n \"LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data):\",\n \"When it fails to quiesce by other errors (e.g. agent\",\n \"compute_driver configuration option or, if supplied, the driver name supplied\",\n \"NotImplementedError() def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance counters associated with\",\n \"the underlying hypervisor technology. A \\\"stronger\\\" version of `pause` is\",\n \"to be freeform) is up to the network implementation layer.\",\n \"etc, but regular switches require pre-configured MAC->network mappings that will\",\n \"of that cache which isn't directly related to other calls\",\n \"driver that manages only one node can safely ignore this\",\n \"name of the host to add to the given `aggregate`.\",\n \"<PASSWORD> for rescue. \\\"\\\"\\\" raise NotImplementedError() def set_bootable(self, instance, is_bootable):\",\n \"of an instance with the supplied ID accessible on the\",\n \":func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should be paused.\",\n \"needed for creating the snapshot. \\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self,\",\n \"halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network information :param\",\n \"uptime of this host since the last boot. :rtype: str\",\n \"usage. :param attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection info\",\n \"type to be checked, the validate values are defined at\",\n \"create_info: The data needed for nova to be able to\",\n \"object instance object that is migrated. :param dest: destination host\",\n \"information. This method is called when nova-compute launches, and as\",\n \"list of DriverVolumeBlockDevice or any of it's more specialized subclasses.\",\n \"the instance is being resized, False otherwise :param block_device_info: instance\",\n \"NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The\",\n \"use CPU cycles or memory of the host anymore. The\",\n \"Instance being live migrated \\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self, instance):\",\n \"given instance. A paused instance doesn't use CPU cycles of\",\n \"dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk of\",\n \"match the actual configuration. Most hypervisors can use the default\",\n \"context :param instance: nova.objects.instance.Instance object :param block_device_info: instance block device\",\n \"returned by Cinder's initialize_connection() API call. In the case of\",\n \"The instance that has the volume attached :param uuid volume_id:\",\n \"def unpause(self, instance): \\\"\\\"\\\"Unpause the given paused instance. The paused\",\n \"get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known host CPU stats. :returns: a\",\n \":param nova.objects.instance.Instance instance: The instance which gets VIFs unplugged. :param\",\n \"instance was deleted from disk, False otherwise. \\\"\\\"\\\" return True\",\n \":param int timeout: The time in seconds to wait for\",\n \"the actual configuration. Most hypervisors can use the default implementation\",\n \"was deleted from disk, False otherwise. \\\"\\\"\\\" return True @property\",\n \"rules to firewall rules can be set faster). Concretely, the\",\n \"0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals =\",\n \"preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this instance. A 'rebuild' effectively purges\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\",\n \"'name' elsewhere. As far as this layer is concerned, 'instance\",\n \"if instance files located on shared storage. :param context: security\",\n \"calls back to the source host to check the results.\",\n \"network_info: instance network information :param reboot_type: Either a HARD or\",\n \":param instance: nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"host: The name of the host whose maintenance mode should\",\n \"preserved on rebuild \\\"\\\"\\\" raise NotImplementedError() def spawn(self, context, instance,\",\n \"per-instance overhead calculations are desired. :param instance_info: Instance/flavor to calculate\",\n \"raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation\",\n \"result of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data):\",\n \"`instance` at instance boot time. The counter action is :func:`unplug_vifs`.\",\n \"if files of an instance with the supplied ID accessible\",\n \"the host anymore. The state of the VM could be\",\n \":param instance: nova.objects.instance.Instance object Returns a InstanceInfo object \\\"\\\"\\\" #\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore the specified\",\n \"LOG.exception(_LE(\\\"Unable to load the virtualization driver\\\")) sys.exit(1) def is_xenapi(): return\",\n \"whether we expect the legacy format of block device info\",\n \"from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"at instance boot time. The counter action is :func:`unplug_vifs`. :param\",\n \":param dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self,\",\n \"directly in aggregate form, without Nova having to do the\",\n \"info for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver) try: driver =\",\n \"NOTE(jichenjc): Return False here so that every hypervisor # need\",\n \"device name. \\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether\",\n \"instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError()\",\n \"NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the specified\",\n \"device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk to the instance at mountpoint\",\n \":func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which should be\",\n \"def suspend(self, context, instance): \\\"\\\"\\\"Suspend the specified instance. A suspended\",\n \"the host should perform. The valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\"\",\n \"commit :returns: a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def\",\n \"guest while waiting for it to shutdown. :return: A list\",\n \"auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True):\",\n \"group the instance is a member of or if the\",\n \"the CPU stat info, eg: | {'kernel': kern, | 'idle':\",\n \"on the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError()\",\n \"including catching up with currently running VM's on the given\",\n \"\\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False,\",\n \"machine as follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname\",\n \"when an instance gets added to or removed from the\",\n \"root_device_name: device name of the root disk - ephemerals: a\",\n \"retrieved directly in aggregate form, without Nova having to do\",\n \"if it is possible to execute live migration. This checks\",\n \"Note that this function takes an instance ID. \\\"\\\"\\\" raise\",\n \"IP address of the dom0 \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"`pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should\",\n \"unplug. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the\",\n \"the migration/resize :param migration: the migrate/resize information :param instance: nova.objects.instance.Instance\",\n \"def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability of this host to\",\n \"a vnc console. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"underlying domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param\",\n \"deallocated on reschedule?\\\"\\\"\\\" return False def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC\",\n \"instance which gets a network interface removed. :param nova.network.model.NetworkInfo vif:\",\n \"an in-progress live migration. :param instance: instance that is live\",\n \"this must be supplied. :param virtapi: a VirtAPI instance :param\",\n \"or SOFT reboot :param block_device_info: Info pertaining to attached volumes\",\n \"of the power action :rtype: : str \\\"\\\"\\\" raise NotImplementedError()\",\n \"directly related to other calls into the driver. The prime\",\n \"task that records the results in the DB. :param nodename:\",\n \"instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk attached to the instance.\\\"\\\"\\\"\",\n \"instance whose volume gets replaced by another one. :param str\",\n \"def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options for this instance. Some\",\n \"to the DHCP service. Most hypervisors can use the default\",\n \"*args, **kwargs): driver_registry = dict() for driver_str in named_driver_config: driver_type,\",\n \"the names of all the instances known to the virtualization\",\n \"has the volume attached. :param uuid volume_id: Attached volume associated\",\n \"False otherwise \\\"\\\"\\\" raise NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause the\",\n \"context: request context :param instance: nova.objects.instance.Instance to be unquiesced :param\",\n \"Return the number of virtual machines that the hypervisor knows\",\n \"The instance where the crash dump should be triggered. :return:\",\n \"their own TFTP service. This requires passing the appropriate options\",\n \"mountpoint using info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint,\",\n \"instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for\",\n \"security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is\",\n \"user indicate the cumulative CPU time (nanoseconds) spent by kernel\",\n \"with the snapshot :param uuid snapshot_id: The snapshot to delete.\",\n \"will also have their own identity schemes, to uniquely identify\",\n \"unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek): Need to\",\n \"block device info :param power_on: True if the instance should\",\n \"and only these MAC addresses'. \\\"\\\"\\\" return None def dhcp_options_for_instance(self,\",\n \"returned. :return: Has a big overlap to the return value\",\n \"or when the 'VM' is actually proxied and needs to\",\n \"disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk to the instance at\",\n \":instance: instance object that was migrated :block_device_info: instance block device\",\n \":param rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\" raise NotImplementedError() def set_bootable(self,\",\n \"paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network\",\n \"def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a reboot on all given\",\n \"form, without Nova having to do the aggregation. On those\",\n \"context :param instance: Instance object as returned by DB layer.\",\n \"accept new instances. :param bool enabled: If this is `True`,\",\n \"information :param block_device_info: Information about block devices to be attached\",\n \"unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance\",\n \"if true, migrate VM disk. :param migrate_data: a LiveMigrateData object\",\n \"guest while waiting for it to shutdown \\\"\\\"\\\" raise NotImplementedError()\",\n \"nova.objects.instance.Instance instance: The instance to which the diagnostic data should\",\n \"switches require pre-configured MAC->network mappings that will match the actual\",\n \"purges all existing data from the system and remakes the\",\n \"Administrator password to set in instance. :param network_info: instance network\",\n \"connection, the name of the iscsi initiator and the hostname\",\n \"is :func:`unpause`. :param nova.objects.instance.Instance instance: The instance which should be\",\n \"injected_files: User files to inject into instance. :param admin_password: <PASSWORD>\",\n \"of qcow2 backed snapshots, this would include the file being\",\n \"was before this call began. :param context: security context :param\",\n \":param instance: nova.objects.instance.Instance object :param block_device_info: instance block device information\",\n \"addresses. This is called during spawn_instance by the compute manager.\",\n \"cycles and memory of the host again. The counter action\",\n \"connection interface. The platform-specific layer is responsible for keeping track\",\n \"security context :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError() def\",\n \"def emit_event(self, event): \\\"\\\"\\\"Dispatches an event to the compute manager.\",\n \"specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it\",\n \"The aggregate which should remove the given `host` :param str\",\n \"at disk API module. \\\"\\\"\\\" # NOTE(jichenjc): Return False here\",\n \"information :param network_info: instance network information :param disk_info: instance disk\",\n \"to be attached to the instance. :param preserve_ephemeral: True if\",\n \"def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of all nodes managed by\",\n \"check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if instance files located on shared\",\n \"\\\"\\\"\\\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a\",\n \"on a given host.- \\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves\",\n \"technology. A \\\"stronger\\\" version of `pause` is :func:'suspend'. The counter\",\n \"also have their own identity schemes, to uniquely identify a\",\n \"have been destroyed from the Hypervisor before calling this method.\",\n \"on shared storage. :param context: security context :param data: result\",\n \"up, and the virtualization platform should be in the state\",\n \"use for rebuild :param detach_block_devices: function to detach block devices.\",\n \"with given 'metadata' and 'personalities'. This base class method shuts\",\n \"that was migrated :block_device_info: instance block device information :param migrate_data:\",\n \"function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param\",\n \"entry to create a new instance based on that. :param\",\n \"option or, if supplied, the driver name supplied as an\",\n \"rebooting instances to be stuck. :param list instances: A list\",\n \"context: security context :param instance: Instance object as returned by\",\n \"the specified security group. An error should be raised if\",\n \"running instance. The counter action to this is :func:`detach_interface`. :param\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"oslo_utils import importutils import nova.conf from nova.i18n import _, _LE,\",\n \"block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object \\\"\\\"\\\" raise\",\n \"known host CPU stats. :returns: a dict containing the CPU\",\n \"bdms, block_device_obj): \\\"\\\"\\\"Get the next device name based on the\",\n \"\\\"\\\"\\\"Check if instance files located on shared storage. This runs\",\n \"'idle': idle, | 'user': user, | 'iowait': wait, | 'frequency':\",\n \"Has a big overlap to the return value of the\",\n \"to use for rebuild :param detach_block_devices: function to detach block\",\n \"def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices,\",\n \"swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {}\",\n \"block device information :param network_info: instance network information :param disk_info:\",\n \"\\\"\\\"\\\"Rescue the specified instance. :param nova.context.RequestContext context: The context for\",\n \"live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration\",\n \"cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the\",\n \"migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the\",\n \"the IP address of the dom0 \\\"\\\"\\\" # TODO(Vek): Need\",\n \"are long integers. \\\"\\\"\\\" raise NotImplementedError() def block_stats(self, instance, disk_id):\",\n \"\\\"\\\"\\\" raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None,\",\n \"datastore, and calling this method should apply the updated rules\",\n \"ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info\",\n \"an instance of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self, context,\",\n \"only these MAC addresses'. \\\"\\\"\\\" return None def dhcp_options_for_instance(self, instance):\",\n \"on the given instance. Stalling instances can be triggered to\",\n \"driver name to override the config opt :returns: a ComputeDriver\",\n \":func:`suspend`. Depending on the underlying hypervisor technology, the guest has\",\n \"to driver format. Virt drivers expect block device mapping to\",\n \"action the host should perform. The valid actions are: \\\"\\\"startup\\\",\",\n \"powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self, migration,\",\n \"metadata of the image of the instance. \\\"\\\"\\\" raise NotImplementedError()\",\n \"of a dict containing the following keys: - root_device_name: device\",\n \"base class method shuts down the VM, detaches all block\",\n \"nova.objects.instance.Instance instance: The instance where the crash dump should be\",\n \"\\\"\\\"\\\" return None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options for\",\n \"the DHCP service. Most hypervisors can use the default implementation\",\n \"of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event) except Exception\",\n \"'192.168.127.12'} | ] \\\"\\\"\\\" return None def manage_image_cache(self, context, all_instances):\",\n \"shared disk files \\\"\\\"\\\" return False def register_event_listener(self, callback): \\\"\\\"\\\"Register\",\n \"migration to complete :param instance: Instance being live migrated \\\"\\\"\\\"\",\n \"(in seconds) for considering rebooting instances to be stuck. :param\",\n \"the instance ID or name is not human-readable or customer-controlled\",\n \"the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the root\",\n \"device name :param bdms: a nova.objects.BlockDeviceMappingList for the instance :param\",\n \"the compute manager, and it is up to that call\",\n \"host): \\\"\\\"\\\"Clean up anything that is necessary for the driver\",\n \"\\\"\\\"\\\" # TODO(Vek): Need to pass context in for access\",\n \"called during spawn_instance by the compute manager. Note that the\",\n \"manager doesn't use this interface, which seems odd # since\",\n \"to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for the\",\n \"device mapping. :param instance: nova.objects.instance.Instance that volume is requesting a\",\n \"virtapi): self.virtapi = virtapi self._compute_event_callback = None def init_host(self, host):\",\n \"anymore, but is still available as a database entry. The\",\n \":param context: security context :instance: instance object that was migrated\",\n \"method\\\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation\",\n \"host anymore. The state of the VM could be stored\",\n \"\\\"\\\"\\\"Return the names of all the instances known to the\",\n \"'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list =\",\n \"MAC limits should return a set of MAC addresses, which\",\n \"The suspended instance gets resumed and will use CPU cycles\",\n \"network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host ID to associate with network\",\n \"be changed. :param bool mode: If `True`, go into maintenance\",\n \"after the snapshot operation has completed. There may be other\",\n \"int resize_to: If the new volume is larger than the\",\n \"thing here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs) from networks. The counter\",\n \"def reset_network(self, instance): \\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\" # TODO(Vek):\",\n \"auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up filtering rules\",\n \"node_is_available(self, nodename): \\\"\\\"\\\"Return whether this compute service manages a particular\",\n \"that this function takes an instance ID. \\\"\\\"\\\" raise NotImplementedError()\",\n \"virtualization platform should ensure that the reboot action has completed\",\n \"a 'name' elsewhere. As far as this layer is concerned,\",\n \"interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self, instances):\",\n \"action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance\",\n \"compute_driver: LOG.error(_LE(\\\"Compute driver option required, but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading\",\n \"nova.network.model.NetworkInfo network_info: The network information of the given `instance`. :param\",\n \"of 'instances' (Amazon EC2 and internal Nova terminology), by which\",\n \"for an instance. :param instance: nova.objects.instance.Instance :returns: True if the\",\n \"when setting-up filtering rules operations are not completed. :param instance:\",\n \"networking failed), this function should still succeed. It's probably a\",\n \"Driver base-classes: (Beginning of) the contract that compute drivers must\",\n \"resize/migration. :param context: the context for the finish_revert_migration :param instance:\",\n \"post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of live\",\n \"translating that generic data structure into terms that are specific\",\n \"law or agreed to in writing, software # distributed under\",\n \"results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info:\",\n \"called when nova-compute launches, and as part of a periodic\",\n \"instance, disk_id): \\\"\\\"\\\"Return performance counters associated with the given disk_id\",\n \"the block devices. :param int timeout: The time in seconds\",\n \"confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying the source\",\n \":param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self,\",\n \":param context: security context :param instance: instance object that is\",\n \"NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a default root\",\n \"NotImplementedError() def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection info for a\",\n \"(for example if networking failed), this function should still succeed.\",\n \"NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it\",\n \"hypervisor - all the cleanup of old state is skipped.\",\n \"by other errors (e.g. agent timeout), NovaException is raised. :param\",\n \"reboot action has completed successfully even in cases in which\",\n \"be preserved on rebuild \\\"\\\"\\\" raise NotImplementedError() def spawn(self, context,\",\n \"instance object that is migrated :param network_info: instance network information\",\n \":param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def\",\n \"to other hosts. Consider the combination with :func:`set_host_enabled`. :param str\",\n \"dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self, context,\",\n \"terminology), by which we mean 'running virtual machine' (XenAPI terminology)\",\n \"and manage their own TFTP service. This requires passing the\",\n \"the host to add to the given `aggregate`. :param dict\",\n \"returned by DB layer. :param network_info: instance network information :param\",\n \"language governing permissions and limitations # under the License. \\\"\\\"\\\"\",\n \"in self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization overhead required\",\n \"this call began. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"on reschedule?\\\"\\\"\\\" return False def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses\",\n \"it is possible to execute live migration. This checks if\",\n \"network information :param disk_info: instance disk information :param migrate_data: a\",\n \"the given `instance`. :param dict new_connection_info: The volume for this\",\n \":param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery\",\n \"having to do the aggregation. On those platforms, this method\",\n \"cannot complete. \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"NotImplementedError() def confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms a resize/migration, destroying\",\n \"\\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None):\",\n \"self._compute_event_callback = callback def emit_event(self, event): \\\"\\\"\\\"Dispatches an event to\",\n \"to the given `aggregate`. :param dict kwargs: A free-form thingy...\",\n \"otherwise. .. note:: Used in rebuild for HA implementation and\",\n \"will be supplied to the allocate_for_instance call by the compute\",\n \"def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0):\",\n \"Instance should have been destroyed from the Hypervisor before calling\",\n \"to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context.\",\n \"get an additional network interface. :param nova.objects.ImageMeta image_meta: The metadata\",\n \"as bare metal) cannot do freeform virtualization of MAC addresses.\",\n \"given disk_id on the given instance. These are returned as\",\n \"of the new instance. :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources . Instance should have\",\n \"context :param instance: nova.objects.instance.Instance that the network ports will be\",\n \"this layer is concerned, 'instance ID' and 'instance name' are\",\n \"wait for GuestOS to shutdown :param retry_interval: How often to\",\n \"the rebooting state than `timeout` seconds. :param int timeout: The\",\n \"the host, False otherwise. .. note:: Used in rebuild for\",\n \"instance. A 'rebuild' effectively purges all existing data from the\",\n \":param bad_volumes_callback: Function to handle any bad volumes encountered \\\"\\\"\\\"\",\n \"succeed, based on the results from check_can_live_migrate_destination. :param context: security\",\n \"the instance metadata to the instance, then this method should\",\n \"kernel and user processes respectively, idle indicates the cumulative idle\",\n \"return value of the newer interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\"\",\n \"shutdown :param retry_interval: How often to signal guest while waiting\",\n \"on the underlying hypervisor technology, the guest has the same\",\n \"action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets\",\n \"def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if\",\n \"on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS\",\n \"security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP\",\n \"parameter is the base64-encoded path to which the file is\",\n \"to an aggregate. The counter action to this is :func:`remove_from_aggregate`\",\n \"the host won't accept new instances. :return: If the host\",\n \"for all drivers, but it is not particularly efficient. Maintainers\",\n \"an instance gets added to or removed from the security\",\n \"| 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, |\",\n \"interface to this class talks in terms of 'instances' (Amazon\",\n \"from oslo_log import log as logging from oslo_utils import importutils\",\n \"be written on the instance; the third is the contents\",\n \"big overlap to the return value of the older interface\",\n \":param dict new_connection_info: The volume for this connection gets attached\",\n \"instance network information :param block_migration: if true, post operation of\",\n \"layer is responsible for translating that generic data structure into\",\n \"for access to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass):\",\n \"\\\"\\\"\\\"Get connection info for a vnc console. :param context: security\",\n \"nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception occurs. expected\",\n \"live migration. :param context: security context :param instance: instance object\",\n \"to instances running the specified security group. An error should\",\n \"database entry. The counter action :func:`restore` uses the database entry\",\n \"migrating \\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info,\",\n \"the root device. \\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name,\",\n \"\\\"stronger\\\" version of `pause` is :func:'suspend'. The counter action for\",\n \"'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable\",\n \"this function takes an instance ID. \\\"\\\"\\\" raise NotImplementedError() def\",\n \":param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about the\",\n \"all drivers, but it is not particularly efficient. Maintainers of\",\n \"not be started when setting-up filtering rules operations are not\",\n \"a snapshot of a volume attached to a specified instance.\",\n \"mountpoint: The mountpoint in the instance where the volume for\",\n \"True if the default ephemeral storage partition must be preserved\",\n \"Hypervisor. If the instance is not found (for example if\",\n \"int timeout: The timeout (in seconds) for considering rebooting instances\",\n \"The volume for this connection gets attached to the given\",\n \"information for the resume. :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"a host is booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance with the supplied ID exists on the host, False\",\n \"to the allocate_for_instance call by the compute manager, and it\",\n \"the case of doing a snapshot, it is the image\",\n \"instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration. :param context:\",\n \"often to signal guest while waiting for it to shutdown\",\n \"only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate,\",\n \"context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def\",\n \"context, instance): \\\"\\\"\\\"Suspend the specified instance. A suspended instance doesn't\",\n \"Instance volume block device info. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"the format of a dict containing the following keys: -\",\n \"name supplied as an argument. Compute drivers constructors take a\",\n \"remakes the VM with given 'metadata' and 'personalities'. This base\",\n \"for driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class\",\n \"the instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The\",\n \"migrate_data=None): \\\"\\\"\\\"Live migration of an instance to another host. :param\",\n \"the virtualization platform. Once this successfully completes, the instance should\",\n \"instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata\",\n \"host, set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self,\",\n \"reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance. After this is\",\n \"to attached volumes :param bad_volumes_callback: Function to handle any bad\",\n \"driver module specified by the compute_driver configuration option or, if\",\n \"image of the instance. \\\"\\\"\\\" raise NotImplementedError() def unquiesce(self, context,\",\n \"allocate_for_instance call by the compute manager, and it is up\",\n \"of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get\",\n \"number of virtual machines. Return the number of virtual machines\",\n \"instance, vif): \\\"\\\"\\\"Use hotunplug to remove a network interface from\",\n \"instance. The first parameter is an instance of nova.compute.service.Instance, and\",\n \"[] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {}\",\n \"attached to vms on a given host.- \\\"\\\"\\\" raise NotImplementedError()\",\n \"signal guest while waiting for it to shutdown. :return: A\",\n \"the stack. Most virtualization platforms will also have their own\",\n \"instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object\",\n \"creating the snapshot. \\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance,\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore the\",\n \"unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute manager\",\n \"op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise NotImplementedError()\",\n \"layer. For instance, with openflow switches, fixed MAC addresses can\",\n \"running (power_state.RUNNING). If this fails, any partial instance should be\",\n \"def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host maintenance window. On start,\",\n \"management of that cache which isn't directly related to other\",\n \"( Waiting only for filtering rules to hypervisor, since filtering\",\n \"block device mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj):\",\n \"SOFT reboot :param block_device_info: Info pertaining to attached volumes :param\",\n \"context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \\\"\\\"\\\"\",\n \"event as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def\",\n \"CPU cycles of the host again. The counter action for\",\n \"'%s'\\\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi)\",\n \":param instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS\",\n \"to get the root device for. :param nova.objects.ImageMeta image_meta: The\",\n \"be triggered to dump the crash data. How the guest\",\n \"needed for nova to be able to attach to the\",\n \"guest OS to shutdown. :param int retry_interval: How often to\",\n \"manager should be the controlling thing here. \\\"\\\"\\\"Unplug virtual interfaces\",\n \"instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails\",\n \"macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses must this instance have? Some\",\n \"implementation if not set. :returns: The chosen device name. \\\"\\\"\\\"\",\n \"this :returns: Dictionary describing resources \\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self,\",\n \"of nova.compute.service.Instance. This is the data structure used by nova.compute\",\n \"instance gets added to or removed from the security group\",\n \"inevitable on destination host. ( Waiting only for filtering rules\",\n \"nova.objects.BlockDeviceMapping root_bdm: The description of the root device. \\\"\\\"\\\" raise\",\n \":param network_info: instance network information :param block_migration: if true, post\",\n \"To migrate an instance, filtering rules to hypervisors and firewalls\",\n \"desired. :param instance_info: Instance/flavor to calculate overhead for. :returns: Dict\",\n \"VM afterwards. It may be overridden by hypervisors that need\",\n \"Neutron client API. :return: None, or a set of DHCP\",\n \"NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo for\",\n \"image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param context: the\",\n \"context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes\",\n \"through migrateToURI(), so , no need to be called. Don't\",\n \"instance where the crash dump should be triggered. :return: None\",\n \":rtype: : str \\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self, host, mode):\",\n \"the volume attached :param uuid volume_id: Volume to be snapshotted\",\n \"volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot of\",\n \"The object which contains information about the VIFs to plug.\",\n \"restored instance will be automatically booted. The counter action for\",\n \"go into maintenance mode. If `False`, leave the maintenance mode.\",\n \"to associate with network ports. :param context: request context :param\",\n \"\\\"\\\"\\\"Live migration of an instance to another host. :param context:\",\n \":param context: request context :param instance: nova.objects.instance.Instance to be unquiesced\",\n \"hypervisor knows about. .. note:: This implementation works for all\",\n \"to pass context in for access to auth_token raise NotImplementedError()\",\n \"\\\"\\\"\\\" pass def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta,\",\n \"destination host :param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param\",\n \"request context :param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta\",\n \":param virtapi: a VirtAPI instance :param compute_driver: a compute driver\",\n \"\\\"\\\"\\\" pass def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network info for\",\n \"VM's on the given host. \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"attached to the given 'instance'. :param nova.objects.instance.Instance instance: The instance\",\n \"network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"set, and should be decided by the driver implementation if\",\n \"\\\"\\\"\\\"Transfers the disk of a running instance in multiple phases,\",\n \"name' are synonyms. Note that the instance ID or name\",\n \"format. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def snapshot(self, context, instance,\",\n \"\\\"softer\\\" way of `suspend` is :func:`pause`. The counter action for\",\n \"_, _LE, _LI from nova import utils from nova.virt import\",\n \"block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain on the virtualization platform. Once\",\n \"instance. Some hypervisors (such as bare metal) require that instances\",\n \"are encouraged to override this method with something more efficient.\",\n \"from oslo_utils import importutils import nova.conf from nova.i18n import _,\",\n \"string representing the host ID \\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi,\",\n \"NotImplementedError() def get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection info for a\",\n \"Some hypervisors (such as bare metal) cannot do freeform virtualization\",\n \"Copyright 2011 <NAME> # All Rights Reserved. # # Licensed\",\n \"that records the results in the DB. :param nodename: node\",\n \"for the resume. :param nova.objects.instance.Instance instance: The suspended instance to\",\n \"to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"instance's state goes back to power_state.RUNNING. The virtualization platform should\",\n \"actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return: The result of\",\n \"context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the\",\n \"# under the License. \\\"\\\"\\\" Driver base-classes: (Beginning of) the\",\n \"and 'personalities'. This base class method shuts down the VM,\",\n \"This is optional. \\\"\\\"\\\" pass def get_info(self, instance): \\\"\\\"\\\"Get the\",\n \"raise NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause the given instance. A\",\n \"instance metadata to the instance, then this method should not\",\n \"# TODO(Vek): Need to pass context in for access to\",\n \"context, op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise\",\n \"rules operations are not completed. :param instance: nova.objects.instance.Instance object \\\"\\\"\\\"\",\n \"is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will get\",\n \"destination host, and then calls back to the source host\",\n \"instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data):\",\n \"# distributed under the License is distributed on an \\\"AS\",\n \"the instance, then this method should not be implemented. :param\",\n \"Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information\",\n \"the caller if the driver requires legacy block device info.\",\n \"in instance. :param network_info: instance network information :param block_device_info: Information\",\n \"driver option required, but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver\",\n \"the controlling thing here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs) from networks.\",\n \"nova.compute.service.Instance, and so the instance is being specified as instance.name.\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest,\",\n \"more efficient. \\\"\\\"\\\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError:\",\n \"is up to the network implementation layer. For instance, with\",\n \"timeout: time to wait for GuestOS to shutdown :param retry_interval:\",\n \":param str host: The name of the host to remove\",\n \"NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up any resources left\",\n \"volume_id: Volume to be snapshotted :param create_info: The data needed\",\n \":returns: a ComputeDriver instance \\\"\\\"\\\" if not compute_driver: compute_driver =\",\n \"raise NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information. This method\",\n \"driver_registry = dict() for driver_str in named_driver_config: driver_type, _sep, driver\",\n \"is possible to execute live migration. This checks if the\",\n \"be used to filter out devices which are actually volumes.\",\n \"attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use hotplug to add a network\",\n \"which seems odd # since the manager should be the\",\n \"None \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"all instances to other hosts. Consider the combination with :func:`set_host_enabled`.\",\n \"or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or\",\n \"nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param nova.objects.ImageMeta\",\n \"configured timeout. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"must be an instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\",\",\n \"This is called during spawn_instance by the compute manager. Note\",\n \"needs to be held across the shutdown + spin up\",\n \"OS reacts in details, depends on the configuration of it.\",\n \"the instance could be persisted on the host and allocate\",\n \"instance. :param admin_password: <PASSWORD> to set in instance. :param bdms:\",\n \"I/O wait CPU time (nanoseconds), since the host is booting\",\n \"context, instance, block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance for\",\n \"drivers should override if per-instance overhead calculations are desired. :param\",\n \"detach. :raise nova.exception.NovaException: If the detach fails. :return: None \\\"\\\"\\\"\",\n \"an instance of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self, context,\",\n \"specified instance. The first parameter is an instance of nova.compute.service.Instance,\",\n \"not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event)) return if not isinstance(event,\",\n \"ID, which is the identifier chosen by Nova to represent\",\n \"contract that compute drivers must follow, and shared types that\",\n \"\\\"\\\"\\\"Get connection info for a MKS console. :param context: security\",\n \"representing the ip of the machine that will be making\",\n \"This base class method shuts down the VM, detaches all\",\n \"\\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host ID\",\n \"used by nova.compute to store details regarding an instance, and\",\n \"fails to quiesce by other errors (e.g. agent timeout), NovaException\",\n \"gets resumed and will use CPU cycles and memory of\",\n \"def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str in\",\n \"nova.network.model.NetworkInfo vif: The object which has the information about the\",\n \"instance: nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"of live migration at source host. :param context: security context\",\n \"Compute drivers constructors take a VirtAPI object as their first\",\n \"image of the instance. :param resize_instance: True if the instance\",\n \"the rescue. :param nova.objects.instance.Instance instance: The instance being rescued. :param\",\n \"security_group_id): \\\"\\\"\\\"This method is called after a change to security\",\n \"for live migration :param context: security context :param instance: nova.objects.instance.Instance\",\n \"def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is called after a change\",\n \"governing permissions and limitations # under the License. \\\"\\\"\\\" Driver\",\n \"to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies\",\n \"to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info:\",\n \"launches, and as part of a periodic task that records\",\n \"is a member of or if the group gains or\",\n \"this file except in compliance with the License. You may\",\n \"instances on disk. This method is an opportunity to do\",\n \"be checked, the validate values are defined at disk API\",\n \"cleanup_host(self, host): \\\"\\\"\\\"Clean up anything that is necessary for the\",\n \"as virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config,\",\n \":param nova.objects.instance.Instance instance: The instance to get the root device\",\n \"filter out devices which are actually volumes. :return: json strings\",\n \"performance counters associated with the given disk_id on the given\",\n \"empty) list of DriverEphemeralBlockDevice instances - swap: An instance of\",\n \"stat info, eg: | {'kernel': kern, | 'idle': idle, |\",\n \"to receive events. Register a callback to receive asynchronous event\",\n \"In the case of doing a snapshot, it is the\",\n \"file except in compliance with the License. You may obtain\",\n \"instance network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does not support\",\n \"this into consideration when provisioning networking for the instance. Mapping\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. See\",\n \"def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering instance files for an\",\n \":return: Has a big overlap to the return value of\",\n \"gets added to or removed from the security group the\",\n \"or if the group gains or loses a rule. \\\"\\\"\\\"\",\n \"get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection info for a serial console.\",\n \"able to attach to the volume. This is the same\",\n \"that need to - e.g. for optimisations, or when the\",\n \"successfully completes, the instance should be running (power_state.RUNNING). If this\",\n \"the License. \\\"\\\"\\\" Driver base-classes: (Beginning of) the contract that\",\n \"\\\"\\\"\\\"resume guest state when a host is booted. :param instance:\",\n \"destination host. :param context: security context :param instance: instance object\",\n \"virtual machines that the hypervisor knows about. .. note:: This\",\n \"is concerned, 'instance ID' and 'instance name' are synonyms. Note\",\n \"number of I/O requests made, bytes is the total number\",\n \":param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration(self,\",\n \"If a driver supports multi compute-nodes, this method returns a\",\n \"Register a callback to receive asynchronous event notifications from hypervisors.\",\n \"boot time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance:\",\n \"live migration. :param instance: instance that is live migrating \\\"\\\"\\\"\",\n \"timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk of a running instance in\",\n \"raise NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the\",\n \"it's an internal ID chosen by Nova. At the nova.virt\",\n \"method should not be implemented. :param context: security context :param\",\n \"located on shared storage. :param context: security context :param data:\",\n \"for access to auth_token raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the\",\n \"the instance is being recreated on a new hypervisor -\",\n \"to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual\",\n \"of old state is skipped. :param block_device_info: Information about block\",\n \"containing the following keys: - root_device_name: device name of the\",\n \"NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the\",\n \"this class talks in terms of 'instances' (Amazon EC2 and\",\n \"block devices, then spins up the new VM afterwards. It\",\n \"thingy... :return: None \\\"\\\"\\\" # NOTE(jogo) Currently only used for\",\n \"int retry_interval: How often to signal guest while waiting for\",\n \"access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up\",\n \"a given host.- \\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the\",\n \"set of MAC addresses. This is called during spawn_instance by\",\n \"The instance to get the root device for. :param nova.objects.ImageMeta\",\n \"The first parameter is an instance of nova.compute.service.Instance, and so\",\n \"on/off an instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def\",\n \"User files to inject into instance. :param admin_password: Administrator password\",\n \"instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk\",\n \"driver's local image cache. Some drivers chose to cache images\",\n \"instance. The paused instance gets unpaused and will use CPU\",\n \"def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None,\",\n \"host.- \\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address\",\n \"as well that is needed for creating the snapshot. \\\"\\\"\\\"\",\n \"up; freq indicates the current CPU frequency (MHz). All values\",\n \"\\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver want\",\n \"from the network, and manage their own TFTP service. This\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information.\",\n \"The counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance:\",\n \"auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual interfaces\",\n \"in for access to auth_token raise NotImplementedError() def change_instance_metadata(self, context,\",\n \"use this file except in compliance with the License. You\",\n \"list of disk information dicts in JSON format. :rtype: str\",\n \"if further instances shouldn't be scheduled to this host, return\",\n \"to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power\",\n \"access to auth_token raise NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause the\",\n \"def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap') or\",\n \"interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self, context, aggregate,\",\n \"The compute manager doesn't use this interface, which seems odd\",\n \"network, and manage their own TFTP service. This requires passing\",\n \"as before the 'suspend'. :param nova.context.RequestContext context: The context for\",\n \"of the instance. \\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self, context, instance):\",\n \"image of the instance. :param rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\"\",\n \"service. This requires passing the appropriate options out to the\",\n \"It may be overridden by hypervisors that need to -\",\n \"client API. :return: None, or a set of DHCP options,\",\n \":param data: result of check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context,\",\n \"since filtering rules to firewall rules can be set faster).\",\n \"VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains information\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); you may #\",\n \"network_info: Necessary network information for the resume. :param dict block_device_info:\",\n \"reboot on all given 'instances'. Reboots the given `instances` which\",\n \"re-make this instance. A 'rebuild' effectively purges all existing data\",\n \"nova.objects.instance.Instance instance: The instance to suspend. :return: None \\\"\\\"\\\" raise\",\n \"instance: nova.objects.instance.Instance This function should use the data there to\",\n \"the given `instance`. :param dict old_connection_info: The volume for this\",\n \"it to shutdown \\\"\\\"\\\" raise NotImplementedError() def power_on(self, context, instance,\",\n \"compute drivers must follow, and shared types that support that\",\n \"callback will be invoked with a single parameter, which will\",\n \"rules to hypervisors and firewalls are inevitable on destination host.\",\n \"metadata of the image of the instance. :param rescue_password: <PASSWORD>\",\n \"the instance. \\\"\\\"\\\" raise NotImplementedError() def destroy(self, context, instance, network_info,\",\n \"file system # type and implement this function at their\",\n \"drivers must follow, and shared types that support that contract\",\n \"to the instance. \\\"\\\"\\\" raise NotImplementedError() def destroy(self, context, instance,\",\n \"\\\"\\\"\\\"Start/Stop host maintenance window. On start, it triggers the migration\",\n \"if true, post operation of block_migration. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"group gains or loses a rule. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"Consider the combination with :func:`set_host_enabled`. :param str host: The name\",\n \"to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which\",\n \"or libvirt terminology). An instance has an ID, which is\",\n \"disk. :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def\",\n \"the crash dump should be triggered. :return: None \\\"\\\"\\\" raise\",\n \"been in rebooting state longer than the configured timeout. :return:\",\n \"dict of nova uuid => dict of usage info \\\"\\\"\\\"\",\n \"LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration(self, context, instance, dest,\",\n \"'host': hostname } \\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns\",\n \"\\\"\\\"\\\" raise NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause the given instance.\",\n \"instance. \\\"\\\"\\\" raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None,\",\n \"to prepare for snapshots. If the specified instance doesn't support\",\n \"host to an aggregate. The counter action to this is\",\n \"that generic data structure into terms that are specific to\",\n \"is responsible for keeping track of which instance ID maps\",\n \"idle indicates the cumulative idle CPU time (nanoseconds), wait indicates\",\n \"nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk. :param migrate_data:\",\n \"for the guest OS to shutdown. :param int retry_interval: How\",\n \"action for 'unpause' is :func:`pause`. Depending on the underlying hypervisor\",\n \"are not completed. :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek):\",\n \"info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach\",\n \"format. Virt drivers expect block device mapping to be presented\",\n \"module specified by the compute_driver configuration option or, if supplied,\",\n \"instance: The instance to soft-delete. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \":param context: security context :param dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return the\",\n \"virtapi: a VirtAPI instance :param compute_driver: a compute driver name\",\n \"skipped. :param block_device_info: Information about block devices to be attached\",\n \"get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given instance. :param\",\n \"to set in instance. :param bdms: block-device-mappings to use for\",\n \"True def volume_snapshot_create(self, context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached\",\n \"be stuck. :param list instances: A list of nova.objects.instance.Instance objects\",\n \"mode: If `True`, go into maintenance mode. If `False`, leave\",\n \"with currently running VM's on the given host. \\\"\\\"\\\" #\",\n \"terminology). An instance has an ID, which is the identifier\",\n \":param migrate_data: a LiveMigrateData object \\\"\\\"\\\" pass def post_live_migration_at_source(self, context,\",\n \"bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make\",\n \"instance, network_info): \\\"\\\"\\\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"return value is specific to the Neutron client API. :return:\",\n \"is called when nova-compute launches, and as part of a\",\n \":param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info:\",\n \"up destination node after a failed live migration. :param context:\",\n \"def volume_snapshot_create(self, context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached to\",\n \"that will hold the snapshot. \\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self,\",\n \"compute drivers. The interface to this class talks in terms\",\n \"instance): \\\"\\\"\\\"Get connector information for the instance for attaching to\",\n \"off the specified instance. :param instance: nova.objects.instance.Instance :param timeout: time\",\n \"migration. :param context: security context :param instance: instance object that\",\n \"snapshot(self, context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified instance. :param\",\n \"counter action for 'resume' is :func:`suspend`. Depending on the underlying\",\n \"true, prepare for block migration :param disk_over_commit: if true, allow\",\n \"phases, turning off the instance before the end. :param nova.objects.instance.Instance\",\n \"swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info):\",\n \"to a pre-created image that will hold the snapshot. \\\"\\\"\\\"\",\n \"def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance counters associated with the\",\n \"format of the return value is specific to the Neutron\",\n \"calls into the driver. The prime example is to clean\",\n \"on the configuration of it. :param nova.objects.instance.Instance instance: The instance\",\n \"def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given instance.\",\n \"instance of the given flavor. Defaults to zero, drivers should\",\n \"machine :param block_migration: if true, prepare for block migration :param\",\n \"if not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event)) return if not\",\n \"constructors take a VirtAPI object as their first object and\",\n \"network ports. :param context: request context :param instance: nova.objects.instance.Instance that\",\n \"\\\"\\\"\\\"Replace the volume attached to the given `instance`. :param dict\",\n \"device name for the driver. :param nova.objects.instance.Instance instance: The instance\",\n \"returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates\",\n \"info :param power_on: True if the instance should be powered\",\n \"dest: The IP address of the destination host. :param nova.objects.flavor.Flavor\",\n \"but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver) try:\",\n \"volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached to a specified instance. The\",\n \"older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self,\",\n \"\\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, } def __init__(self, virtapi): self.virtapi =\",\n \"\\\"\\\"\\\"Prepare an instance for live migration :param context: security context\",\n \"an instance, and pass them into this layer. This layer\",\n \"up due to a full pipeline. All counters are long\",\n \"filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn\",\n \"be called. Don't use thread for this method since migration\",\n \"dicts in JSON format. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def\",\n \"indicates the current CPU frequency (MHz). All values are long\",\n \"not be implemented. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context in for\",\n \"Invokes the event callback registered by the compute manager to\",\n \"for snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported\",\n \"the # License for the specific language governing permissions and\",\n \"= block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info =\",\n \"return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances()\",\n \"try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in\",\n \"processes respectively, idle indicates the cumulative idle CPU time (nanoseconds),\",\n \"decided by the driver implementation if not set. :returns: The\",\n \"follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname } \\\"\\\"\\\"\",\n \"\\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"The counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context:\",\n \"to be called since it defines PROJNET, PROJMASK. but libvirt\",\n \"the compute driver module specified by the compute_driver configuration option\",\n \"of the machine as follows:: { 'ip': ip, 'initiator': initiator,\",\n \"Waiting only for filtering rules to hypervisor, since filtering rules\",\n \"mapping. :param instance: nova.objects.instance.Instance that volume is requesting a device\",\n \"Some hypervisors (such as bare metal) require that instances boot\",\n \"the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When\",\n \"flavor. Defaults to zero, drivers should override if per-instance overhead\",\n \":rtype: str \\\"\\\"\\\" raise NotImplementedError() def snapshot(self, context, instance, image_id,\",\n \"instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of the root device.\",\n \"and the file being merged into (if appropriate). :return: None\",\n \"See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information :param\",\n \"cache which isn't directly related to other calls into the\",\n \"\\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True,\",\n \"context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance.\",\n \"set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means 'these and\",\n \"context, instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot of a\",\n \"from the security group the instance is a member of\",\n \"not support \\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context, instance, network_info,\",\n \"None def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the driver's local image\",\n \"\\\"\\\"\\\" raise NotImplementedError() def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance counters\",\n \"about the receiving machine :param block_migration: if true, prepare for\",\n \"object and this must be supplied. :param virtapi: a VirtAPI\",\n \"which has the information about the interface to attach. :raise\",\n \"action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets\",\n \"cycles of the host anymore. The state of the VM\",\n \"class talks in terms of 'instances' (Amazon EC2 and internal\",\n \"far as this layer is concerned, 'instance ID' and 'instance\",\n \"is booting up; freq indicates the current CPU frequency (MHz).\",\n \"block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info):\",\n \"return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return whether\",\n \"about block devices to be attached to the instance. :param\",\n \"a MKS console. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"an instance of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self, instance):\",\n \"Connector information is a dictionary representing the ip of the\",\n \"is :func:`pause`. Depending on the underlying hypervisor technology, the guest\",\n \"'/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server',\",\n \"any bad volumes encountered \\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self, console_type):\",\n \"for a spice console. :param context: security context :param instance:\",\n \"def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self):\",\n \"True if the instance is being resized, False otherwise :param\",\n \"this connection gets detached from the given `instance`. :param dict\",\n \"interface to attach. :raise nova.exception.NovaException: If the attach fails. :return:\",\n \"instance): \\\"\\\"\\\"Refresh security group rules Gets called when an instance\",\n \"destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified\",\n \"drivers, but it is not particularly efficient. Maintainers of the\",\n \"compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return\",\n \"otherwise :param block_device_info: instance volume block device info :param power_on:\",\n \"bool enabled: If this is `True`, the host will accept\",\n \"of `suspend` is :func:`pause`. The counter action for `suspend` is\",\n \"for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting\",\n \"to the compute manager. Invokes the event callback registered by\",\n \"virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping':\",\n \"context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance\",\n \"freq indicates the current CPU frequency (MHz). All values are\",\n \"raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None,\",\n \"# NOTE(markus_z): 2015-08-18 # The compute manager doesn't use this\",\n \"in for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer\",\n \"instance. The restored instance will be automatically booted. The counter\",\n \"current CPU frequency (MHz). All values are long integers. \\\"\\\"\\\"\",\n \"removed. :param nova.network.model.NetworkInfo vif: The object which has the information\",\n \"the underlying domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance\",\n \"you may # not use this file except in compliance\",\n \"= block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info =\",\n \"may be overridden by hypervisors that need to - e.g.\",\n \"to the platform-specific layer, and never escape the connection interface.\",\n \"state that it was before this call began. :param context:\",\n \"of doing a snapshot, it is the image file Cinder\",\n \"The metadata of the image of the instance. :param injected_files:\",\n \"There may be other data included as well that is\",\n \"replaced by another one. :param str mountpoint: The mountpoint in\",\n \"the same state as before the 'suspend'. :param nova.context.RequestContext context:\",\n \":param nova.objects.instance.Instance instance: The instance whose disk should be migrated.\",\n \"rules to hypervisor, since filtering rules to firewall rules can\",\n \"specified by the compute_driver configuration option or, if supplied, the\",\n \"IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of IPTables\",\n \":param instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context,\",\n \"instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about actual disk sizes of an\",\n \"to auth_token raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the result of\",\n \"block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param context: the context for\",\n \"is the number of requests held up due to a\",\n \"host again. The counter action for 'resume' is :func:`suspend`. Depending\",\n \"should add the given `host` :param str host: The name\",\n \"will get an additional network interface. :param nova.objects.ImageMeta image_meta: The\",\n \"host, **kwargs): \\\"\\\"\\\"Add a compute host to an aggregate. The\",\n \"_sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args,\",\n \"the state that it was before this call began. :param\",\n \"attached. :param uuid volume_id: Attached volume associated with the snapshot\",\n \"be the controlling thing here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs) from\",\n \"(nanoseconds) spent by kernel and user processes respectively, idle indicates\",\n \"shutdown + spin up steps. :param context: security context :param\",\n \"the specified instance. The first parameter is an instance of\",\n \"return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return\",\n \"set faster). Concretely, the below method must be called. -\",\n \"snapshot. \\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id,\",\n \"remove the given `host` :param str host: The name of\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage info\",\n \"instance): \\\"\\\"\\\"Force live migration to complete :param instance: Instance being\",\n \"of the instance could be persisted on the host and\",\n \"method shuts down the VM, detaches all block devices, then\",\n \"`instance`. :param dict new_connection_info: The volume for this connection gets\",\n \"context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending\",\n \"than `timeout` seconds. :param int timeout: The timeout (in seconds)\",\n \"instance that has the volume attached. :param uuid volume_id: Attached\",\n \"of calling the Linux command `uptime` on this host. :return:\",\n \"snapshot to delete. :param dict delete_info: Volume backend technology specific\",\n \"is called successfully, the instance's state goes back to power_state.RUNNING.\",\n \":param nova.objects.instance.Instance instance: The soft-deleted instance which should be restored\",\n \"for the resume. :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"host ID to associate with network ports. :param context: request\",\n \"\\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check\",\n \"to zero, drivers should override if per-instance overhead calculations are\",\n \"the new instance. :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"this method returns a list of nodenames managed by the\",\n \"\\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True):\",\n \"a ComputeDriver instance \\\"\\\"\\\" if not compute_driver: compute_driver = CONF.compute_driver\",\n \"NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy\",\n \"to dump the crash data. How the guest OS reacts\",\n \":func:`add_to_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate:\",\n \"also base64-encoded. NOTE(russellb) This method is deprecated and will be\",\n \"an additional network interface. :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"result of the power action :rtype: : str \\\"\\\"\\\" raise\",\n \":func:`pause`. The counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext\",\n \"to signal guest while waiting for it to shutdown \\\"\\\"\\\"\",\n \"API. :return: None, or a set of DHCP options, eg:\",\n \"instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event) except\",\n \"supplied ID exists on the host, False otherwise. .. note::\",\n \"or a set of DHCP options, eg: | [{'opt_name': 'bootfile-name',\",\n \"raise NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the file format\",\n \"network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of live migration at destination\",\n \"to cache images for instances on disk. This method is\",\n \"to methods that expect it. \\\"\\\"\\\" return True def volume_snapshot_create(self,\",\n \"def detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug to remove a network\",\n \"the resume. :param nova.objects.instance.Instance instance: The suspended instance to resume.\",\n \"specified soft-deleted instance. The restored instance will be automatically booted.\",\n \"block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name')\",\n \"of the host again. The counter action for 'resume' is\",\n \"instance: The instance whose disk should be migrated. :param str\",\n \"of this host to accept new instances. :param bool enabled:\",\n \"own TFTP service. This requires passing the appropriate options out\",\n \"there to guide the creation of the new instance. :param\",\n \"pass def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a reboot on all\",\n \"rebuild for HA implementation and required for validation of access\",\n \"of the host anymore. The state of the instance could\",\n \"are defined at disk API module. \\\"\\\"\\\" # NOTE(jichenjc): Return\",\n \":param nova.objects.instance.Instance instance: The suspended instance to resume. :param nova.network.model.NetworkInfo\",\n \"path to which the file is to be written on\",\n \"} \\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of\",\n \"usage counters for each interface on each running VM. :param\",\n \"optional driver method which is used to publish changes to\",\n \"powers up the host. :param str action: The action the\",\n \"in for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance):\",\n \"related to other calls into the driver. The prime example\",\n \"attach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def detach_interface(self, instance,\",\n \"can be set faster). Concretely, the below method must be\",\n \"nova.i18n import _, _LE, _LI from nova import utils from\",\n \"this method should return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def node_is_available(self,\",\n \"ValueError( _(\\\"Event must be an instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting\",\n \"Some methods here take an instance of nova.compute.service.Instance. This is\",\n \"it. \\\"\\\"\\\" return True def volume_snapshot_create(self, context, instance, volume_id, create_info):\",\n \"big overlap to the return value of the newer interface\",\n \"the UUIDS of all the instances known to the virtualization\",\n \"resources from a driver that manages only one node can\",\n \"compute manager doesn't use this interface, which seems odd #\",\n \"a compute driver name to override the config opt :returns:\",\n \"an instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event)\",\n \"given 'metadata' and 'personalities'. This base class method shuts down\",\n \"removed from the security group the instance is a member\",\n \"block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals') or\",\n \"guest has the same state as before the 'pause'. :param\",\n \"that was being migrated :param network_info: instance network information :param\",\n \"software # distributed under the License is distributed on an\",\n \"block_device_info=None): \\\"\\\"\\\"Check if it is possible to execute live migration.\",\n \"it is not particularly efficient. Maintainers of the virt drivers\",\n \"None def init_host(self, host): \\\"\\\"\\\"Initialize anything that is necessary for\",\n \"an instance with the supplied ID accessible on the host,\",\n \"dict kwargs: A free-form thingy... :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"on shared storage. This runs check on the destination host,\",\n \"be destroyed :param migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError()\",\n \"| ] \\\"\\\"\\\" return None def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage\",\n \"from nova.virt import block_device as virt_block_device block_device_info = { 'root_device_name':\",\n \"NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting\",\n \"to be stuck. :param list instances: A list of nova.objects.instance.Instance\",\n \"rebooting state longer than the configured timeout. :return: None \\\"\\\"\\\"\",\n \"called. Don't use thread for this method since migration should\",\n \"the instance. \\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get\",\n \"to the instance metadata. This is an optional driver method\",\n \"instance object that was being migrated :param network_info: instance network\",\n \"be freeform) is up to the network implementation layer. For\",\n \"nova-instance-instance-xxx, etc.) to_xml may have to be called since it\",\n \"nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry =\",\n \"at destination during cleanup :param migrate_data: a LiveMigrateData object \\\"\\\"\\\"\",\n \"initiator, 'host': hostname } \\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self, refresh=False):\",\n \"raised if the operation cannot complete. \\\"\\\"\\\" # TODO(Vek): Need\",\n \"nova.virt import event as virtevent CONF = nova.conf.CONF LOG =\",\n \"not need to be set, and should be decided by\",\n \"of the iscsi initiator and the hostname of the machine\",\n \"the results from check_can_live_migrate_destination. :param context: security context :param instance:\",\n \"device name based on the block device mapping. :param instance:\",\n \"instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain\",\n \"perform. The valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\". :return:\",\n \"The context for the suspend. :param nova.objects.instance.Instance instance: The instance\",\n \"instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest state when a host is\",\n \"_LE, _LI from nova import utils from nova.virt import event\",\n \"additional network interface. :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False,\",\n \"volumes attached to vms on a given host.- \\\"\\\"\\\" raise\",\n \"supplied ID accessible on the host, False otherwise. .. note::\",\n \"which should be paused. :return: None \\\"\\\"\\\" # TODO(Vek): Need\",\n \"console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection\",\n \":param disk_info: the newly transferred disk information :param network_info: instance\",\n \"str(event)) return if not isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event must\",\n \"| {'kernel': kern, | 'idle': idle, | 'user': user, |\",\n \"instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError()\",\n \"being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information for the\",\n \"disk sizes of an instance. :param instance: nova.objects.Instance :param block_device_info:\",\n \"The result of the power action :rtype: : str \\\"\\\"\\\"\",\n \"build an instance of the given flavor. Defaults to zero,\",\n \"is called during spawn_instance by the compute manager. :return: None,\",\n \"or None - block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice\",\n \"False otherwise. .. note:: Used in rebuild for HA implementation\",\n \"or {} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping\",\n \"information :param network_info: instance network information :param nova.objects.ImageMeta image_meta: The\",\n \"virtualization overhead required to build an instance of the given\",\n \"IDs must stay internal to the platform-specific layer, and never\",\n \"this method. :param context: security context :param instance: Instance object\",\n \"representing the host ID \\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None):\",\n \"instance. :param nova.objects.instance.Instance instance: The instance to which the diagnostic\",\n \"parameter, which will be an instance of the nova.virt.event.Event class.\",\n \"dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns:\",\n \"bad_volumes_callback: Function to handle any bad volumes encountered \\\"\\\"\\\" raise\",\n \":func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance which will get an\",\n \":param nova.network.model.NetworkInfo network_info: The network information of the given `instance`.\",\n \"a dict containing the following keys: - root_device_name: device name\",\n \"# Refresh and check again. return nodename in self.get_available_nodes(refresh=True) def\",\n \"raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is called after\",\n \"shared types that support that contract \\\"\\\"\\\" import sys from\",\n \"KIND, either express or implied. See the # License for\",\n \"\\\"\\\"\\\" # NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError()\",\n \"instance of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self, context, instance):\",\n \"argument. Compute drivers constructors take a VirtAPI object as their\",\n \"can still be virtualized onto any L2 domain, with arbitrary\",\n \"rebuild :param detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl\",\n \"volume attached to a specified instance. The counter action to\",\n \"def cleanup_host(self, host): \\\"\\\"\\\"Clean up anything that is necessary for\",\n \"data from the system and remakes the VM with given\",\n \"of requests held up due to a full pipeline. All\",\n \"another one. :param str mountpoint: The mountpoint in the instance\",\n \"specified instance. After this is called successfully, the instance's state\",\n \"all the instances known to the virtualization layer, as a\",\n \"instance, network_info): # NOTE(markus_z): 2015-08-18 # The compute manager doesn't\",\n \"is a dictionary representing the ip of the machine that\",\n \"LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"), {'event': event, 'ex': ex}) def\",\n \"destroyed from the Hypervisor before calling this method. :param context:\",\n \"name for the driver. :param nova.objects.instance.Instance instance: The instance to\",\n \"instance. :param preserve_ephemeral: True if the default ephemeral storage partition\",\n \"NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage counters for each\",\n \"\\\"\\\"\\\"Power off the specified instance. :param instance: nova.objects.instance.Instance :param timeout:\",\n \"be set faster). Concretely, the below method must be called.\",\n \"the compute manager to dispatch the event. This must only\",\n \"attaching to volumes. Connector information is a dictionary representing the\",\n \"def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the driver's local image cache.\",\n \"reboot_type: Either a HARD or SOFT reboot :param block_device_info: Info\",\n \"all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs):\",\n \"This is the same data format returned by Cinder's initialize_connection()\",\n \"module. \\\"\\\"\\\" # NOTE(jichenjc): Return False here so that every\",\n \":param admin_password: Administrator password to set in instance. :param network_info:\",\n \"an instance of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self, context,\",\n \"before the 'suspend'. :param nova.context.RequestContext context: The context for the\",\n \"to be presented in the format of a dict containing\",\n \"def get_info(self, instance): \\\"\\\"\\\"Get the current status of an instance,\",\n \"kern, | 'idle': idle, | 'user': user, | 'iowait': wait,\",\n \"disk_info: instance disk information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\"\",\n \"return value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\"\",\n \"instance: nova.objects.instance.Instance object Returns a InstanceInfo object \\\"\\\"\\\" # TODO(Vek):\",\n \"which instance ID maps to which platform-specific ID, and vice\",\n \"be detached from the instance. :param destroy_disks: Indicates if disks\",\n \"access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics\",\n \"instance block device information :param destroy_disks: if true, destroy disks\",\n \"with the supplied ID accessible on the host, False otherwise.\",\n \"self._compute_event_callback = None def init_host(self, host): \\\"\\\"\\\"Initialize anything that is\",\n \"up the stack. This is unfortunately also called a 'name'\",\n \"Need to pass context in for access to auth_token raise\",\n \"are compatible with the set of MAC addresses. This is\",\n \"from the Hypervisor before calling this method. :param context: security\",\n \"`resize_to`. :return: None \\\"\\\"\\\" raise NotImplementedError() def attach_interface(self, instance, image_meta,\",\n \":param data: result of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self,\",\n \"\\\"\\\"\\\"Pause the given instance. A paused instance doesn't use CPU\",\n \"security groups and their associated rules live in the datastore,\",\n \"options out to the DHCP service. Most hypervisors can use\",\n \"compatible with the set of MAC addresses. This is called\",\n \"with arbitrary VLANs etc, but regular switches require pre-configured MAC->network\",\n \"instance): \\\"\\\"\\\"Suspend the specified instance. A suspended instance doesn't use\",\n \"vif): \\\"\\\"\\\"Use hotunplug to remove a network interface from a\",\n \"one node can safely ignore this :returns: Dictionary describing resources\",\n \"\\\"\\\"\\\"Delete any lingering instance files for an instance. :param instance:\",\n \"the given `instance`. :param dict block_device_info: Information about the block\",\n \"instance, image_meta): \\\"\\\"\\\"Unquiesce the specified instance after snapshots. If the\",\n \"the virtualization driver\\\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver == 'xenapi.XenAPIDriver'\",\n \":param dest: destination host :param post_method: post operation method. expected\",\n \"gets a network interface removed. :param nova.network.model.NetworkInfo vif: The object\",\n \"access to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0):\",\n \"nova.objects.instance.Instance instance: The instance which should be paused. :return: None\",\n \"paused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"= importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except\",\n \"a resize/migration. :param context: the context for the migration/resize :param\",\n \"true, allow disk over commit :returns: a LiveMigrateData object (hypervisor-dependent)\",\n \":param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info\",\n \"considering rebooting instances to be stuck. :param list instances: A\",\n \"instance :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise\",\n \"'unpause' is :func:`pause`. Depending on the underlying hypervisor technology, the\",\n \"a failed live migration. :param context: security context :param instance:\",\n \"to which the file is to be written on the\",\n \"no longer of interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def\",\n \"Refresh and check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self):\",\n \"obtain # a copy of the License at # #\",\n \"VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self, context,\",\n \":param dict kwargs: A free-form thingy... :return: None \\\"\\\"\\\" raise\",\n \"The IP address of the destination host. :param nova.objects.flavor.Flavor flavor:\",\n \"\\\"\\\"\\\"Get connection info for a serial console. :param context: security\",\n \"host will accept new instances. If it is `False`, the\",\n \"for `suspend` is :func:`resume`. :param nova.context.RequestContext context: The context for\",\n \"object \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if\",\n \"block_device_info=None): \\\"\\\"\\\"Retrieve information about actual disk sizes of an instance.\",\n \"need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if the driver requires legacy block\",\n \"block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified instance from the Hypervisor.\",\n \"to delete. :param dict delete_info: Volume backend technology specific data\",\n \"their supported file system # type and implement this function\",\n \"instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def\",\n \"not compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute driver\",\n \"attached to the instance. \\\"\\\"\\\" raise NotImplementedError() def destroy(self, context,\",\n \"\\\"\\\"\\\"Return usage info for volumes attached to vms on a\",\n \"instance: The suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary\",\n \"security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the\",\n \"calling the Linux command `uptime` on this host. :return: A\",\n \"technology specific data needed to be able to complete the\",\n \"given `instance` at instance boot time. The counter action is\",\n \"own identity schemes, to uniquely identify a VM or domain.\",\n \"volume is larger than the old volume, it gets resized\",\n \"long integers. \\\"\\\"\\\" raise NotImplementedError() def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return\",\n \"internal Nova terminology), by which we mean 'running virtual machine'\",\n \"for translating that generic data structure into terms that are\",\n \"hypervisor technology, the guest has the same state as before\",\n \"LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s'\",\n \"must stay internal to the platform-specific layer, and never escape\",\n \"possible to execute live migration. This checks if the live\",\n \"successfully, the instance's state goes back to power_state.RUNNING. The virtualization\",\n \"nova.objects.aggregate.Aggregate aggregate: The aggregate which should add the given `host`\",\n \"will be removed once it can be removed from nova.compute.manager.\",\n \"Note that the format of the return value is specific\",\n \"access to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): #\",\n \"appropriate options out to the DHCP service. Most hypervisors can\",\n \"counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which\",\n \"instance/VM/domain on the virtualization platform. Once this successfully completes, the\",\n \"the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def\",\n \":param timeout: time to wait for GuestOS to shutdown :param\",\n \"or name is not human-readable or customer-controlled -- it's an\",\n \"instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def unrescue(self, instance,\",\n \"NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about actual disk\",\n \"pass def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up filtering rules and\",\n \"host. :param nova.objects.flavor.Flavor flavor: The flavor of the instance whose\",\n \"is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which should\",\n \"the root device for. :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"admin_password: <PASSWORD> to set in instance. :param bdms: block-device-mappings to\",\n \"isn't directly related to other calls into the driver. The\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use\",\n \"instance to soft-delete. :return: None \\\"\\\"\\\" raise NotImplementedError() def restore(self,\",\n \"@property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if the driver requires\",\n \"it to shutdown. :return: A list of disk information dicts\",\n \"LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info,\",\n \"access to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug\",\n \"respectively, idle indicates the cumulative idle CPU time (nanoseconds), wait\",\n \"source host. :param context: security context :instance: instance object that\",\n \"at destination host. :param context: security context :param instance: instance\",\n \"A \\\"softer\\\" way of `suspend` is :func:`pause`. The counter action\",\n \"should not be started when setting-up filtering rules operations are\",\n \"network details are compatible with the set of MAC addresses.\",\n \"NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause the given paused instance. The\",\n \"context in for access to auth_token raise NotImplementedError() def get_console_output(self,\",\n \"the volume attached to the given `instance`. :param dict old_connection_info:\",\n \"raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup on host\",\n \"rdp console. :param context: security context :param instance: nova.objects.instance.Instance :returns\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known host\",\n \"uuid snapshot_id: The snapshot to delete. :param dict delete_info: Volume\",\n \"instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object\",\n \"action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security\",\n \"won't accept new instances. :return: If the host can accept\",\n \"the suspend. :param nova.objects.instance.Instance instance: The instance to suspend. :return:\",\n \"the host can accept further instances, return \\\"enabled\\\", if further\",\n \"False otherwise \\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info):\",\n \"rescue. :param nova.objects.instance.Instance instance: The instance being rescued. :param nova.network.model.NetworkInfo\",\n \"for compute drivers. The interface to this class talks in\",\n \"the 'VM' is actually proxied and needs to be held\",\n \"vif: The object which has the information about the interface\",\n \"_get_instance_block_device_info :returns: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self,\",\n \"should be destroyed :param migrate_data: implementation specific params \\\"\\\"\\\" raise\",\n \"soft-deleted instance doesn't allocate any resources anymore, but is still\",\n \"disk, False otherwise. \\\"\\\"\\\" return True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell\",\n \"is unused. Note that this function takes an instance ID.\",\n \":returns: dict of nova uuid => dict of usage info\",\n \"networking for the instance. Mapping of MAC addresses to actual\",\n \"to maintenance mode or \\\"off_maintenance\\\" if maintenance mode got left.\",\n \"that support that contract \\\"\\\"\\\" import sys from oslo_log import\",\n \"API module. \\\"\\\"\\\" # NOTE(jichenjc): Return False here so that\",\n \"NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump mechanism on the\",\n \"cumulative I/O wait CPU time (nanoseconds), since the host is\",\n \"entry. The counter action :func:`restore` uses the database entry to\",\n \"WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"permissions and limitations # under the License. \\\"\\\"\\\" Driver base-classes:\",\n \"encryption=None): \\\"\\\"\\\"Attach the disk to the instance at mountpoint using\",\n \"seems odd # since the manager should be the controlling\",\n \"of the instance. :param injected_files: User files to inject into\",\n \"| 'iowait': wait, | 'frequency': freq}, where kern and user\",\n \"into instance. :param admin_password: <PASSWORD> to set in instance. :param\",\n \"rescue(self, context, instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified instance.\",\n \"\\\"\\\"\\\"Base class for compute drivers. The interface to this class\",\n \"HARD or SOFT reboot :param block_device_info: Info pertaining to attached\",\n \"block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk of a running instance\",\n \"compute_host_bdms): \\\"\\\"\\\"Return usage info for volumes attached to vms on\",\n \"information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does not support \\\" \\\"post_live_migration_at_source\",\n \"None - block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or\",\n \"volume attached. :param uuid volume_id: Attached volume associated with the\",\n \"free-form thingy... :return: None \\\"\\\"\\\" # NOTE(jogo) Currently only used\",\n \"of IPTables rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of\",\n \"before the 'pause'. :param nova.objects.instance.Instance instance: The instance which should\",\n \"use CPU cycles and memory of the host again. The\",\n \"def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration to complete :param instance:\",\n \"total number of I/O requests made, bytes is the total\",\n \"instance to lookup Returns True if an instance with the\",\n \"about block devices that should be detached from the instance.\",\n \"new instance/VM/domain on the virtualization platform. Once this successfully completes,\",\n \"enabled: If this is `True`, the host will accept new\",\n \"nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach block devices.\",\n \"names of all the instances known to the virtualization layer,\",\n \"def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute driver module. Load the\",\n \"On some platforms (e.g. XenAPI) performance statistics can be retrieved\",\n \"(if appropriate). :return: None \\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self, instance,\",\n \"long integers. This method is optional. On some platforms (e.g.\",\n \"network information for the resume. :param nova.objects.ImageMeta image_meta: The metadata\",\n \"auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None,\",\n \"or domain. These IDs must stay internal to the platform-specific\",\n \"virt drivers are encouraged to override this method with something\",\n \"automatically booted. The counter action for `restore` is :func:`soft_delete`. :param\",\n \"\\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"calculate overhead for. :returns: Dict of estimated overhead values. \\\"\\\"\\\"\",\n \"information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly\",\n \"on disk. This method is an opportunity to do management\",\n \"counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The\",\n \"prime example is to clean the cache and remove images\",\n \"\\\"\\\"\\\" Driver base-classes: (Beginning of) the contract that compute drivers\",\n \"set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def get_volume_connector(self, instance):\",\n \"than the configured timeout. :return: None \\\"\\\"\\\" # TODO(Vek): Need\",\n \"the appropriate options out to the DHCP service. Most hypervisors\",\n \"instance. A suspended instance doesn't use CPU cycles or memory\",\n \"rd_bytes, wr_req, wr_bytes, errs], where rd indicates read, wr indicates\",\n \"cycles of the host again. The counter action for 'unpause'\",\n \"it is the image file Cinder expects to be used\",\n \"of a volume attached to a specified instance. The counter\",\n \"time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The\",\n \"addresses to actual networks (or permitting them to be freeform)\",\n \"be persisted on the host and allocate storage space this\",\n \"service. Otherwise, this method should return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance at mountpoint using info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self, connection_info,\",\n \"them into this layer. This layer is responsible for translating\",\n \"given instance. Stalling instances can be triggered to dump the\",\n \"which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which\",\n \"to signal guest while waiting for it to shutdown. :return:\",\n \"stack. Most virtualization platforms will also have their own identity\",\n \"the host whose maintenance mode should be changed. :param bool\",\n \"return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return\",\n \"instance will be automatically booted. The counter action for `restore`\",\n \"The security context. :param nova.objects.instance.Instance instance: The instance that has\",\n \"console_type): # TODO(Vek): Need to pass context in for access\",\n \"\\\"\\\"\\\"Post operation of live migration at source host. :param context:\",\n \"or implied. See the # License for the specific language\",\n \"maintenance mode got left. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def\",\n \"configuration option or, if supplied, the driver name supplied as\",\n \"drivers constructors take a VirtAPI object as their first object\",\n \"be powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self,\",\n \":param str dest: The IP address of the destination host.\",\n \"down the VM, detaches all block devices, then spins up\",\n \"a new hypervisor - all the cleanup of old state\",\n \"instance. :param instance: nova.objects.instance.Instance :param new_pass: the <PASSWORD> \\\"\\\"\\\" raise\",\n \"errs], where rd indicates read, wr indicates write, req is\",\n \"associated with the given disk_id on the given instance. These\",\n \"migration :param context: security context :param instance: nova.objects.instance.Instance object :param\",\n \"errors (e.g. agent timeout), NovaException is raised. :param context: request\",\n \"destination host. ( Waiting only for filtering rules to hypervisor,\",\n \"The instance to lookup Returns True if an instance with\",\n \"network_info: Necessary network information for the resume. :param nova.objects.ImageMeta image_meta:\",\n \"limits should return a set of MAC addresses, which will\",\n \"compute driver module. Load the compute driver module specified by\",\n \"not human-readable or customer-controlled -- it's an internal ID chosen\",\n \"as part of a periodic task that records the results\",\n \"nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage\",\n \"sending machine :param dst_compute_info: Info about the receiving machine :param\",\n \"if not compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute\",\n \"instance, image_meta, vif): \\\"\\\"\\\"Use hotplug to add a network interface\",\n \"complete. \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a\",\n \"drivers to return a set of MAC addresses that the\",\n \"get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information for the instance for attaching\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def detach_interface(self, instance, vif): \\\"\\\"\\\"Use\",\n \"this host to accept new instances. :param bool enabled: If\",\n \"gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains\",\n \":returns: Dict of estimated overhead values. \\\"\\\"\\\" return {'memory_mb': 0,\",\n \"spice console. :param context: security context :param instance: nova.objects.instance.Instance :returns\",\n \"dict block_device_info: Information about the block devices. :param int timeout:\",\n \"raise NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\"\",\n \"containing the CPU stat info, eg: | {'kernel': kern, |\",\n \"devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information\",\n \"- e.g. for optimisations, or when the 'VM' is actually\",\n \"of an instance to another host. :param context: security context\",\n \"addresses, which will be supplied to the allocate_for_instance call by\",\n \"the format of the return value is specific to the\",\n \"the specified instance. :param nova.context.RequestContext context: The context for the\",\n \"\\\"\\\"\\\"Return the total number of virtual machines. Return the number\",\n \"The name of the host whose maintenance mode should be\",\n \"{'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return the names of\",\n \"signal guest while waiting for it to shutdown \\\"\\\"\\\" raise\",\n \"return swap and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info):\",\n \"attached to the given `instance`. :param dict old_connection_info: The volume\",\n \"\\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute driver\",\n \"NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume guest state\",\n \"method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception\",\n \"(MHz). All values are long integers. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove a compute\",\n \"and user processes respectively, idle indicates the cumulative idle CPU\",\n \"Reserved. # # Licensed under the Apache License, Version 2.0\",\n \"anymore. The state of the instance could be persisted on\",\n \"vnc console. :param context: security context :param instance: nova.objects.instance.Instance :returns\",\n \"merged, and the file being merged into (if appropriate). :return:\",\n \"security group. An error should be raised if the operation\",\n \"that will be making the connection, the name of the\",\n \"to override the config opt :returns: a ComputeDriver instance \\\"\\\"\\\"\",\n \"False here so that every hypervisor # need to define\",\n \"NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address of the dom0\",\n \"maintenance mode. If `False`, leave the maintenance mode. :return: \\\"on_maintenance\\\"\",\n \"the same data format returned by Cinder's initialize_connection() API call.\",\n \"if true, prepare for block migration :param disk_over_commit: if true,\",\n \"instance could be persisted on the host and allocate storage\",\n \"\\\"\\\"\\\" return True def volume_snapshot_create(self, context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots\",\n \"given `host` :param str host: The name of the host\",\n \"pass def post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug VIFs from networks\",\n \"implementation specific params \\\"\\\"\\\" raise NotImplementedError() def cleanup(self, context, instance,\",\n \"migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError() def cleanup(self, context,\",\n \"an opportunity to do management of that cache which isn't\",\n \"here so that every hypervisor # need to define their\",\n \"virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to load the\",\n \"context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified instance\",\n \"\\\"\\\"\\\" raise NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method,\",\n \"instance. :param admin_password: Administrator password to set in instance. :param\",\n \"and firewalls are inevitable on destination host. ( Waiting only\",\n \"default ephemeral storage partition must be preserved on rebuild \\\"\\\"\\\"\",\n \"object \\\"\\\"\\\" raise NotImplementedError() def live_migration(self, context, instance, dest, post_method,\",\n \"context: security context :param instance: nova.objects.instance.Instance :param image_id: Reference to\",\n \"sys from oslo_log import log as logging from oslo_utils import\",\n \"suspend(self, context, instance): \\\"\\\"\\\"Suspend the specified instance. A suspended instance\",\n \"completed. There may be other data included as well that\",\n \"context in for access to auth_token raise NotImplementedError() def attach_volume(self,\",\n \":param block_device_info: instance volume block device info :param power_on: True\",\n \"instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute driver module. Load\",\n \":param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine\",\n \"auth_token raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of all\",\n \"block device mapping to be presented in the format of\",\n \"to be called. Don't use thread for this method since\",\n \"the block device mapping. :param instance: nova.objects.instance.Instance that volume is\",\n \"in for access to auth_token raise NotImplementedError() def suspend(self, context,\",\n \"recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of an instance to another\",\n \"'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError() def\",\n \"of a running instance in multiple phases, turning off the\",\n \"block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of an instance to another host.\",\n \"Gigabyte) of `resize_to`. :return: None \\\"\\\"\\\" raise NotImplementedError() def attach_interface(self,\",\n \"None. This is called during spawn_instance by the compute manager.\",\n \"unplugged. :param nova.network.model.NetworkInfo network_info: The object which contains information about\",\n \"a list. \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"reverting a resize/migration. :param context: the context for the finish_revert_migration\",\n \"object which contains information about the VIFs to plug. :return:\",\n \"A 'rebuild' effectively purges all existing data from the system\",\n \"instance: Instance object as returned by DB layer. :param network_info:\",\n \"resume(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the specified suspended instance.\",\n \"on the block device mapping. :param instance: nova.objects.instance.Instance that volume\",\n \"security context :instance: instance object that was migrated :block_device_info: instance\",\n \"or agreed to in writing, software # distributed under the\",\n \"or storage space of the host, depending on the underlying\",\n \"fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance,\",\n \"to do management of that cache which isn't directly related\",\n \"on the host, False otherwise. .. note:: This implementation works\",\n \"instances. :return: If the host can accept further instances, return\",\n \"execute live migration. This runs checks on the destination host,\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate,\",\n \"pass context in for access to auth_token pass def ensure_filtering_rules_for_instance(self,\",\n \"Can be used to filter out devices which are actually\",\n \"optional. \\\"\\\"\\\" pass def get_info(self, instance): \\\"\\\"\\\"Get the current status\",\n \"specified instance after snapshots. If the specified instance doesn't support\",\n \"object reference :param network_info: instance network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor\",\n \"virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info =\",\n \"interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"reference :param network_info: instance network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver\",\n \"used as the active disk after the snapshot operation has\",\n \"the disk of a running instance in multiple phases, turning\",\n \"context :param instance: instance object that is migrated :param network_info:\",\n \"ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering instance files for\",\n \"instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"nova.objects.instance.Instance being migrated/resized :param network_info: instance network information :param block_device_info:\",\n \"can use the default implementation which returns None. Hypervisors with\",\n \"indicate the cumulative CPU time (nanoseconds) spent by kernel and\",\n \"| 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} |\",\n \"driver does not support \\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self, context,\",\n \"Need to pass context in for access to auth_token pass\",\n \"network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain on the virtualization platform.\",\n \"change to security groups. All security groups and their associated\",\n \"NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the given\",\n \"not set. :returns: The chosen device name. \\\"\\\"\\\" raise NotImplementedError()\",\n \"The instance which should be paused. :return: None \\\"\\\"\\\" #\",\n \"def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver want networks deallocated on\",\n \"MAC addresses'. \\\"\\\"\\\" return None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP\",\n \"def snapshot(self, context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified instance.\",\n \"is to have. allocate_for_instance will take this into consideration when\",\n \"for the suspend. :param nova.objects.instance.Instance instance: The instance to suspend.\",\n \"ports. :param context: request context :param instance: nova.objects.instance.Instance that the\",\n \"<filename>nova/virt/driver.py # Copyright 2011 <NAME> # All Rights Reserved. #\",\n \"otherwise \\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): \\\"\\\"\\\"Confirms\",\n \"the disk to the instance at mountpoint using info.\\\"\\\"\\\" raise\",\n \"eg: | {'kernel': kern, | 'idle': idle, | 'user': user,\",\n \"nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance resource\",\n \":param network_info: instance network information :param block_device_info: Information about block\",\n \"block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping\",\n \"in for access to auth_token raise NotImplementedError() def attach_volume(self, context,\",\n \"used to publish changes to the instance's metadata to the\",\n \"def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address of the dom0 \\\"\\\"\\\"\",\n \"is optional. On some platforms (e.g. XenAPI) performance statistics can\",\n \"is being resized, False otherwise :param block_device_info: instance volume block\",\n \"to be used as the active disk after the snapshot\",\n \"Mapping of MAC addresses to actual networks (or permitting them\",\n \"str host: The name of the host to add to\",\n \"Gets called when an instance gets added to or removed\",\n \"instance): \\\"\\\"\\\"Does the driver want networks deallocated on reschedule?\\\"\\\"\\\" return\",\n \"pass def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network info for specified\",\n \"the platform-specific layer, and never escape the connection interface. The\",\n \":param retry_interval: How often to signal guest while waiting for\",\n \":param power_on: True if the instance should be powered on,\",\n \"provisioning networking for the instance. Mapping of MAC addresses to\",\n \"host: The name of the host to remove from the\",\n \"False def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses must this instance\",\n \"and remakes the VM with given 'metadata' and 'personalities'. This\",\n \"dict old_connection_info: The volume for this connection gets detached from\",\n \"network_info, block_device_info=None): \\\"\\\"\\\"resume the specified suspended instance. The suspended instance\",\n \"nodename): \\\"\\\"\\\"Retrieve resource information. This method is called when nova-compute\",\n \"power on/off an instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError()\",\n \"from a green thread. \\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding event\",\n \"interfaces (VIFs) into the given `instance` at instance boot time.\",\n \"a VM or domain. These IDs must stay internal to\",\n \"the contract that compute drivers must follow, and shared types\",\n \"src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it is possible to\",\n \"quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce by\",\n \"completion. To migrate an instance, filtering rules to hypervisors and\",\n \"NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver want networks deallocated\",\n \"effectively purges all existing data from the system and remakes\",\n \"event notifications from hypervisors. The callback will be invoked with\",\n \"driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str in named_driver_config:\",\n \"we mean 'running virtual machine' (XenAPI terminology) or domain (Xen\",\n \"name of the root disk - ephemerals: a (potentially empty)\",\n \"access to auth_token raise NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean up\",\n \"expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk. :param\",\n \"shared storage. :param context: security context :param data: result of\",\n \"host maintenance window. On start, it triggers the migration of\",\n \"across the shutdown + spin up steps. :param context: security\",\n \"set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the root password on the specified\",\n \"a network interface removed. :param nova.network.model.NetworkInfo vif: The object which\",\n \"the driver name supplied as an argument. Compute drivers constructors\",\n \"self.get_available_nodes(): return True # Refresh and check again. return nodename\",\n \"libvirt terminology). An instance has an ID, which is the\",\n \"of the instance. :param nova.network.model.NetworkInfo vif: The object which has\",\n \"host. \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required cleanup on dest host\",\n \"instance is a member of or if the group gains\",\n \"dest: destination host :param post_method: post operation method. expected nova.compute.manager._post_live_migration.\",\n \"up any resources left after an interrupted snapshot. :param context:\",\n \"Optional; Can be used to filter out devices which are\",\n \"VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which contains information\",\n \"(XenAPI terminology) or domain (Xen or libvirt terminology). An instance\",\n \"has the information about the interface to attach. :raise nova.exception.NovaException:\",\n \"context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \\\"\\\"\\\"\",\n \"raise NotImplementedError() def detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug to remove\",\n \"object that was being migrated :param network_info: instance network information\",\n \"is being specified as instance.name. The second parameter is the\",\n \"network_info: instance network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does not\",\n \"bare metal) cannot do freeform virtualization of MAC addresses. This\",\n \"to be attached to the instance. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"encouraged to override this method with something more efficient. \\\"\\\"\\\"\",\n \"def restore(self, instance): \\\"\\\"\\\"Restore the specified soft-deleted instance. The restored\",\n \"\\\"\\\"\\\"Quiesce the specified instance to prepare for snapshots. If the\",\n \"raise NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return whether this compute service\",\n \"set means 'these and only these MAC addresses'. \\\"\\\"\\\" return\",\n \"instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about\",\n \"the driver implementation if not set. :returns: The chosen device\",\n \"raise NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration to complete\",\n \"log a warning in that case. :param context: security context\",\n \"This runs check on the destination host, and then calls\",\n \"inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network info for specified instance.\\\"\\\"\\\" #\",\n \"fs_type): \\\"\\\"\\\"Check whether the file format is supported by this\",\n \"resource information. This method is called when nova-compute launches, and\",\n \"`uptime` on this host. :return: A text which contains the\",\n \"NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of all nodes managed\",\n \"context: The context for the rescue. :param nova.objects.instance.Instance instance: The\",\n \"on the host, False otherwise. .. note:: Used in rebuild\",\n \"power_on: True if the instance should be powered on, False\",\n \"or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: instance network information\",\n \"is requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList for\",\n \"instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it is possible to execute\",\n \"action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security\",\n \"given 'instance'. :param nova.objects.instance.Instance instance: The instance whose volume gets\",\n \"undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\"\",\n \":param nova.objects.BlockDeviceMapping root_bdm: The description of the root device. \\\"\\\"\\\"\",\n \"trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump mechanism on the given instance.\",\n \"diagnostic data should be returned. :return: Has a big overlap\",\n \"raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required cleanup on\",\n \"compute_driver=None): \\\"\\\"\\\"Load a compute driver module. Load the compute driver\",\n \"concerned, 'instance ID' and 'instance name' are synonyms. Note that\",\n \"def node_is_available(self, nodename): \\\"\\\"\\\"Return whether this compute service manages a\",\n \"the file being merged into (if appropriate). :return: None \\\"\\\"\\\"\",\n \"the virtualization platform should be in the state that it\",\n \"pass def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance,\",\n \"VM or domain. These IDs must stay internal to the\",\n \"get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address of the dom0 \\\"\\\"\\\" #\",\n \"the instance is to have. allocate_for_instance will take this into\",\n \"instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the specified instance. :param instance:\",\n \"occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk.\",\n \"filtering rules to firewall rules can be set faster). Concretely,\",\n \"an instance :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor,\",\n \"chosen device name. \\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check\",\n \"get the root device for. :param nova.objects.ImageMeta image_meta: The metadata\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self, context, migration, instance,\",\n \"number of virtual machines that the hypervisor knows about. ..\",\n \"the sending machine :param dst_compute_info: Info about the receiving machine\",\n \"support. If a driver supports multi compute-nodes, this method returns\",\n \"the volume for `old_connection_info` is attached to. :param int resize_to:\",\n \"to this is :func:`add_to_aggregate` :param nova.context.RequestContext context: The security context.\",\n \"DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, |\",\n \"in for access to auth_token raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns\",\n \"post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up any resources left after an\",\n \"'user': user, | 'iowait': wait, | 'frequency': freq}, where kern\",\n \"necessary for the driver gracefully stop, including ending remote sessions.\",\n \"method must be called. - setup_basic_filtering (for nova-basic, etc.) -\",\n \"which platform-specific ID, and vice versa. Some methods here take\",\n \"data structure into terms that are specific to the virtualization\",\n \"volume for `old_connection_info` is attached to. :param int resize_to: If\",\n \":return: A text which contains the uptime of this host\",\n \"an instance, by name (not ID!) :param instance: nova.objects.instance.Instance object\",\n \"all_instances): \\\"\\\"\\\"Manage the driver's local image cache. Some drivers chose\",\n \"These IDs must stay internal to the platform-specific layer, and\",\n \"an instance to driver format. Virt drivers expect block device\",\n \"The context for the rescue. :param nova.objects.instance.Instance instance: The instance\",\n \"nova.objects.instance.Instance object Returns a InstanceInfo object \\\"\\\"\\\" # TODO(Vek): Need\",\n \"driver to function, including catching up with currently running VM's\",\n \"which will be an instance of the nova.virt.event.Event class. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is called\",\n \"on the host. :param instance: nova.objects.instance.Instance to lookup Returns True\",\n \"utils from nova.virt import event as virtevent CONF = nova.conf.CONF\",\n \"true, post operation of block_migration. \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self,\",\n \"cleanup :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def\",\n \"which gets a network interface removed. :param nova.network.model.NetworkInfo vif: The\",\n \"\\\"\\\"\\\"Clean up destination node after a failed live migration. :param\",\n \"NotImplementedError() def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue the specified instance. :param\",\n \"be able to complete the snapshot. For example, in the\",\n \"or customer-controlled -- it's an internal ID chosen by Nova.\",\n \"# NOTE(jichenjc): Return False here so that every hypervisor #\",\n \"virtualization platform. \\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False,\",\n \"the 'suspend'. :param nova.context.RequestContext context: The context for the resume.\",\n \"could be persisted on the host and allocate storage space\",\n \"Unless required by applicable law or agreed to in writing,\",\n \"implementation layer. For instance, with openflow switches, fixed MAC addresses\",\n \"\\\"\\\"\\\"Writes a file on the specified instance. The first parameter\",\n \"indicates write, req is the total number of I/O requests\",\n \"Nova having to do the aggregation. On those platforms, this\",\n \"instance for attaching to volumes. Connector information is a dictionary\",\n \"for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return\",\n \"diff): \\\"\\\"\\\"Applies a diff to the instance metadata. This is\",\n \"The counter action to this is :func:`add_to_aggregate` :param nova.context.RequestContext context:\",\n \"list instances: A list of nova.objects.instance.Instance objects that have been\",\n \"up anything that is necessary for the driver gracefully stop,\",\n \"the disk attached to the instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self,\",\n \"lookup Returns True if files of an instance with the\",\n \"def power_on(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on the specified\",\n \"counter action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The\",\n \"def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): \\\"\\\"\\\"Deletes a snapshot\",\n \"operation of live migration at source host. :param context: security\",\n \"console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection\",\n \"integers. \\\"\\\"\\\" raise NotImplementedError() def block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance\",\n \"remove from the given `aggregate`. :param dict kwargs: A free-form\",\n \"ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means\",\n \"nova.objects.instance.Instance instance: The instance that has the volume attached. :param\",\n \"waiting for it to shutdown \\\"\\\"\\\" raise NotImplementedError() def power_on(self,\",\n \"either express or implied. See the # License for the\",\n \"of) the contract that compute drivers must follow, and shared\",\n \"host, False otherwise. .. note:: Used in rebuild for HA\",\n \"the machine as follows:: { 'ip': ip, 'initiator': initiator, 'host':\",\n \"User files to inject into instance. :param admin_password: <PASSWORD> to\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host,\",\n \"must be called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for\",\n \"def get_volume_connector(self, instance): \\\"\\\"\\\"Get connector information for the instance for\",\n \"a snapshot, it is the image file Cinder expects to\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage counters\",\n \"defined at disk API module. \\\"\\\"\\\" # NOTE(jichenjc): Return False\",\n \"a spice console. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"necessary for the driver to function, including catching up with\",\n \"raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver want networks\",\n \"migration should not be started when setting-up filtering rules operations\",\n \"timeout. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"without Nova having to do the aggregation. On those platforms,\",\n \"raise NotImplementedError() def get_host_uptime(self): \\\"\\\"\\\"Returns the result of calling the\",\n \"at all -- such things are only known higher up\",\n \"ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping =\",\n \"the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info\",\n \"to be checked, the validate values are defined at disk\",\n \"goes back to power_state.RUNNING. The virtualization platform should ensure that\",\n \"the destination host, and then calls back to the source\",\n \"third is the contents of the file, also base64-encoded. NOTE(russellb)\",\n \"\\\"\\\"\\\" return {} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of instance\",\n \"including ending remote sessions. This is optional. \\\"\\\"\\\" pass def\",\n \"the specified suspended instance. The suspended instance gets resumed and\",\n \"if the operation cannot complete. \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"'initiator': initiator, 'host': hostname } \\\"\\\"\\\" raise NotImplementedError() def get_available_nodes(self,\",\n \"instance metadata. This is an optional driver method which is\",\n \"set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the ability to power on/off an\",\n \"as logging from oslo_utils import importutils import nova.conf from nova.i18n\",\n \"image_meta: The metadata of the image of the instance. :param\",\n \"file system type to be checked, the validate values are\",\n \"to that call to ensure that all assigned network details\",\n \"instance block device information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\"\",\n \"CPU time (nanoseconds) spent by kernel and user processes respectively,\",\n \"context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self, instance, nw_info):\",\n \"block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources . Instance\",\n \"depends on the configuration of it. :param nova.objects.instance.Instance instance: The\",\n \"Returns a InstanceInfo object \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"could be stored in the memory or storage space of\",\n \"multi compute-nodes, this method returns a list of nodenames managed\",\n \"addresses can still be virtualized onto any L2 domain, with\",\n \":param str mountpoint: The mountpoint in the instance where the\",\n \"finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration.\",\n \"this instance. Some hypervisors (such as bare metal) require that\",\n \"for its completion. To migrate an instance, filtering rules to\",\n \"nova.context.RequestContext context: The context for the rescue. :param nova.objects.instance.Instance instance:\",\n \"the virtualization layer, as a list. \\\"\\\"\\\" # TODO(Vek): Need\",\n \"This implementation works for all drivers, but it is not\",\n \"security context :param instance: instance object reference :param network_info: instance\",\n \"controlling thing here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs) from networks. The\",\n \"the combination with :func:`set_host_enabled`. :param str host: The name of\",\n \"the cumulative CPU time (nanoseconds) spent by kernel and user\",\n \"a dict containing the CPU stat info, eg: | {'kernel':\",\n \":param disk_info: instance disk information :param migrate_data: a LiveMigrateData object\",\n \"(VIFs) from networks. The counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance\",\n \"raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove a\",\n \"inject into instance. :param admin_password: <PASSWORD> to set in instance.\",\n \"ID maps to which platform-specific ID, and vice versa. Some\",\n \":param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object\",\n \"instance: The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network\",\n \"NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0,\",\n \"paused instance. The paused instance gets unpaused and will use\",\n \"wait indicates the cumulative I/O wait CPU time (nanoseconds), since\",\n \"The counter action for 'resume' is :func:`suspend`. Depending on the\",\n \"'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return\",\n \"Cinder's initialize_connection() API call. In the case of doing a\",\n \"soft-deleted instance which should be restored from the soft-deleted data.\",\n \"`instances` which are longer in the rebooting state than `timeout`\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"state than `timeout` seconds. :param int timeout: The timeout (in\",\n \"the host is booting up; freq indicates the current CPU\",\n \"NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk\",\n \"destroy disks at destination during cleanup :param migrate_data: a LiveMigrateData\",\n \"auth_token raise NotImplementedError() def get_console_output(self, context, instance): \\\"\\\"\\\"Get console output\",\n \"the connection interface. The platform-specific layer is responsible for keeping\",\n \"refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is called after a change to\",\n \":param instance: instance object reference :param network_info: instance network information\",\n \"should be triggered. :return: None \\\"\\\"\\\" raise NotImplementedError() def soft_delete(self,\",\n \"create a new instance based on that. :param nova.objects.instance.Instance instance:\",\n \"will accept new instances. If it is `False`, the host\",\n \"instance, network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs) into the given `instance`\",\n \"the instance should be powered on, False otherwise \\\"\\\"\\\" raise\",\n \"raised. :param context: request context :param instance: nova.objects.instance.Instance to be\",\n \"class for compute drivers. The interface to this class talks\",\n \"of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data,\",\n \"def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping')\",\n \"migration at source host. :param context: security context :instance: instance\",\n \"volume for this connection gets attached to the given 'instance'.\",\n \"the host to remove from the given `aggregate`. :param dict\",\n \"compute host from an aggregate. The counter action to this\",\n \"can safely ignore this :returns: Dictionary describing resources \\\"\\\"\\\" raise\",\n \"started when setting-up filtering rules operations are not completed. :param\",\n \"pipeline. All counters are long integers. This method is optional.\",\n \"information :param block_device_info: instance volume block device info :param power_on:\",\n \"security context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" pass def\",\n \"The counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance:\",\n \"GuestOS to shutdown :param retry_interval: How often to signal guest\",\n \"raise NotImplementedError() def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection info for\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def unrescue(self, instance, network_info):\",\n \"\\\"\\\"\\\" return True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if\",\n \"a (potentially empty) list of DriverVolumeBlockDevice or any of it's\",\n \"even in cases in which the underlying domain/vm is paused\",\n \"but is still available as a database entry. The counter\",\n \"% compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE(\\\"Unable to\",\n \"the live migration can succeed, based on the results from\",\n \"fixed MAC addresses can still be virtualized onto any L2\",\n \"block_device_info: instance volume block device info :param power_on: True if\",\n \"on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def confirm_migration(self, migration, instance,\",\n \"instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance. After\",\n \":func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs plugged.\",\n \"nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate\",\n \"to security groups. All security groups and their associated rules\",\n \"those value through migrateToURI(), so , no need to be\",\n \"be an instance of nova.virt.event.Event\\\")) try: LOG.debug(\\\"Emitting event %s\\\", str(event))\",\n \"bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance. After this is called successfully,\",\n \"if networking failed), this function should still succeed. It's probably\",\n \"\\\"\\\"\\\"What MAC addresses must this instance have? Some hypervisors (such\",\n \"counters are long integers. This method is optional. On some\",\n \"the default ephemeral storage partition must be preserved on rebuild\",\n \"their # virt layer. return False def quiesce(self, context, instance,\",\n \"data needed to be able to complete the snapshot. For\",\n \"None \\\"\\\"\\\" # NOTE(jogo) Currently only used for XenAPI-Pool raise\",\n \"otherwise \\\"\\\"\\\" raise NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause the given\",\n \"instance, is_bootable): \\\"\\\"\\\"Set the ability to power on/off an instance.\",\n \"and should be decided by the driver implementation if not\",\n \"for access to auth_token raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the\",\n \"to auth_token raise NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean up anything\",\n \"context in for access to auth_token raise NotImplementedError() def power_off(self,\",\n \"if true, destroy disks at destination during cleanup :param migrate_data:\",\n \"raise NotImplementedError() def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop host maintenance window.\",\n \"ports will be associated with :returns: a string representing the\",\n \"instance: The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info:\",\n \"detach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self, context,\",\n \"disk information dicts in JSON format. :rtype: str \\\"\\\"\\\" raise\",\n \"resources left after an interrupted snapshot. :param context: security context\",\n \"include the file being merged, and the file being merged\",\n \":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC \\\"\\\"\\\" raise\",\n \"seconds) for considering rebooting instances to be stuck. :param list\",\n \"quiesced :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"interface to detach. :raise nova.exception.NovaException: If the detach fails. :return:\",\n \"block device information :param destroy_disks: if true, destroy disks at\",\n \":param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info\",\n \"an internal ID chosen by Nova. At the nova.virt layer,\",\n \"making the connection, the name of the iscsi initiator and\",\n \"the receiving machine :param block_migration: if true, prepare for block\",\n \"instance object that is migrated. :param dest: destination host :param\",\n \"(in Gigabyte) of `resize_to`. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"that the instance is to have. allocate_for_instance will take this\",\n \"The snapshot to delete. :param dict delete_info: Volume backend technology\",\n \"being live migrated \\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort\",\n \"into this layer. This layer is responsible for translating that\",\n \"called successfully, the instance's state goes back to power_state.RUNNING. The\",\n \"setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may\",\n \"device mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get\",\n \"cleaned up, and the virtualization platform should be in the\",\n \"change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies a diff to the instance\",\n \"info, eg: | {'kernel': kern, | 'idle': idle, | 'user':\",\n \"return \\\"disabled\\\". :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"data should be returned. :return: Has a big overlap to\",\n \"None means 'no constraints', a set means 'these and only\",\n \"into (if appropriate). :return: None \\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self,\",\n \"distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"(nanoseconds), wait indicates the cumulative I/O wait CPU time (nanoseconds),\",\n \"This must only be invoked from a green thread. \\\"\\\"\\\"\",\n \"to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z):\",\n \"failed), this function should still succeed. It's probably a good\",\n \"instance, network_info): \\\"\\\"\\\"Setting up filtering rules and waiting for its\",\n \"instance: The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info:\",\n \"raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on\",\n \"network ports will be associated with :returns: a string representing\",\n \"contract \\\"\\\"\\\" import sys from oslo_log import log as logging\",\n \"method is called after a change to security groups. All\",\n \"Reboots the given `instances` which are longer in the rebooting\",\n \"for access to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info):\",\n \"inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a file on the specified\",\n \"The context for the resume. :param nova.objects.instance.Instance instance: The suspended\",\n \"hypervisor, since filtering rules to firewall rules can be set\",\n \"an instance to another host. :param context: security context :param\",\n \"the given paused instance. The paused instance gets unpaused and\",\n \"wr indicates write, req is the total number of I/O\",\n \"are no longer of interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass\",\n \"delete_info): \\\"\\\"\\\"Deletes a snapshot of a volume attached to a\",\n \"if supplied, the driver name supplied as an argument. Compute\",\n \"a network interface from a running instance. The counter action\",\n \"auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group rules\",\n \"str \\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability\",\n \"the compute service. This method is for multi compute-nodes support.\",\n \"resize_instance: True if the instance is being resized, False otherwise\",\n \"and the virtualization platform should be in the state that\",\n \"the host again. The counter action for 'unpause' is :func:`pause`.\",\n \"for access to auth_token raise NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots,\",\n \"root_bdm): \\\"\\\"\\\"Provide a default root device name for the driver.\",\n \"# The compute manager doesn't use this interface, which seems\",\n \"still be virtualized onto any L2 domain, with arbitrary VLANs\",\n \"the given instance. A paused instance doesn't use CPU cycles\",\n \"other calls into the driver. The prime example is to\",\n \"of the root device. \\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self, instance,\",\n \"block device information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" pass\",\n \":param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self,\",\n \"remove images which are no longer of interest. :param all_instances:\",\n \"import sys from oslo_log import log as logging from oslo_utils\",\n \"def remove_from_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove a compute host\",\n \"failed live migration. :param context: security context :param instance: instance\",\n \"metal) require that instances boot from the network, and manage\",\n \"instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of live migration at source\",\n \"soft_delete(self, instance): \\\"\\\"\\\"Soft delete the specified instance. A soft-deleted instance\",\n \"`False`, leave the maintenance mode. :return: \\\"on_maintenance\\\" if switched to\",\n \"name of the host whose maintenance mode should be changed.\",\n \"- setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml\",\n \"See the # License for the specific language governing permissions\",\n \"supplied to the allocate_for_instance call by the compute manager, and\",\n \"The state of the VM could be stored in the\",\n \"should be the controlling thing here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs)\",\n \"raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if instance files\",\n \"the base64-encoded path to which the file is to be\",\n \"context for the suspend. :param nova.objects.instance.Instance instance: The instance to\",\n \"dest_check_data): \\\"\\\"\\\"Do required cleanup on dest host after check_can_live_migrate calls\",\n \"block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info\",\n \"instance: The instance to suspend. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"should override if per-instance overhead calculations are desired. :param instance_info:\",\n \"changes to the instance's metadata to the hypervisor. If the\",\n \"down or powers up the host. :param str action: The\",\n \":param nodename: node which the caller want to get resources\",\n \"context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of live migration at\",\n \"option required, but not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"),\",\n \"return False def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses must this\",\n \"If `True`, go into maintenance mode. If `False`, leave the\",\n \"network info for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"longer of interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self,\",\n \"{} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap):\",\n \"whose maintenance mode should be changed. :param bool mode: If\",\n \"context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power on the specified instance. :param\",\n \"block_device_info=None): \\\"\\\"\\\"resume the specified suspended instance. The suspended instance gets\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def unrescue(self, instance, network_info): \\\"\\\"\\\"Unrescue\",\n \"self.virtapi = virtapi self._compute_event_callback = None def init_host(self, host): \\\"\\\"\\\"Initialize\",\n \"return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return the names\",\n \"is raised. When it fails to quiesce by other errors\",\n \"block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info\",\n \"on the instance; the third is the contents of the\",\n \"requests made, bytes is the total number of bytes transferred,\",\n \"of block device info to be passed in to methods\",\n \"the data there to guide the creation of the new\",\n \"a resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \".. note:: This implementation works for all drivers, but it\",\n \"invoked from a green thread. \\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding\",\n \"of all the instances known to the virtualization layer, as\",\n \"permitting them to be freeform) is up to the network\",\n \"compute-nodes support. If a driver supports multi compute-nodes, this method\",\n \"will hold the snapshot. \\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context,\",\n \"by Nova to represent the instance further up the stack.\",\n \"a compute driver module. Load the compute driver module specified\",\n \"a driver that manages only one node can safely ignore\",\n \"in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver)\",\n \"def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] > 0\",\n \"def get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection info for a MKS\",\n \"the given `instance` at instance boot time. The counter action\",\n \"raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None,\",\n \"nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"the image of the instance. :param resize_instance: True if the\",\n \"nodes managed by the compute service. This method is for\",\n \"receive asynchronous event notifications from hypervisors. The callback will be\",\n \"method with something more efficient. \\\"\\\"\\\" try: return instance.uuid in\",\n \"import log as logging from oslo_utils import importutils import nova.conf\",\n \"ImportError: LOG.exception(_LE(\\\"Unable to load the virtualization driver\\\")) sys.exit(1) def is_xenapi():\",\n \"into instance. :param admin_password: Administrator password to set in instance.\",\n \"instance: The instance which will get an additional network interface.\",\n \"image_meta, vif): \\\"\\\"\\\"Use hotplug to add a network interface to\",\n \"vif): \\\"\\\"\\\"Use hotplug to add a network interface to a\",\n \"which isn't directly related to other calls into the driver.\",\n \"def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device mappings for an instance\",\n \"any L2 domain, with arbitrary VLANs etc, but regular switches\",\n \"instance boot time. The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance\",\n \"running instance. The counter action to this is :func:`attach_interface`. :param\",\n \"manages only one node can safely ignore this :returns: Dictionary\",\n \"raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the\",\n \":param block_device_info: instance block device information :param destroy_disks: if true,\",\n \"host to check the results. :param context: security context :param\",\n \"again. The counter action for 'unpause' is :func:`pause`. Depending on\",\n \"for this instance. Some hypervisors (such as bare metal) require\",\n \"\\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a\",\n \"and will use CPU cycles of the host again. The\",\n \"to the source host to check the results. :param context:\",\n \"\\\"\\\"\\\"Confirms a resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance\",\n \"would include the file being merged, and the file being\",\n \"other errors (e.g. agent timeout), NovaException is raised. :param context:\",\n \"NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if instance files located\",\n \"console output for an instance :param context: security context :param\",\n \"\\\"\\\"\\\"Return diagnostics data about the given instance. :param nova.objects.instance.Instance instance:\",\n \"persisted on the host and allocate storage space this way.\",\n \"instance before the end. :param nova.objects.instance.Instance instance: The instance whose\",\n \"instance. :param instance: nova.objects.instance.Instance :param timeout: time to wait for\",\n \"\\\"\\\"\\\" raise NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft delete the specified\",\n \"errs is the number of requests held up due to\",\n \"instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the volume attached to the given\",\n \"NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a file on\",\n \"the guest has the same state as before the 'suspend'.\",\n \"on the specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the\",\n \"further up the stack. This is unfortunately also called a\",\n \":return: \\\"on_maintenance\\\" if switched to maintenance mode or \\\"off_maintenance\\\" if\",\n \"known to the virtualization layer, as a list. \\\"\\\"\\\" raise\",\n \"to shutdown :param retry_interval: How often to signal guest while\",\n \"options for this instance. Some hypervisors (such as bare metal)\",\n \"supplied, the driver name supplied as an argument. Compute drivers\",\n \"Hypervisor before calling this method. :param context: security context :param\",\n \"instance network information :param block_device_info: instance block device information :param\",\n \":param instance: nova.objects.instance.Instance to lookup Returns True if files of\",\n \"hypervisors (such as bare metal) require that instances boot from\",\n \"volumes :param bad_volumes_callback: Function to handle any bad volumes encountered\",\n \"resize/migration. :param context: the context for the migration/resize :param migration:\",\n \"and needs to be held across the shutdown + spin\",\n \"of the image of the instance. :param nova.network.model.NetworkInfo vif: The\",\n \"into the driver. The prime example is to clean the\",\n \"block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration. :param context: the context\",\n \"here. \\\"\\\"\\\"Unplug virtual interfaces (VIFs) from networks. The counter action\",\n \"be removed from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"by the compute manager. :return: None, or a set of\",\n \"given instance. :param nova.objects.instance.Instance instance: The instance to which the\",\n \"to actual networks (or permitting them to be freeform) is\",\n \"should be restored from the soft-deleted data. :return: None \\\"\\\"\\\"\",\n \"load the virtualization driver\\\")) sys.exit(1) def is_xenapi(): return CONF.compute_driver ==\",\n \"the newly transferred disk information :param network_info: instance network information\",\n \"to firewall rules can be set faster). Concretely, the below\",\n \"False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, } def __init__(self, virtapi): self.virtapi\",\n \"for the migration/resize :param migration: the migrate/resize information :param instance:\",\n \":param block_device_info: Information about block devices to be attached to\",\n \"it. :param nova.objects.instance.Instance instance: The instance where the crash dump\",\n \"data structure used by nova.compute to store details regarding an\",\n \"value through migrateToURI(), so , no need to be called.\",\n \"def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live\",\n \"shared storage. This runs check on the destination host, and\",\n \"block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of live migration at source host.\",\n \"the rules now.\\\"\\\"\\\" pass def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering\",\n \"instance: nova.objects.instance.Instance :param network_info: instance network information :param reboot_type: Either\",\n \"name to override the config opt :returns: a ComputeDriver instance\",\n \"LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve\",\n \"layer. :param network_info: instance network information :param block_device_info: Information about\",\n \"def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information. This method is called\",\n \"same state as before the 'suspend'. :param nova.context.RequestContext context: The\",\n \"rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\" raise NotImplementedError() def set_bootable(self, instance,\",\n \"bdms: block-device-mappings to use for rebuild :param detach_block_devices: function to\",\n \"host, **kwargs): \\\"\\\"\\\"Remove a compute host from an aggregate. The\",\n \"instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def\",\n \"migrated \\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress\",\n \"None. Hypervisors with MAC limits should return a set of\",\n \"is :func:'suspend'. The counter action for `pause` is :func:`unpause`. :param\",\n \"on the given instance. These are returned as [rd_req, rd_bytes,\",\n \"cycles or memory of the host anymore. The state of\",\n \"use the data there to guide the creation of the\",\n \"raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\" pass\",\n \"and will use CPU cycles and memory of the host\",\n \"operation of block_migration. \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance):\",\n \"the interface to detach. :raise nova.exception.NovaException: If the detach fails.\",\n \"and vice versa. Some methods here take an instance of\",\n \"of all nodes managed by the compute service. This method\",\n \"retry_interval: How often to signal guest while waiting for it\",\n \"old_connection_info: The volume for this connection gets detached from the\",\n \"context: security context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" pass\",\n \"cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required cleanup on dest host after\",\n \"source. :param context: security context :param instance: instance object reference\",\n \"instance: instance object reference :param network_info: instance network information \\\"\\\"\\\"\",\n \"\\\"\\\"\\\"Set the root password on the specified instance. :param instance:\",\n \"instance. :param rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"event %(event)s: %(ex)s\\\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance):\",\n \"auth_token raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\"\",\n \"take an instance of nova.compute.service.Instance. This is the data structure\",\n \"The object which has the information about the interface to\",\n \"\\\"\\\"\\\" raise NotImplementedError() def node_is_available(self, nodename): \\\"\\\"\\\"Return whether this compute\",\n \"any resources left after an interrupted snapshot. :param context: security\",\n \"which should remove the given `host` :param str host: The\",\n \"devices to be attached to the instance. \\\"\\\"\\\" raise NotImplementedError()\",\n \":param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError()\",\n \"addresses that the instance is to have. allocate_for_instance will take\",\n \"left after an interrupted snapshot. :param context: security context :param\",\n \"that all assigned network details are compatible with the set\",\n \"required cleanup on dest host after check_can_live_migrate calls :param context:\",\n \"being migrated/resized :param disk_info: the newly transferred disk information :param\",\n \"\\\"\\\"\\\"Post operation of live migration at destination host. :param context:\",\n \"info for a MKS console. :param context: security context :param\",\n \":returns: The chosen device name. \\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self,\",\n \"hold the snapshot. \\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance):\",\n \"merged into (if appropriate). :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"publishing the instance metadata to the instance, then this method\",\n \"OF ANY KIND, either express or implied. See the #\",\n \"'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self,\",\n \"to add to the given `aggregate`. :param dict kwargs: A\",\n \"def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals')\",\n \"new instances. :param bool enabled: If this is `True`, the\",\n \"data format returned by Cinder's initialize_connection() API call. In the\",\n \"a set means 'these and only these MAC addresses'. \\\"\\\"\\\"\",\n \"the machine that will be making the connection, the name\",\n \"specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"off deferral of IPTables rules and apply the rules now.\\\"\\\"\\\"\",\n \"'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size']\",\n \"def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs) into the\",\n \"of DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty) list\",\n \"new instances. :return: If the host can accept further instances,\",\n \"from nova import utils from nova.virt import event as virtevent\",\n \"driver name supplied as an argument. Compute drivers constructors take\",\n \"further instances, return \\\"enabled\\\", if further instances shouldn't be scheduled\",\n \"here take an instance of nova.compute.service.Instance. This is the data\",\n \"NotImplementedError() def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host ID to associate\",\n \"with the License. You may obtain # a copy of\",\n \"agent timeout), NovaException is raised. :param context: request context :param\",\n \"raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up any resources\",\n \"a periodic task that records the results in the DB.\",\n \"is responsible for translating that generic data structure into terms\",\n \"\\\"\\\"\\\"resume the specified suspended instance. The suspended instance gets resumed\",\n \"platforms will also have their own identity schemes, to uniquely\",\n \"these MAC addresses'. \\\"\\\"\\\" return None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get\",\n \"nova-compute launches, and as part of a periodic task that\",\n \"start, it triggers the migration of all instances to other\",\n \"from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param\",\n \"shutdown. :param int retry_interval: How often to signal guest while\",\n \"The aggregate which should add the given `host` :param str\",\n \":param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The\",\n \"object Returns a InstanceInfo object \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"the Hypervisor. If the instance is not found (for example\",\n \"which we mean 'running virtual machine' (XenAPI terminology) or domain\",\n \"auth_token raise NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down or\",\n \"return True # Refresh and check again. return nodename in\",\n \"security context. :param nova.objects.instance.Instance instance: The instance that has the\",\n \"with the supplied ID exists on the host, False otherwise.\",\n \"configuration. Most hypervisors can use the default implementation which returns\",\n \"The instance whose disk should be migrated. :param str dest:\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"to execute live migration. This checks if the live migration\",\n \"only one node can safely ignore this :returns: Dictionary describing\",\n \"cleanup on dest host after check_can_live_migrate calls :param context: security\",\n \"context in for access to auth_token raise NotImplementedError() def set_admin_password(self,\",\n \"nova.objects.instance.Instance :param timeout: time to wait for GuestOS to shutdown\",\n \"hotunplug to remove a network interface from a running instance.\",\n \"firewalls are inevitable on destination host. ( Waiting only for\",\n \"being resized, False otherwise :param block_device_info: instance volume block device\",\n \":param src_compute_info: Info about the sending machine :param dst_compute_info: Info\",\n \"{} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info):\",\n \"diagnostics data about the given instance. :param nova.objects.instance.Instance instance: The\",\n \"images for instances on disk. This method is an opportunity\",\n \"network_info): \\\"\\\"\\\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" #\",\n \"\\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger crash dump mechanism\",\n \"versa. Some methods here take an instance of nova.compute.service.Instance. This\",\n \"migration/resize :param migration: the migrate/resize information :param instance: nova.objects.instance.Instance being\",\n \"keys: - root_device_name: device name of the root disk -\",\n \"remove a network interface from a running instance. The counter\",\n \"def list_instances(self): \\\"\\\"\\\"Return the names of all the instances known\",\n \"from nova.virt import event as virtevent CONF = nova.conf.CONF LOG\",\n \"instance; the third is the contents of the file, also\",\n \"node which the caller want to get resources from a\",\n \"params \\\"\\\"\\\" raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type,\",\n \"migrate_data=None): \\\"\\\"\\\"Clean up destination node after a failed live migration.\",\n \"CPU cycles or memory of the host anymore. The state\",\n \"expect it. \\\"\\\"\\\" return True def volume_snapshot_create(self, context, instance, volume_id,\",\n \"to hypervisor, since filtering rules to firewall rules can be\",\n \"be called since it defines PROJNET, PROJMASK. but libvirt migrates\",\n \"context: The security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which\",\n \"check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it\",\n \"for validation of access to instance shared disk files \\\"\\\"\\\"\",\n \"for volumes attached to vms on a given host.- \\\"\\\"\\\"\",\n \"volume gets replaced by another one. :param str mountpoint: The\",\n \"'ip': ip, 'initiator': initiator, 'host': hostname } \\\"\\\"\\\" raise NotImplementedError()\",\n \"object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do\",\n \"A text which contains the uptime of this host since\",\n \"as their first object and this must be supplied. :param\",\n \"have to be called since it defines PROJNET, PROJMASK. but\",\n \"instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The metadata\",\n \"end. :param nova.objects.instance.Instance instance: The instance whose disk should be\",\n \"named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type]\",\n \"instance files located on shared storage. This runs check on\",\n \"context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it is possible to\",\n \":param uuid volume_id: Attached volume associated with the snapshot :param\",\n \"instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None,\",\n \"= virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info\",\n \"be scheduled to this host, return \\\"disabled\\\". :rtype: str \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability of\",\n \"The instance whose volume gets replaced by another one. :param\",\n \"\\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required cleanup\",\n \"for the instance for attaching to volumes. Connector information is\",\n \"True if the instance should be powered on, False otherwise\",\n \"types that support that contract \\\"\\\"\\\" import sys from oslo_log\",\n \"%s\\\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\\\"Exception dispatching event\",\n \"{ \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\":\",\n \"0, 'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return the names of all\",\n \"or powers up the host. :param str action: The action\",\n \"HA implementation and required for validation of access to instance\",\n \"raise NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress live migration.\",\n \"must be supplied. :param virtapi: a VirtAPI instance :param compute_driver:\",\n \"address of the dom0 \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"list of DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice\",\n \"raise NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down or powers\",\n \"importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping):\",\n \"counter action :func:`restore` uses the database entry to create a\",\n \"maps to which platform-specific ID, and vice versa. Some methods\",\n \"about the block devices. :param int timeout: The time in\",\n \"disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance for live migration :param context:\",\n \"The name of the host to remove from the given\",\n \"compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute driver option\",\n \"the caller whether we expect the legacy format of block\",\n \"live migration :param context: security context :param instance: nova.objects.instance.Instance object\",\n \"instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a file on the specified instance.\",\n \"an instance for live migration :param context: security context :param\",\n \"from the given `aggregate`. :param dict kwargs: A free-form thingy...\",\n \"active disk after the snapshot operation has completed. There may\",\n \":returns: a dict containing the CPU stat info, eg: |\",\n \"of `resize_to`. :return: None \\\"\\\"\\\" raise NotImplementedError() def attach_interface(self, instance,\",\n \"for 'unpause' is :func:`pause`. Depending on the underlying hypervisor technology,\",\n \"recover_method: recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param\",\n \"for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"instance): \\\"\\\"\\\"Soft delete the specified instance. A soft-deleted instance doesn't\",\n \"instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine :param\",\n \"Nova. At the nova.virt layer, instances do not have human-readable\",\n \"instances): \\\"\\\"\\\"Perform a reboot on all given 'instances'. Reboots the\",\n \"on dest host after check_can_live_migrate calls :param context: security context\",\n \"image_meta): \\\"\\\"\\\"Quiesce the specified instance to prepare for snapshots. If\",\n \"odd # since the manager should be the controlling thing\",\n \"instance on the host. :param instance: The instance to lookup\",\n \"destination during cleanup :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise\",\n \"driver. The prime example is to clean the cache and\",\n \"this method with something more efficient. \\\"\\\"\\\" return len(self.list_instances()) def\",\n \"volume attached :param uuid volume_id: Volume to be snapshotted :param\",\n \"0} def list_instances(self): \\\"\\\"\\\"Return the names of all the instances\",\n \"this interface, which seems odd # since the manager should\",\n \"restore(self, instance): \\\"\\\"\\\"Restore the specified soft-deleted instance. The restored instance\",\n \"of it's more specialized subclasses. \\\"\\\"\\\" from nova.virt import block_device\",\n \"If the hypervisor has no means of publishing the instance\",\n \"Volume backend technology specific data needed to be able to\",\n \"driver '%s'\\\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver,\",\n \"migrated :param network_info: instance network information :param block_device_info: instance block\",\n \"raise NotImplementedError() def network_binding_host_id(self, context, instance): \\\"\\\"\\\"Get host ID to\",\n \"[] return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class for compute drivers.\",\n \"whose volume gets replaced by another one. :param str mountpoint:\",\n \"use thread for this method since migration should not be\",\n \"for the driver to function, including catching up with currently\",\n \"dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options for this instance. Some hypervisors\",\n \"has an ID, which is the identifier chosen by Nova\",\n \"destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources . Instance should\",\n \"should apply the updated rules to instances running the specified\",\n \"Either a HARD or SOFT reboot :param block_device_info: Info pertaining\",\n \"of the newer interface :func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" # TODO(Vek):\",\n \"cumulative CPU time (nanoseconds) spent by kernel and user processes\",\n \"If the new volume is larger than the old volume,\",\n \"disk get migrated. :param nova.network.model.NetworkInfo network_info: The network information of\",\n \"to dispatch the event. This must only be invoked from\",\n \"check the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance\",\n \"diff to the instance metadata. This is an optional driver\",\n \"of which instance ID maps to which platform-specific ID, and\",\n \":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \\\"\\\"\\\" raise\",\n \"method allows drivers to return a set of MAC addresses\",\n \"returns None. Hypervisors with MAC limits should return a set\",\n \"off the instance before the end. :param nova.objects.instance.Instance instance: The\",\n \"resized, False otherwise :param block_device_info: instance volume block device info\",\n \"with something more efficient. \\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self, instance):\",\n \"migration :param disk_over_commit: if true, allow disk over commit :returns:\",\n \"context: The context for the suspend. :param nova.objects.instance.Instance instance: The\",\n \"represent the instance further up the stack. This is unfortunately\",\n \"DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty) list of\",\n \"by the compute manager. Note that the format of the\",\n \"associated with the snapshot :param uuid snapshot_id: The snapshot to\",\n \"context: security context :param instance: instance object that is migrated\",\n \"an instance, filtering rules to hypervisors and firewalls are inevitable\",\n \"the file is to be written on the instance; the\",\n \"NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot\",\n \"need to define their supported file system # type and\",\n \"specific data needed to be able to complete the snapshot.\",\n \"if disks should be destroyed :param migrate_data: implementation specific params\",\n \"\\\"\\\"\\\"Check whether the file format is supported by this driver\",\n \"must follow, and shared types that support that contract \\\"\\\"\\\"\",\n \"ensure that all assigned network details are compatible with the\",\n \":param context: the context for the migration/resize :param migration: the\",\n \"def rescue(self, context, instance, network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified\",\n \"suspended instance doesn't use CPU cycles or memory of the\",\n \"try: LOG.debug(\\\"Emitting event %s\\\", str(event)) self._compute_event_callback(event) except Exception as ex:\",\n \"to guide the creation of the new instance. :param nova.objects.ImageMeta\",\n \"expect the legacy format of block device info to be\",\n \"stuck. :param list instances: A list of nova.objects.instance.Instance objects that\",\n \"of `pause` is :func:'suspend'. The counter action for `pause` is\",\n \"the file being merged, and the file being merged into\",\n \"of nova.objects.instance.Instance objects that have been in rebooting state longer\",\n \"context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it is\",\n \"network_info: instance network information :param block_device_info: instance block device information\",\n \"\\\"\\\"\\\"Return whether this compute service manages a particular node.\\\"\\\"\\\" if\",\n \"resources anymore, but is still available as a database entry.\",\n \"context :param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta:\",\n \"of the instance. :param rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\" raise\",\n \"disk over commit :returns: a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise\",\n \"VIFs to unplug. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self):\",\n \"block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or\",\n \"NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of all the instances\",\n \"the host, depending on the underlying hypervisor technology. A \\\"stronger\\\"\",\n \"live migration. This runs checks on the destination host, and\",\n \"the specified instance after snapshots. If the specified instance doesn't\",\n \"def get_console_output(self, context, instance): \\\"\\\"\\\"Get console output for an instance\",\n \"VIFs to plug. :return: None \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"of bytes transferred, and errs is the number of requests\",\n \".. note:: Used in rebuild for HA implementation and required\",\n \"express or implied. See the # License for the specific\",\n \"= importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance,\",\n \"the instance further up the stack. This is unfortunately also\",\n \"results from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance\",\n \"def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up any resources left after\",\n \"member of or if the group gains or loses a\",\n \"`restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted instance which\",\n \"reboot :param block_device_info: Info pertaining to attached volumes :param bad_volumes_callback:\",\n \"info. Tell the caller whether we expect the legacy format\",\n \"to or removed from the security group the instance is\",\n \"instances: A list of nova.objects.instance.Instance objects that have been in\",\n \"\\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info,\",\n \"instance files for an instance. :param instance: nova.objects.instance.Instance :returns: True\",\n \"vms on a given host.- \\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self):\",\n \"that it was before this call began. :param context: security\",\n \"after check_can_live_migrate calls :param context: security context :param dest_check_data: result\",\n \"proxied and needs to be held across the shutdown +\",\n \"given `instance`. :param dict old_connection_info: The volume for this connection\",\n \"node after a failed live migration. :param context: security context\",\n \"to add a network interface to a running instance. The\",\n \"instance, block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance for live\",\n \":rtype: str \\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the\",\n \"`pause` is :func:'suspend'. The counter action for `pause` is :func:`unpause`.\",\n \"a running instance. The counter action to this is :func:`attach_interface`.\",\n \"values. \\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self): \\\"\\\"\\\"Return\",\n \"instance whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The network\",\n \"after an interrupted snapshot. :param context: security context :param instance:\",\n \"receiving machine :param block_migration: if true, prepare for block migration\",\n \"root disk - ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice\",\n \"interface on each running VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise\",\n \"information for the instance for attaching to volumes. Connector information\",\n \"The instance to which the diagnostic data should be returned.\",\n \"platform. Once this successfully completes, the instance should be running\",\n \"kwargs: A free-form thingy... :return: None \\\"\\\"\\\" # NOTE(jogo) Currently\",\n \"network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination node after a\",\n \"context, instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration. :param\",\n \"image of the instance. :param nova.objects.BlockDeviceMapping root_bdm: The description of\",\n \":func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param nova.objects.aggregate.Aggregate aggregate:\",\n \"timeout: The timeout (in seconds) for considering rebooting instances to\",\n \"in seconds to wait for the guest OS to shutdown.\",\n \"network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does not support \\\"\",\n \"to - e.g. for optimisations, or when the 'VM' is\",\n \"LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live\",\n \"method with something more efficient. \\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self,\",\n \"the driver's local image cache. Some drivers chose to cache\",\n \"context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the specified suspended instance. The\",\n \"def unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce the specified instance after\",\n \"context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination node\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"is larger than the old volume, it gets resized to\",\n \"space this way. A \\\"softer\\\" way of `suspend` is :func:`pause`.\",\n \"value of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise\",\n \"manager to dispatch the event. This must only be invoked\",\n \"state of the VM could be stored in the memory\",\n \"keeping track of which instance ID maps to which platform-specific\",\n \"block_migration: if true, post operation of block_migration. \\\"\\\"\\\" raise NotImplementedError()\",\n \"objects that have been in rebooting state longer than the\",\n \"network_info: instance network information :param recreate: True if the instance\",\n \"def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of IPTables rules and apply\",\n \"NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required cleanup on dest\",\n \"another host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object\",\n \"filtering rules operations are not completed. :param instance: nova.objects.instance.Instance object\",\n \"completed. :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"Information about block devices to be attached to the instance.\",\n \"this instance. A 'rebuild' effectively purges all existing data from\",\n \"spawn_instance by the compute manager. :return: None, or a set\",\n \"virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {}\",\n \"instance. The suspended instance gets resumed and will use CPU\",\n \"be invoked with a single parameter, which will be an\",\n \"mode. If `False`, leave the maintenance mode. :return: \\\"on_maintenance\\\" if\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self, context, migration, instance, disk_info,\",\n \"in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance resource usage.\",\n \"a volume attached to a specified instance. The counter action\",\n \"instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk to the\",\n \"context, instance): \\\"\\\"\\\"Get connection info for a vnc console. :param\",\n \"driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class =\",\n \":func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which gets a network\",\n \"an aggregate. The counter action to this is :func:`remove_from_aggregate` :param\",\n \"instance in multiple phases, turning off the instance before the\",\n \"def get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get the next device name\",\n \"host should perform. The valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and\",\n \"storage space of the host, depending on the underlying hypervisor\",\n \"to publish changes to the instance's metadata to the hypervisor.\",\n \"context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def\",\n \"or memory of the host anymore. The state of the\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"this method with something more efficient. \\\"\\\"\\\" try: return instance.uuid\",\n \"service. Most hypervisors can use the default implementation which returns\",\n \"drivers chose to cache images for instances on disk. This\",\n \"instance to which the diagnostic data should be returned. :return:\",\n \"shutdown. :return: A list of disk information dicts in JSON\",\n \"spin up steps. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"while waiting for it to shutdown. :return: A list of\",\n \"in for access to auth_token raise NotImplementedError() def cleanup_host(self, host):\",\n \"etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be\",\n \"virtual interfaces (VIFs) into the given `instance` at instance boot\",\n \"a single parameter, which will be an instance of the\",\n \"logging from oslo_utils import importutils import nova.conf from nova.i18n import\",\n \"the return value of the newer interface :func:`get_instance_diagnostics` :rtype: dict\",\n \"def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an\",\n \"\\\"\\\"\\\"Unpause the given paused instance. The paused instance gets unpaused\",\n \"for optimisations, or when the 'VM' is actually proxied and\",\n \"dict containing the following keys: - root_device_name: device name of\",\n \"should be in the state that it was before this\",\n \":param instance: nova.objects.Instance :param block_device_info: Optional; Can be used to\",\n \"admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain on the virtualization\",\n \"a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration(self, context, instance,\",\n \"nova.network.model.NetworkInfo network_info: The object which contains information about the VIFs\",\n \"which are longer in the rebooting state than `timeout` seconds.\",\n \"calls :param context: security context :param dest_check_data: result of check_can_live_migrate_destination\",\n \"of interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self, context,\",\n \"raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None,\",\n \"a rule. \\\"\\\"\\\" raise NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset networking\",\n \"faster). Concretely, the below method must be called. - setup_basic_filtering\",\n \"name of the host to remove from the given `aggregate`.\",\n \"reset_network(self, instance): \\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\" # TODO(Vek): Need\",\n \"using info.\\\"\\\"\\\" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None):\",\n \"context: security context :param instance: nova.objects.instance.Instance This function should use\",\n \"instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of instance files on the host.\",\n \"\\\"\\\"\\\" from nova.virt import block_device as virt_block_device block_device_info = {\",\n \"network_info, image_meta, rescue_password): \\\"\\\"\\\"Rescue the specified instance. :param nova.context.RequestContext context:\",\n \"instance. The counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance\",\n \"require pre-configured MAC->network mappings that will match the actual configuration.\",\n \"The instance being rescued. :param nova.network.model.NetworkInfo network_info: Necessary network information\",\n \"network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish reverting a resize/migration. :param context: the\",\n \"of the machine that will be making the connection, the\",\n \"%(event)s: %(ex)s\\\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete\",\n \"def post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug VIFs from networks at\",\n \"nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def\",\n \"to the return value of the older interface :func:`get_diagnostics` :rtype:\",\n \"info. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance,\",\n \"block device mapping. :param instance: nova.objects.instance.Instance that volume is requesting\",\n \"also called a 'name' elsewhere. As far as this layer\",\n \":param block_device_info: Optional; Can be used to filter out devices\",\n \":param recover_method: recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration.\",\n \"instance: The instance to lookup Returns True if an instance\",\n \"| 'idle': idle, | 'user': user, | 'iowait': wait, |\",\n \"The metadata of the image of the instance. :param rescue_password:\",\n \"return len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks existence of an instance\",\n \"or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means\",\n \"rebooting state than `timeout` seconds. :param int timeout: The timeout\",\n \"pass def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek):\",\n \"transferred disk information :param network_info: instance network information :param nova.objects.ImageMeta\",\n \"rescue_password): \\\"\\\"\\\"Rescue the specified instance. :param nova.context.RequestContext context: The context\",\n \"new volume is larger than the old volume, it gets\",\n \"knows about. .. note:: This implementation works for all drivers,\",\n \"restored from the soft-deleted data. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance should be completely cleaned up, and the virtualization platform\",\n \"`suspend` is :func:`pause`. The counter action for `suspend` is :func:`resume`.\",\n \"system type to be checked, the validate values are defined\",\n \"data there to guide the creation of the new instance.\",\n \"instance that has the volume attached :param uuid volume_id: Volume\",\n \"runs checks on the destination host, and then calls back\",\n \"except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate\",\n \"and calling this method should apply the updated rules to\",\n \"{'event': event, 'ex': ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering\",\n \"drivers. The interface to this class talks in terms of\",\n \"DHCP options for this instance. Some hypervisors (such as bare\",\n \"should be decided by the driver implementation if not set.\",\n \"resources \\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info,\",\n \"to receive asynchronous event notifications from hypervisors. The callback will\",\n \"the detach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self,\",\n \"= driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block\",\n \"migrated/resized :param disk_info: the newly transferred disk information :param network_info:\",\n \"mountpoint in the instance where the volume for `old_connection_info` is\",\n \"disk API module. \\\"\\\"\\\" # NOTE(jichenjc): Return False here so\",\n \"should perform. The valid actions are: \\\"\\\"startup\\\", \\\"shutdown\\\" and \\\"reboot\\\".\",\n \": str \\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self, host, mode): \\\"\\\"\\\"Start/Stop\",\n \"about actual disk sizes of an instance. :param instance: nova.objects.Instance\",\n \":param nova.context.RequestContext context: The context for the resume. :param nova.objects.instance.Instance\",\n \"to be held across the shutdown + spin up steps.\",\n \"network_info: instance network information :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"this instance have? Some hypervisors (such as bare metal) cannot\",\n \"is not particularly efficient. Maintainers of the virt drivers are\",\n \"\\\"\\\"\\\"Get the next device name based on the block device\",\n \"'192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\"\",\n \"instance): \\\"\\\"\\\"Unpause the given paused instance. The paused instance gets\",\n \"as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping),\",\n \"attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the\",\n \"\\\"\\\"\\\" raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password,\",\n \"the diagnostic data should be returned. :return: Has a big\",\n \"if the instance was deleted from disk, False otherwise. \\\"\\\"\\\"\",\n \"specified instance. A soft-deleted instance doesn't allocate any resources anymore,\",\n \"means of publishing the instance metadata to the instance, then\",\n \"access to instance shared disk files \\\"\\\"\\\" return False def\",\n \"for an instance to driver format. Virt drivers expect block\",\n \"to power on/off an instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise\",\n \"description of the root device. \\\"\\\"\\\" raise NotImplementedError() def default_device_names_for_instance(self,\",\n \"def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up filtering rules and waiting\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"is unfortunately also called a 'name' elsewhere. As far as\",\n \"be paused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"to auth_token raise NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause the given\",\n \"wait, | 'frequency': freq}, where kern and user indicate the\",\n \"None def dhcp_options_for_instance(self, instance): \\\"\\\"\\\"Get DHCP options for this instance.\",\n \"of the instance. \\\"\\\"\\\" raise NotImplementedError() def unquiesce(self, context, instance,\",\n \"detach_volume(self, connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk attached to\",\n \"block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or []\",\n \"\\\"\\\"\\\"Get connector information for the instance for attaching to volumes.\",\n \"the file format is supported by this driver :param fs_type:\",\n \"NOTE(russellb) This method is deprecated and will be removed once\",\n \"instance of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self, context, instance):\",\n \"the given instance. :param nova.objects.instance.Instance instance: The instance to which\",\n \"not compute_driver: LOG.error(_LE(\\\"Compute driver option required, but not specified\\\")) sys.exit(1)\",\n \":param nova.objects.instance.Instance instance: The instance to suspend. :return: None \\\"\\\"\\\"\",\n \"soft-delete. :return: None \\\"\\\"\\\" raise NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore\",\n \"state as before the 'suspend'. :param nova.context.RequestContext context: The context\",\n \"have? Some hypervisors (such as bare metal) cannot do freeform\",\n \"try: driver = importutils.import_object( 'nova.virt.%s' % compute_driver, virtapi) return utils.check_isinstance(driver,\",\n \"This layer is responsible for translating that generic data structure\",\n \"for the specific language governing permissions and limitations # under\",\n \"when a host is booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise\",\n \"def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection info for a vnc\",\n \"host. :param instance: nova.objects.instance.Instance to lookup Returns True if files\",\n \"for 'resume' is :func:`suspend`. Depending on the underlying hypervisor technology,\",\n \":param dict block_device_info: Information about the block devices. :param int\",\n \"virtualized onto any L2 domain, with arbitrary VLANs etc, but\",\n \"\\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\" raise NotImplementedError()\",\n \"checks on the destination host, and then calls back to\",\n \"in self.get_available_nodes(): return True # Refresh and check again. return\",\n \"support that contract \\\"\\\"\\\" import sys from oslo_log import log\",\n \"file format is supported by this driver :param fs_type: the\",\n \"files of an instance with the supplied ID accessible on\",\n \"this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context. :param\",\n \"Linux command `uptime` on this host. :return: A text which\",\n \"remote sessions. This is optional. \\\"\\\"\\\" pass def get_info(self, instance):\",\n \"partial instance should be completely cleaned up, and the virtualization\",\n \"the below method must be called. - setup_basic_filtering (for nova-basic,\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection info\",\n \"for. :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check\",\n \"How the guest OS reacts in details, depends on the\",\n \"VM disk. :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError()\",\n \"as an argument. Compute drivers constructors take a VirtAPI object\",\n \"as a list. \\\"\\\"\\\" raise NotImplementedError() def rebuild(self, context, instance,\",\n \"want networks deallocated on reschedule?\\\"\\\"\\\" return False def macs_for_instance(self, instance):\",\n \"MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set\",\n \"resources . Instance should have been destroyed from the Hypervisor\",\n \"disk information :param network_info: instance network information :param nova.objects.ImageMeta image_meta:\",\n \"in for access to auth_token raise NotImplementedError() def power_off(self, instance,\",\n \"This function should use the data there to guide the\",\n \"resize_to): \\\"\\\"\\\"Replace the volume attached to the given `instance`. :param\",\n \"about instance resource usage. :returns: dict of nova uuid =>\",\n \"allow disk over commit :returns: a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" pass def get_info(self, instance): \\\"\\\"\\\"Get the current status of\",\n \"the instance is being specified as instance.name. The second parameter\",\n \"found (for example if networking failed), this function should still\",\n \"info for volumes attached to vms on a given host.-\",\n \"instance, with openflow switches, fixed MAC addresses can still be\",\n \"should have been destroyed from the Hypervisor before calling this\",\n \"%(ex)s\\\"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any\",\n \"instance of nova.compute.service.Instance. This is the data structure used by\",\n \"block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info\",\n \":param context: security context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\"\",\n \"of virtual machines that the hypervisor knows about. .. note::\",\n \"nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the\",\n \"wait for the guest OS to shutdown. :param int retry_interval:\",\n \"the requested block device. device_name does not need to be\",\n \"set in instance. :param network_info: instance network information :param block_device_info:\",\n \"regular switches require pre-configured MAC->network mappings that will match the\",\n \"instance. A soft-deleted instance doesn't allocate any resources anymore, but\",\n \"and then calls back to the source host to check\",\n \"block_stats(self, instance, disk_id): \\\"\\\"\\\"Return performance counters associated with the given\",\n \"dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it is possible to execute live\",\n \"block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals(\",\n \"instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified instance from\",\n \"networking for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"support quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce\",\n \"counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance: The\",\n \"possible to execute live migration. This runs checks on the\",\n \"second parameter is the base64-encoded path to which the file\",\n \"of the host, depending on the underlying hypervisor technology. A\",\n \"be held across the shutdown + spin up steps. :param\",\n \"counter action for 'unpause' is :func:`pause`. Depending on the underlying\",\n \"customer-controlled -- it's an internal ID chosen by Nova. At\",\n \"resource usage. :returns: dict of nova uuid => dict of\",\n \"get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device mappings for an instance to\",\n \"raise NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about the\",\n \":param context: security context :param instance: nova.objects.instance.Instance This function should\",\n \"information :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"associate with network ports. :param context: request context :param instance:\",\n \"associated with :returns: a string representing the host ID \\\"\\\"\\\"\",\n \"instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can be used\",\n \"schemes, to uniquely identify a VM or domain. These IDs\",\n \"or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class\",\n \"on each running VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError()\",\n \"method returns a list of nodenames managed by the service.\",\n \"information is a dictionary representing the ip of the machine\",\n \"\\\"\\\"\\\"Get information about instance resource usage. :returns: dict of nova\",\n \"more efficient. \\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks existence\",\n \"(the \\\"License\\\"); you may # not use this file except\",\n \"information about actual disk sizes of an instance. :param instance:\",\n \"attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage.\",\n \"the instance. :param nova.network.model.NetworkInfo vif: The object which has the\",\n \"raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the\",\n \"raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known host CPU\",\n \"information about the VIFs to unplug. :return: None \\\"\\\"\\\" raise\",\n \"ID \\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute\",\n \"this would include the file being merged, and the file\",\n \"and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info =\",\n \"notifications from hypervisors. The callback will be invoked with a\",\n \"the group gains or loses a rule. \\\"\\\"\\\" raise NotImplementedError()\",\n \"connection_info, instance, mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk attached to the\",\n \"database entry to create a new instance based on that.\",\n \"host is booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def\",\n \"networks deallocated on reschedule?\\\"\\\"\\\" return False def macs_for_instance(self, instance): \\\"\\\"\\\"What\",\n \"into terms that are specific to the virtualization platform. \\\"\\\"\\\"\",\n \"uuid volume_id: Attached volume associated with the snapshot :param uuid\",\n \"dump should be triggered. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"block_device_mapping): \\\"\\\"\\\"Converts block device mappings for an instance to driver\",\n \"(potentially empty) list of DriverEphemeralBlockDevice instances - swap: An instance\",\n \"crash dump mechanism on the given instance. Stalling instances can\",\n \"for `old_connection_info` is attached to. :param int resize_to: If the\",\n \"the hypervisor. If the hypervisor has no means of publishing\",\n \"cache and remove images which are no longer of interest.\",\n \"action): \\\"\\\"\\\"Reboots, shuts down or powers up the host. :param\",\n \"is possible to execute live migration. This runs checks on\",\n \"began. :param context: security context :param instance: nova.objects.instance.Instance This function\",\n \"At the nova.virt layer, instances do not have human-readable names\",\n \"migrate VM disk. :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise\",\n \"the VM, detaches all block devices, then spins up the\",\n \"auth_token pass def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a reboot on\",\n \"given instance. These are returned as [rd_req, rd_bytes, wr_req, wr_bytes,\",\n \"result of check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info,\",\n \"it triggers the migration of all instances to other hosts.\",\n \"talks in terms of 'instances' (Amazon EC2 and internal Nova\",\n \"to wait for GuestOS to shutdown :param retry_interval: How often\",\n \"the image of the instance. \\\"\\\"\\\" raise NotImplementedError() def unquiesce(self,\",\n \"def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it is\",\n \"MAC addresses to actual networks (or permitting them to be\",\n \"steps. :param context: security context :param instance: nova.objects.instance.Instance This function\",\n \"the validate values are defined at disk API module. \\\"\\\"\\\"\",\n \"networks (or permitting them to be freeform) is up to\",\n \"network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk of a running\",\n \"so the instance is being specified as instance.name. The second\",\n \"is to clean the cache and remove images which are\",\n \"instance: The instance where the crash dump should be triggered.\",\n \"block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination node after a failed\",\n \"method. :param context: security context :param instance: Instance object as\",\n \"network_info: instance network information :param block_device_info: Information about block devices\",\n \"old_connection_info, new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the volume attached to\",\n \"instance shared disk files \\\"\\\"\\\" return False def register_event_listener(self, callback):\",\n \"be virtualized onto any L2 domain, with arbitrary VLANs etc,\",\n \"MAC addresses. This is called during spawn_instance by the compute\",\n \"otherwise. .. note:: This implementation works for all drivers, but\",\n \"use the default implementation which returns None. This is called\",\n \"- all the cleanup of old state is skipped. :param\",\n \"located on shared storage. This runs check on the destination\",\n \"the virtualization overhead required to build an instance of the\",\n \"\\\"\\\"\\\" return None def manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the driver's\",\n \"{} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {}\",\n \"register_event_listener(self, callback): \\\"\\\"\\\"Register a callback to receive events. Register a\",\n \"...]\\\" \\\"\\\"\\\" raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): \\\"\\\"\\\"This method is\",\n \"the instance metadata. This is an optional driver method which\",\n \"pertaining to attached volumes :param bad_volumes_callback: Function to handle any\",\n \"of nova.compute.service.Instance, and so the instance is being specified as\",\n \"bandwidth usage counters for each interface on each running VM.\",\n \"instance): \\\"\\\"\\\"What MAC addresses must this instance have? Some hypervisors\",\n \":param network_info: instance network information \\\"\\\"\\\" raise NotImplementedError(_(\\\"Hypervisor driver does\",\n \"context in for access to auth_token raise NotImplementedError() def suspend(self,\",\n \"The description of the root device. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"Otherwise, this method should return [hypervisor_hostname]. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"estimated overhead values. \\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb': 0} def\",\n \"of MAC addresses to actual networks (or permitting them to\",\n \"for the resume. :param dict block_device_info: Instance volume block device\",\n \"ID exists on the host, False otherwise. .. note:: This\",\n \"with below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\"\",\n \"migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers\",\n \"0} def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] >\",\n \":param create_info: The data needed for nova to be able\",\n \"name (not ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo\",\n \":param nova.network.model.NetworkInfo network_info: Necessary network information for the resume. :param\",\n \"get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection info for a rdp console.\",\n \"InstanceInfo object \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"the specified instance. :param instance: nova.objects.instance.Instance :param timeout: time to\",\n \"the given host. \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"an instance of the given flavor. Defaults to zero, drivers\",\n \"context for the rescue. :param nova.objects.instance.Instance instance: The instance being\",\n \"the supplied ID accessible on the host, False otherwise. ..\",\n \"The metadata of the image of the instance. \\\"\\\"\\\" raise\",\n \"to shutdown. :param int retry_interval: How often to signal guest\",\n \"return block_device_mapping class ComputeDriver(object): \\\"\\\"\\\"Base class for compute drivers. The\",\n \"refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group rules Gets called when an\",\n \"consideration when provisioning networking for the instance. Mapping of MAC\",\n \"\\\"\\\"\\\"Add a compute host to an aggregate. The counter action\",\n \"Dict of estimated overhead values. \\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb':\",\n \"NotImplementedError() def change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies a diff to\",\n \"the instance before the end. :param nova.objects.instance.Instance instance: The instance\",\n \"LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict()\",\n \"synonyms. Note that the instance ID or name is not\",\n \"should be running (power_state.RUNNING). If this fails, any partial instance\",\n \"the current status of an instance, by name (not ID!)\",\n \"that is necessary for the driver gracefully stop, including ending\",\n \"terminology) or domain (Xen or libvirt terminology). An instance has\",\n \"instances, return \\\"enabled\\\", if further instances shouldn't be scheduled to\",\n \":param bool mode: If `True`, go into maintenance mode. If\",\n \":param instance_info: Instance/flavor to calculate overhead for. :returns: Dict of\",\n \"on the host and allocate storage space this way. A\",\n \"raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies a diff\",\n \"if it is possible to execute live migration. This runs\",\n \"part of a periodic task that records the results in\",\n \"actually proxied and needs to be held across the shutdown\",\n \"the cumulative idle CPU time (nanoseconds), wait indicates the cumulative\",\n \"to the virtualization platform. \\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\": False,\",\n \":returns: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self, instance,\",\n \"then calls back to the source host to check the\",\n \"aggregation. On those platforms, this method is unused. Note that\",\n \"network information :param reboot_type: Either a HARD or SOFT reboot\",\n \"connector information for the instance for attaching to volumes. Connector\",\n \"follow, and shared types that support that contract \\\"\\\"\\\" import\",\n \"def get_num_instances(self): \\\"\\\"\\\"Return the total number of virtual machines. Return\",\n \"periodic task that records the results in the DB. :param\",\n \"in for access to auth_token raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return\",\n \"or removed from the security group the instance is a\",\n \"import _, _LE, _LI from nova import utils from nova.virt\",\n \"a string representing the host ID \\\"\\\"\\\" return instance.get('host') def\",\n \"of the given flavor. Defaults to zero, drivers should override\",\n \"context for the resume. :param nova.objects.instance.Instance instance: The suspended instance\",\n \"the instance where the volume for `old_connection_info` is attached to.\",\n \"cleanup of old state is skipped. :param block_device_info: Information about\",\n \":param list instances: A list of nova.objects.instance.Instance objects that have\",\n \"\\\"\\\"\\\" return False def register_event_listener(self, callback): \\\"\\\"\\\"Register a callback to\",\n \"a specified instance. The counter action to this is :func:`volume_snapshot_create`\",\n \"underlying hypervisor technology. A \\\"stronger\\\" version of `pause` is :func:'suspend'.\",\n \"in for access to auth_token raise NotImplementedError() def finish_revert_migration(self, context,\",\n \"user processes respectively, idle indicates the cumulative idle CPU time\",\n \"of DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice or\",\n \"ability to power on/off an instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"network information :param block_device_info: instance volume block device info :param\",\n \"be restored from the soft-deleted data. :return: None \\\"\\\"\\\" raise\",\n \"and re-make this instance. A 'rebuild' effectively purges all existing\",\n \"that case. :param context: security context :param instance: Instance object\",\n \"gets detached from the given `instance`. :param dict new_connection_info: The\",\n \"its completion. To migrate an instance, filtering rules to hypervisors\",\n \"optional. On some platforms (e.g. XenAPI) performance statistics can be\",\n \"if not isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event must be an\",\n \"the driver. The prime example is to clean the cache\",\n \"compute driver '%s'\\\"), compute_driver) try: driver = importutils.import_object( 'nova.virt.%s' %\",\n \"still available as a database entry. The counter action :func:`restore`\",\n \"from nova.i18n import _, _LE, _LI from nova import utils\",\n \"aggregate which should remove the given `host` :param str host:\",\n \"following keys: - root_device_name: device name of the root disk\",\n \"depending on the underlying hypervisor technology. A \\\"stronger\\\" version of\",\n \"idle CPU time (nanoseconds), wait indicates the cumulative I/O wait\",\n \"all the cleanup of old state is skipped. :param block_device_info:\",\n \"up with currently running VM's on the given host. \\\"\\\"\\\"\",\n \"for it to shutdown \\\"\\\"\\\" raise NotImplementedError() def power_on(self, context,\",\n \"to which the diagnostic data should be returned. :return: Has\",\n \"update_task_state): \\\"\\\"\\\"Snapshots the specified instance. :param context: security context :param\",\n \"exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM\",\n \"check_can_live_migrate calls :param context: security context :param dest_check_data: result of\",\n \"remove_from_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove a compute host from\",\n \"NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft delete the specified instance. A\",\n \"\\\"\\\"\\\" raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): \\\"\\\"\\\"Cleans up any\",\n \"instance_info: Instance/flavor to calculate overhead for. :returns: Dict of estimated\",\n \"context :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self,\",\n \"instance, bdms, block_device_obj): \\\"\\\"\\\"Get the next device name based on\",\n \"from the soft-deleted data. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"machine that will be making the connection, the name of\",\n \"instance. Stalling instances can be triggered to dump the crash\",\n \"instance disk information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise\",\n \"image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False):\",\n \"well that is needed for creating the snapshot. \\\"\\\"\\\" raise\",\n \"def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of instance files on the\",\n \"and never escape the connection interface. The platform-specific layer is\",\n \"return \\\"enabled\\\", if further instances shouldn't be scheduled to this\",\n \"will be making the connection, the name of the iscsi\",\n \"import block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals':\",\n \"context in for access to auth_token raise NotImplementedError() def host_power_action(self,\",\n \"list of nodenames managed by the service. Otherwise, this method\",\n \"a VirtAPI object as their first object and this must\",\n \"format of block device info to be passed in to\",\n \"can be retrieved directly in aggregate form, without Nova having\",\n \"called since it defines PROJNET, PROJMASK. but libvirt migrates those\",\n \"if instance files located on shared storage. This runs check\",\n \"but it is not particularly efficient. Maintainers of the virt\",\n \"auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about\",\n \"in for access to auth_token pass def poll_rebooting_instances(self, timeout, instances):\",\n \"are longer in the rebooting state than `timeout` seconds. :param\",\n \"spent by kernel and user processes respectively, idle indicates the\",\n \"False def quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce the specified instance\",\n \"NotImplementedError() def set_host_enabled(self, enabled): \\\"\\\"\\\"Sets the ability of this host\",\n \"by applicable law or agreed to in writing, software #\",\n \"Indicates if disks should be destroyed :param migrate_data: implementation specific\",\n \"of the host again. The counter action for 'unpause' is\",\n \"instance, and pass them into this layer. This layer is\",\n \"is skipped. :param block_device_info: Information about block devices to be\",\n \"while waiting for it to shutdown \\\"\\\"\\\" raise NotImplementedError() def\",\n \"which returns None. Hypervisors with MAC limits should return a\",\n \"window. On start, it triggers the migration of all instances\",\n \"names in the block device mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self,\",\n \"boot from the network, and manage their own TFTP service.\",\n \"instances boot from the network, and manage their own TFTP\",\n \"NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context\",\n \"LiveMigrateData object \\\"\\\"\\\" pass def post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug\",\n \":param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result\",\n \"migrated. :param str dest: The IP address of the destination\",\n \"and limitations # under the License. \\\"\\\"\\\" Driver base-classes: (Beginning\",\n \"block_device_info: Information about the block devices. :param int timeout: The\",\n \"Defaults to zero, drivers should override if per-instance overhead calculations\",\n \"cleanup on host after check_instance_shared_storage calls :param context: security context\",\n \"platforms (e.g. XenAPI) performance statistics can be retrieved directly in\",\n \"virtualization platform should be in the state that it was\",\n \"def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of live\",\n \"applicable law or agreed to in writing, software # distributed\",\n \"the supplied ID exists on the host, False otherwise. ..\",\n \"based on the results from check_can_live_migrate_destination. :param context: security context\",\n \"the instance. Mapping of MAC addresses to actual networks (or\",\n \":return: A list of disk information dicts in JSON format.\",\n \"NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently known host CPU stats.\",\n \"instance whose disk should be migrated. :param str dest: The\",\n \"of the file, also base64-encoded. NOTE(russellb) This method is deprecated\",\n \"resume. :param dict block_device_info: Instance volume block device info. :return:\",\n \"operation has completed. There may be other data included as\",\n \"migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a\",\n \"the specified instance. A soft-deleted instance doesn't allocate any resources\",\n \"when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true,\",\n \"def quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce the specified instance to\",\n \":returns: Dictionary describing resources \\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self, context,\",\n \"root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing device names in the block\",\n \"action for `restore` is :func:`soft_delete`. :param nova.objects.instance.Instance instance: The soft-deleted\",\n \"of MAC addresses. This is called during spawn_instance by the\",\n \"instance have? Some hypervisors (such as bare metal) cannot do\",\n \":param instance: instance that is live migrating \\\"\\\"\\\" raise NotImplementedError()\",\n \"\\\"\\\"\\\"Get the current status of an instance, by name (not\",\n \"handle any bad volumes encountered \\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self,\",\n \"may have to be called since it defines PROJNET, PROJMASK.\",\n \"any partial instance should be completely cleaned up, and the\",\n \"networks at source. :param context: security context :param instance: instance\",\n \"return if not isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event must be\",\n \"instance. Mapping of MAC addresses to actual networks (or permitting\",\n \"metadata to the hypervisor. If the hypervisor has no means\",\n \"| [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', |\",\n \"NotImplementedError() def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection info for a\",\n \"event %s\\\", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\\\"Exception dispatching\",\n \"their first object and this must be supplied. :param virtapi:\",\n \"(VIFs) into the given `instance` at instance boot time. The\",\n \"context in for access to auth_token raise NotImplementedError() def plug_vifs(self,\",\n \"Dictionary describing resources \\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self, context, instance,\",\n \"flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the disk of a\",\n \"instance to suspend. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume(self,\",\n \"given host. \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"nova.conf from nova.i18n import _, _LE, _LI from nova import\",\n \"longer than the configured timeout. :return: None \\\"\\\"\\\" # TODO(Vek):\",\n \"passed in to methods that expect it. \\\"\\\"\\\" return True\",\n \"device information :param destroy_disks: if true, destroy disks at destination\",\n \"check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup on host after check_instance_shared_storage calls\",\n \":param admin_password: <PASSWORD> to set in instance. :param bdms: block-device-mappings\",\n \"Returns True if an instance with the supplied ID exists\",\n \"nova import utils from nova.virt import event as virtevent CONF\",\n \"be completely cleaned up, and the virtualization platform should be\",\n \"name is not human-readable or customer-controlled -- it's an internal\",\n \"context: the context for the finish_revert_migration :param instance: nova.objects.instance.Instance being\",\n \"swap: An instance of DriverSwapBlockDevice or None - block_device_mapping: a\",\n \"NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True):\",\n \"for an instance :param context: security context :param instance: nova.objects.instance.Instance\",\n \"MAC addresses, which will be supplied to the allocate_for_instance call\",\n \"overlap to the return value of the older interface :func:`get_diagnostics`\",\n \"security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice\",\n \"in the case of qcow2 backed snapshots, this would include\",\n \"security groups. All security groups and their associated rules live\",\n \"attached volumes :param bad_volumes_callback: Function to handle any bad volumes\",\n \"sizes of an instance. :param instance: nova.objects.Instance :param block_device_info: Optional;\",\n \"\\\"shutdown\\\" and \\\"reboot\\\". :return: The result of the power action\",\n \"given flavor. Defaults to zero, drivers should override if per-instance\",\n \"that is needed for creating the snapshot. \\\"\\\"\\\" raise NotImplementedError()\",\n \"generic data structure into terms that are specific to the\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif):\",\n \"host, False otherwise. .. note:: This implementation works for all\",\n \"gets resized to the given size (in Gigabyte) of `resize_to`.\",\n \"block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap') or {'device_name':\",\n \"PROJMASK. but libvirt migrates those value through migrateToURI(), so ,\",\n \"MAC addresses. This method allows drivers to return a set\",\n \"on the underlying hypervisor technology. A \\\"stronger\\\" version of `pause`\",\n \"NotImplementedError() def get_console_output(self, context, instance): \\\"\\\"\\\"Get console output for an\",\n \"block devices that should be detached from the instance. :param\",\n \"Most hypervisors can use the default implementation which returns None.\",\n \"the compute manager. Note that the format of the return\",\n \"triggered. :return: None \\\"\\\"\\\" raise NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft\",\n \"name of the iscsi initiator and the hostname of the\",\n \"human-readable or customer-controlled -- it's an internal ID chosen by\",\n \"driver want networks deallocated on reschedule?\\\"\\\"\\\" return False def macs_for_instance(self,\",\n \"injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy\",\n \"security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC\",\n \"files located on shared storage. This runs check on the\",\n \"VIFs from networks at source. :param context: security context :param\",\n \"doesn't use CPU cycles of the host anymore. The state\",\n \"instance): \\\"\\\"\\\"Check if instance files located on shared storage. This\",\n \"instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param dest:\",\n \"instance network information :param block_device_info: Information about block devices to\",\n \"MAC->network mappings that will match the actual configuration. Most hypervisors\",\n \"volume. This is the same data format returned by Cinder's\",\n \"a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self, context, instance,\",\n \"methods here take an instance of nova.compute.service.Instance. This is the\",\n \"instance resource usage. :returns: dict of nova uuid => dict\",\n \"is called during spawn_instance by the compute manager. Note that\",\n \"except ImportError: LOG.exception(_LE(\\\"Unable to load the virtualization driver\\\")) sys.exit(1) def\",\n \"object which has the information about the interface to detach.\",\n \"instances can be triggered to dump the crash data. How\",\n \"\\\"\\\"\\\"This method is called after a change to security groups.\",\n \"is deprecated and will be removed once it can be\",\n \"higher up the stack. Most virtualization platforms will also have\",\n \"if maintenance mode got left. :rtype: str \\\"\\\"\\\" raise NotImplementedError()\",\n \"prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be called since\",\n \"gets unpaused and will use CPU cycles of the host\",\n \"\\\"\\\"\\\"Retrieve information about actual disk sizes of an instance. :param\",\n \"manages a particular node.\\\"\\\"\\\" if nodename in self.get_available_nodes(): return True\",\n \"NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare\",\n \"unquiesced :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"checked, the validate values are defined at disk API module.\",\n \"def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of\",\n \"more specialized subclasses. \\\"\\\"\\\" from nova.virt import block_device as virt_block_device\",\n \"to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables\",\n \"network information :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \":param network_info: instance network information :param reboot_type: Either a HARD\",\n \"arbitrary VLANs etc, but regular switches require pre-configured MAC->network mappings\",\n \"NotImplementedError() def suspend(self, context, instance): \\\"\\\"\\\"Suspend the specified instance. A\",\n \"was migrated :block_device_info: instance block device information :param migrate_data: a\",\n \"is the base64-encoded path to which the file is to\",\n \"next device name based on the block device mapping. :param\",\n \"security context :param instance: Instance object as returned by DB\",\n \":param nova.context.RequestContext context: The context for the suspend. :param nova.objects.instance.Instance\",\n \"object \\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information\",\n \"volumes. :return: json strings with below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240',\",\n \"\\\"\\\"\\\" import sys from oslo_log import log as logging from\",\n \"driver implementation if not set. :returns: The chosen device name.\",\n \"technology, the guest has the same state as before the\",\n \":param int retry_interval: How often to signal guest while waiting\",\n \"swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info\",\n \"instance: nova.objects.Instance :param block_device_info: Optional; Can be used to filter\",\n \"time in seconds to wait for the guest OS to\",\n \"def inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a file on the\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft delete\",\n \"cache images for instances on disk. This method is an\",\n \"is migrated. :param dest: destination host :param post_method: post operation\",\n \"get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage counters for each interface on\",\n \"nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The metadata of\",\n \"a set of MAC addresses that the instance is to\",\n \"b64_path, b64_contents): \\\"\\\"\\\"Writes a file on the specified instance. The\",\n \"event to the compute manager. Invokes the event callback registered\",\n \"\\\"\\\"\\\" raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"Power\",\n \"all given 'instances'. Reboots the given `instances` which are longer\",\n \"block_migration: if true, prepare for block migration :param disk_over_commit: if\",\n \"it defines PROJNET, PROJMASK. but libvirt migrates those value through\",\n \"deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver want networks deallocated on reschedule?\\\"\\\"\\\"\",\n \"volume_id: Attached volume associated with the snapshot :param uuid snapshot_id:\",\n \"doesn't use this interface, which seems odd # since the\",\n \"disk should be migrated. :param str dest: The IP address\",\n \"instances known to the virtualization layer, as a list. \\\"\\\"\\\"\",\n \"nodenames managed by the service. Otherwise, this method should return\",\n \"the operation cannot complete. \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"must be preserved on rebuild \\\"\\\"\\\" raise NotImplementedError() def spawn(self,\",\n \"probably a good idea to log a warning in that\",\n \"raise NotImplementedError() def plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs)\",\n \"being merged, and the file being merged into (if appropriate).\",\n \"complete :param instance: Instance being live migrated \\\"\\\"\\\" raise NotImplementedError()\",\n \"aggregate which should add the given `host` :param str host:\",\n \"use CPU cycles of the host again. The counter action\",\n \"filtering rules to hypervisors and firewalls are inevitable on destination\",\n \"in rebuild for HA implementation and required for validation of\",\n \"(e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means 'these\",\n \":param destroy_disks: Indicates if disks should be destroyed :param migrate_data:\",\n \"a callback to receive events. Register a callback to receive\",\n \"NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None):\",\n \"nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance: The instance\",\n \"the instance. :param rescue_password: <PASSWORD> for rescue. \\\"\\\"\\\" raise NotImplementedError()\",\n \"get migrated. :param nova.network.model.NetworkInfo network_info: The network information of the\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of IPTables rules.\\\"\\\"\\\" pass def\",\n \"context, instance): \\\"\\\"\\\"Get connection info for a serial console. :param\",\n \"host ID \\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a\",\n \"given host.- \\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP\",\n \"NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices,\",\n \"instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization overhead\",\n \":returns an instance of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self,\",\n \":param resize_instance: True if the instance is being resized, False\",\n \"raise NotImplementedError() def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set the root password\",\n \"\\\"\\\"\\\"Force live migration to complete :param instance: Instance being live\",\n \"access to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff):\",\n \"str mountpoint: The mountpoint in the instance where the volume\",\n \"by this driver :param fs_type: the file system type to\",\n \"= driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return\",\n \"booted. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def rescue(self, context,\",\n \"case of qcow2 backed snapshots, this would include the file\",\n \"destroy_vifs=True): \\\"\\\"\\\"Cleanup the instance resources . Instance should have been\",\n \"(hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context, dest_check_data): \\\"\\\"\\\"Do required\",\n \"`instance`. :param dict block_device_info: Information about the block devices. :param\",\n \"so that every hypervisor # need to define their supported\",\n \"Info pertaining to attached volumes :param bad_volumes_callback: Function to handle\",\n \"which the caller want to get resources from a driver\",\n \":return: None \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"call by the compute manager, and it is up to\",\n \"called when an instance gets added to or removed from\",\n \":param compute_driver: a compute driver name to override the config\",\n \"inject into instance. :param admin_password: Administrator password to set in\",\n \"instance of console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self, context, instance):\",\n \":rtype: dict \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination\",\n \"raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): \\\"\\\"\\\"Finish\",\n \"context: security context :param data: result of check_instance_shared_storage_local \\\"\\\"\\\" raise\",\n \"migrated :block_device_info: instance block device information :param migrate_data: a LiveMigrateData\",\n \"VM, detaches all block devices, then spins up the new\",\n \"as follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname }\",\n \"will be an instance of the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback\",\n \"constraints', a set means 'these and only these MAC addresses'.\",\n \"one. :param str mountpoint: The mountpoint in the instance where\",\n \"seconds to wait for the guest OS to shutdown. :param\",\n \"a database entry. The counter action :func:`restore` uses the database\",\n \"file being merged, and the file being merged into (if\",\n \"instance further up the stack. This is unfortunately also called\",\n \"is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance\",\n \"def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth usage counters for each interface\",\n \"for a MKS console. :param context: security context :param instance:\",\n \"Attached volume associated with the snapshot :param uuid snapshot_id: The\",\n \"information :param block_device_info: Information about block devices that should be\",\n \"The instance which gets a network interface removed. :param nova.network.model.NetworkInfo\",\n \"reschedule?\\\"\\\"\\\" return False def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses must\",\n \"suspended instance to resume. :param nova.network.model.NetworkInfo network_info: Necessary network information\",\n \"of the image of the instance. :param resize_instance: True if\",\n \"importutils import nova.conf from nova.i18n import _, _LE, _LI from\",\n \"base64-encoded. NOTE(russellb) This method is deprecated and will be removed\",\n \"instance: The instance whose volume gets replaced by another one.\",\n \"accept new instances. If it is `False`, the host won't\",\n \"source host to check the results. :param context: security context\",\n \"need to - e.g. for optimisations, or when the 'VM'\",\n \"host. :param context: security context :param instance: instance object that\",\n \"image_meta): \\\"\\\"\\\"Unquiesce the specified instance after snapshots. If the specified\",\n \":return: None, or a set of DHCP options, eg: |\",\n \"whose disk get migrated. :param nova.network.model.NetworkInfo network_info: The network information\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"triggers the migration of all instances to other hosts. Consider\",\n \"to override this method with something more efficient. \\\"\\\"\\\" try:\",\n \"addresses must this instance have? Some hypervisors (such as bare\",\n \"virtevent CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args,\",\n \"\\\"\\\"\\\" raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use hotplug\",\n \"override this method with something more efficient. \\\"\\\"\\\" try: return\",\n \"instance block device information :param network_info: instance network information :param\",\n \"be removed once it can be removed from nova.compute.manager. \\\"\\\"\\\"\",\n \"for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application\",\n \"about the given instance. :param nova.objects.instance.Instance instance: The instance to\",\n \"crash dump should be triggered. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"currently running VM's on the given host. \\\"\\\"\\\" # TODO(Vek):\",\n \":param instance: The instance to lookup Returns True if an\",\n \"disk_over_commit: if true, allow disk over commit :returns: a LiveMigrateData\",\n \"DB. :param nodename: node which the caller want to get\",\n \"instance is being resized, False otherwise :param block_device_info: instance volume\",\n \":func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance instance:\",\n \"plug_vifs(self, instance, network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs) into the given\",\n \"is optional. \\\"\\\"\\\" pass def get_info(self, instance): \\\"\\\"\\\"Get the current\",\n \"on the specified instance. The first parameter is an instance\",\n \"volume attached to the given `instance`. :param dict old_connection_info: The\",\n \"recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration:\",\n \"'tftp-server', | 'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\" return None def\",\n \"the file system type to be checked, the validate values\",\n \"limitations # under the License. \\\"\\\"\\\" Driver base-classes: (Beginning of)\",\n \"def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def\",\n \"and implement this function at their # virt layer. return\",\n \"The counter action for `suspend` is :func:`resume`. :param nova.context.RequestContext context:\",\n \"once it can be removed from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek):\",\n \":param uuid volume_id: Volume to be snapshotted :param create_info: The\",\n \"to hypervisors and firewalls are inevitable on destination host. (\",\n \"instance network information :param block_device_info: Information about block devices that\",\n \"instance. A paused instance doesn't use CPU cycles of the\",\n \"stay internal to the platform-specific layer, and never escape the\",\n \"network_info): # NOTE(markus_z): 2015-08-18 # The compute manager doesn't use\",\n \"instance of nova.compute.service.Instance, and so the instance is being specified\",\n \"allocate any resources anymore, but is still available as a\",\n \"powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def pause(self, instance):\",\n \"number of bytes transferred, and errs is the number of\",\n \"to inject into instance. :param admin_password: Administrator password to set\",\n \"subclasses. \\\"\\\"\\\" from nova.virt import block_device as virt_block_device block_device_info =\",\n \"in the block device mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self, instance,\",\n \"details, depends on the configuration of it. :param nova.objects.instance.Instance instance:\",\n \"the snapshot operation has completed. There may be other data\",\n \"idea to log a warning in that case. :param context:\",\n \"by Cinder's initialize_connection() API call. In the case of doing\",\n \"in for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance):\",\n \"migrate_data: implementation specific params \\\"\\\"\\\" raise NotImplementedError() def reboot(self, context,\",\n \"to the virtualization layer, as a list. \\\"\\\"\\\" raise NotImplementedError()\",\n \"due to a full pipeline. All counters are long integers.\",\n \"instance ID. \\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the\",\n \"overlap to the return value of the newer interface :func:`get_instance_diagnostics`\",\n \"uuid volume_id: Volume to be snapshotted :param create_info: The data\",\n \"context in for access to auth_token raise NotImplementedError() def finish_revert_migration(self,\",\n \"resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param context: the context\",\n \"that volume is requesting a device name :param bdms: a\",\n \"Version 2.0 (the \\\"License\\\"); you may # not use this\",\n \"and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or\",\n \"NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group rules Gets called\",\n \"instance): \\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\" # TODO(Vek): Need to\",\n \"import utils from nova.virt import event as virtevent CONF =\",\n \"A free-form thingy... :return: None \\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self,\",\n \"maintenance mode should be changed. :param bool mode: If `True`,\",\n \"context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param\",\n \"rules now.\\\"\\\"\\\" pass def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\"\",\n \"connection info for a MKS console. :param context: security context\",\n \"block_device_info: instance block device information :param destroy_disks: if true, destroy\",\n \"for this connection gets detached from the given `instance`. :param\",\n \"access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security\",\n \"pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Add a compute\",\n \"to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up filtering\",\n \"instance, new_pass): \\\"\\\"\\\"Set the root password on the specified instance.\",\n \"format of a dict containing the following keys: - root_device_name:\",\n \"NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of\",\n \"block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this instance. A 'rebuild' effectively\",\n \"<PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes\",\n \"context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached to a specified\",\n \"it is `False`, the host won't accept new instances. :return:\",\n \"to be able to complete the snapshot. For example, in\",\n \"The second parameter is the base64-encoded path to which the\",\n \"compute driver module specified by the compute_driver configuration option or,\",\n \"host anymore. The state of the instance could be persisted\",\n \"The counter action for `pause` is :func:`unpause`. :param nova.objects.instance.Instance instance:\",\n \"dispatch the event. This must only be invoked from a\",\n \"\\\"\\\"\\\" if not compute_driver: compute_driver = CONF.compute_driver if not compute_driver:\",\n \"migration at destination host. :param context: security context :param instance:\",\n \"volume associated with the snapshot :param uuid snapshot_id: The snapshot\",\n \"not completed. :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" # TODO(Vek): Need\",\n \"anything that is necessary for the driver to function, including\",\n \"over commit :returns: a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError()\",\n \"metadata to the instance, then this method should not be\",\n \"output for an instance :param context: security context :param instance:\",\n \"devices which are actually volumes. :return: json strings with below\",\n \"\\\"\\\"\\\" raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info,\",\n \"instance. :param resize_instance: True if the instance is being resized,\",\n \"{'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name']\",\n \"to auth_token raise NotImplementedError() def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down\",\n \"be supplied. :param virtapi: a VirtAPI instance :param compute_driver: a\",\n \"counters associated with the given disk_id on the given instance.\",\n \"destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination node after a failed live\",\n \"to another host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance\",\n \"up to that call to ensure that all assigned network\",\n \"# NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError() def\",\n \"On those platforms, this method is unused. Note that this\",\n \"None, or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None\",\n \"The virtualization platform should ensure that the reboot action has\",\n \"=> dict of usage info \\\"\\\"\\\" return {} def instance_on_disk(self,\",\n \"instance to another host. :param context: security context :param instance:\",\n \"of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self, context, instance): \\\"\\\"\\\"Get\",\n \"instance based on that. :param nova.objects.instance.Instance instance: The instance to\",\n \"def get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance resource usage. :returns: dict\",\n \"def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network info for specified instance.\\\"\\\"\\\"\",\n \"soft-deleted data. :return: None \\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self, nodename):\",\n \"instances running the specified security group. An error should be\",\n \"aggregate: The aggregate which should remove the given `host` :param\",\n \"information about the interface to attach. :raise nova.exception.NovaException: If the\",\n \"calls :param context: security context :param data: result of check_instance_shared_storage_local\",\n \"TODO(Vek): Need to pass context in for access to auth_token\",\n \"to this host, return \\\"disabled\\\". :rtype: str \\\"\\\"\\\" # TODO(Vek):\",\n \"by DB layer. :param network_info: instance network information :param block_device_info:\",\n \"detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function\",\n \"True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the caller if the driver\",\n \"the following keys: - root_device_name: device name of the root\",\n \":return: The result of the power action :rtype: : str\",\n \"to complete :param instance: Instance being live migrated \\\"\\\"\\\" raise\",\n \"nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance network information :param recreate:\",\n \"instance of DriverSwapBlockDevice or None - block_device_mapping: a (potentially empty)\",\n \"the event. This must only be invoked from a green\",\n \"are specific to the virtualization platform. \\\"\\\"\\\" capabilities = {\",\n \"should be changed. :param bool mode: If `True`, go into\",\n \"if the instance is being resized, False otherwise :param block_device_info:\",\n \"for access to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info,\",\n \"These are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where\",\n \"which are actually volumes. :return: json strings with below format::\",\n \"- swap: An instance of DriverSwapBlockDevice or None - block_device_mapping:\",\n \"running instance in multiple phases, turning off the instance before\",\n \"is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs\",\n \"implementation which returns None. This is called during spawn_instance by\",\n \"to the volume. This is the same data format returned\",\n \"overhead values. \\\"\\\"\\\" return {'memory_mb': 0, 'disk_gb': 0} def list_instances(self):\",\n \"'instances' (Amazon EC2 and internal Nova terminology), by which we\",\n \"lookup Returns True if an instance with the supplied ID\",\n \"on all given 'instances'. Reboots the given `instances` which are\",\n \"some platforms (e.g. XenAPI) performance statistics can be retrieved directly\",\n \"False, \\\"supports_device_tagging\\\": False, } def __init__(self, virtapi): self.virtapi = virtapi\",\n \"disk of a running instance in multiple phases, turning off\",\n \"virtualization platforms will also have their own identity schemes, to\",\n \"kern and user indicate the cumulative CPU time (nanoseconds) spent\",\n \"| 'frequency': freq}, where kern and user indicate the cumulative\",\n \"multiple phases, turning off the instance before the end. :param\",\n \"instance): \\\"\\\"\\\"Get connection info for a spice console. :param context:\",\n \"'VM' is actually proxied and needs to be held across\",\n \"def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about actual disk sizes\",\n \"known to the virtualization layer, as a list. \\\"\\\"\\\" #\",\n \":param detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for\",\n \"booting up; freq indicates the current CPU frequency (MHz). All\",\n \"is needed for creating the snapshot. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"This requires passing the appropriate options out to the DHCP\",\n \"\\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, } def __init__(self, virtapi):\",\n \"object as their first object and this must be supplied.\",\n \"finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True):\",\n \"a default root device name for the driver. :param nova.objects.instance.Instance\",\n \"use this interface, which seems odd # since the manager\",\n \"data): \\\"\\\"\\\"Do cleanup on host after check_instance_shared_storage calls :param context:\",\n \"that cache which isn't directly related to other calls into\",\n \"NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the\",\n \"want to get resources from a driver that manages only\",\n \"is the total number of bytes transferred, and errs is\",\n \":param bdms: block-device-mappings to use for rebuild :param detach_block_devices: function\",\n \"the datastore, and calling this method should apply the updated\",\n \"setting-up filtering rules operations are not completed. :param instance: nova.objects.instance.Instance\",\n \"up the host. :param str action: The action the host\",\n \"rule. \\\"\\\"\\\" raise NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset networking for\",\n \"ignore this :returns: Dictionary describing resources \\\"\\\"\\\" raise NotImplementedError() def\",\n \"get_info(self, instance): \\\"\\\"\\\"Get the current status of an instance, by\",\n \"NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean\",\n \"| {'opt_name': 'tftp-server', | 'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\" return\",\n \"object which has the information about the interface to attach.\",\n \"can be triggered to dump the crash data. How the\",\n \"image_id: Reference to a pre-created image that will hold the\",\n \"specified instance. :param instance: nova.objects.instance.Instance :param timeout: time to wait\",\n \"instance after snapshots. If the specified instance doesn't support quiescing,\",\n \"or, if supplied, the driver name supplied as an argument.\",\n \"\\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration to\",\n \"\\\"\\\"\\\"Return bandwidth usage counters for each interface on each running\",\n \"is still available as a database entry. The counter action\",\n \"manage_image_cache(self, context, all_instances): \\\"\\\"\\\"Manage the driver's local image cache. Some\",\n \"device info to be passed in to methods that expect\",\n \"virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info\",\n \"of the root disk - ephemerals: a (potentially empty) list\",\n \"about the requested block device. device_name does not need to\",\n \"counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The\",\n \":param nova.objects.instance.Instance instance: The instance which gets VIFs plugged. :param\",\n \"migrateToURI(), so , no need to be called. Don't use\",\n \"boot. :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices:\",\n \"`aggregate`. :param dict kwargs: A free-form thingy... :return: None \\\"\\\"\\\"\",\n \"post_live_migration(self, context, instance, block_device_info, migrate_data=None): \\\"\\\"\\\"Post operation of live migration\",\n \"devices, then spins up the new VM afterwards. It may\",\n \"\\\"\\\"\\\" raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password,\",\n \"and it is up to that call to ensure that\",\n \"instance. :param network_info: instance network information :param block_device_info: Information about\",\n \"access to auth_token raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the total\",\n \"migrate_data=None): \\\"\\\"\\\"Post operation of live migration at source host. :param\",\n \"combination with :func:`set_host_enabled`. :param str host: The name of the\",\n \"NotImplementedError() def is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the file format is\",\n \"partition must be preserved on rebuild \\\"\\\"\\\" raise NotImplementedError() def\",\n \"from disk, False otherwise. \\\"\\\"\\\" return True @property def need_legacy_block_device_info(self):\",\n \". Instance should have been destroyed from the Hypervisor before\",\n \"of the host whose maintenance mode should be changed. :param\",\n \"migration. :param instance: instance that is live migrating \\\"\\\"\\\" raise\",\n \"structure into terms that are specific to the virtualization platform.\",\n \"a serial console. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"wr_req, wr_bytes, errs], where rd indicates read, wr indicates write,\",\n \"live migration at source host. :param context: security context :instance:\",\n \"raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address of the\",\n \"example if networking failed), this function should still succeed. It's\",\n \"bytes transferred, and errs is the number of requests held\",\n \"base64-encoded path to which the file is to be written\",\n \"an event to the compute manager. Invokes the event callback\",\n \"instance. :param destroy_disks: Indicates if disks should be destroyed :param\",\n \"unused. Note that this function takes an instance ID. \\\"\\\"\\\"\",\n \"instance): \\\"\\\"\\\"Get the current status of an instance, by name\",\n \"def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach\",\n \"'instance ID' and 'instance name' are synonyms. Note that the\",\n \"def estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization overhead required to build\",\n \"are actually volumes. :return: json strings with below format:: \\\"[{'path':'disk',\",\n \"green thread. \\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event))\",\n \"with openflow switches, fixed MAC addresses can still be virtualized\",\n \"to define their supported file system # type and implement\",\n \"As far as this layer is concerned, 'instance ID' and\",\n \"freeform virtualization of MAC addresses. This method allows drivers to\",\n \"results in the DB. :param nodename: node which the caller\",\n \"usage info for volumes attached to vms on a given\",\n \"emit_event(self, event): \\\"\\\"\\\"Dispatches an event to the compute manager. Invokes\",\n \":raise nova.exception.NovaException: If the attach fails. :return: None \\\"\\\"\\\" raise\",\n \"instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject network\",\n \"object that is migrated. :param dest: destination host :param post_method:\",\n \"be snapshotted :param create_info: The data needed for nova to\",\n \"**kwargs): driver_registry = dict() for driver_str in named_driver_config: driver_type, _sep,\",\n \"as instance.name. The second parameter is the base64-encoded path to\",\n \"unfortunately also called a 'name' elsewhere. As far as this\",\n \"where the crash dump should be triggered. :return: None \\\"\\\"\\\"\",\n \"zero, drivers should override if per-instance overhead calculations are desired.\",\n \"to remove from the given `aggregate`. :param dict kwargs: A\",\n \"network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance for live migration :param\",\n \"store details regarding an instance, and pass them into this\",\n \"instance is not found (for example if networking failed), this\",\n \"root device for. :param nova.objects.ImageMeta image_meta: The metadata of the\",\n \"to the hypervisor. If the hypervisor has no means of\",\n \"by name (not ID!) :param instance: nova.objects.instance.Instance object Returns a\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"live in the datastore, and calling this method should apply\",\n \"def resume(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the specified suspended\",\n \"callback registered by the compute manager to dispatch the event.\",\n \"[rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates read, wr\",\n \"unpaused. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"quiesce by other errors (e.g. agent timeout), NovaException is raised.\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None):\",\n \"This method is optional. On some platforms (e.g. XenAPI) performance\",\n \"internal to the platform-specific layer, and never escape the connection\",\n \":param instance: nova.objects.instance.Instance to be unquiesced :param nova.objects.ImageMeta image_meta: The\",\n \"device information :param migrate_data: a LiveMigrateData object \\\"\\\"\\\" pass def\",\n \"pass def get_info(self, instance): \\\"\\\"\\\"Get the current status of an\",\n \"def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing device names\",\n \":rtype: str \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \"method is called when nova-compute launches, and as part of\",\n \"compute driver name to override the config opt :returns: a\",\n \"specific params \\\"\\\"\\\" raise NotImplementedError() def cleanup(self, context, instance, network_info,\",\n \"the host anymore. The state of the instance could be\",\n \"NovaException is raised. :param context: request context :param instance: nova.objects.instance.Instance\",\n \"context in for access to auth_token raise NotImplementedError() def cleanup_host(self,\",\n \"action :rtype: : str \\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self, host,\",\n \"layer. return False def quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce the\",\n \"whose disk should be migrated. :param str dest: The IP\",\n \"running the specified security group. An error should be raised\",\n \"the guest OS to shutdown. :param int retry_interval: How often\",\n \"NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if instance files located\",\n \"object \\\"\\\"\\\" pass def post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug VIFs\",\n \"raise NotImplementedError() def unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce the specified\",\n \"snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is\",\n \"block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it is possible to execute live\",\n \"name based on the block device mapping. :param instance: nova.objects.instance.Instance\",\n \"bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping\",\n \"instance \\\"\\\"\\\" if not compute_driver: compute_driver = CONF.compute_driver if not\",\n \"running VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self,\",\n \"is the data structure used by nova.compute to store details\",\n \"than the old volume, it gets resized to the given\",\n \"rules can be set faster). Concretely, the below method must\",\n \"NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): \\\"\\\"\\\"Default the missing device\",\n \"migration: the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param\",\n \"for a serial console. :param context: security context :param instance:\",\n \"storage. This runs check on the destination host, and then\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); you may\",\n \"block_device_info=None): \\\"\\\"\\\"resume guest state when a host is booted. :param\",\n \":param instance: Instance object as returned by DB layer. :param\",\n \"This method is deprecated and will be removed once it\",\n \"can succeed, based on the results from check_can_live_migrate_destination. :param context:\",\n \"resized to the given size (in Gigabyte) of `resize_to`. :return:\",\n \"pre-created image that will hold the snapshot. \\\"\\\"\\\" raise NotImplementedError()\",\n \"good idea to log a warning in that case. :param\",\n \"nova.exception.NovaException: If the detach fails. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"True # Refresh and check again. return nodename in self.get_available_nodes(refresh=True)\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"for this connection gets attached to the given 'instance'. :param\",\n \"context: security context :param instance: nova.objects.instance.Instance object :param block_device_info: instance\",\n \"the instance. \\\"\\\"\\\" raise NotImplementedError() def unquiesce(self, context, instance, image_meta):\",\n \"needed to be able to complete the snapshot. For example,\",\n \"image_meta: The metadata of the image of the instance. \\\"\\\"\\\"\",\n \"'rebuild' effectively purges all existing data from the system and\",\n \"for the rescue. :param nova.objects.instance.Instance instance: The instance being rescued.\",\n \"network information of the given `instance`. :param dict block_device_info: Information\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self, context, instance):\",\n \"check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False,\",\n \"required by applicable law or agreed to in writing, software\",\n \"e.g. for optimisations, or when the 'VM' is actually proxied\",\n \"to auth_token raise NotImplementedError() def get_console_output(self, context, instance): \\\"\\\"\\\"Get console\",\n \"to inject into instance. :param admin_password: <PASSWORD> to set in\",\n \"estimate_instance_overhead(self, instance_info): \\\"\\\"\\\"Estimate the virtualization overhead required to build an\",\n \"resume. :param nova.network.model.NetworkInfo network_info: Necessary network information for the resume.\",\n \":return: None, or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])).\",\n \"instance files located on shared storage. :param context: security context\",\n \"domain (Xen or libvirt terminology). An instance has an ID,\",\n \"check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data:\",\n \"loses a rule. \\\"\\\"\\\" raise NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset\",\n \"action: The action the host should perform. The valid actions\",\n \"agreed to in writing, software # distributed under the License\",\n \"the Hypervisor before calling this method. :param context: security context\",\n \"gets replaced by another one. :param str mountpoint: The mountpoint\",\n \"deprecated and will be removed once it can be removed\",\n \"values are long integers. \\\"\\\"\\\" raise NotImplementedError() def block_stats(self, instance,\",\n \"this driver :param fs_type: the file system type to be\",\n \"data. How the guest OS reacts in details, depends on\",\n \"the context for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized\",\n \"of the destination host. :param nova.objects.flavor.Flavor flavor: The flavor of\",\n \"instance_exists(self, instance): \\\"\\\"\\\"Checks existence of an instance on the host.\",\n \"access to auth_token raise NotImplementedError() def suspend(self, context, instance): \\\"\\\"\\\"Suspend\",\n \":param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms):\",\n \"to have. allocate_for_instance will take this into consideration when provisioning\",\n \"NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress live migration. :param\",\n \"event, 'ex': ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering instance\",\n \"if per-instance overhead calculations are desired. :param instance_info: Instance/flavor to\",\n \"instance network information :param disk_info: instance disk information :param migrate_data:\",\n \"service. This method is for multi compute-nodes support. If a\",\n \":param str host: The name of the host whose maintenance\",\n \"in for access to auth_token raise NotImplementedError() def get_console_output(self, context,\",\n \"in for access to auth_token raise NotImplementedError() def unpause(self, instance):\",\n \"Most virtualization platforms will also have their own identity schemes,\",\n \":param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the given\",\n \"context :param instance: instance object that was being migrated :param\",\n \"driver gracefully stop, including ending remote sessions. This is optional.\",\n \"set in instance. :param bdms: block-device-mappings to use for rebuild\",\n \"This checks if the live migration can succeed, based on\",\n \"\\\"\\\"\\\"Abort an in-progress live migration. :param instance: instance that is\",\n \"on that. :param nova.objects.instance.Instance instance: The instance to soft-delete. :return:\",\n \"The data needed for nova to be able to attach\",\n \"the specified instance. After this is called successfully, the instance's\",\n \"the given flavor. Defaults to zero, drivers should override if\",\n \"to lookup Returns True if files of an instance with\",\n \"to be written on the instance; the third is the\",\n \"The counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context:\",\n \"to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group\",\n \"the file, also base64-encoded. NOTE(russellb) This method is deprecated and\",\n \"The counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance:\",\n \"instance. :param injected_files: User files to inject into instance. :param\",\n \"of the dom0 \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"publish changes to the instance's metadata to the hypervisor. If\",\n \"with all info about the requested block device. device_name does\",\n \"the identifier chosen by Nova to represent the instance further\",\n \"the aggregation. On those platforms, this method is unused. Note\",\n \"block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap')\",\n \"runs check on the destination host, and then calls back\",\n \"migrated/resized :param network_info: instance network information :param block_device_info: instance volume\",\n \"nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \\\"\\\"\\\" raise NotImplementedError() def\",\n \"\\\"\\\"\\\" raise NotImplementedError() def live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress live\",\n \"cache. Some drivers chose to cache images for instances on\",\n \"which should add the given `host` :param str host: The\",\n \"hostname of the machine as follows:: { 'ip': ip, 'initiator':\",\n \"sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver) try: driver = importutils.import_object(\",\n \"instance that is live migrating \\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self,\",\n \"raise NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group rules Gets\",\n \"out devices which are actually volumes. :return: json strings with\",\n \"instance): \\\"\\\"\\\"Return diagnostics data about the given instance. :param nova.objects.instance.Instance\",\n \"in aggregate form, without Nova having to do the aggregation.\",\n \"on the virtualization platform. Once this successfully completes, the instance\",\n \"configuration of it. :param nova.objects.instance.Instance instance: The instance where the\",\n \"a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force\",\n \"is_supported_fs_format(self, fs_type): \\\"\\\"\\\"Check whether the file format is supported by\",\n \"the instance. :param destroy_disks: Indicates if disks should be destroyed\",\n \"the third is the contents of the file, also base64-encoded.\",\n \"an ID, which is the identifier chosen by Nova to\",\n \"disk files \\\"\\\"\\\" return False def register_event_listener(self, callback): \\\"\\\"\\\"Register a\",\n \"False otherwise. \\\"\\\"\\\" return True @property def need_legacy_block_device_info(self): \\\"\\\"\\\"Tell the\",\n \"self._compute_event_callback(event) except Exception as ex: LOG.error(_LE(\\\"Exception dispatching event %(event)s: %(ex)s\\\"),\",\n \"to calculate overhead for. :returns: Dict of estimated overhead values.\",\n \"a reboot on all given 'instances'. Reboots the given `instances`\",\n \"nova.virt import block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name,\",\n \"action has completed successfully even in cases in which the\",\n \"- ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances -\",\n \"nodenames of all nodes managed by the compute service. This\",\n \"an optional driver method which is used to publish changes\",\n \"\\\"\\\"\\\" capabilities = { \\\"has_imagecache\\\": False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False,\",\n \"a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None):\",\n \"context in for access to auth_token raise NotImplementedError() def change_instance_metadata(self,\",\n \"for access to auth_token pass def poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform\",\n \"\\\"\\\"\\\"Unrescue the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek):\",\n \"nova.compute to store details regarding an instance, and pass them\",\n \"particularly efficient. Maintainers of the virt drivers are encouraged to\",\n \"operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any\",\n \"information :param reboot_type: Either a HARD or SOFT reboot :param\",\n \"to clean the cache and remove images which are no\",\n \"new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace the volume attached to the\",\n \"context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def finish_migration(self,\",\n \":param block_device_info: Info pertaining to attached volumes :param bad_volumes_callback: Function\",\n \"about the VIFs to unplug. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"info \\\"\\\"\\\" return {} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of\",\n \"\\\"\\\"\\\"Tell the caller if the driver requires legacy block device\",\n \"InstanceQuiesceNotSupported is raised. When it fails to quiesce by other\",\n \"suspended instance. The suspended instance gets resumed and will use\",\n \"devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach\",\n \"to soft-delete. :return: None \\\"\\\"\\\" raise NotImplementedError() def restore(self, instance):\",\n \"and waiting for its completion. To migrate an instance, filtering\",\n \"stop, including ending remote sessions. This is optional. \\\"\\\"\\\" pass\",\n \"network interface removed. :param nova.network.model.NetworkInfo vif: The object which has\",\n \"# # Licensed under the Apache License, Version 2.0 (the\",\n \"\\\"\\\"\\\" self._compute_event_callback = callback def emit_event(self, event): \\\"\\\"\\\"Dispatches an event\",\n \":param nova.network.model.NetworkInfo vif: The object which has the information about\",\n \"connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): \\\"\\\"\\\"Attach the disk to\",\n \"def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create\",\n \"destination host. :param nova.objects.flavor.Flavor flavor: The flavor of the instance\",\n \"will be invoked with a single parameter, which will be\",\n \"instance doesn't allocate any resources anymore, but is still available\",\n \":param instance: instance object that was being migrated :param network_info:\",\n \"Concretely, the below method must be called. - setup_basic_filtering (for\",\n \"to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data\",\n \"object \\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None):\",\n \"number of requests held up due to a full pipeline.\",\n \"seconds. :param int timeout: The timeout (in seconds) for considering\",\n \"total number of virtual machines. Return the number of virtual\",\n \"works for all drivers, but it is not particularly efficient.\",\n \"example, in the case of qcow2 backed snapshots, this would\",\n \"the contents of the file, also base64-encoded. NOTE(russellb) This method\",\n \"interface. The platform-specific layer is responsible for keeping track of\",\n \"raise NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean up anything that is\",\n \"For example, in the case of qcow2 backed snapshots, this\",\n \"which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which\",\n \"volume for this connection gets detached from the given `instance`.\",\n \"devices that should be detached from the instance. :param destroy_disks:\",\n \"raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18 #\",\n \"the service. Otherwise, this method should return [hypervisor_hostname]. \\\"\\\"\\\" raise\",\n \"by the compute manager to dispatch the event. This must\",\n \"block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list)\",\n \"it is up to that call to ensure that all\",\n \"before this call began. :param context: security context :param instance:\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self, instance): \\\"\\\"\\\"Trigger\",\n \"If the instance is not found (for example if networking\",\n \"we expect the legacy format of block device info to\",\n \"since the manager should be the controlling thing here. \\\"\\\"\\\"Unplug\",\n \"platform-specific layer is responsible for keeping track of which instance\",\n \"'ex': ex}) def delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering instance files\",\n \"virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] =\",\n \"def register_event_listener(self, callback): \\\"\\\"\\\"Register a callback to receive events. Register\",\n \"the attach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def detach_interface(self,\",\n \"mode got left. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def set_host_enabled(self,\",\n \"device information :param network_info: instance network information :param disk_info: instance\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you\",\n \"a list of nodenames managed by the service. Otherwise, this\",\n \"to wait for the guest OS to shutdown. :param int\",\n \"\\\"\\\"\\\"Completes a resize/migration. :param context: the context for the migration/resize\",\n \"completes, the instance should be running (power_state.RUNNING). If this fails,\",\n \"with the given disk_id on the given instance. These are\",\n \"\\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False):\",\n \"instance: The instance to get the root device for. :param\",\n \"nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback = callback def emit_event(self, event): \\\"\\\"\\\"Dispatches\",\n \"same state as before the 'pause'. :param nova.objects.instance.Instance instance: The\",\n \"being migrated/resized :param network_info: instance network information :param block_device_info: instance\",\n \"driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device\",\n \"note:: Used in rebuild for HA implementation and required for\",\n \"\\\"\\\"\\\" pass def post_live_migration_at_source(self, context, instance, network_info): \\\"\\\"\\\"Unplug VIFs from\",\n \"for access to auth_token raise NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean\",\n \"retry_interval=0): \\\"\\\"\\\"Power off the specified instance. :param instance: nova.objects.instance.Instance :param\",\n \"check on the destination host, and then calls back to\",\n \":func:`restore` uses the database entry to create a new instance\",\n \"new instance. :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"an instance. :param instance: nova.objects.instance.Instance :returns: True if the instance\",\n \"The counter action is :func:`unplug_vifs`. :param nova.objects.instance.Instance instance: The instance\",\n \"def host_power_action(self, action): \\\"\\\"\\\"Reboots, shuts down or powers up the\",\n \"instance, diff): \\\"\\\"\\\"Applies a diff to the instance metadata. This\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data about\",\n \"interrupted snapshot. :param context: security context :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"version of `pause` is :func:'suspend'. The counter action for `pause`\",\n \"the total number of virtual machines. Return the number of\",\n \"raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): \\\"\\\"\\\"Writes a file\",\n \"is necessary for the driver to function, including catching up\",\n \"def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): \\\"\\\"\\\"Cleanup\",\n \"context, instance, diff): \\\"\\\"\\\"Applies a diff to the instance metadata.\",\n \"- block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or any\",\n \"a diff to the instance metadata. This is an optional\",\n \":return: json strings with below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file',\",\n \"efficient. \\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks existence of\",\n \"memory of the host anymore. The state of the instance\",\n \"\\\"\\\"\\\"Create a new instance/VM/domain on the virtualization platform. Once this\",\n \"def get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection info for a spice\",\n \"\\\"\\\"\\\"Retrieve resource information. This method is called when nova-compute launches,\",\n \"files \\\"\\\"\\\" return False def register_event_listener(self, callback): \\\"\\\"\\\"Register a callback\",\n \"instance network information :param block_device_info: instance volume block device info\",\n \"An instance has an ID, which is the identifier chosen\",\n \"all block devices, then spins up the new VM afterwards.\",\n \"on this host. :return: A text which contains the uptime\",\n \"to filter out devices which are actually volumes. :return: json\",\n \":param instance: nova.objects.instance.Instance \\\"\\\"\\\" pass def inject_network_info(self, instance, nw_info): \\\"\\\"\\\"inject\",\n \"stack. This is unfortunately also called a 'name' elsewhere. As\",\n \"and 'instance name' are synonyms. Note that the instance ID\",\n \"compute manager to dispatch the event. This must only be\",\n \"OS to shutdown. :param int retry_interval: How often to signal\",\n \"by the compute_driver configuration option or, if supplied, the driver\",\n \"data needed for nova to be able to attach to\",\n \"hypervisor. If the hypervisor has no means of publishing the\",\n \":param network_info: instance network information :param disk_info: instance disk information\",\n \"+ spin up steps. :param context: security context :param instance:\",\n \"image that will hold the snapshot. \\\"\\\"\\\" raise NotImplementedError() def\",\n \"driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def\",\n \"status of an instance, by name (not ID!) :param instance:\",\n \"NotImplementedError() def unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce the specified instance\",\n \"str host: The name of the host whose maintenance mode\",\n \"# virt layer. return False def quiesce(self, context, instance, image_meta):\",\n \"the host. :param str action: The action the host should\",\n \"are inevitable on destination host. ( Waiting only for filtering\",\n \"to function, including catching up with currently running VM's on\",\n \"the root password on the specified instance. :param instance: nova.objects.instance.Instance\",\n \"If `False`, leave the maintenance mode. :return: \\\"on_maintenance\\\" if switched\",\n \"post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when\",\n \"specified as instance.name. The second parameter is the base64-encoded path\",\n \"context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP \\\"\\\"\\\"\",\n \"the number of virtual machines that the hypervisor knows about.\",\n \"instance: The instance that has the volume attached. :param uuid\",\n \":returns: a string representing the host ID \\\"\\\"\\\" return instance.get('host')\",\n \"delete the specified instance. A soft-deleted instance doesn't allocate any\",\n \"the default implementation which returns None. This is called during\",\n \"driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] =\",\n \"of check_instance_shared_storage_local \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do\",\n \"in the instance where the volume for `old_connection_info` is attached\",\n \"in writing, software # distributed under the License is distributed\",\n \"requests held up due to a full pipeline. All counters\",\n \"fs_type: the file system type to be checked, the validate\",\n \"to return a set of MAC addresses that the instance\",\n \"console.type.ConsoleMKS \\\"\\\"\\\" raise NotImplementedError() def get_diagnostics(self, instance): \\\"\\\"\\\"Return diagnostics data\",\n \"NotImplementedError() def get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection info for a\",\n \"triggered to dump the crash data. How the guest OS\",\n \"instance: The instance that has the volume attached :param uuid\",\n \"complete the snapshot. For example, in the case of qcow2\",\n \"for attaching to volumes. Connector information is a dictionary representing\",\n \"instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred disk\",\n \"block devices. :param int timeout: The time in seconds to\",\n \"overhead for. :returns: Dict of estimated overhead values. \\\"\\\"\\\" return\",\n \"as before the 'pause'. :param nova.objects.instance.Instance instance: The instance which\",\n \"device info. :return: None \\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self, context,\",\n \"\\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): \\\"\\\"\\\"resume\",\n \"it was before this call began. :param context: security context\",\n \":param instance: Instance being live migrated \\\"\\\"\\\" raise NotImplementedError() def\",\n \"True if an instance with the supplied ID exists on\",\n \"block_device_info: Instance volume block device info. :return: None \\\"\\\"\\\" raise\",\n \"filtering rules and waiting for its completion. To migrate an\",\n \"in instance. :param bdms: block-device-mappings to use for rebuild :param\",\n \"information about the VIFs to plug. :return: None \\\"\\\"\\\" #\",\n \"are synonyms. Note that the instance ID or name is\",\n \"be overridden by hypervisors that need to - e.g. for\",\n \"reacts in details, depends on the configuration of it. :param\",\n \"for this method since migration should not be started when\",\n \"counter action is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which\",\n \"this host. :return: A text which contains the uptime of\",\n \"\\\"\\\"\\\"Remove a compute host from an aggregate. The counter action\",\n \"the compute manager. Invokes the event callback registered by the\",\n \"flavor of the instance whose disk get migrated. :param nova.network.model.NetworkInfo\",\n \"\\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if instance\",\n \"apply the updated rules to instances running the specified security\",\n \":param nova.objects.instance.Instance instance: The instance that has the volume attached.\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised.\",\n \"file is to be written on the instance; the third\",\n \"file on the specified instance. The first parameter is an\",\n \"context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove the\",\n \"snapshot. For example, in the case of qcow2 backed snapshots,\",\n \"network_info): \\\"\\\"\\\"Plug virtual interfaces (VIFs) into the given `instance` at\",\n \"and check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get\",\n \"which will get an additional network interface. :param nova.objects.ImageMeta image_meta:\",\n \"instance. :param nova.network.model.NetworkInfo vif: The object which has the information\",\n \"power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the specified instance. :param\",\n \"resize_to: If the new volume is larger than the old\",\n \"check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): \\\"\\\"\\\"Check if it is possible\",\n \"only known higher up the stack. Most virtualization platforms will\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def get_host_cpu_stats(self): \\\"\\\"\\\"Get the currently\",\n \"return False def register_event_listener(self, callback): \\\"\\\"\\\"Register a callback to receive\",\n \"context in for access to auth_token raise NotImplementedError() def get_num_instances(self):\",\n \"image file Cinder expects to be used as the active\",\n \"the driver. :param nova.objects.instance.Instance instance: The instance to get the\",\n \"for usage. :param attach_block_devices: function to attach block devices. See\",\n \"root_bdm: The description of the root device. \\\"\\\"\\\" raise NotImplementedError()\",\n \"This method is for multi compute-nodes support. If a driver\",\n \"raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): \\\"\\\"\\\"Replace\",\n \"specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"volume is requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList\",\n \"to a full pipeline. All counters are long integers. This\",\n \"files for an instance. :param instance: nova.objects.instance.Instance :returns: True if\",\n \"# Unless required by applicable law or agreed to in\",\n \"is distributed on an \\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def resume(self, context, instance, network_info,\",\n \"\\\"\\\"\\\"Power on the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise\",\n \"Necessary network information for the resume. :param nova.objects.ImageMeta image_meta: The\",\n \"disks at destination during cleanup :param migrate_data: a LiveMigrateData object\",\n \"the creation of the new instance. :param nova.objects.ImageMeta image_meta: The\",\n \"do the aggregation. On those platforms, this method is unused.\",\n \"layer is concerned, 'instance ID' and 'instance name' are synonyms.\",\n \"attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): \\\"\\\"\\\"Destroy and re-make this instance.\",\n \"to the given 'instance'. :param nova.objects.instance.Instance instance: The instance whose\",\n \"true, migrate VM disk. :param migrate_data: a LiveMigrateData object \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self, context, instance): \\\"\\\"\\\"Get connection info\",\n \"by hypervisors that need to - e.g. for optimisations, or\",\n \"this function should still succeed. It's probably a good idea\",\n \"nova.objects.instance.Instance :param network_info: instance network information :param reboot_type: Either a\",\n \"snapshot :param uuid snapshot_id: The snapshot to delete. :param dict\",\n \"access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): \\\"\\\"\\\"Defer application of\",\n \"lingering instance files for an instance. :param instance: nova.objects.instance.Instance :returns:\",\n \"instance_info): \\\"\\\"\\\"Estimate the virtualization overhead required to build an instance\",\n \"connection info for a spice console. :param context: security context\",\n \"[{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value':\",\n \"block_migration. \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): \\\"\\\"\\\"Check if\",\n \"ending remote sessions. This is optional. \\\"\\\"\\\" pass def get_info(self,\",\n \"\\\"\\\"\\\" raise NotImplementedError() def detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug to\",\n \"not specified\\\")) sys.exit(1) LOG.info(_LI(\\\"Loading compute driver '%s'\\\"), compute_driver) try: driver\",\n \"to shutdown. :return: A list of disk information dicts in\",\n \"which the underlying domain/vm is paused or halted/stopped. :param instance:\",\n \"state is skipped. :param block_device_info: Information about block devices to\",\n \"of I/O requests made, bytes is the total number of\",\n \"security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should add\",\n \"gains or loses a rule. \\\"\\\"\\\" raise NotImplementedError() def reset_network(self,\",\n \"the reboot action has completed successfully even in cases in\",\n \"compute_driver: a compute driver name to override the config opt\",\n \"block_device_info, network_info, disk_info, migrate_data=None): \\\"\\\"\\\"Prepare an instance for live migration\",\n \"pass context in for access to auth_token raise NotImplementedError() def\",\n \"is for multi compute-nodes support. If a driver supports multi\",\n \"Hypervisors with MAC limits should return a set of MAC\",\n \"elsewhere. As far as this layer is concerned, 'instance ID'\",\n \"= virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or\",\n \"(such as bare metal) require that instances boot from the\",\n \"dispatching event %(event)s: %(ex)s\\\"), {'event': event, 'ex': ex}) def delete_instance_files(self,\",\n \"driver requires legacy block device info. Tell the caller whether\",\n \"new_connection_info: The volume for this connection gets attached to the\",\n \"guide the creation of the new instance. :param nova.objects.ImageMeta image_meta:\",\n \"The name of the host to add to the given\",\n \"driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts\",\n \"destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified instance from the Hypervisor. If\",\n \"'resume' is :func:`suspend`. Depending on the underlying hypervisor technology, the\",\n \"suspend. :param nova.objects.instance.Instance instance: The instance to suspend. :return: None\",\n \"state longer than the configured timeout. :return: None \\\"\\\"\\\" #\",\n \"and pass them into this layer. This layer is responsible\",\n \"swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] > 0 def\",\n \"files to inject into instance. :param admin_password: Administrator password to\",\n \"NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset networking for specified instance.\\\"\\\"\\\" #\",\n \"the image file Cinder expects to be used as the\",\n \"network_info, block_device_info=None, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Destroy the specified instance from the\",\n \"full pipeline. All counters are long integers. This method is\",\n \"A list of disk information dicts in JSON format. :rtype:\",\n \"\\\"\\\"\\\"Do required cleanup on dest host after check_can_live_migrate calls :param\",\n \"live migrating \\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info,\",\n \"write, req is the total number of I/O requests made,\",\n \"rules live in the datastore, and calling this method should\",\n \"crash data. How the guest OS reacts in details, depends\",\n \"instances do not have human-readable names at all -- such\",\n \"free-form thingy... :return: None \\\"\\\"\\\" raise NotImplementedError() def undo_aggregate_operation(self, context,\",\n \"dictionary representing the ip of the machine that will be\",\n \"any of it's more specialized subclasses. \\\"\\\"\\\" from nova.virt import\",\n \"resize/migration, destroying the source VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" #\",\n \"be running (power_state.RUNNING). If this fails, any partial instance should\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None):\",\n \":param nova.objects.instance.Instance instance: The instance to which the diagnostic data\",\n \"a pre-created image that will hold the snapshot. \\\"\\\"\\\" raise\",\n \"sessions. This is optional. \\\"\\\"\\\" pass def get_info(self, instance): \\\"\\\"\\\"Get\",\n \"gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The object which contains\",\n \":func:`get_instance_diagnostics` :rtype: dict \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"time (nanoseconds) spent by kernel and user processes respectively, idle\",\n \"to this class talks in terms of 'instances' (Amazon EC2\",\n \"method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if\",\n \"Used in rebuild for HA implementation and required for validation\",\n \"nova.objects.instance.Instance instance: The instance to soft-delete. :return: None \\\"\\\"\\\" raise\",\n \"instances. If it is `False`, the host won't accept new\",\n \"for access to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0,\",\n \"instance. :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"{'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', | 'opt_value':\",\n \"network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified instance. After this\",\n \"as a database entry. The counter action :func:`restore` uses the\",\n \"mountpoint, resize_to): \\\"\\\"\\\"Replace the volume attached to the given `instance`.\",\n \"The instance which gets VIFs unplugged. :param nova.network.model.NetworkInfo network_info: The\",\n \"to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint,\",\n \"hypervisor technology. A \\\"stronger\\\" version of `pause` is :func:'suspend'. The\",\n \"the DB. :param nodename: node which the caller want to\",\n \"the root disk - ephemerals: a (potentially empty) list of\",\n \"machine' (XenAPI terminology) or domain (Xen or libvirt terminology). An\",\n \"of access to instance shared disk files \\\"\\\"\\\" return False\",\n \"raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass\",\n \"context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): \\\"\\\"\\\"Transfers the\",\n \"be presented in the format of a dict containing the\",\n \"events. Register a callback to receive asynchronous event notifications from\",\n \"None, or a set of DHCP options, eg: | [{'opt_name':\",\n \"auth_token raise NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause the given paused\",\n \"check the results. :param context: security context :param instance: nova.objects.instance.Instance\",\n \"volumes. Connector information is a dictionary representing the ip of\",\n \"the case of qcow2 backed snapshots, this would include the\",\n \"freeform) is up to the network implementation layer. For instance,\",\n \"in for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info):\",\n \"'opt_value': '192.168.127.12'} | ] \\\"\\\"\\\" return None def manage_image_cache(self, context,\",\n \"is the identifier chosen by Nova to represent the instance\",\n \"host. :param instance: The instance to lookup Returns True if\",\n \"pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of IPTables rules and\",\n \"with network ports. :param context: request context :param instance: nova.objects.instance.Instance\",\n \"parameter is an instance of nova.compute.service.Instance, and so the instance\",\n \"by Nova. At the nova.virt layer, instances do not have\",\n \"return {} def instance_on_disk(self, instance): \\\"\\\"\\\"Checks access of instance files\",\n \"instance to get the root device for. :param nova.objects.ImageMeta image_meta:\",\n \"instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network information :param\",\n \"VM with given 'metadata' and 'personalities'. This base class method\",\n \"which are no longer of interest. :param all_instances: nova.objects.instance.InstanceList \\\"\\\"\\\"\",\n \"to get resources from a driver that manages only one\",\n \"the License. You may obtain # a copy of the\",\n \"counters for each interface on each running VM. :param instances:\",\n \"attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: instance\",\n \"= { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) }\",\n \"NotImplementedError() def pause(self, instance): \\\"\\\"\\\"Pause the given instance. A paused\",\n \"nova.objects.Instance :param block_device_info: Optional; Can be used to filter out\",\n \"mode. :return: \\\"on_maintenance\\\" if switched to maintenance mode or \\\"off_maintenance\\\"\",\n \"override the config opt :returns: a ComputeDriver instance \\\"\\\"\\\" if\",\n \"hosts. Consider the combination with :func:`set_host_enabled`. :param str host: The\",\n \"in to methods that expect it. \\\"\\\"\\\" return True def\",\n \"guest has the same state as before the 'suspend'. :param\",\n \"should ensure that the reboot action has completed successfully even\",\n \"context :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self, context,\",\n \"isinstance(event, virtevent.Event): raise ValueError( _(\\\"Event must be an instance of\",\n \"\\\"\\\"\\\"Trigger crash dump mechanism on the given instance. Stalling instances\",\n \"{} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object):\",\n \"NotImplementedError() def cleanup_host(self, host): \\\"\\\"\\\"Clean up anything that is necessary\",\n \"raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): \\\"\\\"\\\"Power off the\",\n \"\\\"\\\"\\\"Estimate the virtualization overhead required to build an instance of\",\n \"should be migrated. :param str dest: The IP address of\",\n \"raise NotImplementedError() def unpause(self, instance): \\\"\\\"\\\"Unpause the given paused instance.\",\n \"of the image of the instance. :param injected_files: User files\",\n \"raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of all the\",\n \"The metadata of the image of the instance. :param nova.network.model.NetworkInfo\",\n \"image_meta, root_bdm): \\\"\\\"\\\"Provide a default root device name for the\",\n \"\\\"\\\"\\\"Reboot the specified instance. After this is called successfully, the\",\n \"shutdown \\\"\\\"\\\" raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None):\",\n \"| {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'}, | {'opt_name': 'tftp-server', |\",\n \"migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def live_migration(self, context,\",\n \"Some drivers chose to cache images for instances on disk.\",\n \"driver :param fs_type: the file system type to be checked,\",\n \"into maintenance mode. If `False`, leave the maintenance mode. :return:\",\n \"ComputeDriver instance \\\"\\\"\\\" if not compute_driver: compute_driver = CONF.compute_driver if\",\n \"poll_rebooting_instances(self, timeout, instances): \\\"\\\"\\\"Perform a reboot on all given 'instances'.\",\n \"oslo_log import log as logging from oslo_utils import importutils import\",\n \"calculations are desired. :param instance_info: Instance/flavor to calculate overhead for.\",\n \"is raised. :param context: request context :param instance: nova.objects.instance.Instance to\",\n \"is an opportunity to do management of that cache which\",\n \"This method is an opportunity to do management of that\",\n \"instance, network_info): \\\"\\\"\\\"Stop filtering instance.\\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \"info for a serial console. :param context: security context :param\",\n \"the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param\",\n \"able to complete the snapshot. For example, in the case\",\n \"freq}, where kern and user indicate the cumulative CPU time\",\n \"The soft-deleted instance which should be restored from the soft-deleted\",\n \"\\\"disabled\\\". :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"function should use the data there to guide the creation\",\n \"host to accept new instances. :param bool enabled: If this\",\n \"\\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\", str(event)) return if\",\n \"interface. :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"rd indicates read, wr indicates write, req is the total\",\n \"PROJNET, PROJMASK. but libvirt migrates those value through migrateToURI(), so\",\n \"def pause(self, instance): \\\"\\\"\\\"Pause the given instance. A paused instance\",\n \"specified suspended instance. The suspended instance gets resumed and will\",\n \"event callback registered by the compute manager to dispatch the\",\n \"currently known host CPU stats. :returns: a dict containing the\",\n \"context, instance, network_info, block_migration=False, block_device_info=None): \\\"\\\"\\\"Post operation of live migration\",\n \"of the older interface :func:`get_diagnostics` :rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError()\",\n \"instance. :param instance: nova.objects.instance.Instance :returns: True if the instance was\",\n \"A soft-deleted instance doesn't allocate any resources anymore, but is\",\n \"expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception occurs.\",\n \":param network_info: instance network information :param block_device_info: instance block device\",\n \"take a VirtAPI object as their first object and this\",\n \"requires legacy block device info. Tell the caller whether we\",\n \"info for a vnc console. :param context: security context :param\",\n \"\\\"\\\"\\\" raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True,\",\n \"accept further instances, return \\\"enabled\\\", if further instances shouldn't be\",\n \"def default_root_device_name(self, instance, image_meta, root_bdm): \\\"\\\"\\\"Provide a default root device\",\n \"the given instance. Stalling instances can be triggered to dump\",\n \"context, instance): \\\"\\\"\\\"Get host ID to associate with network ports.\",\n \":param context: request context :param instance: nova.objects.instance.Instance that the network\",\n \"\\\"\\\"\\\"Clean up anything that is necessary for the driver gracefully\",\n \"mapping.\\\"\\\"\\\" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): \\\"\\\"\\\"Get the\",\n \"actual networks (or permitting them to be freeform) is up\",\n \"the instance; the third is the contents of the file,\",\n \"asynchronous event notifications from hypervisors. The callback will be invoked\",\n \"attach. :raise nova.exception.NovaException: If the attach fails. :return: None \\\"\\\"\\\"\",\n \"up the stack. Most virtualization platforms will also have their\",\n \"the configured timeout. :return: None \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"and internal Nova terminology), by which we mean 'running virtual\",\n \"have. allocate_for_instance will take this into consideration when provisioning networking\",\n \"instance: instance object that was being migrated :param network_info: instance\",\n \"data: result of check_instance_shared_storage_local \\\"\\\"\\\" pass def check_can_live_migrate_destination(self, context, instance,\",\n \"virtualization platform. Once this successfully completes, the instance should be\",\n \"thread for this method since migration should not be started\",\n \":param network_info: instance network information :param recreate: True if the\",\n \":rtype: nova.virt.diagnostics.Diagnostics \\\"\\\"\\\" raise NotImplementedError() def get_all_bw_counters(self, instances): \\\"\\\"\\\"Return bandwidth\",\n \"context, data): \\\"\\\"\\\"Check if instance files located on shared storage.\",\n \"to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The instance which\",\n \"that has the volume attached :param uuid volume_id: Volume to\",\n \"in for access to auth_token raise NotImplementedError() def plug_vifs(self, instance,\",\n \"instance files on the host. :param instance: nova.objects.instance.Instance to lookup\",\n \"list of nova.objects.instance.Instance objects that have been in rebooting state\",\n \"If the detach fails. :return: None \\\"\\\"\\\" raise NotImplementedError() def\",\n \"to the given size (in Gigabyte) of `resize_to`. :return: None\",\n \"context, dest_check_data): \\\"\\\"\\\"Do required cleanup on dest host after check_can_live_migrate\",\n \"security context :param dest_check_data: result of check_can_live_migrate_destination \\\"\\\"\\\" raise NotImplementedError()\",\n \"\\\"\\\"\\\"Cleans up any resources left after an interrupted snapshot. :param\",\n \"is_bootable): \\\"\\\"\\\"Set the ability to power on/off an instance. :param\",\n \"check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object\",\n \"License for the specific language governing permissions and limitations #\",\n \"be attached to the instance. :param preserve_ephemeral: True if the\",\n \"that expect it. \\\"\\\"\\\" return True def volume_snapshot_create(self, context, instance,\",\n \"DB layer. :param network_info: instance network information :param block_device_info: Information\",\n \"at source. :param context: security context :param instance: instance object\",\n \"migrate an instance, filtering rules to hypervisors and firewalls are\",\n \"accept new instances. :return: If the host can accept further\",\n \"The chosen device name. \\\"\\\"\\\" raise NotImplementedError() def is_supported_fs_format(self, fs_type):\",\n \"attached to the instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info,\",\n \"instance. :param nova.context.RequestContext context: The context for the rescue. :param\",\n \"NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information. This method is\",\n \"resume. :param nova.objects.instance.Instance instance: The suspended instance to resume. :param\",\n \"virtevent.Event): raise ValueError( _(\\\"Event must be an instance of nova.virt.event.Event\\\"))\",\n \"\\\"\\\"\\\" raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True,\",\n \"migrate_data=None): \\\"\\\"\\\"Destroy the specified instance from the Hypervisor. If the\",\n \"`host` :param str host: The name of the host to\",\n \"which returns None. This is called during spawn_instance by the\",\n \"migration. This checks if the live migration can succeed, based\",\n \"network_info): \\\"\\\"\\\"Unplug VIFs from networks at source. :param context: security\",\n \"the number of requests held up due to a full\",\n \"'running virtual machine' (XenAPI terminology) or domain (Xen or libvirt\",\n \"def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh security group rules Gets called when\",\n \"return driver_registry def get_block_device_info(instance, block_device_mapping): \\\"\\\"\\\"Converts block device mappings for\",\n \"the missing device names in the block device mapping.\\\"\\\"\\\" raise\",\n \"NotImplementedError(_(\\\"Hypervisor driver does not support \\\" \\\"post_live_migration_at_source method\\\")) def post_live_migration_at_destination(self,\",\n \"of DriverVolumeBlockDevice or any of it's more specialized subclasses. \\\"\\\"\\\"\",\n \"instance gets resumed and will use CPU cycles and memory\",\n \"of all instances to other hosts. Consider the combination with\",\n \"image of the instance. \\\"\\\"\\\" raise NotImplementedError() def network_binding_host_id(self, context,\",\n \"drivers are encouraged to override this method with something more\",\n \"block_migration: if true, migrate VM disk. :param migrate_data: a LiveMigrateData\",\n \"the instance is not found (for example if networking failed),\",\n \"2015-08-18 # The compute manager doesn't use this interface, which\",\n \"`True`, the host will accept new instances. If it is\",\n \"DriverEphemeralBlockDevice instances - swap: An instance of DriverSwapBlockDevice or None\",\n \"nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check\",\n \"should be powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def\",\n \"instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination node after\",\n \"action to this is :func:`detach_interface`. :param nova.objects.instance.Instance instance: The instance\",\n \"a member of or if the group gains or loses\",\n \":param dst_compute_info: Info about the receiving machine :param block_migration: if\",\n \"in that case. :param context: security context :param instance: Instance\",\n \"raise NotImplementedError() def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set the ability to\",\n \"context :param instance: nova.objects.instance.Instance This function should use the data\",\n \"'instances'. Reboots the given `instances` which are longer in the\",\n \"instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result\",\n \"Nova to represent the instance further up the stack. This\",\n \"override this method with something more efficient. \\\"\\\"\\\" return len(self.list_instances())\",\n \"CONF = nova.conf.CONF LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs):\",\n \"the dom0 \\\"\\\"\\\" # TODO(Vek): Need to pass context in\",\n \":param image_id: Reference to a pre-created image that will hold\",\n \"\\\"\\\"\\\"Soft delete the specified instance. A soft-deleted instance doesn't allocate\",\n \"the host. :param instance: The instance to lookup Returns True\",\n \"that the hypervisor knows about. .. note:: This implementation works\",\n \"that the format of the return value is specific to\",\n \"def macs_for_instance(self, instance): \\\"\\\"\\\"What MAC addresses must this instance have?\",\n \"ensure_filtering_rules_for_instance(self, instance, network_info): \\\"\\\"\\\"Setting up filtering rules and waiting for\",\n \"log as logging from oslo_utils import importutils import nova.conf from\",\n \"Load the compute driver module specified by the compute_driver configuration\",\n \":param preserve_ephemeral: True if the default ephemeral storage partition must\",\n \"local image cache. Some drivers chose to cache images for\",\n \"the driver to function, including catching up with currently running\",\n \"\\\"\\\"\\\"Initialize anything that is necessary for the driver to function,\",\n \"the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info:\",\n \"for nova to be able to attach to the volume.\",\n \"retry_interval=0): \\\"\\\"\\\"Transfers the disk of a running instance in multiple\",\n \"the shutdown + spin up steps. :param context: security context\",\n \"openflow switches, fixed MAC addresses can still be virtualized onto\",\n \"VLANs etc, but regular switches require pre-configured MAC->network mappings that\",\n \"disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): \\\"\\\"\\\"Completes a resize/migration. :param\",\n \"API call. In the case of doing a snapshot, it\",\n \"def init_host(self, host): \\\"\\\"\\\"Initialize anything that is necessary for the\",\n \":param recreate: True if the instance is being recreated on\",\n \":param context: security context :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise\",\n \"name :param bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj:\",\n \"for it to shutdown. :return: A list of disk information\",\n \"used to filter out devices which are actually volumes. :return:\",\n \"manager. Invokes the event callback registered by the compute manager\",\n \"class method shuts down the VM, detaches all block devices,\",\n \"from the system and remakes the VM with given 'metadata'\",\n \"of nova uuid => dict of usage info \\\"\\\"\\\" return\",\n \"structure used by nova.compute to store details regarding an instance,\",\n \"that contract \\\"\\\"\\\" import sys from oslo_log import log as\",\n \"below method must be called. - setup_basic_filtering (for nova-basic, etc.)\",\n \"attached :param uuid volume_id: Volume to be snapshotted :param create_info:\",\n \"or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def\",\n \"it gets resized to the given size (in Gigabyte) of\",\n \"size (in Gigabyte) of `resize_to`. :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"is the contents of the file, also base64-encoded. NOTE(russellb) This\",\n \"is live migrating \\\"\\\"\\\" raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance,\",\n \"} swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def\",\n \"def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup on host after check_instance_shared_storage\",\n \"host, depending on the underlying hypervisor technology. A \\\"stronger\\\" version\",\n \"the given `instances` which are longer in the rebooting state\",\n \"= callback def emit_event(self, event): \\\"\\\"\\\"Dispatches an event to the\",\n \"after a change to security groups. All security groups and\",\n \"This method allows drivers to return a set of MAC\",\n \"device. device_name does not need to be set, and should\",\n \"Returns True if files of an instance with the supplied\",\n \"encryption=None): \\\"\\\"\\\"Detach the disk attached to the instance.\\\"\\\"\\\" raise NotImplementedError()\",\n \"security context :param instance: nova.objects.instance.Instance object :param block_device_info: instance block\",\n \"which contains information about the VIFs to plug. :return: None\",\n \"hotplug to add a network interface to a running instance.\",\n \"the return value is specific to the Neutron client API.\",\n \"context, data): \\\"\\\"\\\"Do cleanup on host after check_instance_shared_storage calls :param\",\n \"registered by the compute manager to dispatch the event. This\",\n \"instance): \\\"\\\"\\\"Get DHCP options for this instance. Some hypervisors (such\",\n \"\\\"\\\"\\\"Provide a default root device name for the driver. :param\",\n \"the specified instance. A suspended instance doesn't use CPU cycles\",\n \"raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None):\",\n \"to log a warning in that case. :param context: security\",\n \"mountpoint, encryption=None): \\\"\\\"\\\"Detach the disk attached to the instance.\\\"\\\"\\\" raise\",\n \"A list of nova.objects.instance.Instance objects that have been in rebooting\",\n \"host since the last boot. :rtype: str \\\"\\\"\\\" # TODO(Vek):\",\n \"cases in which the underlying domain/vm is paused or halted/stopped.\",\n \":return: None \\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm):\",\n \"compute manager. :return: None, or a set of MAC ids\",\n \"object that is migrated :param network_info: instance network information :param\",\n \"nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with\",\n \"context, instance): \\\"\\\"\\\"Get connection info for a spice console. :param\",\n \"for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info:\",\n \"instance: nova.objects.instance.Instance to lookup Returns True if files of an\",\n \"method is unused. Note that this function takes an instance\",\n \"by the compute service. This method is for multi compute-nodes\",\n \"config opt :returns: a ComputeDriver instance \\\"\\\"\\\" if not compute_driver:\",\n \":param disk_over_commit: if true, allow disk over commit :returns: a\",\n \"= CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute driver option required, but\",\n \"# WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"layer, instances do not have human-readable names at all --\",\n \"\\\"\\\"\\\"Applies a diff to the instance metadata. This is an\",\n \"instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached to a specified instance.\",\n \"the source host to check the results. :param context: security\",\n \"\\\"\\\"\\\"Do cleanup on host after check_instance_shared_storage calls :param context: security\",\n \"TFTP service. This requires passing the appropriate options out to\",\n \"must only be invoked from a green thread. \\\"\\\"\\\" if\",\n \"to this is :func:`remove_from_aggregate` :param nova.context.RequestContext context: The security context.\",\n \"each running VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def\",\n \"firewall rules can be set faster). Concretely, the below method\",\n \"The instance which should be unpaused. :return: None \\\"\\\"\\\" #\",\n \"interface removed. :param nova.network.model.NetworkInfo vif: The object which has the\",\n \"specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def trigger_crash_dump(self,\",\n \"nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of\",\n \"migration can succeed, based on the results from check_can_live_migrate_destination. :param\",\n \"completed successfully even in cases in which the underlying domain/vm\",\n \"specified security group. An error should be raised if the\",\n \"the specified soft-deleted instance. The restored instance will be automatically\",\n \"to power_state.RUNNING. The virtualization platform should ensure that the reboot\",\n \"which is used to publish changes to the instance's metadata\",\n \"block_device_info = block_device_info or {} return block_device_info.get('swap') or {'device_name': None,\",\n \"plug. :return: None \\\"\\\"\\\" # TODO(Vek): Need to pass context\",\n \"raise NotImplementedError(_(\\\"Hypervisor driver does not support \\\" \\\"post_live_migration_at_source method\\\")) def\",\n \"the compute manager. :return: None, or a set of MAC\",\n \"instance with the supplied ID accessible on the host, False\",\n \"which the file is to be written on the instance;\",\n \"aggregate, host, set_error=True): \\\"\\\"\\\"Undo for Resource Pools.\\\"\\\"\\\" raise NotImplementedError() def\",\n \"instance.\\\"\\\"\\\" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to):\",\n \"\\\"\\\"\\\"Use hotunplug to remove a network interface from a running\",\n \"if an instance with the supplied ID exists on the\",\n \"caller want to get resources from a driver that manages\",\n \"is :func:`plug_vifs`. :param nova.objects.instance.Instance instance: The instance which gets VIFs\",\n \"dump mechanism on the given instance. Stalling instances can be\",\n \"the information about the interface to detach. :raise nova.exception.NovaException: If\",\n \"instance object reference :param network_info: instance network information \\\"\\\"\\\" raise\",\n \"If this fails, any partial instance should be completely cleaned\",\n \"\\\"\\\"\\\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name\",\n \"passing the appropriate options out to the DHCP service. Most\",\n \"compute host to an aggregate. The counter action to this\",\n \"a (potentially empty) list of DriverEphemeralBlockDevice instances - swap: An\",\n \"instance should be running (power_state.RUNNING). If this fails, any partial\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): \\\"\\\"\\\"Retrieve information about\",\n \":param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred\",\n \"for considering rebooting instances to be stuck. :param list instances:\",\n \"ComputeDriver(object): \\\"\\\"\\\"Base class for compute drivers. The interface to this\",\n \"layer, as a list. \\\"\\\"\\\" # TODO(Vek): Need to pass\",\n \":func:`pause`. Depending on the underlying hypervisor technology, the guest has\",\n \"about. .. note:: This implementation works for all drivers, but\",\n \"return False def quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce the specified\",\n \"be in the state that it was before this call\",\n \"CPU stat info, eg: | {'kernel': kern, | 'idle': idle,\",\n \"add to the given `aggregate`. :param dict kwargs: A free-form\",\n \"None, 'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name'] and\",\n \"used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host,\",\n \"- prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be called\",\n \"NotImplementedError() def restore(self, instance): \\\"\\\"\\\"Restore the specified soft-deleted instance. The\",\n \"NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified\",\n \"timeout (in seconds) for considering rebooting instances to be stuck.\",\n \":param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network information\",\n \"block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info\",\n \"for access to auth_token raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the\",\n \"of the host anymore. The state of the VM could\",\n \"anymore. The state of the VM could be stored in\",\n \"class. \\\"\\\"\\\" self._compute_event_callback = callback def emit_event(self, event): \\\"\\\"\\\"Dispatches an\",\n \":param network_info: instance network information :param nova.objects.ImageMeta image_meta: The metadata\",\n \"callback to receive asynchronous event notifications from hypervisors. The callback\",\n \"opportunity to do management of that cache which isn't directly\",\n \"reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the specified\",\n \"disk_info: the newly transferred disk information :param network_info: instance network\",\n \"manager. :return: None, or a set of MAC ids (e.g.\",\n \"nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def\",\n \"group. An error should be raised if the operation cannot\",\n \"called during spawn_instance by the compute manager. :return: None, or\",\n \":func:`resume`. :param nova.context.RequestContext context: The context for the suspend. :param\",\n \"CONF.compute_driver if not compute_driver: LOG.error(_LE(\\\"Compute driver option required, but not\",\n \"override if per-instance overhead calculations are desired. :param instance_info: Instance/flavor\",\n \"counter action to this is :func:`volume_snapshot_delete` :param nova.context.RequestContext context: The\",\n \"Once this successfully completes, the instance should be running (power_state.RUNNING).\",\n \"initiator and the hostname of the machine as follows:: {\",\n \"\\\"\\\"\\\"Destroy the specified instance from the Hypervisor. If the instance\",\n \"get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance resource usage. :returns: dict of\",\n \"is being recreated on a new hypervisor - all the\",\n \"the snapshot :param uuid snapshot_id: The snapshot to delete. :param\",\n \":param instance: nova.objects.instance.Instance to be quiesced :param nova.objects.ImageMeta image_meta: The\",\n \"for the instance. Mapping of MAC addresses to actual networks\",\n \"An instance of DriverSwapBlockDevice or None - block_device_mapping: a (potentially\",\n \"hypervisors can use the default implementation which returns None. This\",\n \"the VM with given 'metadata' and 'personalities'. This base class\",\n \"instance of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self, context, instance):\",\n \"below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]\\\" \\\"\\\"\\\"\",\n \"since it defines PROJNET, PROJMASK. but libvirt migrates those value\",\n \"do not have human-readable names at all -- such things\",\n \"nova.objects.instance.InstanceList \\\"\\\"\\\" raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage\",\n \"to uniquely identify a VM or domain. These IDs must\",\n \"made, bytes is the total number of bytes transferred, and\",\n \"in the format of a dict containing the following keys:\",\n \"from hypervisors. The callback will be invoked with a single\",\n \"can be removed from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"instance: The instance which should be paused. :return: None \\\"\\\"\\\"\",\n \"context, aggregate, host, **kwargs): \\\"\\\"\\\"Add a compute host to an\",\n \"for block migration :param disk_over_commit: if true, allow disk over\",\n \"for access to auth_token raise NotImplementedError() def suspend(self, context, instance):\",\n \"image cache. Some drivers chose to cache images for instances\",\n \"2011 <NAME> # All Rights Reserved. # # Licensed under\",\n \"instance): \\\"\\\"\\\"Get host ID to associate with network ports. :param\",\n \"security context. :param nova.objects.aggregate.Aggregate aggregate: The aggregate which should remove\",\n \"nova.objects.instance.Instance \\\"\\\"\\\" raise NotImplementedError() def get_vnc_console(self, context, instance): \\\"\\\"\\\"Get connection\",\n \"and apply the rules now.\\\"\\\"\\\" pass def unfilter_instance(self, instance, network_info):\",\n \"be able to attach to the volume. This is the\",\n \"set of MAC addresses, which will be supplied to the\",\n \"\\\"AS IS\\\" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY\",\n \"'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '192.168.127.12'},\",\n \"live_migration_abort(self, instance): \\\"\\\"\\\"Abort an in-progress live migration. :param instance: instance\",\n \"= virtapi self._compute_event_callback = None def init_host(self, host): \\\"\\\"\\\"Initialize anything\",\n \"block-device-mappings to use for rebuild :param detach_block_devices: function to detach\",\n \"len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks existence of an instance on\",\n \"The prime example is to clean the cache and remove\",\n \"mechanism on the given instance. Stalling instances can be triggered\",\n \"removed once it can be removed from nova.compute.manager. \\\"\\\"\\\" #\",\n \"maintenance mode or \\\"off_maintenance\\\" if maintenance mode got left. :rtype:\",\n \"contains the uptime of this host since the last boot.\",\n \"instances to be stuck. :param list instances: A list of\",\n \"and will be removed once it can be removed from\",\n \"to. :param int resize_to: If the new volume is larger\",\n \"(potentially empty) list of DriverVolumeBlockDevice or any of it's more\",\n \"live migration at destination host. :param context: security context :param\",\n \"def list_instance_uuids(self): \\\"\\\"\\\"Return the UUIDS of all the instances known\",\n \"dst_compute_info, block_migration=False, disk_over_commit=False): \\\"\\\"\\\"Check if it is possible to execute\",\n \"to the instance, then this method should not be implemented.\",\n \"the instance was deleted from disk, False otherwise. \\\"\\\"\\\" return\",\n \"timeout), NovaException is raised. :param context: request context :param instance:\",\n \"no means of publishing the instance metadata to the instance,\",\n \"instance from the Hypervisor. If the instance is not found\",\n \"data about the given instance. :param nova.objects.instance.Instance instance: The instance\",\n \"host again. The counter action for 'unpause' is :func:`pause`. Depending\",\n \"in JSON format. :rtype: str \\\"\\\"\\\" raise NotImplementedError() def snapshot(self,\",\n \"be powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError() def pause(self,\",\n \"an instance of console.type.ConsoleVNC \\\"\\\"\\\" raise NotImplementedError() def get_spice_console(self, context,\",\n \":param nova.objects.instance.Instance instance: The instance which should be paused. :return:\",\n \"attached to a specified instance. The counter action to this\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_host_ip_addr(self): \\\"\\\"\\\"Retrieves the IP address of\",\n \"str dest: The IP address of the destination host. :param\",\n \"context: the context for the migration/resize :param migration: the migrate/resize\",\n \"the ip of the machine that will be making the\",\n \"metadata of the image of the instance. :param nova.objects.BlockDeviceMapping root_bdm:\",\n \"defines PROJNET, PROJMASK. but libvirt migrates those value through migrateToURI(),\",\n \"to the given `instance`. :param dict old_connection_info: The volume for\",\n \"the total number of bytes transferred, and errs is the\",\n \"raised. When it fails to quiesce by other errors (e.g.\",\n \"unquiesce(self, context, instance, image_meta): \\\"\\\"\\\"Unquiesce the specified instance after snapshots.\",\n \"host CPU stats. :returns: a dict containing the CPU stat\",\n \"virtapi self._compute_event_callback = None def init_host(self, host): \\\"\\\"\\\"Initialize anything that\",\n \"interface, which seems odd # since the manager should be\",\n \"the nova.virt.event.Event class. \\\"\\\"\\\" self._compute_event_callback = callback def emit_event(self, event):\",\n \"info about the requested block device. device_name does not need\",\n \"context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove a compute host from an\",\n \"time (nanoseconds), since the host is booting up; freq indicates\",\n \":param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \\\"\\\"\\\" raise\",\n \"console.type.ConsoleRDP \\\"\\\"\\\" raise NotImplementedError() def get_serial_console(self, context, instance): \\\"\\\"\\\"Get connection\",\n \"The paused instance gets unpaused and will use CPU cycles\",\n \"from a running instance. The counter action to this is\",\n \"things are only known higher up the stack. Most virtualization\",\n \"of an instance on the host. :param instance: The instance\",\n \"fails, any partial instance should be completely cleaned up, and\",\n \"get_num_instances(self): \\\"\\\"\\\"Return the total number of virtual machines. Return the\",\n \"\\\"\\\"\\\"Restore the specified soft-deleted instance. The restored instance will be\",\n \"default root device name for the driver. :param nova.objects.instance.Instance instance:\",\n \"rules.\\\"\\\"\\\" pass def filter_defer_apply_off(self): \\\"\\\"\\\"Turn off deferral of IPTables rules\",\n \"ID accessible on the host, False otherwise. .. note:: Used\",\n \"appropriate). :return: None \\\"\\\"\\\" raise NotImplementedError() def default_root_device_name(self, instance, image_meta,\",\n \"NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup on host after\",\n \"in for access to auth_token raise NotImplementedError() def unplug_vifs(self, instance,\",\n \"describing resources \\\"\\\"\\\" raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info,\",\n \"raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): \\\"\\\"\\\"Undo\",\n \"the hypervisor knows about. .. note:: This implementation works for\",\n \"use the default implementation which returns None. Hypervisors with MAC\",\n \"def change_instance_metadata(self, context, instance, diff): \\\"\\\"\\\"Applies a diff to the\",\n \"of the VM could be stored in the memory or\",\n \"device name of the root disk - ephemerals: a (potentially\",\n \"virt layer. return False def quiesce(self, context, instance, image_meta): \\\"\\\"\\\"Quiesce\",\n \"a driver supports multi compute-nodes, this method returns a list\",\n \"to lookup Returns True if an instance with the supplied\",\n \"of MAC addresses that the instance is to have. allocate_for_instance\",\n \"is up to that call to ensure that all assigned\",\n \"return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): \\\"\\\"\\\"Get information about instance\",\n \"access to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): \\\"\\\"\\\"Set\",\n \"\\\"\\\"\\\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Add a\",\n \"host and allocate storage space this way. A \\\"softer\\\" way\",\n \"under the License. \\\"\\\"\\\" Driver base-classes: (Beginning of) the contract\",\n \"a set of MAC addresses, which will be supplied to\",\n \"swap and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info\",\n \"'iowait': wait, | 'frequency': freq}, where kern and user indicate\",\n \"to execute live migration. This runs checks on the destination\",\n \"the snapshot. \\\"\\\"\\\" raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id,\",\n \":param migration: the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized\",\n \"which should be restored from the soft-deleted data. :return: None\",\n \"for a vnc console. :param context: security context :param instance:\",\n \"instance, filtering rules to hypervisors and firewalls are inevitable on\",\n \"aggregate, host, **kwargs): \\\"\\\"\\\"Remove a compute host from an aggregate.\",\n \"allows drivers to return a set of MAC addresses that\",\n \"On start, it triggers the migration of all instances to\",\n \"a dictionary representing the ip of the machine that will\",\n \"in details, depends on the configuration of it. :param nova.objects.instance.Instance\",\n \"the currently known host CPU stats. :returns: a dict containing\",\n \"\\\"\\\"\\\" raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password):\",\n \"file, also base64-encoded. NOTE(russellb) This method is deprecated and will\",\n \"def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): \\\"\\\"\\\"Reboot the\",\n \"system # type and implement this function at their #\",\n \"NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage info for volumes\",\n \"refresh=False): \\\"\\\"\\\"Returns nodenames of all nodes managed by the compute\",\n \"to be snapshotted :param create_info: The data needed for nova\",\n \"disk after the snapshot operation has completed. There may be\",\n \"that have been in rebooting state longer than the configured\",\n \"then spins up the new VM afterwards. It may be\",\n \"raise NotImplementedError() def get_available_nodes(self, refresh=False): \\\"\\\"\\\"Returns nodenames of all nodes\",\n \"\\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): \\\"\\\"\\\"Do cleanup on\",\n \"context in for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self):\",\n \"UUIDS of all the instances known to the virtualization layer,\",\n \"for access to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance,\",\n \"device for. :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"this method since migration should not be started when setting-up\",\n \"For instance, with openflow switches, fixed MAC addresses can still\",\n \"nova.objects.BlockDeviceMapping instance with all info about the requested block device.\",\n \"raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): \\\"\\\"\\\"Use hotplug to\",\n \"nova.virt layer, instances do not have human-readable names at all\",\n \"should return a set of MAC addresses, which will be\",\n \"nodename in self.get_available_nodes(): return True # Refresh and check again.\",\n \"unpaused and will use CPU cycles of the host again.\",\n \"or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap and\",\n \"all existing data from the system and remakes the VM\",\n \"stored in the memory or storage space of the host,\",\n \":param bool enabled: If this is `True`, the host will\",\n \"context, instance, image_id, update_task_state): \\\"\\\"\\\"Snapshots the specified instance. :param context:\",\n \"the cleanup of old state is skipped. :param block_device_info: Information\",\n \"A suspended instance doesn't use CPU cycles or memory of\",\n \"information :param disk_info: instance disk information :param migrate_data: a LiveMigrateData\",\n \"by the compute manager, and it is up to that\",\n \"XenAPI) performance statistics can be retrieved directly in aggregate form,\",\n \"logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str\",\n \"overridden by hypervisors that need to - e.g. for optimisations,\",\n \"has completed. There may be other data included as well\",\n \"host. :param str action: The action the host should perform.\",\n \"detached from the given `instance`. :param dict new_connection_info: The volume\",\n \"instance. :param bdms: block-device-mappings to use for rebuild :param detach_block_devices:\",\n \"list. \\\"\\\"\\\" raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files,\",\n \"\\\"\\\"\\\" # NOTE(jichenjc): Return False here so that every hypervisor\",\n \"if the group gains or loses a rule. \\\"\\\"\\\" raise\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def detach_interface(self, instance, vif): \\\"\\\"\\\"Use hotunplug\",\n \"the network implementation layer. For instance, with openflow switches, fixed\",\n \"domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info:\",\n \"as this layer is concerned, 'instance ID' and 'instance name'\",\n \"auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): # NOTE(markus_z): 2015-08-18\",\n \"instance): \\\"\\\"\\\"Trigger crash dump mechanism on the given instance. Stalling\",\n \"\\\"\\\"\\\"Converts block device mappings for an instance to driver format.\",\n \":param context: security context :param instance: instance object reference :param\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"\\\"\\\"\\\" raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): \\\"\\\"\\\"Check if instance\",\n \"rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): \\\"\\\"\\\"Clean up destination\",\n \"that. :param nova.objects.instance.Instance instance: The instance to soft-delete. :return: None\",\n \"block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to\",\n \"metadata. This is an optional driver method which is used\",\n \"snapshotted :param create_info: The data needed for nova to be\",\n \"required to build an instance of the given flavor. Defaults\",\n \"in for access to auth_token raise NotImplementedError() def host_power_action(self, action):\",\n \"compute-nodes, this method returns a list of nodenames managed by\",\n \"the specified instance from the Hypervisor. If the instance is\",\n \"instances - swap: An instance of DriverSwapBlockDevice or None -\",\n \"network_info: instance network information :param disk_info: instance disk information :param\",\n \"\\\"\\\"\\\"Does the driver want networks deallocated on reschedule?\\\"\\\"\\\" return False\",\n \"default implementation which returns None. This is called during spawn_instance\",\n \"raise NotImplementedError() def get_mks_console(self, context, instance): \\\"\\\"\\\"Get connection info for\",\n \"require that instances boot from the network, and manage their\",\n \"the instance for attaching to volumes. Connector information is a\",\n \":param block_device_info: result of _get_instance_block_device_info :returns: a LiveMigrateData object \\\"\\\"\\\"\",\n \"state as before the 'pause'. :param nova.objects.instance.Instance instance: The instance\",\n \"for rescue. \\\"\\\"\\\" raise NotImplementedError() def set_bootable(self, instance, is_bootable): \\\"\\\"\\\"Set\",\n \"completely cleaned up, and the virtualization platform should be in\",\n \"metal) cannot do freeform virtualization of MAC addresses. This method\",\n \":returns an instance of console.type.ConsoleSerial \\\"\\\"\\\" raise NotImplementedError() def get_mks_console(self,\",\n \"in for access to auth_token raise NotImplementedError() def list_instance_uuids(self): \\\"\\\"\\\"Return\",\n \"None \\\"\\\"\\\" raise NotImplementedError() def soft_delete(self, instance): \\\"\\\"\\\"Soft delete the\",\n \"instance, network_info, block_device_info=None): \\\"\\\"\\\"resume the specified suspended instance. The suspended\",\n \"context: security context :param instance: instance object that was being\",\n \"ID. \\\"\\\"\\\" raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): \\\"\\\"\\\"Does the driver\",\n \"idle, | 'user': user, | 'iowait': wait, | 'frequency': freq},\",\n \"this connection gets attached to the given 'instance'. :param nova.objects.instance.Instance\",\n \"virtual interfaces (VIFs) from networks. The counter action is :func:`plug_vifs`.\",\n \"the given instance. These are returned as [rd_req, rd_bytes, wr_req,\",\n \"self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info):\",\n \"and as part of a periodic task that records the\",\n \"context: The security context. :param nova.objects.instance.Instance instance: The instance that\",\n \"added to or removed from the security group the instance\",\n \"that is migrated. :param dest: destination host :param post_method: post\",\n \"for HA implementation and required for validation of access to\",\n \"should not be implemented. :param context: security context :param instance:\",\n \"nova.objects.instance.Instance instance: The instance to get the root device for.\",\n \"resume. :param nova.objects.ImageMeta image_meta: The metadata of the image of\",\n \"a good idea to log a warning in that case.\",\n \"instance network information :param reboot_type: Either a HARD or SOFT\",\n \"the host ID \\\"\\\"\\\" return instance.get('host') def load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load\",\n \"context: The context for the resume. :param nova.objects.instance.Instance instance: The\",\n \"counter action to this is :func:`attach_interface`. :param nova.objects.instance.Instance instance: The\",\n \"instance: nova.objects.instance.Instance that volume is requesting a device name :param\",\n \"a LiveMigrateData object (hypervisor-dependent) \\\"\\\"\\\" raise NotImplementedError() def cleanup_live_migration_destination_check(self, context,\",\n \"the results. :param context: security context :param instance: nova.objects.instance.Instance object\",\n \"the source VM. :param instance: nova.objects.instance.Instance \\\"\\\"\\\" # TODO(Vek): Need\",\n \"callback def emit_event(self, event): \\\"\\\"\\\"Dispatches an event to the compute\",\n \"power action :rtype: : str \\\"\\\"\\\" raise NotImplementedError() def host_maintenance_mode(self,\",\n \"platform should be in the state that it was before\",\n \"context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): \\\"\\\"\\\"Live migration of\",\n \"instance should be powered on, False otherwise \\\"\\\"\\\" raise NotImplementedError()\",\n \"NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None):\",\n \"shuts down or powers up the host. :param str action:\",\n \"Info about the sending machine :param dst_compute_info: Info about the\",\n \"instances): \\\"\\\"\\\"Return bandwidth usage counters for each interface on each\",\n \"to instance shared disk files \\\"\\\"\\\" return False def register_event_listener(self,\",\n \"False, \\\"supports_recreate\\\": False, \\\"supports_migrate_to_same_host\\\": False, \\\"supports_attach_interface\\\": False, \\\"supports_device_tagging\\\": False, }\",\n \"efficient. \\\"\\\"\\\" try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return\",\n \"in for access to auth_token raise NotImplementedError() def set_admin_password(self, instance,\",\n \"then this method should not be implemented. :param context: security\",\n \"the last boot. :rtype: str \\\"\\\"\\\" # TODO(Vek): Need to\",\n \"it can be removed from nova.compute.manager. \\\"\\\"\\\" # TODO(Vek): Need\",\n \"file Cinder expects to be used as the active disk\",\n \"raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): \\\"\\\"\\\"Return usage info for\",\n \":param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param\",\n \"the <PASSWORD> \\\"\\\"\\\" raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents):\",\n \"host. :param context: security context :instance: instance object that was\",\n \"access to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance,\",\n \"machines. Return the number of virtual machines that the hypervisor\",\n \"ephemeral storage partition must be preserved on rebuild \\\"\\\"\\\" raise\",\n \":param dict old_connection_info: The volume for this connection gets detached\",\n \"is :func:`volume_snapshot_create` :param nova.context.RequestContext context: The security context. :param nova.objects.instance.Instance\",\n \"instance. The counter action to this is :func:`detach_interface`. :param nova.objects.instance.Instance\",\n \"switched to maintenance mode or \\\"off_maintenance\\\" if maintenance mode got\",\n \":param context: security context :param instance: instance object that was\",\n \"is :func:`suspend`. Depending on the underlying hypervisor technology, the guest\",\n \"(not ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo object\",\n \"allocate storage space this way. A \\\"softer\\\" way of `suspend`\",\n \"being merged into (if appropriate). :return: None \\\"\\\"\\\" raise NotImplementedError()\",\n \"block_device_info=None): \\\"\\\"\\\"Power on the specified instance. :param instance: nova.objects.instance.Instance \\\"\\\"\\\"\",\n \"if not compute_driver: LOG.error(_LE(\\\"Compute driver option required, but not specified\\\"))\",\n \"of IPTables rules and apply the rules now.\\\"\\\"\\\" pass def\",\n \"the end. :param nova.objects.instance.Instance instance: The instance whose disk should\",\n \"connection gets attached to the given 'instance'. :param nova.objects.instance.Instance instance:\",\n \"called after a change to security groups. All security groups\",\n \"block devices to be attached to the instance. \\\"\\\"\\\" raise\",\n \"instance has an ID, which is the identifier chosen by\",\n \"other hosts. Consider the combination with :func:`set_host_enabled`. :param str host:\",\n \"an instance of nova.compute.service.Instance. This is the data structure used\",\n \"image_meta, injected_files, admin_password, network_info=None, block_device_info=None): \\\"\\\"\\\"Create a new instance/VM/domain on\",\n \"the underlying hypervisor technology, the guest has the same state\",\n \"specified instance. The counter action to this is :func:`volume_snapshot_delete` :param\",\n \"nova.objects.instance.InstanceList \\\"\\\"\\\" pass def add_to_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Add\",\n \"identity schemes, to uniquely identify a VM or domain. These\",\n \"of nodenames managed by the service. Otherwise, this method should\",\n \"\\\"\\\"\\\"Plug virtual interfaces (VIFs) into the given `instance` at instance\",\n \"migrate_data: a LiveMigrateData object \\\"\\\"\\\" raise NotImplementedError() def post_live_migration(self, context,\",\n \"\\\"\\\"\\\"Set the ability to power on/off an instance. :param instance:\",\n \"\\\"\\\"\\\"Unplug virtual interfaces (VIFs) from networks. The counter action is\",\n \"get resources from a driver that manages only one node\",\n \"be quiesced :param nova.objects.ImageMeta image_meta: The metadata of the image\",\n \"aggregate. The counter action to this is :func:`remove_from_aggregate` :param nova.context.RequestContext\",\n \"XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): \\\"\\\"\\\"Remove\",\n \"volume_snapshot_create(self, context, instance, volume_id, create_info): \\\"\\\"\\\"Snapshots volumes attached to a\",\n \"something more efficient. \\\"\\\"\\\" return len(self.list_instances()) def instance_exists(self, instance): \\\"\\\"\\\"Checks\",\n \"dom0 \\\"\\\"\\\" # TODO(Vek): Need to pass context in for\",\n \"raise NotImplementedError() def get_console_output(self, context, instance): \\\"\\\"\\\"Get console output for\",\n \"is to be written on the instance; the third is\",\n \"manager. Note that the format of the return value is\",\n \"a warning in that case. :param context: security context :param\",\n \"them to be freeform) is up to the network implementation\",\n \"load_compute_driver(virtapi, compute_driver=None): \\\"\\\"\\\"Load a compute driver module. Load the compute\",\n \"context: security context :param instance: nova.objects.instance.Instance object \\\"\\\"\\\" raise NotImplementedError()\",\n \"The instance which gets VIFs plugged. :param nova.network.model.NetworkInfo network_info: The\",\n \"encountered \\\"\\\"\\\" raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need\",\n \"dict() for driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=')\",\n \"underlying hypervisor technology, the guest has the same state as\",\n \":param nova.objects.flavor.Flavor flavor: The flavor of the instance whose disk\",\n \"context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice \\\"\\\"\\\"\",\n \"if the instance should be powered on, False otherwise \\\"\\\"\\\"\",\n \"True if files of an instance with the supplied ID\",\n \"block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping)\",\n \"from an aggregate. The counter action to this is :func:`add_to_aggregate`\",\n \"raise NotImplementedError() def get_num_instances(self): \\\"\\\"\\\"Return the total number of virtual\",\n \"doesn't allocate any resources anymore, but is still available as\",\n \"that has the volume attached. :param uuid volume_id: Attached volume\",\n \"finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info: instance network\",\n \"for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): \\\"\\\"\\\"Refresh\",\n \"CPU frequency (MHz). All values are long integers. \\\"\\\"\\\" raise\",\n \"LOG.debug(\\\"Discarding event %s\\\", str(event)) return if not isinstance(event, virtevent.Event): raise\",\n \"If this is `True`, the host will accept new instances.\",\n \"# since the manager should be the controlling thing here.\",\n \"onto any L2 domain, with arbitrary VLANs etc, but regular\",\n \"License. You may obtain # a copy of the License\",\n \"params \\\"\\\"\\\" raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None,\",\n \"system and remakes the VM with given 'metadata' and 'personalities'.\",\n \"get_console_output(self, context, instance): \\\"\\\"\\\"Get console output for an instance :param\",\n \"ANY KIND, either express or implied. See the # License\",\n \"cannot do freeform virtualization of MAC addresses. This method allows\",\n \"each interface on each running VM. :param instances: nova.objects.instance.InstanceList \\\"\\\"\\\"\",\n \"result of calling the Linux command `uptime` on this host.\",\n \":param nova.network.model.NetworkInfo network_info: The object which contains information about the\",\n \"apply the rules now.\\\"\\\"\\\" pass def unfilter_instance(self, instance, network_info): \\\"\\\"\\\"Stop\",\n \"instance where the volume for `old_connection_info` is attached to. :param\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_rdp_console(self, context, instance): \\\"\\\"\\\"Get connection info\",\n \"delete_instance_files(self, instance): \\\"\\\"\\\"Delete any lingering instance files for an instance.\",\n \"is actually proxied and needs to be held across the\",\n \"spawn_instance by the compute manager. Note that the format of\",\n \"a green thread. \\\"\\\"\\\" if not self._compute_event_callback: LOG.debug(\\\"Discarding event %s\\\",\",\n \"nova.objects.instance.Instance instance: The soft-deleted instance which should be restored from\",\n \"-- it's an internal ID chosen by Nova. At the\",\n \"This method is called when nova-compute launches, and as part\",\n \"ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances - swap:\",\n \"way of `suspend` is :func:`pause`. The counter action for `suspend`\",\n \"live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration to complete :param instance: Instance\",\n \"json strings with below format:: \\\"[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080'\",\n \"further instances shouldn't be scheduled to this host, return \\\"disabled\\\".\",\n \"nova.objects.instance.Instance :param image_id: Reference to a pre-created image that will\",\n \"object \\\"\\\"\\\" raise NotImplementedError() def live_migration_force_complete(self, instance): \\\"\\\"\\\"Force live migration\",\n \"to vms on a given host.- \\\"\\\"\\\" raise NotImplementedError() def\",\n \"\\\"\\\"\\\" raise NotImplementedError() def reset_network(self, instance): \\\"\\\"\\\"reset networking for specified\",\n \"\\\"\\\"\\\" raise NotImplementedError() def get_available_resource(self, nodename): \\\"\\\"\\\"Retrieve resource information. This\"\n]","isConversational":false}}},{"rowIdx":162,"cells":{"ngram":{"kind":"list like","value":["= 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId","self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId)","0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0","base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName","sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self):","from direct.gui.DirectGui import * from pandac.PandaModules import * from otp.otpbase","return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate()","= None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos =","__execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules","if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self,","if extraInfo: return str(extraInfo) else: return str(exception) except: exception =","chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos","eval: %s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except","% message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception","deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up')","self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text): if","self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self, posX, posY","printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0]","exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo)","globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal","isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message, globals(),","def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0,","self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None","self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def","= sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) except:","extraInfo: return str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0]","def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del","sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text:","self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self, posX,","return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get())","except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return","0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId =","%= len(self.history) def setPos(self, posX, posY = None, posZ =","= whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text']","OTPGlobals import sys from direct.gui.DirectGui import * from pandac.PandaModules import","self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else: if self.chatMgr.execChat:","text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if","self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text)","importExecNamespace(self): pass def addToHistory(self, text): self.history = [text] + self.history[:self.historySize","else: return str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1]","if self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text,","= 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return","getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self):","else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get())","+ self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex])","sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) except: exception","base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def","overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace =","import DirectObject from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui","None self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text,","otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self,","self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if","Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer","otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import * from","typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame","= sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) def","self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history = [text]","= None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName","text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if","message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import","base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text):","self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] =","self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def","self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer","Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName =","ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec:","self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex","= None self.toPlayer = 0 wantHistory = 0 if __dev__:","if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack()","= None, posZ = None): if posX and posY and","= None self.whisperAvatarId = None self.toPlayer = 0 wantHistory =","printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not","self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0","= 0 if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history',","self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos)","from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def","text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout)","self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show()","CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText):","0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up')","= None else: if self.chatMgr.execChat: if text[0] == '>': text","= chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0,","self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s'","self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' %","del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId =","self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos =","toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName","None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083,","from direct.showbase import DirectObject from otp.otpbase import OTPGlobals import sys","None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId","1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1","1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1","self.history = [text] + self.history[:self.historySize - 1] self.historyIndex = 0","self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer =","import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING","= Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName","try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message)","= 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %=","self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self):","0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId,","if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech |","CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def","return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient():","del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr","= self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory:","if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer =","base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return","print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace)","def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace:","self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self):","* from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None","if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None","= base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel %","getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self,","%= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %=","activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer = toPlayer","ChatInputNormal eval: %s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace))","message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception =","= None self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0]","pandac.PandaModules import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace","try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message)","self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('')","'>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text)","self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0","self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message):","base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex","% message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try:","= sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else:","extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu')","None self.toPlayer = 0 wantHistory = 0 if __dev__: wantHistory","ChatInputNormal exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return","from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import *","def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass","self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else: if","def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def","None, posZ = None): if posX and posY and posZ:","= base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10)","chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history =","+= 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -=","if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus']","self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex])","self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide()","self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if","- 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex +=","return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def","def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up')","self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex","self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del","not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message,","self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del","extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del","whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] =","== '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return","exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not","pass def addToHistory(self, text): self.history = [text] + self.history[:self.historySize -","%s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError:","False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu')","SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' %","= Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None","10) self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def","print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return str(eval(message, globals(),","__init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804)","import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace =","self.historyIndex %= len(self.history) def setPos(self, posX, posY = None, posZ","exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok'","1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self):","posZ = None): if posX and posY and posZ: self.chatFrame.setPos(posX,","wantHistory = 0 if __dev__: wantHistory = 1 self.wantHistory =","0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False","self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del","ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr = chatMgr","toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId:","= OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] =","1 self.historyIndex %= len(self.history) def setPos(self, posX, posY = None,","not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(),","OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1","self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId","None): if posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ)","import sys from direct.gui.DirectGui import * from pandac.PandaModules import *","self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus']","not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return","cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def","OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr","self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory)","wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = ['']","sys from direct.gui.DirectGui import * from pandac.PandaModules import * from","None self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0] ==","if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else:","pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient():","ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace)","return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName","checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return","self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex","extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception)","message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if","* from pandac.PandaModules import * from otp.otpbase import OTPLocalizer class","self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71)","self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos)","else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up',","self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory)","ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace()","class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr =","self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self,","posY = None, posZ = None): if posX and posY","0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId","whisperAvatarId = None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId","sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return","self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer","return str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0] extraInfo","[''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def","del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer = 0):","self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech","text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId =","self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size',","self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel","if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer:","0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None","import * from pandac.PandaModules import * from otp.otpbase import OTPLocalizer","import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr):","self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId:","return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy()","self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None","from pandac.PandaModules import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject):","= None): if posX and posY and posZ: self.chatFrame.setPos(posX, posY,","posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ) else: self.chatFrame.setPos(posX)","setPos(self, posX, posY = None, posZ = None): if posX","= 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName =","import OTPGlobals import sys from direct.gui.DirectGui import * from pandac.PandaModules","if posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ) else:","0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history)","= None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId =","0 if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory)","str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0] extraInfo =","self.whisperAvatarId = None self.toPlayer = 0 wantHistory = 0 if","= 0 wantHistory = 0 if __dev__: wantHistory = 1","len(self.history) def setPos(self, posX, posY = None, posZ = None):","self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0] == '>':","= [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return","self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer)","def checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('')","exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo","if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *',","posX, posY = None, posZ = None): if posX and","self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId =","%s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except:","= 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up')","= 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize","if extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('')","self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self,","self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide()","ExecNamespace = None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos","= 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def","-= 1 self.historyIndex %= len(self.history) def setPos(self, posX, posY =","self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0 elif","except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s'","globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval:","None else: if self.chatMgr.execChat: if text[0] == '>': text =","def activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer =","= toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if","return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def","self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName =","messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton","return 'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if","DirectObject from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import","self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if","self.whisperAvatarName = None self.whisperAvatarId = None else: if self.chatMgr.execChat: if","self.toPlayer = 0 wantHistory = 0 if __dev__: wantHistory =","self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme()","self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide():","str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING","self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def","self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {}","0 wantHistory = 0 if __dev__: wantHistory = 1 self.wantHistory","def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def","None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName =","self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else:","else: if self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:])","{} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if","return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not","wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex =","self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None,","direct.showbase import DirectObject from otp.otpbase import OTPGlobals import sys from","__dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history =","def setPos(self, posX, posY = None, posZ = None): if","direct.gui.DirectGui import * from pandac.PandaModules import * from otp.otpbase import","str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo:","self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up',","*', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal","self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self,","self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text):","def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history","self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0 wantHistory","def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if","base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat')","globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo =","isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return str(eval(message,","del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel","self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex","if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history","len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history)","def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del","del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer","addToHistory(self, text): self.history = [text] + self.history[:self.historySize - 1] self.historyIndex","'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo:","def importExecNamespace(self): pass def addToHistory(self, text): self.history = [text] +","str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self):","text): self.history = [text] + self.history[:self.historySize - 1] self.historyIndex =","def addToHistory(self, text): self.history = [text] + self.history[:self.historySize - 1]","chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0,","= {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try:","chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace","if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId","self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory:","% self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show()","def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from","del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self,","elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None","self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0","self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry","1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize =","str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self):","= [text] + self.history[:self.historySize - 1] self.historyIndex = 0 def","self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide()","0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer =","if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack()","= base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self, extraArgs):","delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton","return str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if","| CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self,","[text] + self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self):","= None self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId:","ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1]","None self.whisperAvatarId = None self.toPlayer = 0 wantHistory = 0"],"string":"[\n \"= 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId\",\n \"self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId)\",\n \"0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0\",\n \"base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName\",\n \"sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self):\",\n \"from direct.gui.DirectGui import * from pandac.PandaModules import * from otp.otpbase\",\n \"return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate()\",\n \"= None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos =\",\n \"__execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules\",\n \"if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self,\",\n \"if extraInfo: return str(extraInfo) else: return str(exception) except: exception =\",\n \"chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos\",\n \"eval: %s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except\",\n \"% message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception\",\n \"deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up')\",\n \"self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text): if\",\n \"self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self, posX, posY\",\n \"printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0]\",\n \"exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo)\",\n \"globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal\",\n \"isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message, globals(),\",\n \"def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0,\",\n \"self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None\",\n \"self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def\",\n \"= sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) except:\",\n \"extraInfo: return str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0]\",\n \"def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del\",\n \"sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text:\",\n \"self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self, posX,\",\n \"return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get())\",\n \"except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return\",\n \"0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId =\",\n \"%= len(self.history) def setPos(self, posX, posY = None, posZ =\",\n \"= whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text']\",\n \"OTPGlobals import sys from direct.gui.DirectGui import * from pandac.PandaModules import\",\n \"self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else: if self.chatMgr.execChat:\",\n \"text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if\",\n \"self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text)\",\n \"importExecNamespace(self): pass def addToHistory(self, text): self.history = [text] + self.history[:self.historySize\",\n \"else: return str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1]\",\n \"if self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text,\",\n \"= 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return\",\n \"getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self):\",\n \"else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get())\",\n \"+ self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex])\",\n \"sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) except: exception\",\n \"base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def\",\n \"overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace =\",\n \"import DirectObject from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui\",\n \"None self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text,\",\n \"otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self,\",\n \"self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if\",\n \"Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer\",\n \"otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import * from\",\n \"typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame\",\n \"= sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception) def\",\n \"self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history = [text]\",\n \"= None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName\",\n \"text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if\",\n \"message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import\",\n \"base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text):\",\n \"self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] =\",\n \"self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def\",\n \"self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer\",\n \"Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName =\",\n \"ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec:\",\n \"self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex\",\n \"= None self.toPlayer = 0 wantHistory = 0 if __dev__:\",\n \"if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack()\",\n \"= None, posZ = None): if posX and posY and\",\n \"= None self.whisperAvatarId = None self.toPlayer = 0 wantHistory =\",\n \"printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not\",\n \"self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0\",\n \"= 0 if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history',\",\n \"self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos)\",\n \"from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def\",\n \"text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout)\",\n \"self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show()\",\n \"CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText):\",\n \"0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up')\",\n \"= None else: if self.chatMgr.execChat: if text[0] == '>': text\",\n \"= chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0,\",\n \"self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s'\",\n \"self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' %\",\n \"del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId =\",\n \"self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos =\",\n \"toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName\",\n \"None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083,\",\n \"from direct.showbase import DirectObject from otp.otpbase import OTPGlobals import sys\",\n \"None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId\",\n \"1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1\",\n \"1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1\",\n \"self.history = [text] + self.history[:self.historySize - 1] self.historyIndex = 0\",\n \"self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer =\",\n \"import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING\",\n \"= Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName\",\n \"try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message)\",\n \"= 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %=\",\n \"self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self):\",\n \"0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId,\",\n \"if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech |\",\n \"CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def\",\n \"return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient():\",\n \"del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr\",\n \"= self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory:\",\n \"if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer =\",\n \"base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return\",\n \"print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace)\",\n \"def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace:\",\n \"self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self):\",\n \"* from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None\",\n \"if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None\",\n \"= base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel %\",\n \"getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def setPos(self,\",\n \"%= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %=\",\n \"activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer = toPlayer\",\n \"ChatInputNormal eval: %s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace))\",\n \"message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception =\",\n \"= None self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0]\",\n \"pandac.PandaModules import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace\",\n \"try: if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message)\",\n \"self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('')\",\n \"'>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return base.talkAssistant.sendOpenTalk(text)\",\n \"self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0\",\n \"self.wantHistory: self.addToHistory(text) return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message):\",\n \"base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex\",\n \"% message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try:\",\n \"= sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else:\",\n \"extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu')\",\n \"None self.toPlayer = 0 wantHistory = 0 if __dev__: wantHistory\",\n \"ChatInputNormal exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return\",\n \"from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import *\",\n \"def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass\",\n \"self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else: if\",\n \"def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def\",\n \"None, posZ = None): if posX and posY and posZ:\",\n \"= base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10)\",\n \"chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history =\",\n \"+= 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -=\",\n \"if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus']\",\n \"self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex])\",\n \"self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide()\",\n \"self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if\",\n \"- 1] self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex +=\",\n \"return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def\",\n \"def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up')\",\n \"self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex\",\n \"self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel del\",\n \"not isClient(): print('EXECWARNING ChatInputNormal exec: %s' % message) printStack() exec(message,\",\n \"self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del\",\n \"extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del\",\n \"whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] =\",\n \"== '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech | CFTimeout) return\",\n \"exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not\",\n \"pass def addToHistory(self, text): self.history = [text] + self.history[:self.historySize -\",\n \"%s' % message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError:\",\n \"False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu')\",\n \"SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s' %\",\n \"= Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None\",\n \"10) self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def\",\n \"print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return str(eval(message, globals(),\",\n \"__init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos = Vec3(-1.083, 0, 0.804)\",\n \"import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace =\",\n \"self.historyIndex %= len(self.history) def setPos(self, posX, posY = None, posZ\",\n \"exec: %s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok'\",\n \"1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def deactivate(self):\",\n \"posZ = None): if posX and posY and posZ: self.chatFrame.setPos(posX,\",\n \"wantHistory = 0 if __dev__: wantHistory = 1 self.wantHistory =\",\n \"0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False\",\n \"self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del\",\n \"ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr = chatMgr\",\n \"toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId:\",\n \"= OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] =\",\n \"1 self.historyIndex %= len(self.history) def setPos(self, posX, posY = None,\",\n \"not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(),\",\n \"OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1\",\n \"self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId\",\n \"None): if posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ)\",\n \"import sys from direct.gui.DirectGui import * from pandac.PandaModules import *\",\n \"self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus']\",\n \"not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return\",\n \"cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def\",\n \"OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr\",\n \"self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory)\",\n \"wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = ['']\",\n \"sys from direct.gui.DirectGui import * from pandac.PandaModules import * from\",\n \"None self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0] ==\",\n \"if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else:\",\n \"pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient():\",\n \"ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace)\",\n \"return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName\",\n \"checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return\",\n \"self.historyIndex += 1 self.historyIndex %= len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex\",\n \"extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return str(exception)\",\n \"message) printStack() return str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if\",\n \"* from pandac.PandaModules import * from otp.otpbase import OTPLocalizer class\",\n \"self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71)\",\n \"self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos)\",\n \"else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up',\",\n \"self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory)\",\n \"ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace()\",\n \"class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr): self.chatMgr =\",\n \"self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self,\",\n \"posY = None, posZ = None): if posX and posY\",\n \"0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId\",\n \"whisperAvatarId = None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId\",\n \"sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo: return str(extraInfo) else: return\",\n \"self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer\",\n \"return str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0] extraInfo\",\n \"[''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def\",\n \"del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer = 0):\",\n \"self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:]) base.localAvatar.setChatAbsolute(text, CFSpeech\",\n \"text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId =\",\n \"self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size',\",\n \"self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if self.whisperAvatarId: self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel\",\n \"if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer:\",\n \"0, 0.804) self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None\",\n \"import * from pandac.PandaModules import * from otp.otpbase import OTPLocalizer\",\n \"import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject): ExecNamespace = None def __init__(self, chatMgr):\",\n \"self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId:\",\n \"return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy()\",\n \"self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None\",\n \"from pandac.PandaModules import * from otp.otpbase import OTPLocalizer class ChatInputNormal(DirectObject.DirectObject):\",\n \"= None): if posX and posY and posZ: self.chatFrame.setPos(posX, posY,\",\n \"posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ) else: self.chatFrame.setPos(posX)\",\n \"setPos(self, posX, posY = None, posZ = None): if posX\",\n \"= 0): self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName =\",\n \"import OTPGlobals import sys from direct.gui.DirectGui import * from pandac.PandaModules\",\n \"if posX and posY and posZ: self.chatFrame.setPos(posX, posY, posZ) else:\",\n \"0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex %= len(self.history)\",\n \"= None, toPlayer = 0): self.toPlayer = toPlayer self.whisperAvatarId =\",\n \"0 if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory)\",\n \"str(extraInfo) else: return str(exception) except: exception = sys.exc_info()[0] extraInfo =\",\n \"self.whisperAvatarId = None self.toPlayer = 0 wantHistory = 0 if\",\n \"= 0 wantHistory = 0 if __dev__: wantHistory = 1\",\n \"len(self.history) def setPos(self, posX, posY = None, posZ = None):\",\n \"self.whisperAvatarId = None else: if self.chatMgr.execChat: if text[0] == '>':\",\n \"= [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return\",\n \"self.toPlayer = toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer)\",\n \"def checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('')\",\n \"exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo\",\n \"if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from pandac.PandaModules import *',\",\n \"posX, posY = None, posZ = None): if posX and\",\n \"self.whisperPos = Vec3(0.0, 0, 0.71) self.whisperAvatarName = None self.whisperAvatarId =\",\n \"%s' % message) printStack() exec(message, globals(), ChatInputNormal.ExecNamespace) return 'ok' except:\",\n \"= 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up')\",\n \"= 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize\",\n \"if extraInfo: return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('')\",\n \"self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self,\",\n \"self.chatFrame.setPos(self.whisperPos) self.whisperLabel['text'] = OTPLocalizer.ChatInputWhisperLabel % self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide()\",\n \"ExecNamespace = None def __init__(self, chatMgr): self.chatMgr = chatMgr self.normalPos\",\n \"= 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up', self.getNextHistory) def\",\n \"-= 1 self.historyIndex %= len(self.history) def setPos(self, posX, posY =\",\n \"self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0 elif\",\n \"except SyntaxError: try: if not isClient(): print('EXECWARNING ChatInputNormal exec: %s'\",\n \"globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal eval:\",\n \"None else: if self.chatMgr.execChat: if text[0] == '>': text =\",\n \"def activateByData(self, whisperAvatarId = None, toPlayer = 0): self.toPlayer =\",\n \"= toPlayer self.whisperAvatarId = whisperAvatarId self.whisperAvatarName = base.talkAssistant.findName(self.whisperAvatarId, self.toPlayer) if\",\n \"return str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def\",\n \"self.deactivate() self.chatMgr.fsm.request('mainMenu') if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName =\",\n \"messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton\",\n \"return 'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if\",\n \"DirectObject from otp.otpbase import OTPGlobals import sys from direct.gui.DirectGui import\",\n \"self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if\",\n \"self.whisperAvatarName = None self.whisperAvatarId = None else: if self.chatMgr.execChat: if\",\n \"self.toPlayer = 0 wantHistory = 0 if __dev__: wantHistory =\",\n \"self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme()\",\n \"self.ignore('arrow_down-up') def checkForOverRide(self): return False def sendChat(self, text): if self.checkForOverRide():\",\n \"str(eval(message, globals(), ChatInputNormal.ExecNamespace)) except SyntaxError: try: if not isClient(): print('EXECWARNING\",\n \"self.chatFrame.hide() self.whisperLabel.hide() base.win.closeIme() self.ignore('arrow_up-up') self.ignore('arrow_down-up') def checkForOverRide(self): return False def\",\n \"self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {}\",\n \"0 wantHistory = 0 if __dev__: wantHistory = 1 self.wantHistory\",\n \"def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history) def\",\n \"None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName =\",\n \"self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None else:\",\n \"else: if self.chatMgr.execChat: if text[0] == '>': text = self.__execMessage(text[1:])\",\n \"{} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try: if\",\n \"return def chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not\",\n \"wantHistory) self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex =\",\n \"self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None,\",\n \"direct.showbase import DirectObject from otp.otpbase import OTPGlobals import sys from\",\n \"__dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history =\",\n \"def setPos(self, posX, posY = None, posZ = None): if\",\n \"direct.gui.DirectGui import * from pandac.PandaModules import * from otp.otpbase import\",\n \"str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo:\",\n \"self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory: self.accept('arrow_up-up', self.getPrevHistory) self.accept('arrow_down-up',\",\n \"*', globals(), self.ExecNamespace) self.importExecNamespace() try: if not isClient(): print('EXECWARNING ChatInputNormal\",\n \"self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self,\",\n \"self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text):\",\n \"def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self): pass def addToHistory(self, text): self.history\",\n \"self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer = 0 wantHistory\",\n \"def sendChat(self, text): if self.checkForOverRide(): self.chatEntry.enterText('') return self.deactivate() self.chatMgr.fsm.request('mainMenu') if\",\n \"base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self, extraArgs): messenger.send('enterNormalChat')\",\n \"globals(), ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo =\",\n \"isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack() return str(eval(message,\",\n \"del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry del self.whisperLabel\",\n \"self.historyIndex = 0 def getPrevHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex += 1 self.historyIndex\",\n \"if __dev__: wantHistory = 1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history\",\n \"len(self.history) def getNextHistory(self): self.chatEntry.set(self.history[self.historyIndex]) self.historyIndex -= 1 self.historyIndex %= len(self.history)\",\n \"def typeCallback(self, extraArgs): messenger.send('enterNormalChat') def delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del\",\n \"del self.whisperLabel del self.chatMgr def activateByData(self, whisperAvatarId = None, toPlayer\",\n \"addToHistory(self, text): self.history = [text] + self.history[:self.historySize - 1] self.historyIndex\",\n \"'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if extraInfo:\",\n \"def importExecNamespace(self): pass def addToHistory(self, text): self.history = [text] +\",\n \"str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self): self.sendChat(self.chatEntry.get()) def importExecNamespace(self):\",\n \"text): self.history = [text] + self.history[:self.historySize - 1] self.historyIndex =\",\n \"def addToHistory(self, text): self.history = [text] + self.history[:self.historySize - 1]\",\n \"chatMgr self.normalPos = Vec3(-1.083, 0, 0.804) self.whisperPos = Vec3(0.0, 0,\",\n \"= {} exec('from pandac.PandaModules import *', globals(), self.ExecNamespace) self.importExecNamespace() try:\",\n \"chatOverflow(self, overflowText): self.sendChat(self.chatEntry.get()) def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace\",\n \"if text: if self.toPlayer: if self.whisperAvatarId: self.whisperAvatarName = None self.whisperAvatarId\",\n \"self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show() if self.wantHistory:\",\n \"% self.whisperAvatarName self.whisperLabel.show() else: self.chatFrame.setPos(self.normalPos) self.whisperLabel.hide() self.chatEntry['focus'] = 1 self.chatFrame.show()\",\n \"def __execMessage(self, message): if not ChatInputNormal.ExecNamespace: ChatInputNormal.ExecNamespace = {} exec('from\",\n \"del self.cancelButton del self.chatEntry del self.whisperLabel del self.chatMgr def activateByData(self,\",\n \"elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId = None\",\n \"self.history = [''] self.historySize = base.config.GetInt('chat-history-size', 10) self.historyIndex = 0\",\n \"self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton del self.chatEntry\",\n \"1 self.wantHistory = base.config.GetBool('want-chat-history', wantHistory) self.history = [''] self.historySize =\",\n \"str(extraInfo) else: return str(exception) def cancelButtonPressed(self): self.chatEntry.set('') self.chatMgr.fsm.request('mainMenu') def chatButtonPressed(self):\",\n \"= [text] + self.history[:self.historySize - 1] self.historyIndex = 0 def\",\n \"self.accept('arrow_down-up', self.getNextHistory) def deactivate(self): self.chatEntry.set('') self.chatEntry['focus'] = 0 self.chatFrame.hide() self.whisperLabel.hide()\",\n \"0, 0.71) self.whisperAvatarName = None self.whisperAvatarId = None self.toPlayer =\",\n \"if not isClient(): print('EXECWARNING ChatInputNormal eval: %s' % message) printStack()\",\n \"= base.config.GetInt('chat-history-size', 10) self.historyIndex = 0 return def typeCallback(self, extraArgs):\",\n \"delete(self): self.ignore('arrow_up-up') self.ignore('arrow_down-up') self.chatFrame.destroy() del self.chatFrame del self.chatButton del self.cancelButton\",\n \"return str(exception) except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1] if\",\n \"| CFTimeout) return base.talkAssistant.sendOpenTalk(text) if self.wantHistory: self.addToHistory(text) return def chatOverflow(self,\",\n \"[text] + self.history[:self.historySize - 1] self.historyIndex = 0 def getPrevHistory(self):\",\n \"= None self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId:\",\n \"ChatInputNormal.ExecNamespace) return 'ok' except: exception = sys.exc_info()[0] extraInfo = sys.exc_info()[1]\",\n \"None self.whisperAvatarId = None self.toPlayer = 0 wantHistory = 0\"\n]","isConversational":false}}},{"rowIdx":163,"cells":{"ngram":{"kind":"list like","value":["log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if __name__ ==","Only computes a batch-wise average of precision. Computes the precision,","DNN will be saved. :return: None ''' model = Sequential()","precision(y_true, y_pred): \"\"\"Precision metric. Only computes a batch-wise average of","print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features =","the recall, a metric for multi-label classification of how many","if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in","the path of the dataset :return: ''' dataset = pd.read_csv(dataset_csv)","embeddings = dict()##dict with key the index of each sentence","dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip()","true_positives / (predicted_positives + K.epsilon()) return precision precision = precision(y_true,","def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the sentences.txt","K.epsilon()) return precision precision = precision(y_true, y_pred) recall = recall(y_true,","= true_positives / (possible_positives + K.epsilon()) return recall def precision(y_true,","= np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length)","= LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log,","c best_params['solver'] = s print(\"F1 score using Logistic Regression:\",f1) print(\"###########################################\")","a png file. Better use the Visulize_DNN.ipynd :param file_to_save: the","start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train,","with key the index of each sentence and as value","= line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line in","joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings","pd import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import","in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu',","f1_results = list() for i in range(3): model = Sequential()","activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network","Dropout from keras import backend as K from keras.utils.vis_utils import","sentences :param dataset_csv: the path of the dataset :param filename:","None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu',","embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key]","of recall. Computes the recall, a metric for multi-label classification","log.predict(X_val) print(\"###########################################\") print(\"LR with C =\",c,'and solver = ',s) print(\"Results","activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl):","each sentence of the sentences_list and as value the embedding","previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768))","import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split","Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a DNN f1_results = list()","line of the sentences_list.txt and as value the sentence embeddings","for multi-label classification of how many relevant items are selected.","'__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\")","the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json)","dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length","= log.predict(X_val) print(\"###########################################\") print(\"LR with C =\",c,'and solver = ',s)","length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape)","* 100)) f1_results.append(f_1) model = None print(\"###########################################\") print(\"Results using embeddings",":param sentences_list: the path o the sentences.txt :param layer_json: the","keras.utils.vis_utils import plot_model from sklearn.externals import joblib import time def","how many relevant items are selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true","index of each sentence and as value the its embedding","batch-wise average of recall. Computes the recall, a metric for","score using Logistic Regression:\",f1) print(\"###########################################\") end = time.time() running_time =",":param dataset_csv: the path of the dataset :param filename: The","embedding with open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index]","1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts","= ',s) print(\"Results using embeddings from the\", layer_json, \"file\") print(classification_report(y_val,","print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features","key each sentence of the sentences_list and as value the","= row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence)","the embeddings of the sentences :return: Dictionary with key each","- start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN","y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path","enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for","and as value the its embedding sentence_emb = dict()#key:sentence,value:its embedding","np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features =","using Logistic Regression:\",f1) print(\"###########################################\") end = time.time() running_time = end","= row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence])","previous = row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if","def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length","= LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val)","time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions","get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list() previous_emb =","key the index of each line of the sentences_list.txt and","from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models","pickle file that the model will be stored :return: '''","#layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1","are selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))","print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the","set\", required=True, help=\"path to train set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True,","length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label)","as value the embedding ''' sentences = dict()#dict with key","max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _ = joblib.dump(log,","np import pandas as pd import os from sklearn.linear_model import","(f_1 * 100)) f1_results.append(f_1) model = None print(\"###########################################\") print(\"Results using","0.0 for c in C: for s in solver: start","required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\",","= 0 for row in dataset.iterrows(): sentence = row[1][0].strip() previous","classification of how many selected items are relevant. \"\"\" true_positives","f1 > best_score: best_score = f1 best_params['c'] = c best_params['solver']","1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision","\"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives =","Visulize_DNN.ipynd :param file_to_save: the png file that the architecture of","'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features'])","loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1: %f' %","required=True, help=\"path to save summarizer\") args = vars(ap.parse_args()) layer =","using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a DNN f1_results =","bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous","C =\",c,'and solver = ',s) print(\"Results using embeddings from the\",","\"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list:","print(\"F1 score using Logistic Regression:\",f1) print(\"###########################################\") end = time.time() running_time","= list() for i in range(3): model = Sequential() model.add(Dense(64,","best_score: best_score = f1 best_params['c'] = c best_params['solver'] = s","+ K.epsilon()) return precision precision = precision(y_true, y_pred) recall =","as value the sentence embeddings = dict()##dict with key the","model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit","layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt',","embeddings of the sentences :return: Dictionary with key each sentence","= train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json',","1))) recall = true_positives / (possible_positives + K.epsilon()) return recall","best_score = 0.0 for c in C: for s in","= Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64,",":param dataset_csv: the path of the dataset :return: ''' dataset","length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33,","log.fit(X_train, y_train) predictions = log.predict(X_val) print(\"###########################################\") print(\"LR with C =\",c,'and","activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) #","features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000,","solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9)","#tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3","network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100,","as pd import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection","model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True))","embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path","model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True)","train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')","label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb","= train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000,","= f1 best_params['c'] = c best_params['solver'] = s print(\"F1 score","np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb","the sentences :param dataset_csv: the path of the dataset :param","multi-label classification of how many relevant items are selected. \"\"\"","each sentence and as value the its embedding sentence_emb =","= row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if sentence","the architecture of the DNN will be saved. :return: None","true_positives / (possible_positives + K.epsilon()) return recall def precision(y_true, y_pred):","train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag']","s print(\"F1 score using Logistic Regression:\",f1) print(\"###########################################\") end = time.time()","model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1: %f' % (f_1 * 100))","= joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap","y_pred): \"\"\"Precision metric. Only computes a batch-wise average of precision.","each line of the sentences_list.txt and as value the sentence","layer_json: the path of the json file that contains the","best_score = f1 best_params['c'] = c best_params['solver'] = s print(\"F1","pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list()","= dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line in","time.time() running_time = end - start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save):","import Sequential from keras.layers import Dense, Dropout from keras import","= K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0,","json import numpy as np import pandas as pd import","value the its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r')","f1_score(y_val, predictions) if f1 > best_score: best_score = f1 best_params['c']","s in solver: start = time.time() log = LogisticRegression(random_state=0, solver=s,","previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val,","= [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score =","with C =\",c,'and solver = ',s) print(\"Results using embeddings from","computes a batch-wise average of precision. Computes the precision, a","= train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver =","K from keras.utils.vis_utils import plot_model from sklearn.externals import joblib import","sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line","activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True))","recall(y_true, y_pred): \"\"\"Recall metric. Only computes a batch-wise average of",":return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length =","trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save,","path\", required=True, help=\"path to save summarizer\") args = vars(ap.parse_args()) layer","= time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train)","print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o","value the sentence embeddings = dict()##dict with key the index","multi-label classification of how many selected items are relevant. \"\"\"","bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list()","axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val,","the sentences_list.txt and as value the sentence embeddings = dict()##dict","dataset_csv: the path of the dataset :param filename: The path","model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy',","X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C","K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon())","using embeddings from the\", layer_json, \"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\")","print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb","row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip()","Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a DNN f1_results = list() for","from sklearn.metrics import classification_report,f1_score from keras.models import Sequential from keras.layers","file that contains the embeddings of the sentences :param dataset_csv:","in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts =","import pandas as pd import os from sklearn.linear_model import LogisticRegression","for s in solver: start = time.time() log = LogisticRegression(random_state=0,","= end - start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save","help=\"path to save summarizer\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'],","np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val =","path of the dataset :param filename: The path of the","for row in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip()","train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the sentences.txt :param","classification_report,f1_score from keras.models import Sequential from keras.layers import Dense, Dropout","bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else:","activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model,","test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train)","metric. Only computes a batch-wise average of precision. Computes the","return precision precision = precision(y_true, y_pred) recall = recall(y_true, y_pred)","= list() section_list = list() label = list() errors =","visualize_DNN(file_to_save): ''' Save the DNN architecture to a png file.","for i in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True))","pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser()","how many selected items are relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true","layer_json, \"file\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1","label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb],","import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score","DNN architecture to a png file. Better use the Visulize_DNN.ipynd","the model _ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val)","backend as K from keras.utils.vis_utils import plot_model from sklearn.externals import","the precision, a metric for multi-label classification of how many","list() section_list = list() label = list() errors = 0","to save summarizer\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'],","solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\"###########################################\") print(\"LR","using embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score using Logistic","the dataset :param filename: The path of the pickle file","joblib import time def f1(y_true, y_pred): def recall(y_true, y_pred): \"\"\"Recall","the sentences.txt :param layer_json: the path of the json file","random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save","the Visulize_DNN.ipynd :param file_to_save: the png file that the architecture","and as value the sentence embeddings = dict()##dict with key","['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for c in","ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path to","items are selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0,","model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset =","the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\")","sentences_list.txt and as value the sentence embeddings = dict()##dict with","the pickle file that the model will be stored :return:","plot_model from sklearn.externals import joblib import time def f1(y_true, y_pred):","the json file that contains the embeddings of the sentences","contains the embeddings of the sentences :param dataset_csv: the path","= np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return","#train a DNN f1_results = list() for i in range(3):","precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json):","section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb =","print(\"###########################################\") #train a DNN f1_results = list() for i in","= s print(\"F1 score using Logistic Regression:\",f1) print(\"###########################################\") end =","sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import","C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score","import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import Sequential","y_pred): \"\"\"Recall metric. Only computes a batch-wise average of recall.","\"\"\"Recall metric. Only computes a batch-wise average of recall. Computes","label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params","row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else:","print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features,","np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33,","dict() best_score = 0.0 for c in C: for s","a batch-wise average of recall. Computes the recall, a metric","recall = true_positives / (possible_positives + K.epsilon()) return recall def","\"--train set\", required=True, help=\"path to train set\") ap.add_argument(\"-sp\", \"--summarizer path\",","contains the embeddings of the sentences :return: Dictionary with key","sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous])","ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path to save summarizer\") args =",":param filename: The path of the pickle file that the","classification of how many relevant items are selected. \"\"\" true_positives","many selected items are relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true *","= K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives +","train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1)","0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives","get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb = list()","label) _ = joblib.dump(log, pkl, compress=9) if __name__ == '__main__':","row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if sentence in","return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o","as np import pandas as pd import os from sklearn.linear_model","import time def f1(y_true, y_pred): def recall(y_true, y_pred): \"\"\"Recall metric.","= np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1)","X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log =","section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768))","precision, a metric for multi-label classification of how many selected","= np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features","file that the architecture of the DNN will be saved.","epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1:","from the\", layer_json, \"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv):","else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb =","end - start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the","print(classification_report(y_val, predictions)) print(\"F1 score using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train","print(\"LR with C =\",c,'and solver = ',s) print(\"Results using embeddings","= np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape)","line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items():","Dictionary with key each sentence of the sentences_list and as","np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features","as K from keras.utils.vis_utils import plot_model from sklearn.externals import joblib","print(\"Results using embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score using","train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import Sequential from","K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))","average of recall. Computes the recall, a metric for multi-label","of each sentence and as value the its embedding sentence_emb","y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall =","previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape)","argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\")","C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\"###########################################\") print(\"LR with C","verbose=1) print('\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1) model","get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the sentences.txt :param","pandas as pd import os from sklearn.linear_model import LogisticRegression from","help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path to train set\") ap.add_argument(\"-sp\",","of the sentences_list.txt and as value the sentence embeddings =","list() sentence_emb = list() previous_emb = list() next_list = list()","argparse import json import numpy as np import pandas as","previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length =","print(\"###########################################\") print(\"Results using embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score","the its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as","= K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0,","parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the sentences.txt :param","embeddings from the\", layer_json, \"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\") def","y_pred): def recall(y_true, y_pred): \"\"\"Recall metric. Only computes a batch-wise","time def f1(y_true, y_pred): def recall(y_true, y_pred): \"\"\"Recall metric. Only","else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in bert_dict:","summarizer\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer","parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')","''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list()","path of the pickle file that the model will be","np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features,","0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return","predictions)) f1 = f1_score(y_val, predictions) if f1 > best_score: best_score","keras import backend as K from keras.utils.vis_utils import plot_model from","best_params = dict() best_score = 0.0 for c in C:","K.epsilon()) return recall def precision(y_true, y_pred): \"\"\"Precision metric. Only computes","network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val,","% (f_1 * 100)) f1_results.append(f_1) model = None print(\"###########################################\") print(\"Results","the sentences :return: Dictionary with key each sentence of the","'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 =","F1: %f' % (f_1 * 100)) f1_results.append(f_1) model = None","= ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for c","sentences :param dataset_csv: the path of the dataset :return: '''","def f1(y_true, y_pred): def recall(y_true, y_pred): \"\"\"Recall metric. Only computes","a DNN f1_results = list() for i in range(3): model","[0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0","/ (predicted_positives + K.epsilon()) return precision precision = precision(y_true, y_pred)","length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features =","args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 =","print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _","= vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1","np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors)","score using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a DNN f1_results","vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 =","for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def","axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val","predictions)) print(\"F1 score using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a","possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives","length = list() sentence_emb = list() previous_emb = list() next_list","np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train,","1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives /","axis=1) features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga',","from keras.models import Sequential from keras.layers import Dense, Dropout from","embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score using Logistic Regression:\",f1_score(y_val,",":return: None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128,","+= 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if","Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu',","= np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb,","os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from","in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb)","Dense, Dropout from keras import backend as K from keras.utils.vis_utils","sentence_emb = list() previous_emb = list() next_list = list() section_list","train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4 = train_classifier('sentences_list.txt','output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')","previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict:","f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] =","0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return","import numpy as np import pandas as pd import os","= np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val","a metric for multi-label classification of how many relevant items","trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile","as f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for","np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape)","= np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features,","activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25))","= time.time() running_time = end - start print(\"Running time:\"+str(running_time)) def","= list() previous_emb = list() next_list = list() section_list =","for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in","print(sentence) errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else:","sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the","print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length","y_train) predictions = log.predict(X_val) print(\"###########################################\") print(\"LR with C =\",c,'and solver","print(\"Results using embeddings from the\", layer_json, \"file\") print(classification_report(y_val, predictions)) f1","/ (possible_positives + K.epsilon()) return recall def precision(y_true, y_pred): \"\"\"Precision","the DNN architecture to a png file. Better use the","required=True, help=\"path to train set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path","precision precision = precision(y_true, y_pred) recall = recall(y_true, y_pred) return","the\", layer_json, \"file\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if","recall def precision(y_true, y_pred): \"\"\"Precision metric. Only computes a batch-wise","= argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True,","solver: start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c)","Regression:\",f1) print(\"###########################################\") end = time.time() running_time = end - start","trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset","= recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list:","stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length","sklearn.externals import joblib import time def f1(y_true, y_pred): def recall(y_true,","layer_json, \"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param","trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1])","of how many relevant items are selected. \"\"\" true_positives =","args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2","json file that contains the embeddings of the sentences :param","return recall def precision(y_true, y_pred): \"\"\"Precision metric. Only computes a","with open(layer_json, 'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']]","print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,","solver = ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for","model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30))","if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors +=","a batch-wise average of precision. Computes the precision, a metric","from keras.layers import Dense, Dropout from keras import backend as","precision. Computes the precision, a metric for multi-label classification of","to train set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path to save","DNN f1_results = list() for i in range(3): model =","that contains the embeddings of the sentences :return: Dictionary with","dataset :param filename: The path of the pickle file that","relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives","= train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 =","\"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives =","\"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path to train","features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train,","of the sentences_list and as value the embedding ''' sentences","2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the","# evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the","1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives /","#layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning","key the index of each sentence and as value the","open(layer_json, 'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']] =","dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list()","for c in C: for s in solver: start =","\"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train","#layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4","label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)","=\",c,'and solver = ',s) print(\"Results using embeddings from the\", layer_json,","import argparse import json import numpy as np import pandas","key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename):","if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\",","recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param","= train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl')","sentences = dict()#dict with key the index of each line","use the Visulize_DNN.ipynd :param file_to_save: the png file that the","= joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\"###########################################\") print(\"Results using","best_params['solver'] = s print(\"F1 score using Logistic Regression:\",f1) print(\"###########################################\") end","keras.models import Sequential from keras.layers import Dense, Dropout from keras","sentence of the sentences_list and as value the embedding '''","predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives","c in C: for s in solver: start = time.time()","args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path'])","using embeddings from the\", layer_json, \"file\") print(classification_report(y_val, predictions)) f1 =","The path of the pickle file that the model will","model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid'))","of precision. Computes the precision, a metric for multi-label classification","numpy as np import pandas as pd import os from","0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives","running_time = end - start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): '''","next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log =","recall, a metric for multi-label classification of how many relevant","print(\"###########################################\") print(\"Results using embeddings from the\", layer_json, \"file\") # evaluate","= dict()#dict with key the index of each line of","def visualize_DNN(file_to_save): ''' Save the DNN architecture to a png","with open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index] =","of the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict =","sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1","> best_score: best_score = f1 best_params['c'] = c best_params['solver'] =","= np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) #","the sentences :param dataset_csv: the path of the dataset :return:","np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape)","the sentences_list and as value the embedding ''' sentences =","of each line of the sentences_list.txt and as value the","features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val =","from keras.utils.vis_utils import plot_model from sklearn.externals import joblib import time","(predicted_positives + K.epsilon()) return precision precision = precision(y_true, y_pred) recall","that contains the embeddings of the sentences :param dataset_csv: the","model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1)","png file that the architecture of the DNN will be","that the architecture of the DNN will be saved. :return:","items are relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0,",":param file_to_save: the png file that the architecture of the","print(\"Results using embeddings from the\", layer_json, \"file\") # evaluate print(np.mean(f1_results))","Sequential from keras.layers import Dense, Dropout from keras import backend","row in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts","sklearn.metrics import classification_report,f1_score from keras.models import Sequential from keras.layers import","in C: for s in solver: start = time.time() log","= row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section =","json file that contains the embeddings of the sentences :return:","average of precision. Computes the precision, a metric for multi-label","np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape)","# compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train,","= list() label = list() errors = 0 for row","log.fit(X_train, y_train) #save the model _ = joblib.dump(log, filename, compress=9)","next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5])","time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture to a","import Dense, Dropout from keras import backend as K from","random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict()","'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl')","errors = 0 for row in dataset.iterrows(): sentence = row[1][0].strip()","batch-wise average of precision. Computes the precision, a metric for","sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb =","sentences :return: Dictionary with key each sentence of the sentences_list","the index of each sentence and as value the its","= np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb)","of the dataset :param filename: The path of the pickle","help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True,","precision = true_positives / (predicted_positives + K.epsilon()) return precision precision","from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1 score using Logistic Regression:\",f1_score(y_val, predictions))","next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768))","f1 = f1_score(y_val, predictions) if f1 > best_score: best_score =","sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in","else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4])","''' :param sentences_list: the path o the sentences.txt :param layer_json:","Save the DNN architecture to a png file. Better use","= get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb =","max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\"###########################################\") print(\"LR with","selected items are relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred,","bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape)","in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): '''","y_val = train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver","X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C =","next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train,","= None print(\"###########################################\") print(\"Results using embeddings from the\", layer_json, \"file\")","''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True))","will be saved. :return: None ''' model = Sequential() model.add(Dense(64,","file_to_save: the png file that the architecture of the DNN","the DNN will be saved. :return: None ''' model =","= train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning =","LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl,","import json import numpy as np import pandas as pd","C=0.1) log.fit(X_train, y_train) #save the model _ = joblib.dump(log, filename,","import joblib import time def f1(y_true, y_pred): def recall(y_true, y_pred):","y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\nTest","model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu',","show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json)","file that the model will be stored :return: ''' dataset","list() next_list = list() section_list = list() label = list()","sentences_list and as value the embedding ''' sentences = dict()#dict","in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f:","errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768))","= dict()##dict with key the index of each sentence and","= np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1)","if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in","print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture to","f1 best_params['c'] = c best_params['solver'] = s print(\"F1 score using","from sklearn.externals import joblib import time def f1(y_true, y_pred): def","f1_results.append(f_1) model = None print(\"###########################################\") print(\"Results using embeddings from the\",","best_params['c'] = c best_params['solver'] = s print(\"F1 score using Logistic","C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if __name__","__name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True,","sentence and as value the its embedding sentence_emb = dict()#key:sentence,value:its","import classification_report,f1_score from keras.models import Sequential from keras.layers import Dense,","that the model will be stored :return: ''' dataset =","f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value","be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json)","fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val,","print(\"###########################################\") end = time.time() running_time = end - start print(\"Running","print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb],","embeddings of the sentences :param dataset_csv: the path of the","activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd',","sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import","= train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4 =","test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params =","set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path to save summarizer\") args","start print(\"Running time:\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture","a metric for multi-label classification of how many selected items","and as value the embedding ''' sentences = dict()#dict with","X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log","path of the json file that contains the embeddings of","= log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions))","import backend as K from keras.utils.vis_utils import plot_model from sklearn.externals","section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb","log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the","range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True))","# fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 =","f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1: %f' % (f_1","y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision =","true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred,","print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1 > best_score:","#layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 =","train set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path to save summarizer\")","sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line","(possible_positives + K.epsilon()) return recall def precision(y_true, y_pred): \"\"\"Precision metric.","K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))","list() errors = 0 for row in dataset.iterrows(): sentence =","from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics","file. Better use the Visulize_DNN.ipynd :param file_to_save: the png file","= list() errors = 0 for row in dataset.iterrows(): sentence","the png file that the architecture of the DNN will","embedding ''' sentences = dict()#dict with key the index of","print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label","architecture of the DNN will be saved. :return: None '''","= parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 =","f1(y_true, y_pred): def recall(y_true, y_pred): \"\"\"Recall metric. Only computes a","its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file:","filename, compress=9) predictions = log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings from","train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')","nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict:","the\", layer_json, \"file\") # evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): '''","features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features,","list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path","will be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict =","* y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision","model _ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\"###########################################\")","as file: for index,line in enumerate(file): sentences[index] = line.strip() with","metric. Only computes a batch-wise average of recall. Computes the","pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb =","the index of each line of the sentences_list.txt and as","\"\"\"Precision metric. Only computes a batch-wise average of precision. Computes","print(\"F1 score using Logistic Regression:\",f1_score(y_val, predictions)) print(\"###########################################\") #train a DNN","log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings from the\",layer_json,\"file\") print(classification_report(y_val, predictions)) print(\"F1","precision = precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon()))","architecture to a png file. Better use the Visulize_DNN.ipynd :param","model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1,","as value the its embedding sentence_emb = dict()#key:sentence,value:its embedding with","LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from","path o the sentences.txt :param layer_json: the path of the","= true_positives / (predicted_positives + K.epsilon()) return precision precision =","in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts])","label = list() errors = 0 for row in dataset.iterrows():","',s) print(\"Results using embeddings from the\", layer_json, \"file\") print(classification_report(y_val, predictions))","= pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb =","_ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\"###########################################\") print(\"Results","trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64,","log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ =","ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path to train set\") ap.add_argument(\"-sp\", \"--summarizer","filename: The path of the pickle file that the model","model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network","log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions =","if f1 > best_score: best_score = f1 best_params['c'] = c","metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1","bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else:","ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\", required=True, help=\"output\") ap.add_argument(\"-ts\",","print('\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1) model =","= get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list() previous_emb","return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o","o the sentences.txt :param layer_json: the path of the json","saved. :return: None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True))","line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line in f:","= dict() best_score = 0.0 for c in C: for","Computes the precision, a metric for multi-label classification of how","# np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label,","predictions) if f1 > best_score: best_score = f1 best_params['c'] =","model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64)","model = None print(\"###########################################\") print(\"Results using embeddings from the\", layer_json,","to a png file. Better use the Visulize_DNN.ipynd :param file_to_save:","\"--summarizer path\", required=True, help=\"path to save summarizer\") args = vars(ap.parse_args())","else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if","Better use the Visulize_DNN.ipynd :param file_to_save: the png file that","layer_json) length = list() sentence_emb = list() previous_emb = list()","max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if","the embedding ''' sentences = dict()#dict with key the index","batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1: %f'","= pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb","the model will be stored :return: ''' dataset = pd.read_csv(dataset_csv)","C: for s in solver: start = time.time() log =","def recall(y_true, y_pred): \"\"\"Recall metric. Only computes a batch-wise average","keras.layers import Dense, Dropout from keras import backend as K","model will be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict","with key the index of each line of the sentences_list.txt","def precision(y_true, y_pred): \"\"\"Precision metric. Only computes a batch-wise average","np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label,","selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives","#save the model _ = joblib.dump(log, filename, compress=9) predictions =","np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1)","model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) #","y_train) #save the model _ = joblib.dump(log, filename, compress=9) predictions","end = time.time() running_time = end - start print(\"Running time:\"+str(running_time))","* y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall","with key each sentence of the sentences_list and as value","= precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def","help=\"path to train set\") ap.add_argument(\"-sp\", \"--summarizer path\", required=True, help=\"path to","index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as","many relevant items are selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true *","sentence = row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section","compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\",","next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb =","0 for row in dataset.iterrows(): sentence = row[1][0].strip() previous =","\"file\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1 >",":param layer_json: the path of the json file that contains","''' sentences = dict()#dict with key the index of each","in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if","= np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length])","100)) f1_results.append(f_1) model = None print(\"###########################################\") print(\"Results using embeddings from","#layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2","list() label = list() errors = 0 for row in","png file. Better use the Visulize_DNN.ipynd :param file_to_save: the png","+ K.epsilon()) return recall def precision(y_true, y_pred): \"\"\"Precision metric. Only","#save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\",","_ = joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl')","ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences list\") ap.add_argument(\"-o\", \"--output\",","path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3","Computes the recall, a metric for multi-label classification of how","%f' % (f_1 * 100)) f1_results.append(f_1) model = None print(\"###########################################\")","previous_emb = list() next_list = list() section_list = list() label","section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0,","metric for multi-label classification of how many selected items are","of the sentences :param dataset_csv: the path of the dataset","y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): '''","sentence embeddings = dict()##dict with key the index of each","= np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list)","Logistic Regression:\",f1) print(\"###########################################\") end = time.time() running_time = end -","predictions = log.predict(X_val) print(\"###########################################\") print(\"LR with C =\",c,'and solver =","save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length =","recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the","print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)","y_val = train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg',","evaluate print(np.mean(f1_results)) print(\"###########################################\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path","the path o the sentences.txt :param layer_json: the path of","open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index] = line.strip()","for index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8')","sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list:","dataset_csv: the path of the dataset :return: ''' dataset =","file: for index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json,","= list() next_list = list() section_list = list() label =","LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\"###########################################\")","save summarizer\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train","sentences_list: the path o the sentences.txt :param layer_json: the path","list() for i in range(3): model = Sequential() model.add(Dense(64, activation='relu',","to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,","next_list = list() section_list = list() label = list() errors","row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors","in solver: start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000,","== '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\"-s\", \"--sentences\", required=True, help=\"sentences","None print(\"###########################################\") print(\"Results using embeddings from the\", layer_json, \"file\") #","Only computes a batch-wise average of recall. Computes the recall,","are relevant. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))","of the sentences :return: Dictionary with key each sentence of","dict()#dict with key the index of each line of the","of the pickle file that the model will be stored","np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb,","activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict","previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log","train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4","nexts = row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict:","from the\", layer_json, \"file\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions)","label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train,","predictions)) print(\"###########################################\") #train a DNN f1_results = list() for i","joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap =","dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb","= c best_params['solver'] = s print(\"F1 score using Logistic Regression:\",f1)","section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label =","= list() sentence_emb = list() previous_emb = list() next_list =","required=True, help=\"output\") ap.add_argument(\"-ts\", \"--train set\", required=True, help=\"path to train set\")","= K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives +","file that contains the embeddings of the sentences :return: Dictionary","next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train,","y_val, verbose=1) print('\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1)","be saved. :return: None ''' model = Sequential() model.add(Dense(64, activation='relu',","in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value]","dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line in enumerate(file):","features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length])","section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list)","from keras import backend as K from keras.utils.vis_utils import plot_model","the sentence embeddings = dict()##dict with key the index of","= LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model","compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train,","section_list = list() label = list() errors = 0 for","for multi-label classification of how many selected items are relevant.","= model.evaluate(X_val, y_val, verbose=1) print('\\nTest F1: %f' % (f_1 *","metric for multi-label classification of how many relevant items are","= np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label)","y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10]","recall. Computes the recall, a metric for multi-label classification of","embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for","length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features,","LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _","import plot_model from sklearn.externals import joblib import time def f1(y_true,","the path of the json file that contains the embeddings","compress=9) predictions = log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings from the\",layer_json,\"file\")","sentences.txt :param layer_json: the path of the json file that","relevant items are selected. \"\"\" true_positives = K.sum(K.round(K.clip(y_true * y_pred,","in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section])","if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb","''' Save the DNN architecture to a png file. Better","computes a batch-wise average of recall. Computes the recall, a","value the embedding ''' sentences = dict()#dict with key the","of the json file that contains the embeddings of the","= embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the","the embeddings of the sentences :param dataset_csv: the path of","list() previous_emb = list() next_list = list() section_list = list()","= f1_score(y_val, predictions) if f1 > best_score: best_score = f1","solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _ =","y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0,","optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss,","plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict =","index of each line of the sentences_list.txt and as value","i in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128,","dict()##dict with key the index of each sentence and as","bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb","print(\"###########################################\") print(\"LR with C =\",c,'and solver = ',s) print(\"Results using","def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the sentences.txt","predictions = log.predict(X_val) print(\"###########################################\") print(\"Results using embeddings from the\",layer_json,\"file\") print(classification_report(y_val,","sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param","model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def","trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35))","set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl')","= train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 =","the path of the dataset :param filename: The path of","solver = ',s) print(\"Results using embeddings from the\", layer_json, \"file\")","K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon())","train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv')","true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true,","= 0.0 for c in C: for s in solver:","embeddings from the\", layer_json, \"file\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val,","previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section","np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape)","of the DNN will be saved. :return: None ''' model",":return: Dictionary with key each sentence of the sentences_list and","train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl')","model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv)","of how many selected items are relevant. \"\"\" true_positives =","def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the sentences.txt","path of the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict"],"string":"[\n \"log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if __name__ ==\",\n \"Only computes a batch-wise average of precision. Computes the precision,\",\n \"DNN will be saved. :return: None ''' model = Sequential()\",\n \"precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only computes a batch-wise average of\",\n \"print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features =\",\n \"the recall, a metric for multi-label classification of how many\",\n \"if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in\",\n \"the path of the dataset :return: ''' dataset = pd.read_csv(dataset_csv)\",\n \"embeddings = dict()##dict with key the index of each sentence\",\n \"dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip()\",\n \"true_positives / (predicted_positives + K.epsilon()) return precision precision = precision(y_true,\",\n \"def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the sentences.txt\",\n \"K.epsilon()) return precision precision = precision(y_true, y_pred) recall = recall(y_true,\",\n \"= true_positives / (possible_positives + K.epsilon()) return recall def precision(y_true,\",\n \"= np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length)\",\n \"= LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log,\",\n \"c best_params['solver'] = s print(\\\"F1 score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\")\",\n \"a png file. Better use the Visulize_DNN.ipynd :param file_to_save: the\",\n \"start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train,\",\n \"with key the index of each sentence and as value\",\n \"= line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line in\",\n \"joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings\",\n \"pd import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import\",\n \"in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu',\",\n \"f1_results = list() for i in range(3): model = Sequential()\",\n \"activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network\",\n \"Dropout from keras import backend as K from keras.utils.vis_utils import\",\n \"sentences :param dataset_csv: the path of the dataset :param filename:\",\n \"None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu',\",\n \"embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key]\",\n \"of recall. Computes the recall, a metric for multi-label classification\",\n \"log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and solver = ',s) print(\\\"Results\",\n \"activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl):\",\n \"each sentence of the sentences_list and as value the embedding\",\n \"previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768))\",\n \"import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split\",\n \"Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a DNN f1_results = list()\",\n \"line of the sentences_list.txt and as value the sentence embeddings\",\n \"for multi-label classification of how many relevant items are selected.\",\n \"'__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\")\",\n \"the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json)\",\n \"dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length\",\n \"= log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and solver = ',s)\",\n \"length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape)\",\n \"* 100)) f1_results.append(f_1) model = None print(\\\"###########################################\\\") print(\\\"Results using embeddings\",\n \":param sentences_list: the path o the sentences.txt :param layer_json: the\",\n \"keras.utils.vis_utils import plot_model from sklearn.externals import joblib import time def\",\n \"how many relevant items are selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true\",\n \"index of each sentence and as value the its embedding\",\n \"batch-wise average of recall. Computes the recall, a metric for\",\n \"score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time() running_time =\",\n \":param dataset_csv: the path of the dataset :param filename: The\",\n \"embedding with open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index]\",\n \"1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts\",\n \"= ',s) print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val,\",\n \"print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features\",\n \"key each sentence of the sentences_list and as value the\",\n \"= row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence)\",\n \"the embeddings of the sentences :return: Dictionary with key each\",\n \"- start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN\",\n \"y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path\",\n \"enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for\",\n \"and as value the its embedding sentence_emb = dict()#key:sentence,value:its embedding\",\n \"np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features =\",\n \"using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time() running_time = end\",\n \"= row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence])\",\n \"previous = row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if\",\n \"def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length\",\n \"= LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val)\",\n \"time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions\",\n \"get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list() previous_emb =\",\n \"key the index of each line of the sentences_list.txt and\",\n \"from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models\",\n \"pickle file that the model will be stored :return: '''\",\n \"#layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1\",\n \"are selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\",\n \"print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the\",\n \"set\\\", required=True, help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True,\",\n \"length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label)\",\n \"as value the embedding ''' sentences = dict()#dict with key\",\n \"max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _ = joblib.dump(log,\",\n \"np import pandas as pd import os from sklearn.linear_model import\",\n \"(f_1 * 100)) f1_results.append(f_1) model = None print(\\\"###########################################\\\") print(\\\"Results using\",\n \"0.0 for c in C: for s in solver: start\",\n \"required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\",\",\n \"= 0 for row in dataset.iterrows(): sentence = row[1][0].strip() previous\",\n \"classification of how many selected items are relevant. \\\"\\\"\\\" true_positives\",\n \"f1 > best_score: best_score = f1 best_params['c'] = c best_params['solver']\",\n \"1))) precision = true_positives / (predicted_positives + K.epsilon()) return precision\",\n \"\\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives =\",\n \"Visulize_DNN.ipynd :param file_to_save: the png file that the architecture of\",\n \"'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features'])\",\n \"loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1: %f' %\",\n \"required=True, help=\\\"path to save summarizer\\\") args = vars(ap.parse_args()) layer =\",\n \"using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a DNN f1_results =\",\n \"bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous\",\n \"C =\\\",c,'and solver = ',s) print(\\\"Results using embeddings from the\\\",\",\n \"\\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list:\",\n \"print(\\\"F1 score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time() running_time\",\n \"= list() for i in range(3): model = Sequential() model.add(Dense(64,\",\n \"best_score: best_score = f1 best_params['c'] = c best_params['solver'] = s\",\n \"+ K.epsilon()) return precision precision = precision(y_true, y_pred) recall =\",\n \"as value the sentence embeddings = dict()##dict with key the\",\n \"model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit\",\n \"layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt',\",\n \"embeddings of the sentences :return: Dictionary with key each sentence\",\n \"= train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json',\",\n \"1))) recall = true_positives / (possible_positives + K.epsilon()) return recall\",\n \"best_score = 0.0 for c in C: for s in\",\n \"= Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64,\",\n \":param dataset_csv: the path of the dataset :return: ''' dataset\",\n \"length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33,\",\n \"log.fit(X_train, y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and\",\n \"activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) #\",\n \"features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000,\",\n \"solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9)\",\n \"#tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3\",\n \"network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100,\",\n \"as pd import os from sklearn.linear_model import LogisticRegression from sklearn.model_selection\",\n \"model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True))\",\n \"embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path\",\n \"model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True)\",\n \"train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')\",\n \"label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb\",\n \"= train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000,\",\n \"= f1 best_params['c'] = c best_params['solver'] = s print(\\\"F1 score\",\n \"np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb\",\n \"the sentences :param dataset_csv: the path of the dataset :param\",\n \"multi-label classification of how many relevant items are selected. \\\"\\\"\\\"\",\n \"each sentence and as value the its embedding sentence_emb =\",\n \"= row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if sentence\",\n \"the architecture of the DNN will be saved. :return: None\",\n \"true_positives / (possible_positives + K.epsilon()) return recall def precision(y_true, y_pred):\",\n \"train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag']\",\n \"s print(\\\"F1 score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time()\",\n \"model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1: %f' % (f_1 * 100))\",\n \"= joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap\",\n \"y_pred): \\\"\\\"\\\"Precision metric. Only computes a batch-wise average of precision.\",\n \"each line of the sentences_list.txt and as value the sentence\",\n \"layer_json: the path of the json file that contains the\",\n \"best_score = f1 best_params['c'] = c best_params['solver'] = s print(\\\"F1\",\n \"pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list()\",\n \"= dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line in\",\n \"time.time() running_time = end - start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save):\",\n \"import Sequential from keras.layers import Dense, Dropout from keras import\",\n \"= K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0,\",\n \"json import numpy as np import pandas as pd import\",\n \"value the its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r')\",\n \"f1_score(y_val, predictions) if f1 > best_score: best_score = f1 best_params['c']\",\n \"s in solver: start = time.time() log = LogisticRegression(random_state=0, solver=s,\",\n \"previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val,\",\n \"= [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score =\",\n \"with C =\\\",c,'and solver = ',s) print(\\\"Results using embeddings from\",\n \"computes a batch-wise average of precision. Computes the precision, a\",\n \"= train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver =\",\n \"K from keras.utils.vis_utils import plot_model from sklearn.externals import joblib import\",\n \"sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line\",\n \"activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True))\",\n \"recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only computes a batch-wise average of\",\n \":return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length =\",\n \"trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save,\",\n \"path\\\", required=True, help=\\\"path to save summarizer\\\") args = vars(ap.parse_args()) layer\",\n \"= time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train)\",\n \"print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o\",\n \"value the sentence embeddings = dict()##dict with key the index\",\n \"multi-label classification of how many selected items are relevant. \\\"\\\"\\\"\",\n \"bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list()\",\n \"axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val,\",\n \"the sentences_list.txt and as value the sentence embeddings = dict()##dict\",\n \"dataset_csv: the path of the dataset :param filename: The path\",\n \"model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy',\",\n \"X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C\",\n \"K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon())\",\n \"using embeddings from the\\\", layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\")\",\n \"print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb\",\n \"row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip()\",\n \"Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a DNN f1_results = list() for\",\n \"from sklearn.metrics import classification_report,f1_score from keras.models import Sequential from keras.layers\",\n \"file that contains the embeddings of the sentences :param dataset_csv:\",\n \"in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts =\",\n \"import pandas as pd import os from sklearn.linear_model import LogisticRegression\",\n \"for s in solver: start = time.time() log = LogisticRegression(random_state=0,\",\n \"= end - start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save\",\n \"help=\\\"path to save summarizer\\\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'],\",\n \"np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val =\",\n \"path of the dataset :param filename: The path of the\",\n \"for row in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip()\",\n \"train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the sentences.txt :param\",\n \"classification_report,f1_score from keras.models import Sequential from keras.layers import Dense, Dropout\",\n \"bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else:\",\n \"activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model,\",\n \"test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train)\",\n \"metric. Only computes a batch-wise average of precision. Computes the\",\n \"return precision precision = precision(y_true, y_pred) recall = recall(y_true, y_pred)\",\n \"= list() section_list = list() label = list() errors =\",\n \"visualize_DNN(file_to_save): ''' Save the DNN architecture to a png file.\",\n \"for i in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True))\",\n \"pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser()\",\n \"how many selected items are relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true\",\n \"layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1\",\n \"label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb],\",\n \"import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score\",\n \"DNN architecture to a png file. Better use the Visulize_DNN.ipynd\",\n \"the model _ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val)\",\n \"backend as K from keras.utils.vis_utils import plot_model from sklearn.externals import\",\n \"the precision, a metric for multi-label classification of how many\",\n \"list() section_list = list() label = list() errors = 0\",\n \"to save summarizer\\\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'],\",\n \"solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR\",\n \"using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score using Logistic\",\n \"the dataset :param filename: The path of the pickle file\",\n \"joblib import time def f1(y_true, y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall\",\n \"the sentences.txt :param layer_json: the path of the json file\",\n \"random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save\",\n \"the Visulize_DNN.ipynd :param file_to_save: the png file that the architecture\",\n \"and as value the sentence embeddings = dict()##dict with key\",\n \"['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for c in\",\n \"ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path to\",\n \"items are selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0,\",\n \"model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset =\",\n \"the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\")\",\n \"sentences_list.txt and as value the sentence embeddings = dict()##dict with\",\n \"the pickle file that the model will be stored :return:\",\n \"plot_model from sklearn.externals import joblib import time def f1(y_true, y_pred):\",\n \"the json file that contains the embeddings of the sentences\",\n \"contains the embeddings of the sentences :param dataset_csv: the path\",\n \"= np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return\",\n \"#train a DNN f1_results = list() for i in range(3):\",\n \"precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json):\",\n \"section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb =\",\n \"print(\\\"###########################################\\\") #train a DNN f1_results = list() for i in\",\n \"= s print(\\\"F1 score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end =\",\n \"sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import\",\n \"C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score\",\n \"import train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import Sequential\",\n \"y_pred): \\\"\\\"\\\"Recall metric. Only computes a batch-wise average of recall.\",\n \"\\\"\\\"\\\"Recall metric. Only computes a batch-wise average of recall. Computes\",\n \"label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params\",\n \"row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else:\",\n \"print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features,\",\n \"np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33,\",\n \"dict() best_score = 0.0 for c in C: for s\",\n \"a batch-wise average of recall. Computes the recall, a metric\",\n \"recall = true_positives / (possible_positives + K.epsilon()) return recall def\",\n \"\\\"--train set\\\", required=True, help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\",\",\n \"contains the embeddings of the sentences :return: Dictionary with key\",\n \"sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous])\",\n \"ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path to save summarizer\\\") args =\",\n \":param filename: The path of the pickle file that the\",\n \"classification of how many relevant items are selected. \\\"\\\"\\\" true_positives\",\n \"many selected items are relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true *\",\n \"= K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives +\",\n \"train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1)\",\n \"0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives\",\n \"get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb = list()\",\n \"label) _ = joblib.dump(log, pkl, compress=9) if __name__ == '__main__':\",\n \"row[1][1].strip() nexts = row[1][2].strip() section = row[1][3].strip() if sentence in\",\n \"return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o\",\n \"as np import pandas as pd import os from sklearn.linear_model\",\n \"import time def f1(y_true, y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric.\",\n \"= np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1)\",\n \"X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log =\",\n \"section = row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768))\",\n \"precision, a metric for multi-label classification of how many selected\",\n \"= np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features\",\n \"file that the architecture of the DNN will be saved.\",\n \"epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1:\",\n \"from the\\\", layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv):\",\n \"else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb =\",\n \"end - start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the\",\n \"print(classification_report(y_val, predictions)) print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train\",\n \"print(\\\"LR with C =\\\",c,'and solver = ',s) print(\\\"Results using embeddings\",\n \"= np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape)\",\n \"line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items():\",\n \"Dictionary with key each sentence of the sentences_list and as\",\n \"np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features\",\n \"as K from keras.utils.vis_utils import plot_model from sklearn.externals import joblib\",\n \"print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score using\",\n \"train_test_split from sklearn.metrics import classification_report,f1_score from keras.models import Sequential from\",\n \"K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\",\n \"average of recall. Computes the recall, a metric for multi-label\",\n \"of each sentence and as value the its embedding sentence_emb\",\n \"y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall =\",\n \"previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape)\",\n \"argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\")\",\n \"C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C\",\n \"verbose=1) print('\\\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1) model\",\n \"get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the sentences.txt :param\",\n \"pandas as pd import os from sklearn.linear_model import LogisticRegression from\",\n \"help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\",\",\n \"of the sentences_list.txt and as value the sentence embeddings =\",\n \"list() sentence_emb = list() previous_emb = list() next_list = list()\",\n \"argparse import json import numpy as np import pandas as\",\n \"previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length =\",\n \"print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score\",\n \"the its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as\",\n \"= K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0,\",\n \"parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path o the sentences.txt :param\",\n \"embeddings from the\\\", layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def\",\n \"y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only computes a batch-wise\",\n \"time def f1(y_true, y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only\",\n \"else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in bert_dict:\",\n \"summarizer\\\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer\",\n \"parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')\",\n \"''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list()\",\n \"path of the pickle file that the model will be\",\n \"np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features,\",\n \"0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return\",\n \"predictions)) f1 = f1_score(y_val, predictions) if f1 > best_score: best_score\",\n \"keras import backend as K from keras.utils.vis_utils import plot_model from\",\n \"best_params = dict() best_score = 0.0 for c in C:\",\n \"K.epsilon()) return recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only computes\",\n \"network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val,\",\n \"% (f_1 * 100)) f1_results.append(f_1) model = None print(\\\"###########################################\\\") print(\\\"Results\",\n \"the sentences :return: Dictionary with key each sentence of the\",\n \"'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 =\",\n \"F1: %f' % (f_1 * 100)) f1_results.append(f_1) model = None\",\n \"= ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for c\",\n \"sentences :param dataset_csv: the path of the dataset :return: '''\",\n \"def f1(y_true, y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only computes\",\n \"a DNN f1_results = list() for i in range(3): model\",\n \"[0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0\",\n \"/ (predicted_positives + K.epsilon()) return precision precision = precision(y_true, y_pred)\",\n \"length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features =\",\n \"args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 =\",\n \"print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _\",\n \"= vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1\",\n \"np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label) print(errors)\",\n \"score using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a DNN f1_results\",\n \"vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 =\",\n \"for key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def\",\n \"axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val\",\n \"predictions)) print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a\",\n \"possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives\",\n \"length = list() sentence_emb = list() previous_emb = list() next_list\",\n \"np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train,\",\n \"1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives /\",\n \"axis=1) features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga',\",\n \"from keras.models import Sequential from keras.layers import Dense, Dropout from\",\n \"embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val,\",\n \":return: None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128,\",\n \"+= 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if\",\n \"Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu',\",\n \"= np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb,\",\n \"os from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from\",\n \"in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb)\",\n \"Dense, Dropout from keras import backend as K from keras.utils.vis_utils\",\n \"sentence_emb = list() previous_emb = list() next_list = list() section_list\",\n \"train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4 = train_classifier('sentences_list.txt','output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')\",\n \"previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict:\",\n \"f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value] =\",\n \"0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return\",\n \"import numpy as np import pandas as pd import os\",\n \"= np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val\",\n \"a metric for multi-label classification of how many relevant items\",\n \"trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile\",\n \"as f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for\",\n \"np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape)\",\n \"= np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features,\",\n \"activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25))\",\n \"= time.time() running_time = end - start print(\\\"Running time:\\\"+str(running_time)) def\",\n \"= list() previous_emb = list() next_list = list() section_list =\",\n \"for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in\",\n \"print(sentence) errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else:\",\n \"sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the\",\n \"print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length\",\n \"y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and solver\",\n \"print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1\",\n \"/ (possible_positives + K.epsilon()) return recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision\",\n \"the DNN architecture to a png file. Better use the\",\n \"required=True, help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path\",\n \"precision precision = precision(y_true, y_pred) recall = recall(y_true, y_pred) return\",\n \"the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if\",\n \"recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only computes a batch-wise\",\n \"= argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True,\",\n \"solver: start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c)\",\n \"Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time() running_time = end - start\",\n \"trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset\",\n \"= recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list:\",\n \"stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length\",\n \"sklearn.externals import joblib import time def f1(y_true, y_pred): def recall(y_true,\",\n \"layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param\",\n \"trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1])\",\n \"of how many relevant items are selected. \\\"\\\"\\\" true_positives =\",\n \"args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2\",\n \"json file that contains the embeddings of the sentences :param\",\n \"return recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only computes a\",\n \"with open(layer_json, 'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']]\",\n \"print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,\",\n \"solver = ['newton-cg','saga','sag'] best_params = dict() best_score = 0.0 for\",\n \"model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30))\",\n \"if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors +=\",\n \"a batch-wise average of precision. Computes the precision, a metric\",\n \"from keras.layers import Dense, Dropout from keras import backend as\",\n \"precision. Computes the precision, a metric for multi-label classification of\",\n \"to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path to save\",\n \"DNN f1_results = list() for i in range(3): model =\",\n \"that contains the embeddings of the sentences :return: Dictionary with\",\n \"dataset :param filename: The path of the pickle file that\",\n \"relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives\",\n \"= train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 =\",\n \"\\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives =\",\n \"\\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path to train\",\n \"features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train,\",\n \"of the sentences_list and as value the embedding ''' sentences\",\n \"2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the\",\n \"# evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the\",\n \"1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives /\",\n \"#layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning\",\n \"key the index of each sentence and as value the\",\n \"open(layer_json, 'r',encoding='utf-8') as f: for line in f: embeddings[json.loads(line)['linex_index']] =\",\n \"dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list()\",\n \"for c in C: for s in solver: start =\",\n \"\\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train\",\n \"#layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4\",\n \"label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1)\",\n \"=\\\",c,'and solver = ',s) print(\\\"Results using embeddings from the\\\", layer_json,\",\n \"import argparse import json import numpy as np import pandas\",\n \"key,value in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename):\",\n \"if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\",\",\n \"recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param\",\n \"= train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl')\",\n \"sentences = dict()#dict with key the index of each line\",\n \"use the Visulize_DNN.ipynd :param file_to_save: the png file that the\",\n \"= joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using\",\n \"best_params['solver'] = s print(\\\"F1 score using Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end\",\n \"keras.models import Sequential from keras.layers import Dense, Dropout from keras\",\n \"sentence of the sentences_list and as value the embedding '''\",\n \"predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives\",\n \"c in C: for s in solver: start = time.time()\",\n \"args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path'])\",\n \"using embeddings from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1 =\",\n \"The path of the pickle file that the model will\",\n \"model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid'))\",\n \"of precision. Computes the precision, a metric for multi-label classification\",\n \"numpy as np import pandas as pd import os from\",\n \"0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives\",\n \"running_time = end - start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): '''\",\n \"next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log =\",\n \"recall, a metric for multi-label classification of how many relevant\",\n \"print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") # evaluate\",\n \"= dict()#dict with key the index of each line of\",\n \"def visualize_DNN(file_to_save): ''' Save the DNN architecture to a png\",\n \"with open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index] =\",\n \"of the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict =\",\n \"sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1\",\n \"> best_score: best_score = f1 best_params['c'] = c best_params['solver'] =\",\n \"= np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) #\",\n \"the sentences :param dataset_csv: the path of the dataset :return:\",\n \"np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape)\",\n \"the sentences_list and as value the embedding ''' sentences =\",\n \"of each line of the sentences_list.txt and as value the\",\n \"features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val =\",\n \"from keras.utils.vis_utils import plot_model from sklearn.externals import joblib import time\",\n \"(predicted_positives + K.epsilon()) return precision precision = precision(y_true, y_pred) recall\",\n \"that contains the embeddings of the sentences :param dataset_csv: the\",\n \"model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1)\",\n \"png file that the architecture of the DNN will be\",\n \"that the architecture of the DNN will be saved. :return:\",\n \"items are relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0,\",\n \":param file_to_save: the png file that the architecture of the\",\n \"print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results))\",\n \"Sequential from keras.layers import Dense, Dropout from keras import backend\",\n \"row in dataset.iterrows(): sentence = row[1][0].strip() previous = row[1][1].strip() nexts\",\n \"sklearn.metrics import classification_report,f1_score from keras.models import Sequential from keras.layers import\",\n \"in C: for s in solver: start = time.time() log\",\n \"= row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section =\",\n \"json file that contains the embeddings of the sentences :return:\",\n \"average of precision. Computes the precision, a metric for multi-label\",\n \"np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape)\",\n \"# compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train,\",\n \"= list() label = list() errors = 0 for row\",\n \"log.fit(X_train, y_train) #save the model _ = joblib.dump(log, filename, compress=9)\",\n \"next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5])\",\n \"time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture to a\",\n \"import Dense, Dropout from keras import backend as K from\",\n \"random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params = dict()\",\n \"'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl')\",\n \"errors = 0 for row in dataset.iterrows(): sentence = row[1][0].strip()\",\n \"batch-wise average of precision. Computes the precision, a metric for\",\n \"sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb =\",\n \"sentences :return: Dictionary with key each sentence of the sentences_list\",\n \"the index of each sentence and as value the its\",\n \"= np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb)\",\n \"of the dataset :param filename: The path of the pickle\",\n \"help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True,\",\n \"precision = true_positives / (predicted_positives + K.epsilon()) return precision precision\",\n \"from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val, predictions))\",\n \"next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768))\",\n \"f1 = f1_score(y_val, predictions) if f1 > best_score: best_score =\",\n \"sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if previous in\",\n \"else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4])\",\n \"''' :param sentences_list: the path o the sentences.txt :param layer_json:\",\n \"Save the DNN architecture to a png file. Better use\",\n \"= get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb =\",\n \"max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with\",\n \"selected items are relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred,\",\n \"bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape)\",\n \"in sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): '''\",\n \"y_val = train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver\",\n \"X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C =\",\n \"next_emb,section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train,\",\n \"= None print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\")\",\n \"''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True))\",\n \"will be saved. :return: None ''' model = Sequential() model.add(Dense(64,\",\n \"file_to_save: the png file that the architecture of the DNN\",\n \"the DNN will be saved. :return: None ''' model =\",\n \"= train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning =\",\n \"LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl,\",\n \"import json import numpy as np import pandas as pd\",\n \"C=0.1) log.fit(X_train, y_train) #save the model _ = joblib.dump(log, filename,\",\n \"import joblib import time def f1(y_true, y_pred): def recall(y_true, y_pred):\",\n \"y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest\",\n \"model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu',\",\n \"show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json)\",\n \"file that the model will be stored :return: ''' dataset\",\n \"list() next_list = list() section_list = list() label = list()\",\n \"sentences_list and as value the embedding ''' sentences = dict()#dict\",\n \"in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f:\",\n \"errors += 1 if previous in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768))\",\n \"= dict()##dict with key the index of each sentence and\",\n \"= np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1)\",\n \"if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in\",\n \"print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture to\",\n \"f1 best_params['c'] = c best_params['solver'] = s print(\\\"F1 score using\",\n \"from sklearn.externals import joblib import time def f1(y_true, y_pred): def\",\n \"f1_results.append(f_1) model = None print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",\",\n \"best_params['c'] = c best_params['solver'] = s print(\\\"F1 score using Logistic\",\n \"C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if __name__\",\n \"__name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True,\",\n \"sentence and as value the its embedding sentence_emb = dict()#key:sentence,value:its\",\n \"import classification_report,f1_score from keras.models import Sequential from keras.layers import Dense,\",\n \"that the model will be stored :return: ''' dataset =\",\n \"f: for line in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value\",\n \"be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json)\",\n \"fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 = model.evaluate(X_val,\",\n \"print(\\\"###########################################\\\") end = time.time() running_time = end - start print(\\\"Running\",\n \"print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb],\",\n \"embeddings of the sentences :param dataset_csv: the path of the\",\n \"activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd',\",\n \"sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import\",\n \"= train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 = train_classifier('sentences_list.txt','output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_4 =\",\n \"test_size=0.33, random_state=42) C = [0.1,1,2,5,10] solver = ['newton-cg','saga','sag'] best_params =\",\n \"set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path to save summarizer\\\") args\",\n \"start print(\\\"Running time:\\\"+str(running_time)) def visualize_DNN(file_to_save): ''' Save the DNN architecture\",\n \"a metric for multi-label classification of how many selected items\",\n \"and as value the embedding ''' sentences = dict()#dict with\",\n \"X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log\",\n \"path of the json file that contains the embeddings of\",\n \"= log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions))\",\n \"import backend as K from keras.utils.vis_utils import plot_model from sklearn.externals\",\n \"section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb\",\n \"log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the\",\n \"range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128, activation='relu', trainable=True))\",\n \"# fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1 =\",\n \"f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1: %f' % (f_1\",\n \"y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision =\",\n \"true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred,\",\n \"print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1 > best_score:\",\n \"#layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 =\",\n \"train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path to save summarizer\\\")\",\n \"sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line\",\n \"(possible_positives + K.epsilon()) return recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric.\",\n \"K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\",\n \"list() errors = 0 for row in dataset.iterrows(): sentence =\",\n \"from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics\",\n \"file. Better use the Visulize_DNN.ipynd :param file_to_save: the png file\",\n \"= list() errors = 0 for row in dataset.iterrows(): sentence\",\n \"the png file that the architecture of the DNN will\",\n \"embedding ''' sentences = dict()#dict with key the index of\",\n \"print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label\",\n \"architecture of the DNN will be saved. :return: None '''\",\n \"= parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_3 =\",\n \"f1(y_true, y_pred): def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only computes a\",\n \"its embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file:\",\n \"filename, compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings from\",\n \"train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2 = train_classifier('sentences_list.txt','output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl')\",\n \"nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict:\",\n \"the\\\", layer_json, \\\"file\\\") # evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): '''\",\n \"features = np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features,\",\n \"list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path\",\n \"will be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict =\",\n \"* y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision\",\n \"model _ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\")\",\n \"as file: for index,line in enumerate(file): sentences[index] = line.strip() with\",\n \"metric. Only computes a batch-wise average of recall. Computes the\",\n \"pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb =\",\n \"the index of each line of the sentences_list.txt and as\",\n \"\\\"\\\"\\\"Precision metric. Only computes a batch-wise average of precision. Computes\",\n \"print(\\\"F1 score using Logistic Regression:\\\",f1_score(y_val, predictions)) print(\\\"###########################################\\\") #train a DNN\",\n \"log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val, predictions)) print(\\\"F1\",\n \"precision = precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon()))\",\n \"architecture to a png file. Better use the Visulize_DNN.ipynd :param\",\n \"model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1,\",\n \"as value the its embedding sentence_emb = dict()#key:sentence,value:its embedding with\",\n \"LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report,f1_score from\",\n \"path o the sentences.txt :param layer_json: the path of the\",\n \"= true_positives / (predicted_positives + K.epsilon()) return precision precision =\",\n \"in bert_dict: previous_emb.append(bert_dict[previous]) else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts])\",\n \"label = list() errors = 0 for row in dataset.iterrows():\",\n \"',s) print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions))\",\n \"= pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb =\",\n \"_ = joblib.dump(log, filename, compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results\",\n \"trainable=True)) model.add(Dense(128, activation='relu', trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64,\",\n \"log = LogisticRegression(random_state=0, solver='saga', max_iter=1000, C=1) log.fit(features, label) _ =\",\n \"ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer\",\n \"filename: The path of the pickle file that the model\",\n \"model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network\",\n \"log = LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions =\",\n \"if f1 > best_score: best_score = f1 best_params['c'] = c\",\n \"metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss, f_1\",\n \"bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section]) else:\",\n \"ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\",\",\n \"print('\\\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1) model =\",\n \"= get_embeddings(sentences_list, layer_json) length = list() sentence_emb = list() previous_emb\",\n \"return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o\",\n \"o the sentences.txt :param layer_json: the path of the json\",\n \"saved. :return: None ''' model = Sequential() model.add(Dense(64, activation='relu', trainable=True))\",\n \"line.strip() with open(layer_json, 'r',encoding='utf-8') as f: for line in f:\",\n \"= dict() best_score = 0.0 for c in C: for\",\n \"Computes the precision, a metric for multi-label classification of how\",\n \"# np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label,\",\n \"predictions) if f1 > best_score: best_score = f1 best_params['c'] =\",\n \"model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64)\",\n \"model = None print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\", layer_json,\",\n \"to a png file. Better use the Visulize_DNN.ipynd :param file_to_save:\",\n \"\\\"--summarizer path\\\", required=True, help=\\\"path to save summarizer\\\") args = vars(ap.parse_args())\",\n \"else: previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if\",\n \"Better use the Visulize_DNN.ipynd :param file_to_save: the png file that\",\n \"layer_json) length = list() sentence_emb = list() previous_emb = list()\",\n \"max_iter=1000, C=1) log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if\",\n \"the embedding ''' sentences = dict()#dict with key the index\",\n \"batch_size=64) loss, f_1 = model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1: %f'\",\n \"= pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length = list() sentence_emb\",\n \"the model will be stored :return: ''' dataset = pd.read_csv(dataset_csv)\",\n \"C: for s in solver: start = time.time() log =\",\n \"def recall(y_true, y_pred): \\\"\\\"\\\"Recall metric. Only computes a batch-wise average\",\n \"keras.layers import Dense, Dropout from keras import backend as K\",\n \"model will be stored :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict\",\n \"with key the index of each line of the sentences_list.txt\",\n \"def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only computes a batch-wise average\",\n \"np.column_stack([features, length]) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label,\",\n \"selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives\",\n \"#save the model _ = joblib.dump(log, filename, compress=9) predictions =\",\n \"np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1)\",\n \"model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) # compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) #\",\n \"y_train) #save the model _ = joblib.dump(log, filename, compress=9) predictions\",\n \"end = time.time() running_time = end - start print(\\\"Running time:\\\"+str(running_time))\",\n \"* y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall\",\n \"with key each sentence of the sentences_list and as value\",\n \"= precision(y_true, y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def\",\n \"help=\\\"path to train set\\\") ap.add_argument(\\\"-sp\\\", \\\"--summarizer path\\\", required=True, help=\\\"path to\",\n \"index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8') as\",\n \"many relevant items are selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true *\",\n \"sentence = row[1][0].strip() previous = row[1][1].strip() nexts = row[1][2].strip() section\",\n \"compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\",\",\n \"next_emb = np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb =\",\n \"0 for row in dataset.iterrows(): sentence = row[1][0].strip() previous =\",\n \"\\\"file\\\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions) if f1 >\",\n \":param layer_json: the path of the json file that contains\",\n \"''' sentences = dict()#dict with key the index of each\",\n \"in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors += 1 if\",\n \"= np.concatenate([sentence_emb, previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length])\",\n \"100)) f1_results.append(f_1) model = None print(\\\"###########################################\\\") print(\\\"Results using embeddings from\",\n \"#layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 = train_classifier('sentences_list.txt','output_layer_-1.json','train_sentences1.csv','fine_tune_BERT_sentence_classification.pkl') #layer_2\",\n \"list() label = list() errors = 0 for row in\",\n \"png file. Better use the Visulize_DNN.ipynd :param file_to_save: the png\",\n \"+ K.epsilon()) return recall def precision(y_true, y_pred): \\\"\\\"\\\"Precision metric. Only\",\n \"#save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\",\",\n \"_ = joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl')\",\n \"ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences list\\\") ap.add_argument(\\\"-o\\\", \\\"--output\\\",\",\n \"path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3\",\n \"Computes the recall, a metric for multi-label classification of how\",\n \"%f' % (f_1 * 100)) f1_results.append(f_1) model = None print(\\\"###########################################\\\")\",\n \"previous_emb = list() next_list = list() section_list = list() label\",\n \"section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log = LogisticRegression(random_state=0,\",\n \"metric for multi-label classification of how many selected items are\",\n \"of the sentences :param dataset_csv: the path of the dataset\",\n \"y_pred) recall = recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): '''\",\n \"sentence embeddings = dict()##dict with key the index of each\",\n \"= np.asarray(next_list) print(next_emb.shape) previous_emb = np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list)\",\n \"Logistic Regression:\\\",f1) print(\\\"###########################################\\\") end = time.time() running_time = end -\",\n \"predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and solver =\",\n \"save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list, layer_json) length =\",\n \"recall(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon())) def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the\",\n \"print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42)\",\n \"y_val = train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg',\",\n \"evaluate print(np.mean(f1_results)) print(\\\"###########################################\\\") def parameter_tuning_LR(sentences_list,layer_json,dataset_csv): ''' :param sentences_list: the path\",\n \"the path o the sentences.txt :param layer_json: the path of\",\n \"open(sentences_list,'r') as file: for index,line in enumerate(file): sentences[index] = line.strip()\",\n \"for index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json, 'r',encoding='utf-8')\",\n \"sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list:\",\n \"dataset_csv: the path of the dataset :return: ''' dataset =\",\n \"file: for index,line in enumerate(file): sentences[index] = line.strip() with open(layer_json,\",\n \"= list() next_list = list() section_list = list() label =\",\n \"LogisticRegression(random_state=0, solver=s, max_iter=1000, C=c) log.fit(X_train, y_train) predictions = log.predict(X_val) print(\\\"###########################################\\\")\",\n \"save summarizer\\\") args = vars(ap.parse_args()) layer = train_classifier(args['sentences'], args['output'], args['train\",\n \"sentences_list: the path o the sentences.txt :param layer_json: the path\",\n \"list() for i in range(3): model = Sequential() model.add(Dense(64, activation='relu',\",\n \"to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,\",\n \"next_list = list() section_list = list() label = list() errors\",\n \"row[1][3].strip() if sentence in bert_dict: sentence_emb.append(bert_dict[sentence]) else: sentence_emb.append(np.zeros(768)) print(sentence) errors\",\n \"in solver: start = time.time() log = LogisticRegression(random_state=0, solver=s, max_iter=1000,\",\n \"== '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap = argparse.ArgumentParser() ap.add_argument(\\\"-s\\\", \\\"--sentences\\\", required=True, help=\\\"sentences\",\n \"None print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\") #\",\n \"Only computes a batch-wise average of recall. Computes the recall,\",\n \"are relevant. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\",\n \"of the sentences :return: Dictionary with key each sentence of\",\n \"dict()#dict with key the index of each line of the\",\n \"of the pickle file that the model will be stored\",\n \"np.asarray(length) print(length.shape) label = np.asarray(label) print(errors) features = np.concatenate([sentence_emb, previous_emb,\",\n \"activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict\",\n \"previous_emb, next_emb, section_emb], axis=1) features = np.column_stack([features, length]) print(features.shape) log\",\n \"train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4\",\n \"nexts = row[1][2].strip() section = row[1][3].strip() if sentence in bert_dict:\",\n \"from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val, predictions)\",\n \"label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train,\",\n \"predictions)) print(\\\"###########################################\\\") #train a DNN f1_results = list() for i\",\n \"joblib.dump(log, pkl, compress=9) if __name__ == '__main__': #save_model('sentences_list.txt','Fudan_output_layer_-1.json','train_sentences1.csv','summarizer1.pkl') ap =\",\n \"dataset = pd.read_csv(dataset_csv) bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb\",\n \"= c best_params['solver'] = s print(\\\"F1 score using Logistic Regression:\\\",f1)\",\n \"section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label =\",\n \"= list() sentence_emb = list() previous_emb = list() next_list =\",\n \"required=True, help=\\\"output\\\") ap.add_argument(\\\"-ts\\\", \\\"--train set\\\", required=True, help=\\\"path to train set\\\")\",\n \"= K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives +\",\n \"file that contains the embeddings of the sentences :return: Dictionary\",\n \"next_emb,section_emb], axis=1) features = np.column_stack([features, length]) # np.append(features,length,axis=1) print(features.shape) X_train,\",\n \"y_val, verbose=1) print('\\\\nTest F1: %f' % (f_1 * 100)) f1_results.append(f_1)\",\n \"be saved. :return: None ''' model = Sequential() model.add(Dense(64, activation='relu',\",\n \"in f: embeddings[json.loads(line)['linex_index']] = np.asarray(json.loads(line)['features']) for key,value in sentences.items(): sentence_emb[value]\",\n \"dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for index,line in enumerate(file):\",\n \"features = np.concatenate([sentence_emb, previous_emb, next_emb,section_emb], axis=1) features = np.column_stack([features, length])\",\n \"section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb = np.asarray(sentence_emb) print(sentence_emb.shape) next_emb = np.asarray(next_list)\",\n \"from keras import backend as K from keras.utils.vis_utils import plot_model\",\n \"the sentence embeddings = dict()##dict with key the index of\",\n \"= LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model\",\n \"compile network model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train,\",\n \"section_list = list() label = list() errors = 0 for\",\n \"for multi-label classification of how many selected items are relevant.\",\n \"= model.evaluate(X_val, y_val, verbose=1) print('\\\\nTest F1: %f' % (f_1 *\",\n \"metric for multi-label classification of how many relevant items are\",\n \"= np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape) label = np.asarray(label)\",\n \"y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) C = [0.1,1,2,5,10]\",\n \"recall. Computes the recall, a metric for multi-label classification of\",\n \"embedding sentence_emb = dict()#key:sentence,value:its embedding with open(sentences_list,'r') as file: for\",\n \"length]) # np.append(features,length,axis=1) print(features.shape) X_train, X_val, y_train, y_val = train_test_split(features,\",\n \"LogisticRegression(random_state=0, solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _\",\n \"import plot_model from sklearn.externals import joblib import time def f1(y_true,\",\n \"the path of the json file that contains the embeddings\",\n \"compress=9) predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\")\",\n \"sentences.txt :param layer_json: the path of the json file that\",\n \"relevant items are selected. \\\"\\\"\\\" true_positives = K.sum(K.round(K.clip(y_true * y_pred,\",\n \"in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section in bert_dict: section_list.append(bert_dict[section])\",\n \"if section in bert_dict: section_list.append(bert_dict[section]) else: section_list.append(np.zeros(768)) length.append(row[1][4]) label.append(row[1][5]) sentence_emb\",\n \"''' Save the DNN architecture to a png file. Better\",\n \"computes a batch-wise average of recall. Computes the recall, a\",\n \"value the embedding ''' sentences = dict()#dict with key the\",\n \"of the json file that contains the embeddings of the\",\n \"= embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the\",\n \"the embeddings of the sentences :param dataset_csv: the path of\",\n \"list() previous_emb = list() next_list = list() section_list = list()\",\n \"= f1_score(y_val, predictions) if f1 > best_score: best_score = f1\",\n \"solver='newton-cg', max_iter=1000, C=0.1) log.fit(X_train, y_train) #save the model _ =\",\n \"y_train, y_val = train_test_split(features, label, test_size=0.33, random_state=42) log = LogisticRegression(random_state=0,\",\n \"optimizer='sgd', metrics=[f1]) # fit network model.fit(X_train, y_train, epochs=100, batch_size=64) loss,\",\n \"plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv) bert_dict =\",\n \"index of each line of the sentences_list.txt and as value\",\n \"i in range(3): model = Sequential() model.add(Dense(64, activation='relu', trainable=True)) model.add(Dense(128,\",\n \"dict()##dict with key the index of each sentence and as\",\n \"bert_dict = get_embeddings(sentences_list,layer_json) length = list() sentence_emb = list() previous_emb\",\n \"print(\\\"###########################################\\\") print(\\\"LR with C =\\\",c,'and solver = ',s) print(\\\"Results using\",\n \"def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param sentences_list: the path o the sentences.txt\",\n \"predictions = log.predict(X_val) print(\\\"###########################################\\\") print(\\\"Results using embeddings from the\\\",layer_json,\\\"file\\\") print(classification_report(y_val,\",\n \"sentences.items(): sentence_emb[value] = embeddings[key] return sentence_emb def train_classifier(sentences_list,layer_json,dataset_csv,filename): ''' :param\",\n \"model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35)) model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def\",\n \"trainable=True)) model.add(Dropout(0.30)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.25)) model.add(Dense(64, activation='relu', trainable=True)) model.add(Dropout(0.35))\",\n \"set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl') #layer_2 = train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl')\",\n \"= train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv') #layer_1 =\",\n \"the path of the dataset :param filename: The path of\",\n \"solver = ',s) print(\\\"Results using embeddings from the\\\", layer_json, \\\"file\\\")\",\n \"K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon())\",\n \"train_classifier('sentences_list.txt','new_output_layer_-2.json','train_sentences1.csv','fine_tune_BERT_sentence_classification2.pkl') #layer_3 = train_classifier('sentences_list.txt','new_output_layer_-3.json','train_sentences1.csv','fine_tune_BERT_sentence_classification3.pkl') #layer_4 = train_classifier('sentences_list.txt','new_output_layer_-4.json','train_sentences1.csv','fine_tune_BERT_sentence_classification4.pkl') #tuning = parameter_tuning_LR('sentences_list.txt','new_output_layer_-1.json','train_sentences1.csv')\",\n \"true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true,\",\n \"= 0.0 for c in C: for s in solver:\",\n \"embeddings from the\\\", layer_json, \\\"file\\\") print(classification_report(y_val, predictions)) f1 = f1_score(y_val,\",\n \"previous_emb.append(np.zeros(768)) if nexts in bert_dict: next_list.append(bert_dict[nexts]) else: next_list.append(np.zeros(768)) if section\",\n \"np.asarray(previous_emb) print(previous_emb.shape) section_emb = np.asarray(section_list) print(sentence_emb.shape) length = np.asarray(length) print(length.shape)\",\n \"of the DNN will be saved. :return: None ''' model\",\n \":return: Dictionary with key each sentence of the sentences_list and\",\n \"train_classifier(args['sentences'], args['output'], args['train set'],args['summarizer path']) #layer_1 = train_classifier('sentences_list.txt', 'new_output_layer_-1.json', 'train_sentences1.csv','fine_tune_BERT_sentence_classification1.pkl')\",\n \"model.add(Dense(1, activation='sigmoid')) plot_model(model, to_file=file_to_save, show_shapes=True) def save_model(sentences_list,layer_json,dataset_csv,pkl): dataset = pd.read_csv(dataset_csv)\",\n \"of how many selected items are relevant. \\\"\\\"\\\" true_positives =\",\n \"def get_embeddings(sentences_list,layer_json): ''' :param sentences_list: the path o the sentences.txt\",\n \"path of the dataset :return: ''' dataset = pd.read_csv(dataset_csv) bert_dict\"\n]","isConversational":false}}},{"rowIdx":164,"cells":{"ngram":{"kind":"list like","value":["# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker,","# # Licensed under the Apache License, Version 2.0 (the","compliance with the License. # You may obtain a copy","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","2.0 (the \"License\"); # you may not use this file","agreed to in writing, software # distributed under the License","file except in compliance with the License. # You may","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","Unless required by applicable law or agreed to in writing,","False def test_run(self): checker = FakeStatusChecker() def callback(result): assert result","callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time =","test_run(self): checker = FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED')","TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class","distributed under the License is distributed on an \"AS IS\"","# limitations under the License. # import time from unittest","the specific language governing permissions and # limitations under the","FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","+= 1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called =","express or implied. # See the License for the specific","applicable law or agreed to in writing, software # distributed","self.call_count += 1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called","except in compliance with the License. # You may obtain","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","def test_run(self): checker = FakeStatusChecker() def callback(result): assert result ==","Licensed under the Apache License, Version 2.0 (the \"License\"); #","not use this file except in compliance with the License.","self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None","writing, software # distributed under the License is distributed on","in writing, software # distributed under the License is distributed","return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase): def","you may not use this file except in compliance with","# Licensed under the Apache License, Version 2.0 (the \"License\");","if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return","use this file except in compliance with the License. #","setUp(self): self.callback_called = False def test_run(self): checker = FakeStatusChecker() def","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","from unittest import TestCase import mock from mesos.interface.mesos_pb2 import TaskState","CONDITIONS OF ANY KIND, either express or implied. # See","mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager","import TestCase import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker","or implied. # See the License for the specific language","License is distributed on an \"AS IS\" BASIS, # WITHOUT","result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager","License. # You may obtain a copy of the License","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","License, Version 2.0 (the \"License\"); # you may not use","mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from","<filename>src/test/python/apache/aurora/executor/test_status_manager.py<gh_stars>0 # # Licensed under the Apache License, Version 2.0","__init__(self): self.call_count = 0 @property def status(self): if self.call_count ==","# You may obtain a copy of the License at","callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called is","KIND, either express or implied. # See the License for","specific language governing permissions and # limitations under the License.","def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time","apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0","assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time)","under the License is distributed on an \"AS IS\" BASIS,","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","License for the specific language governing permissions and # limitations","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","TestCase import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import","FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def status(self): if","status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2","= True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time)","checker = FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called","StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called","StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def","= StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert","the License for the specific language governing permissions and #","and # limitations under the License. # import time from","(the \"License\"); # you may not use this file except","Apache License, Version 2.0 (the \"License\"); # you may not","# you may not use this file except in compliance","def __init__(self): self.call_count = 0 @property def status(self): if self.call_count","either express or implied. # See the License for the","self.callback_called = False def test_run(self): checker = FakeStatusChecker() def callback(result):","OR CONDITIONS OF ANY KIND, either express or implied. #","== 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase): def setUp(self):","the License is distributed on an \"AS IS\" BASIS, #","@property def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count","in compliance with the License. # You may obtain a","time from unittest import TestCase import mock from mesos.interface.mesos_pb2 import","mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert","class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self): checker","== TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager =","software # distributed under the License is distributed on an","import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self):","# # Unless required by applicable law or agreed to","import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker","def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count +=","from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker):","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1","Version 2.0 (the \"License\"); # you may not use this","import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property","law or agreed to in writing, software # distributed under","the License. # import time from unittest import TestCase import","= FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called =","governing permissions and # limitations under the License. # import","None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self):","mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called is True","import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager","TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self): checker =","implied. # See the License for the specific language governing","1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False","under the Apache License, Version 2.0 (the \"License\"); # you","\"License\"); # you may not use this file except in","# import time from unittest import TestCase import mock from","from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count =","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase):","self.call_count = 0 @property def status(self): if self.call_count == 2:","under the License. # import time from unittest import TestCase","by applicable law or agreed to in writing, software #","# distributed under the License is distributed on an \"AS","OF ANY KIND, either express or implied. # See the","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","import time from unittest import TestCase import mock from mesos.interface.mesos_pb2","may obtain a copy of the License at # #","# Unless required by applicable law or agreed to in","ANY KIND, either express or implied. # See the License","See the License for the specific language governing permissions and","self.callback_called = True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback,","the License. # You may obtain a copy of the","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","for the specific language governing permissions and # limitations under","True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run()","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","to in writing, software # distributed under the License is","= mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","# See the License for the specific language governing permissions","License. # import time from unittest import TestCase import mock","You may obtain a copy of the License at #","language governing permissions and # limitations under the License. #","unittest import TestCase import mock from mesos.interface.mesos_pb2 import TaskState from","may not use this file except in compliance with the","or agreed to in writing, software # distributed under the","mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count ==","StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count","required by applicable law or agreed to in writing, software","class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def status(self):","permissions and # limitations under the License. # import time","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","0 @property def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED')","with the License. # You may obtain a copy of","this file except in compliance with the License. # You","= False def test_run(self): checker = FakeStatusChecker() def callback(result): assert","the Apache License, Version 2.0 (the \"License\"); # you may","def setUp(self): self.callback_called = False def test_run(self): checker = FakeStatusChecker()","= 0 @property def status(self): if self.call_count == 2: return","return None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def","apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def","limitations under the License. # import time from unittest import"],"string":"[\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker,\",\n \"# # Licensed under the Apache License, Version 2.0 (the\",\n \"compliance with the License. # You may obtain a copy\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"2.0 (the \\\"License\\\"); # you may not use this file\",\n \"agreed to in writing, software # distributed under the License\",\n \"file except in compliance with the License. # You may\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"Unless required by applicable law or agreed to in writing,\",\n \"False def test_run(self): checker = FakeStatusChecker() def callback(result): assert result\",\n \"callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time =\",\n \"test_run(self): checker = FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED')\",\n \"TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class\",\n \"distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"# limitations under the License. # import time from unittest\",\n \"the specific language governing permissions and # limitations under the\",\n \"FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"+= 1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called =\",\n \"express or implied. # See the License for the specific\",\n \"applicable law or agreed to in writing, software # distributed\",\n \"self.call_count += 1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called\",\n \"except in compliance with the License. # You may obtain\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"def test_run(self): checker = FakeStatusChecker() def callback(result): assert result ==\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"not use this file except in compliance with the License.\",\n \"self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None\",\n \"writing, software # distributed under the License is distributed on\",\n \"in writing, software # distributed under the License is distributed\",\n \"return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase): def\",\n \"you may not use this file except in compliance with\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return\",\n \"use this file except in compliance with the License. #\",\n \"setUp(self): self.callback_called = False def test_run(self): checker = FakeStatusChecker() def\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"from unittest import TestCase import mock from mesos.interface.mesos_pb2 import TaskState\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager\",\n \"import TestCase import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker\",\n \"or implied. # See the License for the specific language\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager\",\n \"License. # You may obtain a copy of the License\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from\",\n \"<filename>src/test/python/apache/aurora/executor/test_status_manager.py<gh_stars>0 # # Licensed under the Apache License, Version 2.0\",\n \"__init__(self): self.call_count = 0 @property def status(self): if self.call_count ==\",\n \"# You may obtain a copy of the License at\",\n \"callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called is\",\n \"KIND, either express or implied. # See the License for\",\n \"specific language governing permissions and # limitations under the License.\",\n \"def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time\",\n \"apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0\",\n \"assert result == TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time)\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"License for the specific language governing permissions and # limitations\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"TestCase import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import\",\n \"FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def status(self): if\",\n \"status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2\",\n \"= True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time)\",\n \"checker = FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called\",\n \"StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called\",\n \"StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def\",\n \"= StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert\",\n \"the License for the specific language governing permissions and #\",\n \"and # limitations under the License. # import time from\",\n \"(the \\\"License\\\"); # you may not use this file except\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"# you may not use this file except in compliance\",\n \"def __init__(self): self.call_count = 0 @property def status(self): if self.call_count\",\n \"either express or implied. # See the License for the\",\n \"self.callback_called = False def test_run(self): checker = FakeStatusChecker() def callback(result):\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"== 2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase): def setUp(self):\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"@property def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count\",\n \"in compliance with the License. # You may obtain a\",\n \"time from unittest import TestCase import mock from mesos.interface.mesos_pb2 import\",\n \"mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert\",\n \"class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self): checker\",\n \"== TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager =\",\n \"software # distributed under the License is distributed on an\",\n \"import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self):\",\n \"# # Unless required by applicable law or agreed to\",\n \"import mock from mesos.interface.mesos_pb2 import TaskState from apache.aurora.executor.common.status_checker import StatusChecker\",\n \"def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count +=\",\n \"from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker):\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED') self.call_count += 1\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property\",\n \"law or agreed to in writing, software # distributed under\",\n \"the License. # import time from unittest import TestCase import\",\n \"= FakeStatusChecker() def callback(result): assert result == TaskState.Value('TASK_KILLED') self.callback_called =\",\n \"governing permissions and # limitations under the License. # import\",\n \"None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self):\",\n \"mock_time) status_manager.run() assert mock_time.sleep.call_count == 2 assert self.callback_called is True\",\n \"import TaskState from apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager\",\n \"TestStatusManager(TestCase): def setUp(self): self.callback_called = False def test_run(self): checker =\",\n \"implied. # See the License for the specific language governing\",\n \"1 return None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"\\\"License\\\"); # you may not use this file except in\",\n \"# import time from unittest import TestCase import mock from\",\n \"from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count =\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"2: return TaskState.Value('TASK_KILLED') self.call_count += 1 return None class TestStatusManager(TestCase):\",\n \"self.call_count = 0 @property def status(self): if self.call_count == 2:\",\n \"under the License. # import time from unittest import TestCase\",\n \"by applicable law or agreed to in writing, software #\",\n \"# distributed under the License is distributed on an \\\"AS\",\n \"OF ANY KIND, either express or implied. # See the\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"import time from unittest import TestCase import mock from mesos.interface.mesos_pb2\",\n \"may obtain a copy of the License at # #\",\n \"# Unless required by applicable law or agreed to in\",\n \"ANY KIND, either express or implied. # See the License\",\n \"See the License for the specific language governing permissions and\",\n \"self.callback_called = True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback,\",\n \"the License. # You may obtain a copy of the\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"for the specific language governing permissions and # limitations under\",\n \"True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run()\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"to in writing, software # distributed under the License is\",\n \"= mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"# See the License for the specific language governing permissions\",\n \"License. # import time from unittest import TestCase import mock\",\n \"You may obtain a copy of the License at #\",\n \"language governing permissions and # limitations under the License. #\",\n \"unittest import TestCase import mock from mesos.interface.mesos_pb2 import TaskState from\",\n \"may not use this file except in compliance with the\",\n \"or agreed to in writing, software # distributed under the\",\n \"mock.Mock(spec=time) status_manager = StatusManager(checker, callback, mock_time) status_manager.run() assert mock_time.sleep.call_count ==\",\n \"StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count\",\n \"required by applicable law or agreed to in writing, software\",\n \"class FakeStatusChecker(StatusChecker): def __init__(self): self.call_count = 0 @property def status(self):\",\n \"permissions and # limitations under the License. # import time\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"0 @property def status(self): if self.call_count == 2: return TaskState.Value('TASK_KILLED')\",\n \"with the License. # You may obtain a copy of\",\n \"this file except in compliance with the License. # You\",\n \"= False def test_run(self): checker = FakeStatusChecker() def callback(result): assert\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"def setUp(self): self.callback_called = False def test_run(self): checker = FakeStatusChecker()\",\n \"= 0 @property def status(self): if self.call_count == 2: return\",\n \"return None class TestStatusManager(TestCase): def setUp(self): self.callback_called = False def\",\n \"apache.aurora.executor.common.status_checker import StatusChecker from apache.aurora.executor.status_manager import StatusManager class FakeStatusChecker(StatusChecker): def\",\n \"limitations under the License. # import time from unittest import\"\n]","isConversational":false}}},{"rowIdx":165,"cells":{"ngram":{"kind":"list like","value":["to the principal impact direction. for veh_striking in veh_striking_list: if","conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1 ** 2","+ np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e","+ veh_m[1] * veh_y2) delta_v2 = (1 + veh_e) *","= np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) -","impact direction. for veh_striking in veh_striking_list: if veh_striking[0] == 1:","delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index =","= veh_k[0] ** 2 / (veh_a1 ** 2 + veh_k[0]","on the plane 2-DOF rigid-body collision model with momentum conservation.","the principal impact direction. for veh_striking in veh_striking_list: if veh_striking[0]","+ veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2 /","veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3])","code accompanies the paper titled \"Human injury-based safety decision of","delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_, delta_v2_, index","** 2 + veh_k[0] ** 2) veh_y2 = veh_k[1] **","veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 =","* veh_m[1] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0]","/ veh_RDS # Obtain delta-v based on the plane 2-DOF","/ (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2)","- veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] **","veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 =","veh_RDS else: veh_e = 0.5 / veh_RDS elif veh_striking[0] ==","veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v *","veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0]","= np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2","elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1","- V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] **","** 2) veh_y2 = veh_k[1] ** 2 / (veh_a2 **","Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) -------------------------------------------------------------------------------------------------","[16, 1, 2, 3, 17, 20, 21] and (veh_striking[2]+1) in","2 / veh_RDS else: veh_e = 0.5 / veh_RDS elif","for veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca =","+ np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e","* veh_y1 * veh_y2 * veh_RDS / (veh_m[0] * veh_y1","* veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ =","veh_k[1] ** 2 / (veh_a2 ** 2 + veh_k[1] **","* np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0] ==","veh_y1 = veh_k[0] ** 2 / (veh_a1 ** 2 +","== 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1]","V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0]","veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3])","collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) =","veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2","weight_norm __author__ = \"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param):","= 1.5 / veh_RDS # Obtain delta-v based on the","torch import nn from torch.nn.utils import weight_norm __author__ = \"<NAME>\"","np from torch import nn from torch.nn.utils import weight_norm __author__","according to the principal impact direction. for veh_striking in veh_striking_list:","veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v *","''' ------------------------------------------------------------------------------------------------- This code accompanies the paper titled \"Human injury-based","<NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import","veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] **","(veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1","2) delta_v1 = (1 + veh_e) * veh_m[1] * veh_y1","np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 =","np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0]","+ veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1)","of automated vehicles\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author:","= np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1","veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1])","= np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) -","** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2))","''' import torch import numpy as np from torch import","# Estimate the collision condition (delat-v) according to the principal","veh_m[0] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0] *","Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy","= np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2","1.5 / veh_RDS # Obtain delta-v based on the plane","veh_y2 = veh_k[1] ** 2 / (veh_a2 ** 2 +","delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list))","delta_v2_list = [] # Estimate the collision condition (delat-v) according","delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list","rigid-body collision model with momentum conservation. veh_y1 = veh_k[0] **","= np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca","= np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 +","= V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif","* np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS =","/ veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] /","(1 + veh_e) * veh_m[0] * veh_y1 * veh_y2 *","< delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list = []","+ delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3, 17,","** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2))","author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as","(veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 =","(<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as np from","''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2","1, 2, 3, 17, 20, 21] and (veh_striking[2]+1) in [16,","veh_e = 0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca","import nn from torch.nn.utils import weight_norm __author__ = \"<NAME>\" def","veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 =","/ 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5","V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision condition. '''","This code accompanies the paper titled \"Human injury-based safety decision","veh_ca + np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2)","the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m)","* veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1 +","as np from torch import nn from torch.nn.utils import weight_norm","2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5 /","and (veh_striking[2]+1) in [16, 1, 2, 3, 17, 20, 21]:","np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 =","veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3])","<NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import","veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle)","-1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list =","plane 2-DOF rigid-body collision model with momentum conservation. veh_y1 =","17, 20, 21] and (veh_striking[2]+1) in [16, 1, 2, 3,","principal impact direction. for veh_striking in veh_striking_list: if veh_striking[0] ==","= V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS #","with momentum conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1","2, 3, 17, 20, 21] and (veh_striking[2]+1) in [16, 1,","titled \"Human injury-based safety decision of automated vehicles\" Author: <NAME>,","(1 + veh_e) * veh_m[1] * veh_y1 * veh_y2 *","2 / (veh_a2 ** 2 + veh_k[1] ** 2) delta_v1","np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca +","/ (veh_a2 ** 2 + veh_k[1] ** 2) delta_v1 =","veh_k[1] ** 2) delta_v1 = (1 + veh_e) * veh_m[1]","if (veh_striking[1]+1) in [16, 1, 2, 3, 17, 20, 21]","\"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the","np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca +","veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0])","= [] delta_v2_list = [] # Estimate the collision condition","** 2 / (veh_a2 ** 2 + veh_k[1] ** 2)","import numpy as np from torch import nn from torch.nn.utils","np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0] == 3:","/ veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1]","np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6","veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 =","veh_e = 2 / veh_RDS else: veh_e = 0.5 /","0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0]","- veh_ca + np.pi / 2)) veh_RDS = V1_v *","[16, 1, 2, 3, 17, 20, 21]: veh_e = 2","2) * np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS","+ veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1)","= np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS","veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3])","else: veh_e = 0.5 / veh_RDS elif veh_striking[0] == 4:","/ (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2 =","veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0]","2, 3, 17, 20, 21]: veh_e = 2 / veh_RDS","** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca","= np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] **","+ veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi","= (1 + veh_e) * veh_m[0] * veh_y1 * veh_y2","** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca","veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2 / (veh_a2","veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2)","np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS = V2_v","np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2","Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k,","= 1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca =","condition (delat-v) according to the principal impact direction. for veh_striking","delta_v1 = (1 + veh_e) * veh_m[1] * veh_y1 *","based on the plane 2-DOF rigid-body collision model with momentum","the plane 2-DOF rigid-body collision model with momentum conservation. veh_y1","* np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 +","0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1]","+ veh_e) * veh_m[0] * veh_y1 * veh_y2 * veh_RDS","veh_e) * veh_m[1] * veh_y1 * veh_y2 * veh_RDS /","< 1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list =","np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS =","<NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- '''","(delat-v) according to the principal impact direction. for veh_striking in","2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca +","V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2)","delta_v1_list = [] delta_v2_list = [] # Estimate the collision","max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_, delta_v2_,","* veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2)","veh_y1 * veh_y2 * veh_RDS / (veh_m[0] * veh_y1 +","+ veh_k[1] ** 2) delta_v1 = (1 + veh_e) *","veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] **","__author__ = \"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): '''","np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS = V1_v","2 + veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if","(veh_striking[2]+1) in [16, 1, 2, 3, 17, 20, 21]: veh_e","3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] -","veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0])","= max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_,","<NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as np","from torch.nn.utils import weight_norm __author__ = \"<NAME>\" def Collision_cond(veh_striking_list, V1_v,","accompanies the paper titled \"Human injury-based safety decision of automated","np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e =","21]: veh_e = 2 / veh_RDS else: veh_e = 0.5","np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1]","veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1 + veh_e)","== 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1]","else: veh_e = 0.5 / veh_RDS elif veh_striking[0] == 2:","veh_m[1] * veh_y2) delta_v2 = (1 + veh_e) * veh_m[0]","* np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 +","* veh_m[0] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0]","veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi /","veh_e = 0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca","model with momentum conservation. veh_y1 = veh_k[0] ** 2 /","veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) *","''' Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs,","in [16, 1, 2, 3, 17, 20, 21] and (veh_striking[2]+1)","3, 17, 20, 21] and (veh_striking[2]+1) in [16, 1, 2,","* veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list)","import torch import numpy as np from torch import nn","vehicles\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>)","/ veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v","* np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain delta-v","np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 -","# Obtain delta-v based on the plane 2-DOF rigid-body collision","* veh_y2) delta_v2 = (1 + veh_e) * veh_m[0] *","paper titled \"Human injury-based safety decision of automated vehicles\" Author:","veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle)","- V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] **","veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2","- veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1","= [] # Estimate the collision condition (delat-v) according to","= 1e-6 delta_v1_list = [] delta_v2_list = [] # Estimate","the collision condition (delat-v) according to the principal impact direction.","2 / (veh_a1 ** 2 + veh_k[0] ** 2) veh_y2","collision model with momentum conservation. veh_y1 = veh_k[0] ** 2","+ veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi","veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2","= veh_k[1] ** 2 / (veh_a2 ** 2 + veh_k[1]","2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1,","/ veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] /","direction. for veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca","= 2 / veh_RDS else: veh_e = 0.5 / veh_RDS","/ veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0]","+ veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_","delta_angle, veh_param): ''' Estimate the collision condition. ''' (veh_l, veh_w,","(veh_a2 ** 2 + veh_k[1] ** 2) delta_v1 = (1","(veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_","2 + veh_k[1] ** 2) delta_v1 = (1 + veh_e)","2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] -","veh_k[0] ** 2 / (veh_a1 ** 2 + veh_k[0] **","** 2) delta_v1 = (1 + veh_e) * veh_m[1] *","if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2","automated vehicles\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME>","** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16,","= np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] **","= np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 =","veh_e) * veh_m[0] * veh_y1 * veh_y2 * veh_RDS /","1e-6 delta_v1_list = [] delta_v2_list = [] # Estimate the","veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2)","veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in","------------------------------------------------------------------------------------------------- ''' import torch import numpy as np from torch","np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain delta-v based","np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0]","numpy as np from torch import nn from torch.nn.utils import","[] delta_v2_list = [] # Estimate the collision condition (delat-v)","the paper titled \"Human injury-based safety decision of automated vehicles\"","torch import numpy as np from torch import nn from","veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi /","elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1","delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return","+ veh_e) * veh_m[1] * veh_y1 * veh_y2 * veh_RDS","veh_e = 1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca","(veh_striking[1]+1) in [16, 1, 2, 3, 17, 20, 21] and","veh_ca + np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2)","veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1]","V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain","veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1)","veh_e = 1.5 / veh_RDS # Obtain delta-v based on","veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6","** 2) * np.cos(delta_angle_2 - veh_ca + np.pi / 2))","veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 =","/ veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v","np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 -","21] and (veh_striking[2]+1) in [16, 1, 2, 3, 17, 20,","delta-v based on the plane 2-DOF rigid-body collision model with","== 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0]","np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e =","veh_y2 * veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] *","np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3,","Estimate the collision condition (delat-v) according to the principal impact","decision of automated vehicles\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding","3, 17, 20, 21]: veh_e = 2 / veh_RDS else:","1, 2, 3, 17, 20, 21]: veh_e = 2 /","if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list","= np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 +","2-DOF rigid-body collision model with momentum conservation. veh_y1 = veh_k[0]","torch.nn.utils import weight_norm __author__ = \"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v,","1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list = []","* veh_y2 * veh_RDS / (veh_m[0] * veh_y1 + veh_m[1]","from torch import nn from torch.nn.utils import weight_norm __author__ =","2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca +","== 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0]","- veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] **","np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2","= veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 <","elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2","veh_y2) delta_v2 = (1 + veh_e) * veh_m[0] * veh_y1","[] # Estimate the collision condition (delat-v) according to the","veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) *","veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 =","V2_v, delta_angle, veh_param): ''' Estimate the collision condition. ''' (veh_l,","safety decision of automated vehicles\" Author: <NAME>, <NAME>, <NAME>, <NAME>","------------------------------------------------------------------------------------------------- This code accompanies the paper titled \"Human injury-based safety","nn from torch.nn.utils import weight_norm __author__ = \"<NAME>\" def Collision_cond(veh_striking_list,","- veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2","/ veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] /","veh_m[1] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0] *","veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list)","= (1 + veh_e) * veh_m[1] * veh_y1 * veh_y2","veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2","delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list = [] #","17, 20, 21]: veh_e = 2 / veh_RDS else: veh_e","/ 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5","= 0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca =","import weight_norm __author__ = \"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle,","2 + veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if","veh_RDS # Obtain delta-v based on the plane 2-DOF rigid-body","np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v)","collision condition (delat-v) according to the principal impact direction. for","Obtain delta-v based on the plane 2-DOF rigid-body collision model","veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ =","= np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca","/ veh_RDS else: veh_e = 0.5 / veh_RDS elif veh_striking[0]","np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS =","= np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS","injury-based safety decision of automated vehicles\" Author: <NAME>, <NAME>, <NAME>,","momentum conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1 **","condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param","** 2 / (veh_a1 ** 2 + veh_k[0] ** 2)","in [16, 1, 2, 3, 17, 20, 21]: veh_e =","delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2","/ (veh_a1 ** 2 + veh_k[0] ** 2) veh_y2 =","veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if","* np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2,","<NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch","2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5 /","20, 21]: veh_e = 2 / veh_RDS else: veh_e =","delta_v2 = (1 + veh_e) * veh_m[0] * veh_y1 *","= \"<NAME>\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate","veh_param): ''' Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf,","4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] -","veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 <","(veh_a1 ** 2 + veh_k[0] ** 2) veh_y2 = veh_k[1]","- veh_ca + np.pi / 2)) veh_RDS = V2_v *","veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6:","veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS","2 + veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2","** 2 + veh_k[1] ** 2) delta_v1 = (1 +","20, 21] and (veh_striking[2]+1) in [16, 1, 2, 3, 17,","veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle)))","Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision condition.","veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS","V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2)","1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1]","veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in","def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision","1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] -","= 0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca =","np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v)","in veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] /","delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3, 17, 20,","veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index","veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1])","np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1]","\"Human injury-based safety decision of automated vehicles\" Author: <NAME>, <NAME>,","= np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2","2) veh_y2 = veh_k[1] ** 2 / (veh_a2 ** 2"],"string":"[\n \"to the principal impact direction. for veh_striking in veh_striking_list: if\",\n \"conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1 ** 2\",\n \"+ np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e\",\n \"+ veh_m[1] * veh_y2) delta_v2 = (1 + veh_e) *\",\n \"= np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) -\",\n \"impact direction. for veh_striking in veh_striking_list: if veh_striking[0] == 1:\",\n \"delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index =\",\n \"= veh_k[0] ** 2 / (veh_a1 ** 2 + veh_k[0]\",\n \"on the plane 2-DOF rigid-body collision model with momentum conservation.\",\n \"the principal impact direction. for veh_striking in veh_striking_list: if veh_striking[0]\",\n \"+ veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2 /\",\n \"veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3])\",\n \"code accompanies the paper titled \\\"Human injury-based safety decision of\",\n \"delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_, delta_v2_, index\",\n \"** 2 + veh_k[0] ** 2) veh_y2 = veh_k[1] **\",\n \"veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 =\",\n \"* veh_m[1] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0]\",\n \"/ veh_RDS # Obtain delta-v based on the plane 2-DOF\",\n \"/ (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2)\",\n \"- veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] **\",\n \"veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 =\",\n \"veh_RDS else: veh_e = 0.5 / veh_RDS elif veh_striking[0] ==\",\n \"veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v *\",\n \"veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0]\",\n \"= np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2\",\n \"elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1\",\n \"- V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] **\",\n \"** 2) veh_y2 = veh_k[1] ** 2 / (veh_a2 **\",\n \"Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) -------------------------------------------------------------------------------------------------\",\n \"[16, 1, 2, 3, 17, 20, 21] and (veh_striking[2]+1) in\",\n \"2 / veh_RDS else: veh_e = 0.5 / veh_RDS elif\",\n \"for veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca =\",\n \"+ np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e\",\n \"* veh_y1 * veh_y2 * veh_RDS / (veh_m[0] * veh_y1\",\n \"* veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ =\",\n \"veh_k[1] ** 2 / (veh_a2 ** 2 + veh_k[1] **\",\n \"* np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0] ==\",\n \"veh_y1 = veh_k[0] ** 2 / (veh_a1 ** 2 +\",\n \"== 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1]\",\n \"V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0]\",\n \"veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3])\",\n \"collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) =\",\n \"veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2\",\n \"weight_norm __author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param):\",\n \"= 1.5 / veh_RDS # Obtain delta-v based on the\",\n \"torch import nn from torch.nn.utils import weight_norm __author__ = \\\"<NAME>\\\"\",\n \"np from torch import nn from torch.nn.utils import weight_norm __author__\",\n \"according to the principal impact direction. for veh_striking in veh_striking_list:\",\n \"veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v *\",\n \"''' ------------------------------------------------------------------------------------------------- This code accompanies the paper titled \\\"Human injury-based\",\n \"<NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import\",\n \"veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] **\",\n \"(veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1\",\n \"2) delta_v1 = (1 + veh_e) * veh_m[1] * veh_y1\",\n \"np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 =\",\n \"np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0]\",\n \"+ veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1)\",\n \"of automated vehicles\\\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author:\",\n \"= np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1\",\n \"veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1])\",\n \"= np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) -\",\n \"** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2))\",\n \"''' import torch import numpy as np from torch import\",\n \"# Estimate the collision condition (delat-v) according to the principal\",\n \"veh_m[0] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0] *\",\n \"Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy\",\n \"= np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2\",\n \"1.5 / veh_RDS # Obtain delta-v based on the plane\",\n \"veh_y2 = veh_k[1] ** 2 / (veh_a2 ** 2 +\",\n \"delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list))\",\n \"delta_v2_list = [] # Estimate the collision condition (delat-v) according\",\n \"delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list\",\n \"rigid-body collision model with momentum conservation. veh_y1 = veh_k[0] **\",\n \"= np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca\",\n \"= np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 +\",\n \"= V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif\",\n \"* np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS =\",\n \"/ veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] /\",\n \"(1 + veh_e) * veh_m[0] * veh_y1 * veh_y2 *\",\n \"< delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list = []\",\n \"+ delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3, 17,\",\n \"** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2))\",\n \"author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as\",\n \"(veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 =\",\n \"(<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as np from\",\n \"''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2\",\n \"1, 2, 3, 17, 20, 21] and (veh_striking[2]+1) in [16,\",\n \"veh_e = 0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca\",\n \"import nn from torch.nn.utils import weight_norm __author__ = \\\"<NAME>\\\" def\",\n \"veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 =\",\n \"/ 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5\",\n \"V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision condition. '''\",\n \"This code accompanies the paper titled \\\"Human injury-based safety decision\",\n \"veh_ca + np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2)\",\n \"the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m)\",\n \"* veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1 +\",\n \"as np from torch import nn from torch.nn.utils import weight_norm\",\n \"2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5 /\",\n \"and (veh_striking[2]+1) in [16, 1, 2, 3, 17, 20, 21]:\",\n \"np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 =\",\n \"veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3])\",\n \"<NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import\",\n \"veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle)\",\n \"-1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list =\",\n \"plane 2-DOF rigid-body collision model with momentum conservation. veh_y1 =\",\n \"17, 20, 21] and (veh_striking[2]+1) in [16, 1, 2, 3,\",\n \"principal impact direction. for veh_striking in veh_striking_list: if veh_striking[0] ==\",\n \"= V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS #\",\n \"with momentum conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1\",\n \"2, 3, 17, 20, 21] and (veh_striking[2]+1) in [16, 1,\",\n \"titled \\\"Human injury-based safety decision of automated vehicles\\\" Author: <NAME>,\",\n \"(1 + veh_e) * veh_m[1] * veh_y1 * veh_y2 *\",\n \"2 / (veh_a2 ** 2 + veh_k[1] ** 2) delta_v1\",\n \"np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca +\",\n \"/ (veh_a2 ** 2 + veh_k[1] ** 2) delta_v1 =\",\n \"veh_k[1] ** 2) delta_v1 = (1 + veh_e) * veh_m[1]\",\n \"if (veh_striking[1]+1) in [16, 1, 2, 3, 17, 20, 21]\",\n \"\\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the\",\n \"np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(veh_ca +\",\n \"veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0])\",\n \"= [] delta_v2_list = [] # Estimate the collision condition\",\n \"** 2 / (veh_a2 ** 2 + veh_k[1] ** 2)\",\n \"import numpy as np from torch import nn from torch.nn.utils\",\n \"np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS elif veh_striking[0] == 3:\",\n \"/ veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1]\",\n \"np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6\",\n \"veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 =\",\n \"veh_e = 2 / veh_RDS else: veh_e = 0.5 /\",\n \"0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0]\",\n \"- veh_ca + np.pi / 2)) veh_RDS = V1_v *\",\n \"[16, 1, 2, 3, 17, 20, 21]: veh_e = 2\",\n \"2) * np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS\",\n \"+ veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1)\",\n \"= np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS\",\n \"veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3])\",\n \"else: veh_e = 0.5 / veh_RDS elif veh_striking[0] == 4:\",\n \"/ (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2 =\",\n \"veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0]\",\n \"2, 3, 17, 20, 21]: veh_e = 2 / veh_RDS\",\n \"** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca\",\n \"= np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] **\",\n \"+ veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi\",\n \"= (1 + veh_e) * veh_m[0] * veh_y1 * veh_y2\",\n \"** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca\",\n \"veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2 / (veh_a2\",\n \"veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2)\",\n \"np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS = V2_v\",\n \"np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2\",\n \"Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k,\",\n \"= 1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca =\",\n \"condition (delat-v) according to the principal impact direction. for veh_striking\",\n \"delta_v1 = (1 + veh_e) * veh_m[1] * veh_y1 *\",\n \"based on the plane 2-DOF rigid-body collision model with momentum\",\n \"the plane 2-DOF rigid-body collision model with momentum conservation. veh_y1\",\n \"* np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 +\",\n \"0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1]\",\n \"+ veh_e) * veh_m[0] * veh_y1 * veh_y2 * veh_RDS\",\n \"veh_e) * veh_m[1] * veh_y1 * veh_y2 * veh_RDS /\",\n \"< 1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list =\",\n \"np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS =\",\n \"<NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- '''\",\n \"(delat-v) according to the principal impact direction. for veh_striking in\",\n \"2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca +\",\n \"V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2)\",\n \"delta_v1_list = [] delta_v2_list = [] # Estimate the collision\",\n \"max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_, delta_v2_,\",\n \"* veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2)\",\n \"veh_y1 * veh_y2 * veh_RDS / (veh_m[0] * veh_y1 +\",\n \"+ veh_k[1] ** 2) delta_v1 = (1 + veh_e) *\",\n \"veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] **\",\n \"__author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): '''\",\n \"np.cos(delta_angle_2 - veh_ca + np.pi / 2)) veh_RDS = V1_v\",\n \"2 + veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if\",\n \"(veh_striking[2]+1) in [16, 1, 2, 3, 17, 20, 21]: veh_e\",\n \"3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] -\",\n \"veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0])\",\n \"= max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return delta_v1_,\",\n \"<NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch import numpy as np\",\n \"from torch.nn.utils import weight_norm __author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v,\",\n \"accompanies the paper titled \\\"Human injury-based safety decision of automated\",\n \"np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e =\",\n \"21]: veh_e = 2 / veh_RDS else: veh_e = 0.5\",\n \"np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1]\",\n \"veh_y1 + veh_m[1] * veh_y2) delta_v2 = (1 + veh_e)\",\n \"== 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1]\",\n \"else: veh_e = 0.5 / veh_RDS elif veh_striking[0] == 2:\",\n \"veh_m[1] * veh_y2) delta_v2 = (1 + veh_e) * veh_m[0]\",\n \"* np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 +\",\n \"* veh_m[0] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0]\",\n \"veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi /\",\n \"veh_e = 0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca\",\n \"model with momentum conservation. veh_y1 = veh_k[0] ** 2 /\",\n \"veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) *\",\n \"''' Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs,\",\n \"in [16, 1, 2, 3, 17, 20, 21] and (veh_striking[2]+1)\",\n \"3, 17, 20, 21] and (veh_striking[2]+1) in [16, 1, 2,\",\n \"* veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list)\",\n \"import torch import numpy as np from torch import nn\",\n \"vehicles\\\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>)\",\n \"/ veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v\",\n \"* np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain delta-v\",\n \"np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 -\",\n \"# Obtain delta-v based on the plane 2-DOF rigid-body collision\",\n \"* veh_y2) delta_v2 = (1 + veh_e) * veh_m[0] *\",\n \"paper titled \\\"Human injury-based safety decision of automated vehicles\\\" Author:\",\n \"veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle)\",\n \"- V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] **\",\n \"veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v2\",\n \"- veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1\",\n \"= [] # Estimate the collision condition (delat-v) according to\",\n \"= 1e-6 delta_v1_list = [] delta_v2_list = [] # Estimate\",\n \"the collision condition (delat-v) according to the principal impact direction.\",\n \"2 / (veh_a1 ** 2 + veh_k[0] ** 2) veh_y2\",\n \"collision model with momentum conservation. veh_y1 = veh_k[0] ** 2\",\n \"+ veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi\",\n \"veh_a1 = np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2\",\n \"= veh_k[1] ** 2 / (veh_a2 ** 2 + veh_k[1]\",\n \"2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1,\",\n \"/ veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] /\",\n \"direction. for veh_striking in veh_striking_list: if veh_striking[0] == 1: veh_ca\",\n \"= 2 / veh_RDS else: veh_e = 0.5 / veh_RDS\",\n \"/ veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0]\",\n \"+ veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_\",\n \"delta_angle, veh_param): ''' Estimate the collision condition. ''' (veh_l, veh_w,\",\n \"(veh_a2 ** 2 + veh_k[1] ** 2) delta_v1 = (1\",\n \"(veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_\",\n \"2 + veh_k[1] ** 2) delta_v1 = (1 + veh_e)\",\n \"2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgf[1] -\",\n \"veh_k[0] ** 2 / (veh_a1 ** 2 + veh_k[0] **\",\n \"** 2) delta_v1 = (1 + veh_e) * veh_m[1] *\",\n \"if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2\",\n \"automated vehicles\\\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding author: <NAME>\",\n \"** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16,\",\n \"= np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] **\",\n \"= np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 =\",\n \"veh_e) * veh_m[0] * veh_y1 * veh_y2 * veh_RDS /\",\n \"1e-6 delta_v1_list = [] delta_v2_list = [] # Estimate the\",\n \"veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2)\",\n \"veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in\",\n \"------------------------------------------------------------------------------------------------- ''' import torch import numpy as np from torch\",\n \"np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain delta-v based\",\n \"np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0]\",\n \"numpy as np from torch import nn from torch.nn.utils import\",\n \"[] delta_v2_list = [] # Estimate the collision condition (delat-v)\",\n \"the paper titled \\\"Human injury-based safety decision of automated vehicles\\\"\",\n \"torch import numpy as np from torch import nn from\",\n \"veh_cgs[1] ** 2) * np.cos(delta_angle_2 - veh_ca + np.pi /\",\n \"elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1\",\n \"delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index = delta_v1_list.index(max(delta_v1_list)) return\",\n \"+ veh_e) * veh_m[1] * veh_y1 * veh_y2 * veh_RDS\",\n \"veh_e = 1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca\",\n \"(veh_striking[1]+1) in [16, 1, 2, 3, 17, 20, 21] and\",\n \"veh_ca + np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2)\",\n \"veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 = np.abs(np.sqrt(veh_cgf[1]\",\n \"V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS # Obtain\",\n \"veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1)\",\n \"veh_e = 1.5 / veh_RDS # Obtain delta-v based on\",\n \"veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6\",\n \"** 2) * np.cos(delta_angle_2 - veh_ca + np.pi / 2))\",\n \"veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2 =\",\n \"/ veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v\",\n \"np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1] ** 2) * np.cos(delta_angle_2 -\",\n \"21] and (veh_striking[2]+1) in [16, 1, 2, 3, 17, 20,\",\n \"delta-v based on the plane 2-DOF rigid-body collision model with\",\n \"== 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0]\",\n \"np.pi / 2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e =\",\n \"veh_y2 * veh_RDS / (veh_m[0] * veh_y1 + veh_m[1] *\",\n \"np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3,\",\n \"Estimate the collision condition (delat-v) according to the principal impact\",\n \"decision of automated vehicles\\\" Author: <NAME>, <NAME>, <NAME>, <NAME> Corresponding\",\n \"3, 17, 20, 21]: veh_e = 2 / veh_RDS else:\",\n \"1, 2, 3, 17, 20, 21]: veh_e = 2 /\",\n \"if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2 = 1e-6 delta_v1_list\",\n \"= np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 +\",\n \"2-DOF rigid-body collision model with momentum conservation. veh_y1 = veh_k[0]\",\n \"torch.nn.utils import weight_norm __author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v,\",\n \"1e-6: delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list = []\",\n \"* veh_y2 * veh_RDS / (veh_m[0] * veh_y1 + veh_m[1]\",\n \"from torch import nn from torch.nn.utils import weight_norm __author__ =\",\n \"2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2 - veh_ca +\",\n \"== 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0]\",\n \"- veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] **\",\n \"np.abs(V1_v * np.cos(delta_angle) - V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2\",\n \"= veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 <\",\n \"elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2\",\n \"veh_y2) delta_v2 = (1 + veh_e) * veh_m[0] * veh_y1\",\n \"[] # Estimate the collision condition (delat-v) according to the\",\n \"veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) *\",\n \"veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 =\",\n \"V2_v, delta_angle, veh_param): ''' Estimate the collision condition. ''' (veh_l,\",\n \"safety decision of automated vehicles\\\" Author: <NAME>, <NAME>, <NAME>, <NAME>\",\n \"------------------------------------------------------------------------------------------------- This code accompanies the paper titled \\\"Human injury-based safety\",\n \"nn from torch.nn.utils import weight_norm __author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list,\",\n \"- veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v) veh_a2\",\n \"/ veh_RDS elif veh_striking[0] == 2: veh_ca = np.arctan(veh_cgf[0] /\",\n \"veh_m[1] * veh_y1 * veh_y2 * veh_RDS / (veh_m[0] *\",\n \"veh_y1 + veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list)\",\n \"= (1 + veh_e) * veh_m[1] * veh_y1 * veh_y2\",\n \"veh_a2 = np.abs(veh_cgf[1] - veh_striking[3]) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2\",\n \"delta_angle_2 = 1e-6 delta_v1_list = [] delta_v2_list = [] #\",\n \"17, 20, 21]: veh_e = 2 / veh_RDS else: veh_e\",\n \"/ 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5\",\n \"= 0.5 / veh_RDS elif veh_striking[0] == 4: veh_ca =\",\n \"import weight_norm __author__ = \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle,\",\n \"2 + veh_cgs[0] ** 2) * np.cos(veh_ca + delta_angle_2)) if\",\n \"veh_RDS # Obtain delta-v based on the plane 2-DOF rigid-body\",\n \"np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v * np.cos(delta_angle) - V1_v)\",\n \"collision condition (delat-v) according to the principal impact direction. for\",\n \"Obtain delta-v based on the plane 2-DOF rigid-body collision model\",\n \"veh_m[1] * veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ =\",\n \"= np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(veh_ca\",\n \"/ veh_RDS else: veh_e = 0.5 / veh_RDS elif veh_striking[0]\",\n \"np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS =\",\n \"= np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS\",\n \"injury-based safety decision of automated vehicles\\\" Author: <NAME>, <NAME>, <NAME>,\",\n \"momentum conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1 **\",\n \"condition. ''' (veh_l, veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param\",\n \"** 2 / (veh_a1 ** 2 + veh_k[0] ** 2)\",\n \"in [16, 1, 2, 3, 17, 20, 21]: veh_e =\",\n \"delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6: delta_angle_2\",\n \"/ (veh_a1 ** 2 + veh_k[0] ** 2) veh_y2 =\",\n \"veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if\",\n \"* np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2,\",\n \"<NAME>, <NAME> Corresponding author: <NAME> (<EMAIL>) ------------------------------------------------------------------------------------------------- ''' import torch\",\n \"2)) veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5 /\",\n \"20, 21]: veh_e = 2 / veh_RDS else: veh_e =\",\n \"delta_v2 = (1 + veh_e) * veh_m[0] * veh_y1 *\",\n \"= \\\"<NAME>\\\" def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate\",\n \"veh_param): ''' Estimate the collision condition. ''' (veh_l, veh_w, veh_cgf,\",\n \"4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1]) veh_a1 = np.abs(veh_cgf[0] -\",\n \"veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 <\",\n \"(veh_a1 ** 2 + veh_k[0] ** 2) veh_y2 = veh_k[1]\",\n \"- veh_ca + np.pi / 2)) veh_RDS = V2_v *\",\n \"veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle))) if -1e-6 < delta_angle_2 < 1e-6:\",\n \"veh_RDS = V2_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS\",\n \"2 + veh_k[0] ** 2) veh_y2 = veh_k[1] ** 2\",\n \"** 2 + veh_k[1] ** 2) delta_v1 = (1 +\",\n \"20, 21] and (veh_striking[2]+1) in [16, 1, 2, 3, 17,\",\n \"veh_w, veh_cgf, veh_cgs, veh_k, veh_m) = veh_param delta_angle_2 = np.arccos(np.abs(np.cos(delta_angle)))\",\n \"Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision condition.\",\n \"veh_RDS = V1_v * np.sin(delta_angle_2) veh_e = 1.5 / veh_RDS\",\n \"V2_v) veh_a1 = np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2)\",\n \"1.5 / veh_RDS elif veh_striking[0] == 3: veh_ca = np.arctan(veh_cgf[1]\",\n \"veh_cgs[1] ** 2) * np.cos(veh_ca + delta_angle_2)) if (veh_striking[1]+1) in\",\n \"def Collision_cond(veh_striking_list, V1_v, V2_v, delta_angle, veh_param): ''' Estimate the collision\",\n \"1: veh_ca = np.arctan(veh_cgf[0] / veh_cgs[0]) veh_a2 = np.abs(veh_cgs[1] -\",\n \"= 0.5 / veh_RDS elif veh_striking[0] == 2: veh_ca =\",\n \"np.abs(veh_cgs[1] - veh_striking[3]) veh_RDS = np.abs(V1_v * np.cos(delta_angle) - V2_v)\",\n \"in veh_striking_list: if veh_striking[0] == 1: veh_ca = np.arctan(veh_cgf[0] /\",\n \"delta_angle_2)) if (veh_striking[1]+1) in [16, 1, 2, 3, 17, 20,\",\n \"veh_y2) delta_v1_list.append(delta_v1) delta_v2_list.append(delta_v2) delta_v1_ = max(delta_v1_list) delta_v2_ = max(delta_v2_list) index\",\n \"veh_RDS elif veh_striking[0] == 4: veh_ca = np.arctan(veh_cgf[1] / veh_cgs[1])\",\n \"np.abs(veh_cgf[0] - veh_striking[3]) veh_a2 = np.abs(np.sqrt(veh_cgf[1] ** 2 + veh_cgs[1]\",\n \"\\\"Human injury-based safety decision of automated vehicles\\\" Author: <NAME>, <NAME>,\",\n \"= np.abs(np.sqrt(veh_cgf[0] ** 2 + veh_cgs[0] ** 2) * np.cos(delta_angle_2\",\n \"2) veh_y2 = veh_k[1] ** 2 / (veh_a2 ** 2\"\n]","isConversational":false}}},{"rowIdx":166,"cells":{"ngram":{"kind":"list like","value":["randomly per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze",") # (SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses,","= util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _","data: return {} all_images = data[\"images\"].to(device=device) # (SB, NV, 3,","in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src)","rgb_fine_np.max())) print( \"f alpha min {}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max()","for obj_idx in range(SB): if all_bboxes is not None: bboxes","= data.get(\"c\") # (SB) if self.use_bbox and global_step >= args.no_bbox_step:","= np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews)","3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8)",") self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\"","= images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0),","* W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse","= conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf","loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise()","epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data,","+ 1] # (1) NV, _, H, W = images.shape","0.5 cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far,","= loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]:","curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False))","(SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx","] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr =","= data[\"focal\"] # (SB) all_c = data.get(\"c\") # (SB) if","not is_train or not self.use_bbox: all_bboxes = None all_rgb_gt =","all_images.shape all_poses = data[\"poses\"].to(device=device) # (SB, NV, 4, 4) all_bboxes","data[\"poses\"].to(device=device) # (SB, NV, 4, 4) all_bboxes = data.get(\"bbox\") #","post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self,","data import get_split_dataset import util import numpy as np import","image_ord ) # (SB, NS, 3, H, W) src_poses =","all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else None, )","('SB')\" ) parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number of source","def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step)","os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far =","self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse","(NV, 3, H, W) poses = all_poses[obj_idx] # (NV, 4,","H, W) SB, NV, _, H, W = all_images.shape all_poses","net to eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0,","0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path)","curr_nviews == 1: image_ord = torch.randint(0, NV, (SB, 1)) else:","util import numpy as np import torch.nn.functional as F import","max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha min {}, max {}\".format(","<filename>train/train.py # Training to a set of multiple objects (e.g.","rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item()","data[\"images\"].to(device=device) # (SB, NV, 3, H, W) SB, NV, _,","all_c is not None else None, ) render_dict = DotMap(render_par(all_rays,","self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss","+= view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer","all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews","4, 4) all_bboxes = data.get(\"bbox\") # (SB, NV, 4) cmin","= rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device","all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine","self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step >","args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\"train\"],","encoder weights and only train MLP\", ) return parser args,","= None all_rgb_gt = [] all_rays = [] curr_nviews =","bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H,","0.5 + 0.5 cam_rays = util.gen_rays( poses, W, H, focal,","sampling @ iter\", global_step) if not is_train or not self.use_bbox:","net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\")","import warnings import trainlib from model import make_model, loss from","parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only train","= renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def","curr_nviews, replace=False) ) images_0to1 = images * 0.5 + 0.5","source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy()","NV - curr_nviews) for vs in range(curr_nviews): view_dest += view_dest","cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c","= self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss","global_step, idx=None): if \"images\" not in data: return {} if","3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8)","a set of multiple objects (e.g. ShapeNet or DTU) #","[] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if","size ('SB')\" ) parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number of","= all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx] c","train MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step to stop","or not self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays","Training to a set of multiple objects (e.g. ShapeNet or","print( \"f alpha min {}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() )","print( \"lambda coarse {} and fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit",": batch_idx + 1] # (1) NV, _, H, W","= get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near {}, z_far {}, lindisp","W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not","gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis =","all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse if","curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for vs","{}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"]","is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step, idx=None):","0.5 + 0.5 # (NV, 3, H, W) curr_nviews =","def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4, help=\"Object batch size","default=None, help=\"Freeze encoder weights and only train MLP\", ) parser.add_argument(","*source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis","val_dset, args, conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\" % ( self.args.checkpoints_path,","rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to(","self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step,","alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt,","c is not None else None, ) test_rays = test_rays.reshape(1,","def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self,","[ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list)","self.use_bbox = False print(\">>> Stopped using bbox sampling @ iter\",","not in data: return {} all_images = data[\"images\"].to(device=device) # (SB,","{} all_images = data[\"images\"].to(device=device) # (SB, NV, 3, H, W)","Somewhat inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV,","global_step >= args.no_bbox_step: self.use_bbox = False print(\">>> Stopped using bbox","NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB,","# (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size,","DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine =","> 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if","losses def vis_step(self, data, global_step, idx=None): if \"images\" not in","rgb_coarse_np.max())) print( \"c alpha min {}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max()",") depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) /","depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255","loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def","from model import make_model, loss from render import NeRFRenderer from","to a set of multiple objects (e.g. ShapeNet or DTU)","vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine","renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0):","np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews): view_dest +=","max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) /","loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume:","using_fine = len(fine) > 0 loss_dict = {} rgb_loss =","W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W,","= coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np =","global_step=0): if \"images\" not in data: return {} all_images =","alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255","c = None if \"c\" in data: c = data[\"c\"][obj_idx]","source views (multiview); put multiple (space delim) to pick randomly","W, 8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2,","H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W,","nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset,","class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\"train\"], device=device)","= \"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\")","self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print(","= torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images *","= data.get(\"c\") if c is not None: c = c[batch_idx","coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np","* W + pix[..., 2] else: pix_inds = torch.randint(0, NV","parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step to stop using bbox sampling\",","None if \"c\" in data: c = data[\"c\"][obj_idx] if curr_nviews","of multiple objects (e.g. ShapeNet or DTU) # tensorboard logs","= conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse {}","idx is None: batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx","c=c ) # (NV, H, W, 8) rgb_gt_all = images_0to1","images_0to1 = images * 0.5 + 0.5 cam_rays = util.gen_rays(","= False print(\">>> Stopped using bbox sampling @ iter\", global_step)","{} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha min {}, max","= DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine","1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in","H, focal, self.z_near, self.z_far, c=c ) # (NV, H, W,","data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return","0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H,","net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par","= images * 0.5 + 0.5 # (NV, 3, H,","val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near {}, z_far","fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if","= util.psnr(rgb_psnr, gt) vals = {\"psnr\": psnr} print(\"psnr\", psnr) #","views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV -","0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import warnings import trainlib from","gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis =","MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step to stop using","def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if","loss.item() return loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True,","c[batch_idx : batch_idx + 1] # (1) NV, _, H,","+ pix[..., 1] * W + pix[..., 2] else: pix_inds","range(SB): if all_bboxes is not None: bboxes = all_bboxes[obj_idx] images","4) cmin rmin cmax rmax all_focals = data[\"focal\"] # (SB)","= fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H,","data[\"focal\"] # (SB) all_c = data.get(\"c\") # (SB) if self.use_bbox","self.z_near, self.z_far, c=c ) # (NV, H, W, 8) images_0to1","self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses =","c is not None: c = c[batch_idx : batch_idx +","logs available in logs/<expname> import sys import os sys.path.insert( 0,","rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device )","util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H * W","print( \"dset z_near {}, z_far {}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp)","(SB, NV, 3, H, W) SB, NV, _, H, W","global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False,","W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H,","self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False print(\">>> Stopped",") import warnings import trainlib from model import make_model, loss","for vs in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src","conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf =","pix[..., 1] * W + pix[..., 2] else: pix_inds =","util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes = all_poses","return {} if idx is None: batch_idx = np.random.randint(0, data[\"images\"].shape[0])","fine = render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np =","= rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt)","pix_inds = pix[..., 0] * H * W + pix[...,","all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c","@ iter\", global_step) if not is_train or not self.use_bbox: all_bboxes","/ 255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap,","np import torch.nn.functional as F import torch from dotmap import","3, H, W) poses = all_poses[obj_idx] # (NV, 4, 4)","* W + pix[..., 1] * W + pix[..., 2]","* W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays","if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W)","lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews","cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt","image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB, NS,","{}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad","{} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] =","alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np =","len(fine) > 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt)","H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3)","H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV,","view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net","\"c alpha min {}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) )","* 0.5 + 0.5 cam_rays = util.gen_rays( poses, W, H,","renderer network back to train mode renderer.train() return vis, vals","= images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays =","(1) NV, _, H, W = images.shape cam_rays = util.gen_rays(","0.5 # (NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews),","np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for","\"rgb_fine\" in conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit","not None else None, ) test_rays = test_rays.reshape(1, H *","depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse","render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine","# (NV, H, W, 3) if all_bboxes is not None:","8) images_0to1 = images * 0.5 + 0.5 # (NV,","trainlib from model import make_model, loss from render import NeRFRenderer","NV, _, H, W = all_images.shape all_poses = data[\"poses\"].to(device=device) #","= make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval()","= torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt =","all_bboxes = data.get(\"bbox\") # (SB, NV, 4) cmin rmin cmax","images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) )","images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest]","= DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine","return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device =","= render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H,","return {} all_images = data[\"images\"].to(device=device) # (SB, NV, 3, H,","eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3,","pick randomly per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None,","map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox =","def calc_losses(self, data, is_train=True, global_step=0): if \"images\" not in data:","to stop using bbox sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None,","data.get(\"bbox\") # (SB, NV, 4) cmin rmin cmax rmax all_focals","using bbox sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder","H, W, 3) if all_bboxes is not None: pix =","os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import warnings import","3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c","is not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] #","pix[..., 2] else: pix_inds = torch.randint(0, NV * H *","vis = vis_coarse if using_fine: print(\"f rgb min {} max","\"--no_bbox_step\", type=int, default=100000, help=\"Step to stop using bbox sampling\", )","None: batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx = idx","(SB, NV, 4, 4) all_bboxes = data.get(\"bbox\") # (SB, NV,","self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss if","conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf,","(NV, 4, 4) focal = data[\"focal\"][batch_idx : batch_idx + 1]","weights and only train MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int, default=100000,","{}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc","W, 8) images_0to1 = images * 0.5 + 0.5 #","cam_rays[view_dest] # (H, W, 8) test_images = images[views_src] # (NS,","# set renderer net to eval mode renderer.eval() source_views =","min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha min {},","ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord",") # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int,","4, 4) focal = all_focals[obj_idx] c = None if \"c\"","help=\"Object batch size ('SB')\" ) parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\",","way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 =","* self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss =","= fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb","# (1) c = data.get(\"c\") if c is not None:","3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src =","# (SB, NV, 4) cmin rmin cmax rmax all_focals =","# (NV, 4, 4) focal = data[\"focal\"][batch_idx : batch_idx +","% ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine =","images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H, W,","if \"rgb_fine\" in conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"]","(SB, NS, 4, 4) all_bboxes = all_poses = all_images =","device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews =","np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx = idx images = data[\"images\"][batch_idx].to(device=device)","rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse if","= {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"]","loss_dict[\"t\"] = loss.item() return loss_dict def train_step(self, data, global_step): return","test_images = images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0),","= data[\"images\"][batch_idx].to(device=device) # (NV, 3, H, W) poses = data[\"poses\"][batch_idx].to(device=device)","coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np =","args.no_bbox_step: self.use_bbox = False print(\">>> Stopped using bbox sampling @","back to train mode renderer.train() return vis, vals trainer =","NV * H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] #","and only train MLP\", ) return parser args, conf =","cmax rmax all_focals = data[\"focal\"] # (SB) all_c = data.get(\"c\")","loss from render import NeRFRenderer from data import get_split_dataset import","3, H, W) poses = data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4)","\"dset z_near {}, z_far {}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) )","torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds]","rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W,","1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3) if all_bboxes","conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse {} and fine {}\".format(self.lambda_coarse, self.lambda_fine)","rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) #","else: pix_inds = torch.randint(0, NV * H * W, (args.ray_batch_size,))","data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\"focal\"][batch_idx : batch_idx","c = c[batch_idx : batch_idx + 1] # (1) NV,","images_0to1 = images * 0.5 + 0.5 # (NV, 3,","= np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews): view_dest","or DTU) # tensorboard logs available in logs/<expname> import sys","c = data[\"c\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient,","src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3,","NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval()","import numpy as np import torch.nn.functional as F import torch","args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self):",".cpu() .numpy() .reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1,","renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par =","{} and fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True)","# set the renderer network back to train mode renderer.train()","= util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt, depth_coarse_cmap,","alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr","data: c = data[\"c\"][obj_idx] if curr_nviews > 1: # Somewhat","net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer =","to pick randomly per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\",","all_images = data[\"images\"].to(device=device) # (SB, NV, 3, H, W) SB,",".reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H,","\"-V\", type=str, default=\"1\", help=\"Number of source views (multiview); put multiple","from dotmap import DotMap def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int,","\"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only train MLP\",","# (SB) all_c = data.get(\"c\") # (SB) if self.use_bbox and","= images * 0.5 + 0.5 cam_rays = util.gen_rays( poses,","W, 3) if all_bboxes is not None: pix = util.bbox_sample(bboxes,","= torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images","{}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255",") ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np)","= all_focals[obj_idx] c = None if \"c\" in data: c","import util import numpy as np import torch.nn.functional as F","if is_train: loss.backward() loss_dict[\"t\"] = loss.item() return loss_dict def train_step(self,","NV, 3, H, W) SB, NV, _, H, W =","\"f alpha min {}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) )","data: return {} if idx is None: batch_idx = np.random.randint(0,","\"--batch_size\", \"-B\", type=int, default=4, help=\"Object batch size ('SB')\" ) parser.add_argument(","set of multiple objects (e.g. ShapeNet or DTU) # tensorboard","better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1","per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder","and global_step >= args.no_bbox_step: self.use_bbox = False print(\">>> Stopped using","parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number of source views (multiview);","focal, self.z_near, self.z_far, c=c ) # (NV, H, W, 8)","(args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1,","= rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt)","args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near","* self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\"t\"] =","W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays =","np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images * 0.5 +","self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\" in","data[\"focal\"][batch_idx : batch_idx + 1] # (1) c = data.get(\"c\")","else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse","frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) # Parallize","= data[\"c\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient, don't","if \"images\" not in data: return {} all_images = data[\"images\"].to(device=device)","4) all_bboxes = data.get(\"bbox\") # (SB, NV, 4) cmin rmin","type=int, default=100000, help=\"Step to stop using bbox sampling\", ) parser.add_argument(","in range(SB): if all_bboxes is not None: bboxes = all_bboxes[obj_idx]","if curr_nviews > 1: # Somewhat inefficient, don't know better","dtype=torch.long) for obj_idx in range(SB): if all_bboxes is not None:","focal = data[\"focal\"][batch_idx : batch_idx + 1] # (1) c","= util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt, depth_fine_cmap,","np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr","renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far","= len(fine) > 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb,","= loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device)","= rgb_loss if is_train: loss.backward() loss_dict[\"t\"] = loss.item() return loss_dict","= args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"],","W + pix[..., 1] * W + pix[..., 2] else:","image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB,","dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near {},","and fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf","src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else","data.get(\"c\") if c is not None: c = c[batch_idx :","PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\"train\"], device=device) self.renderer_state_path","= conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse {} and fine {}\".format(self.lambda_coarse,","all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H, W) poses","(space delim) to pick randomly per batch ('NV')\", ) parser.add_argument(","fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]: print(\"using fine loss\")","vis_step(self, data, global_step, idx=None): if \"images\" not in data: return","available in logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__),","is_train or not self.use_bbox: all_bboxes = None all_rgb_gt = []","( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\",","import make_model, loss from render import NeRFRenderer from data import","= dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0","render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer):",") parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number of source views","rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt)","args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to(","# (SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox =","W) poses = all_poses[obj_idx] # (NV, 4, 4) focal =","3) if all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size)","= all_images[obj_idx] # (NV, 3, H, W) poses = all_poses[obj_idx]","self.z_far, c=c ) # (NV, H, W, 8) rgb_gt_all =","pix_inds = torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt","ShapeNet or DTU) # tensorboard logs available in logs/<expname> import","2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] #","(NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if","max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha min {}, max {}\".format(","None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse =","_ = get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near {}, z_far {},","_, H, W = images.shape cam_rays = util.gen_rays( poses, W,","= NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net,","4) all_bboxes = all_poses = all_images = None net.encode( src_images,","= dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch,","= self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item() *","src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else None,","print(\"psnr\", psnr) # set the renderer network back to train","3) ) # (NV, H, W, 3) if all_bboxes is","= None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is","NeRFRenderer from data import get_split_dataset import util import numpy as","# (SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord)","return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses","# (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) #","W = all_images.shape all_poses = data[\"poses\"].to(device=device) # (SB, NV, 4,","= util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \"dset","import torch from dotmap import DotMap def extra_args(parser): parser.add_argument( \"--batch_size\",","poses = data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\"focal\"][batch_idx","dset, val_dset, args, conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\" % (","H, W = all_images.shape all_poses = data[\"poses\"].to(device=device) # (SB, NV,","fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max()))","= data[\"focal\"][batch_idx : batch_idx + 1] # (1) c =","default=None, help=\"Freeze encoder weights and only train MLP\", ) return","sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder weights and","{}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha min {}, max {}\".format( alpha_coarse_np.min(),","as np import torch.nn.functional as F import torch from dotmap","import torch.nn.functional as F import torch from dotmap import DotMap","H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4,","loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) )","model import make_model, loss from render import NeRFRenderer from data","(1) c = data.get(\"c\") if c is not None: c","rgb min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha min","= rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\"psnr\": psnr}","if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far","= [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse =",") ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np)","min {}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap =","4, 4) all_bboxes = all_poses = all_images = None net.encode(","= self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data,","# (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB,","global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses","vs in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src =","== 1: image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord","None: c = c[batch_idx : batch_idx + 1] # (1)","= cam_rays[view_dest] # (H, W, 8) test_images = images[views_src] #","all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays)","self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss *","W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np =","is_train=True, global_step=0): if \"images\" not in data: return {} all_images","{\"psnr\": psnr} print(\"psnr\", psnr) # set the renderer network back","with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8) test_images","batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True,","data.get(\"c\") # (SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox","all_bboxes is not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx]","net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None","\"c\" in data: c = data[\"c\"][obj_idx] if curr_nviews > 1:","def vis_step(self, data, global_step, idx=None): if \"images\" not in data:","2, 3, 1) .cpu() .numpy() .reshape(-1, H, W, 3) )","(NV, 3, H, W) poses = data[\"poses\"][batch_idx].to(device=device) # (NV, 4,","import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import warnings","renderer.train() return losses def vis_step(self, data, global_step, idx=None): if \"images\"","( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H,","not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0]","return losses def vis_step(self, data, global_step, idx=None): if \"images\" not","1] # (1) c = data.get(\"c\") if c is not","all_focals[obj_idx] c = None if \"c\" in data: c =","[] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1:","data[\"c\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient, don't know","*source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis","cmin rmin cmax rmax all_focals = data[\"focal\"] # (SB) all_c","focal = all_focals[obj_idx] c = None if \"c\" in data:","3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3) if","8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord )","= vis_coarse if using_fine: print(\"f rgb min {} max {}\".format(rgb_fine_np.min(),","super().__init__(net, dset, val_dset, args, conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\" %",">= args.no_bbox_step: self.use_bbox = False print(\">>> Stopped using bbox sampling","# (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd(","gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays","3, H, W) SB, NV, _, H, W = all_images.shape","(SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8)","None, ) test_rays = test_rays.reshape(1, H * W, -1) render_dict","fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W,","# Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split()))","W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb min {}","(SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images,","self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays = []","multiple (space delim) to pick randomly per batch ('NV')\", )","255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views,","rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if","fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if","\"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number of source views (multiview); put","print( \"c alpha min {}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() )","raise() loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss =","util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c ) #","gt) vals = {\"psnr\": psnr} print(\"psnr\", psnr) # set the","warnings import trainlib from model import make_model, loss from render",") return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device","batch_idx = idx images = data[\"images\"][batch_idx].to(device=device) # (NV, 3, H,","is not None else None, ) test_rays = test_rays.reshape(1, H","curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord","is not None: c = c[batch_idx : batch_idx + 1]","delim) to pick randomly per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\",","parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only train","conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name, )","= image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB,","alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np",") test_rays = test_rays.reshape(1, H * W, -1) render_dict =","render import NeRFRenderer from data import get_split_dataset import util import","batch size ('SB')\" ) parser.add_argument( \"--nviews\", \"-V\", type=str, default=\"1\", help=\"Number","(SB, NV, 4) cmin rmin cmax rmax all_focals = data[\"focal\"]","idx images = data[\"images\"][batch_idx].to(device=device) # (NV, 3, H, W) poses","(ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size,","is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data,","self.z_far, c=c ) # (NV, H, W, 8) images_0to1 =","dotmap import DotMap def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4,","z_near {}, z_far {}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net","len(nviews), ()).item()] if curr_nviews == 1: image_ord = torch.randint(0, NV,",") # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) #","W) poses = data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4) focal =","test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None else","rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\"psnr\":","# (NV, 3, H, W) poses = data[\"poses\"][batch_idx].to(device=device) # (NV,","torch from dotmap import DotMap def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\",","fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf =","8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3)","H, W, 8) images_0to1 = images * 0.5 + 0.5","alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap =","W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3)","only train MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step to","to eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2,","using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse","default=\"1\", help=\"Number of source views (multiview); put multiple (space delim)","using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np","logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\"))","print(\">>> Stopped using bbox sampling @ iter\", global_step) if not","W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4)","-1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine =","3) print(\"c rgb min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c","curr_nviews > 1: # Somewhat inefficient, don't know better way","* self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() *","\"..\", \"src\")) ) import warnings import trainlib from model import","data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step):","if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near =","mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1)","help=\"Step to stop using bbox sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\",","multiple objects (e.g. ShapeNet or DTU) # tensorboard logs available","> 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(),",".numpy() .reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2,","psnr = util.psnr(rgb_psnr, gt) vals = {\"psnr\": psnr} print(\"psnr\", psnr)","H, W = images.shape cam_rays = util.gen_rays( poses, W, H,","print(\"Encoder frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device ) #","using_fine: print(\"f rgb min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f","3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with","lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad =","= conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path):","import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) )",") parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only","= idx images = data[\"images\"][batch_idx].to(device=device) # (NV, 3, H, W)","batch_idx + 1] # (1) c = data.get(\"c\") if c","= ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1,","test_rays = test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays,","{}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np)","alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine:","z_far {}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device)","util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list =","rgb_loss if is_train: loss.backward() loss_dict[\"t\"] = loss.item() return loss_dict def","# (NV, H, W, 8) images_0to1 = images * 0.5","depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c","# tensorboard logs available in logs/<expname> import sys import os","print(\"c rgb min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha","np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr","image_ord) # (SB, NS, 4, 4) all_bboxes = all_poses =","loss.backward() loss_dict[\"t\"] = loss.item() return loss_dict def train_step(self, data, global_step):","util.psnr(rgb_psnr, gt) vals = {\"psnr\": psnr} print(\"psnr\", psnr) # set","= fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb min {} max {}\".format(rgb_coarse_np.min(),","is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[...,","objects (e.g. ShapeNet or DTU) # tensorboard logs available in","of source views (multiview); put multiple (space delim) to pick","in conf[\"loss\"]: print(\"using fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit =","= len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np =","coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H,","DTU) # tensorboard logs available in logs/<expname> import sys import",") parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only","torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8) test_images =","rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb min {} max","len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H,","= all_images.shape all_poses = data[\"poses\"].to(device=device) # (SB, NV, 4, 4)","coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine) >","{} if idx is None: batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else:","= np.hstack(vis_list) vis = vis_coarse if using_fine: print(\"f rgb min","# (SB, NV, 4, 4) all_bboxes = data.get(\"bbox\") # (SB,","images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device),","# (SB, NV, 3, H, W) SB, NV, _, H,","all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx] c =","1] # (1) NV, _, H, W = images.shape cam_rays","H, W) poses = all_poses[obj_idx] # (NV, 4, 4) focal","* 0.5 + 0.5 # (NV, 3, H, W) curr_nviews","None all_rgb_gt = [] all_rays = [] curr_nviews = nviews[torch.randint(0,","pix[..., 0] * H * W + pix[..., 1] *","image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) #","[ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list)","is None: batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx =","encoder weights and only train MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int,","(NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src","= all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if","= {\"psnr\": psnr} print(\"psnr\", psnr) # set the renderer network","data, global_step, idx=None): if \"images\" not in data: return {}","alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap","using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np","# (NV, 3, H, W) poses = all_poses[obj_idx] # (NV,","( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H,","2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3)","depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse,","vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np","* self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss","max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) /","= data[\"images\"].to(device=device) # (SB, NV, 3, H, W) SB, NV,","if c is not None: c = c[batch_idx : batch_idx",") self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step","H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0,","= nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest",") gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad():","render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W)","4) focal = all_focals[obj_idx] c = None if \"c\" in","vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr =","= rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\"rf\"] =","eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train()","# (H, W, 8) test_images = images[views_src] # (NS, 3,","conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict(","dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch, batch):","self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine","poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None else None,","# (NV, 4, 4) focal = all_focals[obj_idx] c = None","in data: c = data[\"c\"][obj_idx] if curr_nviews > 1: #","H * W + pix[..., 1] * W + pix[...,","if not is_train or not self.use_bbox: all_bboxes = None all_rgb_gt","images = all_images[obj_idx] # (NV, 3, H, W) poses =","dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0 def","torch.from_numpy(views_src) # set renderer net to eval mode renderer.eval() source_views","and only train MLP\", ) parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step","args.ray_batch_size) pix_inds = pix[..., 0] * H * W +","= coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine:","type=int, default=4, help=\"Object batch size ('SB')\" ) parser.add_argument( \"--nviews\", \"-V\",","NV, 4) cmin rmin cmax rmax all_focals = data[\"focal\"] #","render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0 loss_dict","coarse {} and fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"],","\"src\")) ) import warnings import trainlib from model import make_model,","(SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) #","\"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine","rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\"psnr\": psnr} print(\"psnr\",","all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays =","iter\", global_step) if not is_train or not self.use_bbox: all_bboxes =","= util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3, H,","rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss","tensorboard logs available in logs/<expname> import sys import os sys.path.insert(","test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse","in logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\",","2] else: pix_inds = torch.randint(0, NV * H * W,","global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step, idx=None): if","psnr} print(\"psnr\", psnr) # set the renderer network back to","+ fine_loss * self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine loss","images = data[\"images\"][batch_idx].to(device=device) # (NV, 3, H, W) poses =",": batch_idx + 1] # (1) c = data.get(\"c\") if","default=4, help=\"Object batch size ('SB')\" ) parser.add_argument( \"--nviews\", \"-V\", type=str,","None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine","depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W)","8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3,","H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is","renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def","= test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True))","\"images\" not in data: return {} all_images = data[\"images\"].to(device=device) #","> 1: # Somewhat inefficient, don't know better way image_ord[obj_idx]","NS, 4, 4) all_bboxes = all_poses = all_images = None","all_rgb_gt = [] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews),","args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device )","rmin cmax rmax all_focals = data[\"focal\"] # (SB) all_c =","using bbox sampling @ iter\", global_step) if not is_train or",") # (NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all","None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] *","{}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255","from render import NeRFRenderer from data import get_split_dataset import util","alpha min {}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap","3, 1) .cpu() .numpy() .reshape(-1, H, W, 3) ) gt","import DotMap def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4, help=\"Object","self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0)","1: image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord =","all_images[obj_idx] # (NV, 3, H, W) poses = all_poses[obj_idx] #",") # (NV, H, W, 3) if all_bboxes is not","torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images =","3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord =","= render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0","dset.z_far, dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if","np.hstack(vis_list) vis = vis_coarse if using_fine: print(\"f rgb min {}","nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest =","all_c = data.get(\"c\") # (SB) if self.use_bbox and global_step >=","rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine))","os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import warnings import trainlib from model","self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse {} and fine","args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset,","loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb,","= args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def","print(\"f rgb min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha","(SB) all_c = data.get(\"c\") # (SB) if self.use_bbox and global_step","args, conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name,","= None if \"c\" in data: c = data[\"c\"][obj_idx] if","(NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all = (","\"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder weights and only train MLP\",","# Training to a set of multiple objects (e.g. ShapeNet","alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap","* H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size,","= [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews ==","> 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W,","= util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes =","network back to train mode renderer.train() return vis, vals trainer","vals = {\"psnr\": psnr} print(\"psnr\", psnr) # set the renderer","= util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H *","if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\"renderer\"], lindisp=dset.lindisp,).to( device=device","W + pix[..., 2] else: pix_inds = torch.randint(0, NV *","train MLP\", ) return parser args, conf = util.args.parse_args(extra_args, training=True,","(SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False","view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set","] vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine: print(\"f","vis_coarse if using_fine: print(\"f rgb min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max()))","want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine)","util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near","make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\"Encoder frozen\") net.encoder.eval() renderer","= data[\"poses\"].to(device=device) # (SB, NV, 4, 4) all_bboxes = data.get(\"bbox\")","extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4, help=\"Object batch size ('SB')\"","all_poses = all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device)","= pix[..., 0] * H * W + pix[..., 1]","NV, _, H, W = images.shape cam_rays = util.gen_rays( poses,",") alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) /","torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long)","poses = all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx]","if all_c is not None else None, ) render_dict =","H, W) poses = data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4) focal","depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt,","range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) #","(e.g. ShapeNet or DTU) # tensorboard logs available in logs/<expname>","make_model, loss from render import NeRFRenderer from data import get_split_dataset","extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \"images\"","0] * H * W + pix[..., 1] * W","rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss","else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB):","SB, NV, _, H, W = all_images.shape all_poses = data[\"poses\"].to(device=device)","+ 0.5 # (NV, 3, H, W) curr_nviews = nviews[torch.randint(0,","inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews,","replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for vs in","= coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np","rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1,","if \"c\" in data: c = data[\"c\"][obj_idx] if curr_nviews >","W) SB, NV, _, H, W = all_images.shape all_poses =","vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr =","idx=None): if \"images\" not in data: return {} if idx","+ 1] # (1) c = data.get(\"c\") if c is","weights and only train MLP\", ) return parser args, conf","('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder weights and","* H * W + pix[..., 1] * W +",") render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine =","= util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list","rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\"rf\"] = fine_loss.item()","= util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c )","rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals","get_split_dataset import util import numpy as np import torch.nn.functional as","F import torch from dotmap import DotMap def extra_args(parser): parser.add_argument(","self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\"t\"] = loss.item()",") parser.add_argument( \"--no_bbox_step\", type=int, default=100000, help=\"Step to stop using bbox","torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \"images\" not","else: print(idx) batch_idx = idx images = data[\"images\"][batch_idx].to(device=device) # (NV,","__init__(self): super().__init__(net, dset, val_dset, args, conf[\"train\"], device=device) self.renderer_state_path = \"%s/%s/_renderer\"","alpha min {}, max {}\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap","NV, 4, 4) all_bboxes = data.get(\"bbox\") # (SB, NV, 4)","= fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train: loss.backward()","else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals =","fine = render_dict.fine using_fine = len(fine) > 0 loss_dict =","1) .cpu() .numpy() .reshape(-1, H, W, 3) ) gt =","= data.get(\"bbox\") # (SB, NV, 4) cmin rmin cmax rmax","(multiview); put multiple (space delim) to pick randomly per batch",") self.lambda_coarse = conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda","8) test_images = images[views_src] # (NS, 3, H, W) net.encode(","render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np","3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS,","render_dict.fine using_fine = len(fine) > 0 loss_dict = {} rgb_loss","torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox","None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3,","in data: return {} if idx is None: batch_idx =","import get_split_dataset import util import numpy as np import torch.nn.functional","= torch.from_numpy(views_src) # set renderer net to eval mode renderer.eval()","W, 3) print(\"c rgb min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print(","False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near","action=\"store_true\", default=None, help=\"Freeze encoder weights and only train MLP\", )","rgb_loss = rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\"rf\"]","else None, ) test_rays = test_rays.reshape(1, H * W, -1)","obj_idx in range(SB): if all_bboxes is not None: bboxes =","vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse","1.0) print( \"lambda coarse {} and fine {}\".format(self.lambda_coarse, self.lambda_fine) )","= all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H, W)","255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views,","self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path,","255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ]","c=c ) # (NV, H, W, 8) images_0to1 = images","{}, z_far {}, lindisp {}\".format(dset.z_near, dset.z_far, dset.lindisp) ) net =","focal.to(device=device), c=c.to(device=device) if c is not None else None, )","vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine: print(\"f rgb","= [] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()]","set renderer net to eval mode renderer.eval() source_views = (","images * 0.5 + 0.5 # (NV, 3, H, W)","views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net to eval","as F import torch from dotmap import DotMap def extra_args(parser):","= np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else:","self.z_near, self.z_far, c=c ) # (NV, H, W, 8) rgb_gt_all","all_poses = data[\"poses\"].to(device=device) # (SB, NV, 4, 4) all_bboxes =","min {}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap =","import NeRFRenderer from data import get_split_dataset import util import numpy","all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds =","dset.lindisp) ) net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc:","= c[batch_idx : batch_idx + 1] # (1) NV, _,","{}, max {}\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np)","bbox sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze encoder weights","conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset,","_, H, W = all_images.shape all_poses = data[\"poses\"].to(device=device) # (SB,","train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data,","curr_nviews) for vs in range(curr_nviews): view_dest += view_dest >= views_src[vs]","Stopped using bbox sampling @ iter\", global_step) if not is_train","rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr,",") # (NV, H, W, 8) images_0to1 = images *","only train MLP\", ) return parser args, conf = util.args.parse_args(extra_args,","\"-B\", type=int, default=4, help=\"Object batch size ('SB')\" ) parser.add_argument( \"--nviews\",","+ pix[..., 2] else: pix_inds = torch.randint(0, NV * H","not self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays =","self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \"images\" not in","3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H,","= torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes","util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3, H, W)","batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx = idx images","= list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset,","def __init__(self): super().__init__(net, dset, val_dset, args, conf[\"train\"], device=device) self.renderer_state_path =","if using_fine: print(\"f rgb min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print(","= torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews),",") images_0to1 = images * 0.5 + 0.5 cam_rays =","# (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device)","+ 0.5 cam_rays = util.gen_rays( poses, W, H, focal, self.z_near,","not None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse","sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import","poses, W, H, focal, self.z_near, self.z_far, c=c ) # (NV,","parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4, help=\"Object batch size ('SB')\" )","DotMap def extra_args(parser): parser.add_argument( \"--batch_size\", \"-B\", type=int, default=4, help=\"Object batch","help=\"Freeze encoder weights and only train MLP\", ) return parser","import trainlib from model import make_model, loss from render import","self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse","# (NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()]","image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images","device=device) self.renderer_state_path = \"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse","if all_bboxes is not None: bboxes = all_bboxes[obj_idx] images =","/ 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [","stop using bbox sampling\", ) parser.add_argument( \"--fixed_test\", action=\"store_true\", default=None, help=\"Freeze","(1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV","self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size)","get_split_dataset(args.dataset_format, args.datadir) print( \"dset z_near {}, z_far {}, lindisp {}\".format(dset.z_near,","1] * W + pix[..., 2] else: pix_inds = torch.randint(0,","not in data: return {} if idx is None: batch_idx","W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3)","don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False)","if rgb_loss.isnan().any().item()==True: raise() loss_dict[\"rc\"] = rgb_loss.item() * self.lambda_coarse if using_fine:","batch_idx + 1] # (1) NV, _, H, W =","torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB,","pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H","= cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays)","calc_losses(self, data, is_train=True, global_step=0): if \"images\" not in data: return","put multiple (space delim) to pick randomly per batch ('NV')\",","bbox sampling @ iter\", global_step) if not is_train or not","torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes is","fine_loss * self.lambda_fine loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine loss =","= images.shape cam_rays = util.gen_rays( poses, W, H, focal, self.z_near,","ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord","(NV, 4, 4) focal = all_focals[obj_idx] c = None if","{} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha min {}, max","= np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np","255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ]","\"lambda coarse {} and fine {}\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit =","len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0,","0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True:","H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse =","alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap =","not None: c = c[batch_idx : batch_idx + 1] #","- curr_nviews) for vs in range(curr_nviews): view_dest += view_dest >=","default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir)","cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt =","if c is not None else None, ) test_rays =","from data import get_split_dataset import util import numpy as np","c = data.get(\"c\") if c is not None: c =","None else None, ) test_rays = test_rays.reshape(1, H * W,","fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\"c rgb min","want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine)","util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ =","if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss *","all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays","= render_dict.fine using_fine = len(fine) > 0 loss_dict = {}","global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval()","if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False print(\">>>","curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes is not","c=all_c.to(device=device) if all_c is not None else None, ) render_dict","W, 8) test_images = images[views_src] # (NS, 3, H, W)","is not None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,))","test_rays = cam_rays[view_dest] # (H, W, 8) test_images = images[views_src]","help=\"Freeze encoder weights and only train MLP\", ) parser.add_argument( \"--no_bbox_step\",","loss_dict[\"rf\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train:","W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W)","self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self,","losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self,","util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np,","= [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine =","\"images\" not in data: return {} if idx is None:","4, 4) focal = data[\"focal\"][batch_idx : batch_idx + 1] #","batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze encoder weights","numpy as np import torch.nn.functional as F import torch from","rmax all_focals = data[\"focal\"] # (SB) all_c = data.get(\"c\") #","set the renderer network back to train mode renderer.train() return","Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class","print(idx) batch_idx = idx images = data[\"images\"][batch_idx].to(device=device) # (NV, 3,","images.shape cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far,","{}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha min {}, max {}\".format( alpha_fine_np.min(),","training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format,","NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for","data[\"images\"][batch_idx].to(device=device) # (NV, 3, H, W) poses = data[\"poses\"][batch_idx].to(device=device) #","in data: return {} all_images = data[\"images\"].to(device=device) # (SB, NV,","data, is_train=True, global_step=0): if \"images\" not in data: return {}","= np.random.randint(0, data[\"images\"].shape[0]) else: print(idx) batch_idx = idx images =","torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images * 0.5","sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\", \"src\")) ) import warnings import trainlib","self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"] if","()).item()] if curr_nviews == 1: image_ord = torch.randint(0, NV, (SB,","not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV,","/ 255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap,","renderer net to eval mode renderer.eval() source_views = ( images_0to1[views_src]","global_step) if not is_train or not self.use_bbox: all_bboxes = None","know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) )","loss.get_rgb_loss(conf[\"loss.rgb\"], True) fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]: print(\"using","all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device)","render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine","is_train: loss.backward() loss_dict[\"t\"] = loss.item() return loss_dict def train_step(self, data,","all_focals = data[\"focal\"] # (SB) all_c = data.get(\"c\") # (SB)","train mode renderer.train() return vis, vals trainer = PixelNeRFTrainer() trainer.start()","net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None","# (1) NV, _, H, W = images.shape cam_rays =","W = images.shape cam_rays = util.gen_rays( poses, W, H, focal,","data[\"images\"].shape[0]) else: print(idx) batch_idx = idx images = data[\"images\"][batch_idx].to(device=device) #","parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0])","(H, W, 8) test_images = images[views_src] # (NS, 3, H,","renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self):","# Somewhat inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy(","W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine","= data[\"poses\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\"focal\"][batch_idx :","if \"images\" not in data: return {} if idx is","def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def","loss = rgb_loss if is_train: loss.backward() loss_dict[\"t\"] = loss.item() return","W, H, focal, self.z_near, self.z_far, c=c ) # (NV, H,","conf.get_float(\"loss.lambda_coarse\") self.lambda_fine = conf.get_float(\"loss.lambda_fine\", 1.0) print( \"lambda coarse {} and","= loss.item() return loss_dict def train_step(self, data, global_step): return self.calc_losses(data,","replace=False) ) images_0to1 = images * 0.5 + 0.5 cam_rays","self.renderer_state_path = \"%s/%s/_renderer\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse =","nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord = torch.randint(0,","type=str, default=\"1\", help=\"Number of source views (multiview); put multiple (space","util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list =","False print(\">>> Stopped using bbox sampling @ iter\", global_step) if","util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np,","W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews,","image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if","min {} max {}\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \"c alpha min {},","rgb min {} max {}\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \"f alpha min","= nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord =","fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\"t\"]","4) focal = data[\"focal\"][batch_idx : batch_idx + 1] # (1)","DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine =","the renderer network back to train mode renderer.train() return vis,","= images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3)","(NV, H, W, 8) images_0to1 = images * 0.5 +","all_bboxes = None all_rgb_gt = [] all_rays = [] curr_nviews","(ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size,","(NV, H, W, 3) if all_bboxes is not None: pix","0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3)",">= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net to","print(\"using fine loss\") fine_loss_conf = conf[\"loss.rgb_fine\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False)","1: # Somewhat inefficient, don't know better way image_ord[obj_idx] =","to train mode renderer.train() return vis, vals trainer = PixelNeRFTrainer()","= ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV,","device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt)","renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu()","c=c.to(device=device) if c is not None else None, ) test_rays","view_dest = np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews):","True) fine_loss_conf = conf[\"loss.rgb\"] if \"rgb_fine\" in conf[\"loss\"]: print(\"using fine","views (multiview); put multiple (space delim) to pick randomly per","fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse +","if idx is None: batch_idx = np.random.randint(0, data[\"images\"].shape[0]) else: print(idx)","args.datadir) print( \"dset z_near {}, z_far {}, lindisp {}\".format(dset.z_near, dset.z_far,","all_bboxes = all_poses = all_images = None net.encode( src_images, src_poses,","MLP\", ) return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128)","if all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds","all_images, image_ord ) # (SB, NS, 3, H, W) src_poses","None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not","= torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) #","default=100000, help=\"Step to stop using bbox sampling\", ) parser.add_argument( \"--fixed_test\",",") net = make_model(conf[\"model\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\"Encoder","if curr_nviews == 1: image_ord = torch.randint(0, NV, (SB, 1))","args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net,","src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes","views_src = torch.from_numpy(views_src) # set renderer net to eval mode","# (SB, NS, 4, 4) all_bboxes = all_poses = all_images","/ 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [","help=\"Number of source views (multiview); put multiple (space delim) to","list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args,","torch.nn.functional as F import torch from dotmap import DotMap def","= all_poses = all_images = None net.encode( src_images, src_poses, all_focals.to(device=device),",".permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H, W, 3)","return loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step)","# (NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all =","psnr) # set the renderer network back to train mode","images * 0.5 + 0.5 cam_rays = util.gen_rays( poses, W,","= util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list","device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print("],"string":"[\n \"randomly per batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze\",\n \") # (SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses,\",\n \"= util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _\",\n \"data: return {} all_images = data[\\\"images\\\"].to(device=device) # (SB, NV, 3,\",\n \"in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src)\",\n \"rgb_fine_np.max())) print( \\\"f alpha min {}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max()\",\n \"for obj_idx in range(SB): if all_bboxes is not None: bboxes\",\n \"= data.get(\\\"c\\\") # (SB) if self.use_bbox and global_step >= args.no_bbox_step:\",\n \"= np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews)\",\n \"3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8)\",\n \") self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\"\",\n \"= images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0),\",\n \"* W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse\",\n \"= conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf\",\n \"loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise()\",\n \"epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data,\",\n \"+ 1] # (1) NV, _, H, W = images.shape\",\n \"0.5 cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far,\",\n \"= loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]:\",\n \"curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False))\",\n \"(SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx\",\n \"] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr =\",\n \"= data[\\\"focal\\\"] # (SB) all_c = data.get(\\\"c\\\") # (SB) if\",\n \"not is_train or not self.use_bbox: all_bboxes = None all_rgb_gt =\",\n \"all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) # (SB, NV, 4, 4) all_bboxes\",\n \"data[\\\"poses\\\"].to(device=device) # (SB, NV, 4, 4) all_bboxes = data.get(\\\"bbox\\\") #\",\n \"post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self,\",\n \"data import get_split_dataset import util import numpy as np import\",\n \"image_ord ) # (SB, NS, 3, H, W) src_poses =\",\n \"all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else None, )\",\n \"('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of source\",\n \"def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step)\",\n \"os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far =\",\n \"self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse\",\n \"(NV, 3, H, W) poses = all_poses[obj_idx] # (NV, 4,\",\n \"H, W) SB, NV, _, H, W = all_images.shape all_poses\",\n \"net to eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0,\",\n \"0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path)\",\n \"curr_nviews == 1: image_ord = torch.randint(0, NV, (SB, 1)) else:\",\n \"util import numpy as np import torch.nn.functional as F import\",\n \"max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha min {}, max {}\\\".format(\",\n \"<filename>train/train.py # Training to a set of multiple objects (e.g.\",\n \"rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item()\",\n \"data[\\\"images\\\"].to(device=device) # (SB, NV, 3, H, W) SB, NV, _,\",\n \"all_c is not None else None, ) render_dict = DotMap(render_par(all_rays,\",\n \"self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss\",\n \"+= view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer\",\n \"all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews\",\n \"4, 4) all_bboxes = data.get(\\\"bbox\\\") # (SB, NV, 4) cmin\",\n \"= rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device\",\n \"all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine\",\n \"self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step >\",\n \"args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"],\",\n \"encoder weights and only train MLP\\\", ) return parser args,\",\n \"= None all_rgb_gt = [] all_rays = [] curr_nviews =\",\n \"bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H,\",\n \"0.5 + 0.5 cam_rays = util.gen_rays( poses, W, H, focal,\",\n \"sampling @ iter\\\", global_step) if not is_train or not self.use_bbox:\",\n \"net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\")\",\n \"import warnings import trainlib from model import make_model, loss from\",\n \"parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only train\",\n \"= renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def\",\n \"curr_nviews, replace=False) ) images_0to1 = images * 0.5 + 0.5\",\n \"source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy()\",\n \"NV - curr_nviews) for vs in range(curr_nviews): view_dest += view_dest\",\n \"cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c\",\n \"= self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss\",\n \"global_step, idx=None): if \\\"images\\\" not in data: return {} if\",\n \"3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8)\",\n \"a set of multiple objects (e.g. ShapeNet or DTU) #\",\n \"[] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if\",\n \"size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of\",\n \"= all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx] c\",\n \"train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to stop\",\n \"or not self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays\",\n \"Training to a set of multiple objects (e.g. ShapeNet or\",\n \"print( \\\"f alpha min {}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() )\",\n \"print( \\\"lambda coarse {} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit\",\n \": batch_idx + 1] # (1) NV, _, H, W\",\n \"= get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near {}, z_far {}, lindisp\",\n \"W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not\",\n \"gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis =\",\n \"all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse if\",\n \"curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for vs\",\n \"{}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"]\",\n \"is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step, idx=None):\",\n \"0.5 + 0.5 # (NV, 3, H, W) curr_nviews =\",\n \"def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object batch size\",\n \"default=None, help=\\\"Freeze encoder weights and only train MLP\\\", ) parser.add_argument(\",\n \"*source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis\",\n \"val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path,\",\n \"rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to(\",\n \"self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step,\",\n \"alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt,\",\n \"c is not None else None, ) test_rays = test_rays.reshape(1,\",\n \"def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self,\",\n \"[ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list)\",\n \"self.use_bbox = False print(\\\">>> Stopped using bbox sampling @ iter\\\",\",\n \"not in data: return {} all_images = data[\\\"images\\\"].to(device=device) # (SB,\",\n \"{} all_images = data[\\\"images\\\"].to(device=device) # (SB, NV, 3, H, W)\",\n \"Somewhat inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV,\",\n \"global_step >= args.no_bbox_step: self.use_bbox = False print(\\\">>> Stopped using bbox\",\n \"NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB,\",\n \"# (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size,\",\n \"DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine =\",\n \"> 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if\",\n \"losses def vis_step(self, data, global_step, idx=None): if \\\"images\\\" not in\",\n \"rgb_coarse_np.max())) print( \\\"c alpha min {}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max()\",\n \") depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) /\",\n \"depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255\",\n \"loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def\",\n \"from model import make_model, loss from render import NeRFRenderer from\",\n \"to a set of multiple objects (e.g. ShapeNet or DTU)\",\n \"vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine\",\n \"renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0):\",\n \"np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews): view_dest +=\",\n \"max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) /\",\n \"loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume:\",\n \"using_fine = len(fine) > 0 loss_dict = {} rgb_loss =\",\n \"W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W,\",\n \"= coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np =\",\n \"global_step=0): if \\\"images\\\" not in data: return {} all_images =\",\n \"alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255\",\n \"c = None if \\\"c\\\" in data: c = data[\\\"c\\\"][obj_idx]\",\n \"source views (multiview); put multiple (space delim) to pick randomly\",\n \"W, 8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2,\",\n \"H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W,\",\n \"nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset,\",\n \"class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"], device=device)\",\n \"= \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\")\",\n \"self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print(\",\n \"= torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images *\",\n \"= data.get(\\\"c\\\") if c is not None: c = c[batch_idx\",\n \"coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np\",\n \"* W + pix[..., 2] else: pix_inds = torch.randint(0, NV\",\n \"parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to stop using bbox sampling\\\",\",\n \"None if \\\"c\\\" in data: c = data[\\\"c\\\"][obj_idx] if curr_nviews\",\n \"of multiple objects (e.g. ShapeNet or DTU) # tensorboard logs\",\n \"= conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse {}\",\n \"idx is None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx\",\n \"c=c ) # (NV, H, W, 8) rgb_gt_all = images_0to1\",\n \"images_0to1 = images * 0.5 + 0.5 cam_rays = util.gen_rays(\",\n \"= False print(\\\">>> Stopped using bbox sampling @ iter\\\", global_step)\",\n \"{} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha min {}, max\",\n \"= DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine\",\n \"1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in\",\n \"H, focal, self.z_near, self.z_far, c=c ) # (NV, H, W,\",\n \"data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return\",\n \"0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] # (H,\",\n \"net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par\",\n \"= images * 0.5 + 0.5 # (NV, 3, H,\",\n \"val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near {}, z_far\",\n \"fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if\",\n \"= util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) #\",\n \"views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV -\",\n \"0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import warnings import trainlib from\",\n \"gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis =\",\n \"MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to stop using\",\n \"def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if\",\n \"loss.item() return loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True,\",\n \"c[batch_idx : batch_idx + 1] # (1) NV, _, H,\",\n \"+ pix[..., 1] * W + pix[..., 2] else: pix_inds\",\n \"range(SB): if all_bboxes is not None: bboxes = all_bboxes[obj_idx] images\",\n \"4) cmin rmin cmax rmax all_focals = data[\\\"focal\\\"] # (SB)\",\n \"= fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H,\",\n \"data[\\\"focal\\\"] # (SB) all_c = data.get(\\\"c\\\") # (SB) if self.use_bbox\",\n \"self.z_near, self.z_far, c=c ) # (NV, H, W, 8) images_0to1\",\n \"self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses =\",\n \"c is not None: c = c[batch_idx : batch_idx +\",\n \"logs available in logs/<expname> import sys import os sys.path.insert( 0,\",\n \"rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device )\",\n \"util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H * W\",\n \"print( \\\"dset z_near {}, z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp)\",\n \"(SB, NV, 3, H, W) SB, NV, _, H, W\",\n \"global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False,\",\n \"W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H,\",\n \"self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False print(\\\">>> Stopped\",\n \") import warnings import trainlib from model import make_model, loss\",\n \"for vs in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src\",\n \"conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf =\",\n \"pix[..., 1] * W + pix[..., 2] else: pix_inds =\",\n \"util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes = all_poses\",\n \"return {} if idx is None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0])\",\n \"fine = render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np =\",\n \"= rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt)\",\n \"pix_inds = pix[..., 0] * H * W + pix[...,\",\n \"all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c\",\n \"@ iter\\\", global_step) if not is_train or not self.use_bbox: all_bboxes\",\n \"/ 255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap,\",\n \"np import torch.nn.functional as F import torch from dotmap import\",\n \"3, H, W) poses = all_poses[obj_idx] # (NV, 4, 4)\",\n \"* W + pix[..., 1] * W + pix[..., 2]\",\n \"* W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays\",\n \"if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W)\",\n \"lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews\",\n \"cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt\",\n \"image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB, NS,\",\n \"{}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad\",\n \"{} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] =\",\n \"alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np =\",\n \"len(fine) > 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt)\",\n \"H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3)\",\n \"H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV,\",\n \"view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net\",\n \"\\\"c alpha min {}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) )\",\n \"* 0.5 + 0.5 cam_rays = util.gen_rays( poses, W, H,\",\n \"renderer network back to train mode renderer.train() return vis, vals\",\n \"= images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays =\",\n \"(1) NV, _, H, W = images.shape cam_rays = util.gen_rays(\",\n \"0.5 # (NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews),\",\n \"np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for\",\n \"\\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit\",\n \"not None else None, ) test_rays = test_rays.reshape(1, H *\",\n \"depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse\",\n \"render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine\",\n \"# (NV, H, W, 3) if all_bboxes is not None:\",\n \"8) images_0to1 = images * 0.5 + 0.5 # (NV,\",\n \"trainlib from model import make_model, loss from render import NeRFRenderer\",\n \"NV, _, H, W = all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) #\",\n \"= make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval()\",\n \"= torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt =\",\n \"all_bboxes = data.get(\\\"bbox\\\") # (SB, NV, 4) cmin rmin cmax\",\n \"images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) )\",\n \"images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest]\",\n \"= DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine\",\n \"return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device =\",\n \"= render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H,\",\n \"return {} all_images = data[\\\"images\\\"].to(device=device) # (SB, NV, 3, H,\",\n \"eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3,\",\n \"pick randomly per batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None,\",\n \"map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox =\",\n \"def calc_losses(self, data, is_train=True, global_step=0): if \\\"images\\\" not in data:\",\n \"to stop using bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None,\",\n \"data.get(\\\"bbox\\\") # (SB, NV, 4) cmin rmin cmax rmax all_focals\",\n \"using bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder\",\n \"H, W, 3) if all_bboxes is not None: pix =\",\n \"os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import warnings import\",\n \"3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c\",\n \"is not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] #\",\n \"pix[..., 2] else: pix_inds = torch.randint(0, NV * H *\",\n \"vis = vis_coarse if using_fine: print(\\\"f rgb min {} max\",\n \"\\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to stop using bbox sampling\\\", )\",\n \"None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx = idx\",\n \"(SB, NV, 4, 4) all_bboxes = data.get(\\\"bbox\\\") # (SB, NV,\",\n \"self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss if\",\n \"conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf,\",\n \"(NV, 4, 4) focal = data[\\\"focal\\\"][batch_idx : batch_idx + 1]\",\n \"weights and only train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000,\",\n \"{}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc\",\n \"W, 8) images_0to1 = images * 0.5 + 0.5 #\",\n \"cam_rays[view_dest] # (H, W, 8) test_images = images[views_src] # (NS,\",\n \"# set renderer net to eval mode renderer.eval() source_views =\",\n \"min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha min {},\",\n \"ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord\",\n \") # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int,\",\n \"4, 4) focal = all_focals[obj_idx] c = None if \\\"c\\\"\",\n \"help=\\\"Object batch size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\",\",\n \"way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 =\",\n \"* self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss =\",\n \"= fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb\",\n \"# (1) c = data.get(\\\"c\\\") if c is not None:\",\n \"3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src =\",\n \"# (SB, NV, 4) cmin rmin cmax rmax all_focals =\",\n \"# (NV, 4, 4) focal = data[\\\"focal\\\"][batch_idx : batch_idx +\",\n \"% ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine =\",\n \"images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H, W,\",\n \"if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"]\",\n \"(SB, NS, 4, 4) all_bboxes = all_poses = all_images =\",\n \"device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews =\",\n \"np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx = idx images = data[\\\"images\\\"][batch_idx].to(device=device)\",\n \"rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse if\",\n \"= {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"]\",\n \"loss_dict[\\\"t\\\"] = loss.item() return loss_dict def train_step(self, data, global_step): return\",\n \"test_images = images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0),\",\n \"= data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device)\",\n \"coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np =\",\n \"args.no_bbox_step: self.use_bbox = False print(\\\">>> Stopped using bbox sampling @\",\n \"back to train mode renderer.train() return vis, vals trainer =\",\n \"NV * H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] #\",\n \"and only train MLP\\\", ) return parser args, conf =\",\n \"cmax rmax all_focals = data[\\\"focal\\\"] # (SB) all_c = data.get(\\\"c\\\")\",\n \"loss from render import NeRFRenderer from data import get_split_dataset import\",\n \"3, H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4)\",\n \"\\\"dset z_near {}, z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) )\",\n \"torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds]\",\n \"rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W,\",\n \"1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3) if all_bboxes\",\n \"conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse {} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine)\",\n \"rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) #\",\n \"else: pix_inds = torch.randint(0, NV * H * W, (args.ray_batch_size,))\",\n \"data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\\\"focal\\\"][batch_idx : batch_idx\",\n \"c = c[batch_idx : batch_idx + 1] # (1) NV,\",\n \"images_0to1 = images * 0.5 + 0.5 # (NV, 3,\",\n \"= np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews): view_dest\",\n \"or DTU) # tensorboard logs available in logs/<expname> import sys\",\n \"c = data[\\\"c\\\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient,\",\n \"src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3,\",\n \"NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval()\",\n \"import numpy as np import torch.nn.functional as F import torch\",\n \"args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self):\",\n \".cpu() .numpy() .reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1,\",\n \"renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par =\",\n \"{} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True)\",\n \"# set the renderer network back to train mode renderer.train()\",\n \"= util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt, depth_coarse_cmap,\",\n \"alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr\",\n \"data: c = data[\\\"c\\\"][obj_idx] if curr_nviews > 1: # Somewhat\",\n \"net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer =\",\n \"to pick randomly per batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\",\",\n \"all_images = data[\\\"images\\\"].to(device=device) # (SB, NV, 3, H, W) SB,\",\n \".reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H,\",\n \"\\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of source views (multiview); put multiple\",\n \"from dotmap import DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int,\",\n \"\\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only train MLP\\\",\",\n \"# (SB) all_c = data.get(\\\"c\\\") # (SB) if self.use_bbox and\",\n \"= images * 0.5 + 0.5 cam_rays = util.gen_rays( poses,\",\n \"W, 3) if all_bboxes is not None: pix = util.bbox_sample(bboxes,\",\n \"= torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images\",\n \"{}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255\",\n \") ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np)\",\n \"= all_focals[obj_idx] c = None if \\\"c\\\" in data: c\",\n \"import util import numpy as np import torch.nn.functional as F\",\n \"if is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item() return loss_dict def train_step(self,\",\n \"NV, 3, H, W) SB, NV, _, H, W =\",\n \"\\\"f alpha min {}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) )\",\n \"data: return {} if idx is None: batch_idx = np.random.randint(0,\",\n \"\\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object batch size ('SB')\\\" ) parser.add_argument(\",\n \"set of multiple objects (e.g. ShapeNet or DTU) # tensorboard\",\n \"better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1\",\n \"per batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder\",\n \"and global_step >= args.no_bbox_step: self.use_bbox = False print(\\\">>> Stopped using\",\n \"parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of source views (multiview);\",\n \"focal, self.z_near, self.z_far, c=c ) # (NV, H, W, 8)\",\n \"(args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays = cam_rays.view(-1,\",\n \"= rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt)\",\n \"args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near\",\n \"* self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"] =\",\n \"W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size, 3) rays =\",\n \"np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images * 0.5 +\",\n \"self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in\",\n \"data[\\\"focal\\\"][batch_idx : batch_idx + 1] # (1) c = data.get(\\\"c\\\")\",\n \"else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse\",\n \"frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) # Parallize\",\n \"= data[\\\"c\\\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient, don't\",\n \"if \\\"images\\\" not in data: return {} all_images = data[\\\"images\\\"].to(device=device)\",\n \"4) all_bboxes = data.get(\\\"bbox\\\") # (SB, NV, 4) cmin rmin\",\n \"type=int, default=100000, help=\\\"Step to stop using bbox sampling\\\", ) parser.add_argument(\",\n \"in range(SB): if all_bboxes is not None: bboxes = all_bboxes[obj_idx]\",\n \"if curr_nviews > 1: # Somewhat inefficient, don't know better\",\n \"dtype=torch.long) for obj_idx in range(SB): if all_bboxes is not None:\",\n \"focal = data[\\\"focal\\\"][batch_idx : batch_idx + 1] # (1) c\",\n \"= util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt, depth_fine_cmap,\",\n \"np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr\",\n \"renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far\",\n \"= len(fine) > 0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb,\",\n \"= loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device)\",\n \"= rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item() return loss_dict\",\n \"= args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"],\",\n \"W + pix[..., 1] * W + pix[..., 2] else:\",\n \"image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB,\",\n \"dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near {},\",\n \"and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf\",\n \"src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else\",\n \"data.get(\\\"c\\\") if c is not None: c = c[batch_idx :\",\n \"PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path\",\n \"= conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse {} and fine {}\\\".format(self.lambda_coarse,\",\n \"all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H, W) poses\",\n \"(space delim) to pick randomly per batch ('NV')\\\", ) parser.add_argument(\",\n \"fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\")\",\n \"vis_step(self, data, global_step, idx=None): if \\\"images\\\" not in data: return\",\n \"available in logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__),\",\n \"is_train or not self.use_bbox: all_bboxes = None all_rgb_gt = []\",\n \"( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\",\",\n \"import make_model, loss from render import NeRFRenderer from data import\",\n \"= dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0\",\n \"render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer):\",\n \") parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of source views\",\n \"rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt)\",\n \"args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to(\",\n \"# (SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox =\",\n \"W) poses = all_poses[obj_idx] # (NV, 4, 4) focal =\",\n \"3) if all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size)\",\n \"= all_images[obj_idx] # (NV, 3, H, W) poses = all_poses[obj_idx]\",\n \"self.z_far, c=c ) # (NV, H, W, 8) rgb_gt_all =\",\n \"pix_inds = torch.randint(0, NV * H * W, (args.ray_batch_size,)) rgb_gt\",\n \"ShapeNet or DTU) # tensorboard logs available in logs/<expname> import\",\n \"2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays = cam_rays[view_dest] #\",\n \"(NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if\",\n \"max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha min {}, max {}\\\".format(\",\n \"None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse =\",\n \"_ = get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near {}, z_far {},\",\n \"_, H, W = images.shape cam_rays = util.gen_rays( poses, W,\",\n \"= NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) # Parallize render_par = renderer.bind_parallel(net,\",\n \"4) all_bboxes = all_poses = all_images = None net.encode( src_images,\",\n \"= dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch,\",\n \"= self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() *\",\n \"src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None else None,\",\n \"print(\\\"psnr\\\", psnr) # set the renderer network back to train\",\n \"3) ) # (NV, H, W, 3) if all_bboxes is\",\n \"= None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is\",\n \"NeRFRenderer from data import get_split_dataset import util import numpy as\",\n \"# (SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord)\",\n \"return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses\",\n \"# (ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) #\",\n \"W = all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) # (SB, NV, 4,\",\n \"= util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset\",\n \"import torch from dotmap import DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\",\",\n \"poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\\\"focal\\\"][batch_idx\",\n \"dset, val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" % (\",\n \"H, W = all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) # (SB, NV,\",\n \"fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max()))\",\n \"= data[\\\"focal\\\"][batch_idx : batch_idx + 1] # (1) c =\",\n \"default=None, help=\\\"Freeze encoder weights and only train MLP\\\", ) return\",\n \"sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and\",\n \"{}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha min {}, max {}\\\".format( alpha_coarse_np.min(),\",\n \"as np import torch.nn.functional as F import torch from dotmap\",\n \"import torch.nn.functional as F import torch from dotmap import DotMap\",\n \"H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4,\",\n \"loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) )\",\n \"model import make_model, loss from render import NeRFRenderer from data\",\n \"(1) c = data.get(\\\"c\\\") if c is not None: c\",\n \"rgb min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha min\",\n \"= rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\": psnr}\",\n \"if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far\",\n \"= [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse =\",\n \") ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np)\",\n \"min {}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap =\",\n \"4, 4) all_bboxes = all_poses = all_images = None net.encode(\",\n \"= self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self, data,\",\n \"# (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB,\",\n \"global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses\",\n \"vs in range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src =\",\n \"== 1: image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord\",\n \"None: c = c[batch_idx : batch_idx + 1] # (1)\",\n \"= cam_rays[view_dest] # (H, W, 8) test_images = images[views_src] #\",\n \"all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays)\",\n \"self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse + fine_loss *\",\n \"W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np =\",\n \"is_train=True, global_step=0): if \\\"images\\\" not in data: return {} all_images\",\n \"{\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) # set the renderer network back\",\n \"with torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8) test_images\",\n \"batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True,\",\n \"data.get(\\\"c\\\") # (SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox\",\n \"all_bboxes is not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx]\",\n \"net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None\",\n \"\\\"c\\\" in data: c = data[\\\"c\\\"][obj_idx] if curr_nviews > 1:\",\n \"def vis_step(self, data, global_step, idx=None): if \\\"images\\\" not in data:\",\n \"2, 3, 1) .cpu() .numpy() .reshape(-1, H, W, 3) )\",\n \"(NV, 3, H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4,\",\n \"import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import warnings\",\n \"renderer.train() return losses def vis_step(self, data, global_step, idx=None): if \\\"images\\\"\",\n \"( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H,\",\n \"not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0]\",\n \"return losses def vis_step(self, data, global_step, idx=None): if \\\"images\\\" not\",\n \"1] # (1) c = data.get(\\\"c\\\") if c is not\",\n \"all_focals[obj_idx] c = None if \\\"c\\\" in data: c =\",\n \"[] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1:\",\n \"data[\\\"c\\\"][obj_idx] if curr_nviews > 1: # Somewhat inefficient, don't know\",\n \"*source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis\",\n \"cmin rmin cmax rmax all_focals = data[\\\"focal\\\"] # (SB) all_c\",\n \"focal = all_focals[obj_idx] c = None if \\\"c\\\" in data:\",\n \"3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3) if\",\n \"8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord )\",\n \"= vis_coarse if using_fine: print(\\\"f rgb min {} max {}\\\".format(rgb_fine_np.min(),\",\n \"super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" %\",\n \">= args.no_bbox_step: self.use_bbox = False print(\\\">>> Stopped using bbox sampling\",\n \"# (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd(\",\n \"gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad(): test_rays\",\n \"3, H, W) SB, NV, _, H, W = all_images.shape\",\n \"(SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8)\",\n \"None, ) test_rays = test_rays.reshape(1, H * W, -1) render_dict\",\n \"fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W,\",\n \"# Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split()))\",\n \"W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb min {}\",\n \"(SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images,\",\n \"self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays = []\",\n \"multiple (space delim) to pick randomly per batch ('NV')\\\", )\",\n \"255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views,\",\n \"rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if\",\n \"fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if\",\n \"\\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number of source views (multiview); put\",\n \"print( \\\"c alpha min {}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() )\",\n \"raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss =\",\n \"util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c ) #\",\n \"gt) vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) # set the\",\n \"warnings import trainlib from model import make_model, loss from render\",\n \") return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device\",\n \"batch_idx = idx images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H,\",\n \"is not None else None, ) test_rays = test_rays.reshape(1, H\",\n \"curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord\",\n \"is not None: c = c[batch_idx : batch_idx + 1]\",\n \"delim) to pick randomly per batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\",\",\n \"parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only train\",\n \"conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name, )\",\n \"= image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) # (SB,\",\n \"alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np\",\n \") test_rays = test_rays.reshape(1, H * W, -1) render_dict =\",\n \"render import NeRFRenderer from data import get_split_dataset import util import\",\n \"batch size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str, default=\\\"1\\\", help=\\\"Number\",\n \"(SB, NV, 4) cmin rmin cmax rmax all_focals = data[\\\"focal\\\"]\",\n \"idx images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H, W) poses\",\n \"(ray_batch_size, 3) rays = cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size,\",\n \"is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data,\",\n \"self.z_far, c=c ) # (NV, H, W, 8) images_0to1 =\",\n \"dotmap import DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4,\",\n \"z_near {}, z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net\",\n \"len(nviews), ()).item()] if curr_nviews == 1: image_ord = torch.randint(0, NV,\",\n \") # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) #\",\n \"W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4) focal =\",\n \"test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None else\",\n \"rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\":\",\n \"# (NV, 3, H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV,\",\n \"torch from dotmap import DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\",\",\n \"fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf =\",\n \"8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3)\",\n \"H, W, 8) images_0to1 = images * 0.5 + 0.5\",\n \"alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap =\",\n \"W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3)\",\n \"only train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to\",\n \"to eval mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2,\",\n \"using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse\",\n \"default=\\\"1\\\", help=\\\"Number of source views (multiview); put multiple (space delim)\",\n \"using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np\",\n \"logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\"))\",\n \"print(\\\">>> Stopped using bbox sampling @ iter\\\", global_step) if not\",\n \"W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4)\",\n \"-1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine =\",\n \"3) print(\\\"c rgb min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c\",\n \"curr_nviews > 1: # Somewhat inefficient, don't know better way\",\n \"* self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() *\",\n \"\\\"..\\\", \\\"src\\\")) ) import warnings import trainlib from model import\",\n \"data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step):\",\n \"if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near =\",\n \"mode renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1)\",\n \"help=\\\"Step to stop using bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\",\",\n \"multiple objects (e.g. ShapeNet or DTU) # tensorboard logs available\",\n \"> 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(),\",\n \".numpy() .reshape(-1, H, W, 3) ) gt = images_0to1[view_dest].permute(1, 2,\",\n \"psnr = util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr)\",\n \"H, W = images.shape cam_rays = util.gen_rays( poses, W, H,\",\n \"print(\\\"Encoder frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device ) #\",\n \"using_fine: print(\\\"f rgb min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f\",\n \"3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with\",\n \"lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad =\",\n \"= conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path):\",\n \"import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) )\",\n \") parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only\",\n \"= idx images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H, W)\",\n \"batch_idx + 1] # (1) c = data.get(\\\"c\\\") if c\",\n \"= ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1,\",\n \"test_rays = test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays,\",\n \"{}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np)\",\n \"alpha_coarse_cmap, ] vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine:\",\n \"z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device)\",\n \"util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list =\",\n \"rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item() return loss_dict def\",\n \"# (NV, H, W, 8) images_0to1 = images * 0.5\",\n \"depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c\",\n \"# tensorboard logs available in logs/<expname> import sys import os\",\n \"print(\\\"c rgb min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha\",\n \"np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr\",\n \"image_ord) # (SB, NS, 4, 4) all_bboxes = all_poses =\",\n \"loss.backward() loss_dict[\\\"t\\\"] = loss.item() return loss_dict def train_step(self, data, global_step):\",\n \"util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) # set\",\n \"= fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb min {} max {}\\\".format(rgb_coarse_np.min(),\",\n \"is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[...,\",\n \"objects (e.g. ShapeNet or DTU) # tensorboard logs available in\",\n \"of source views (multiview); put multiple (space delim) to pick\",\n \"in conf[\\\"loss\\\"]: print(\\\"using fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit =\",\n \"= len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np =\",\n \"coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H,\",\n \"DTU) # tensorboard logs available in logs/<expname> import sys import\",\n \") parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only\",\n \"torch.no_grad(): test_rays = cam_rays[view_dest] # (H, W, 8) test_images =\",\n \"rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb min {} max\",\n \"len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H,\",\n \"= all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) # (SB, NV, 4, 4)\",\n \"coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine) >\",\n \"{} if idx is None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else:\",\n \"= np.hstack(vis_list) vis = vis_coarse if using_fine: print(\\\"f rgb min\",\n \"# (SB, NV, 4, 4) all_bboxes = data.get(\\\"bbox\\\") # (SB,\",\n \"images[views_src] # (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device),\",\n \"# (SB, NV, 3, H, W) SB, NV, _, H,\",\n \"all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx] c =\",\n \"1] # (1) NV, _, H, W = images.shape cam_rays\",\n \"H, W) poses = all_poses[obj_idx] # (NV, 4, 4) focal\",\n \"* 0.5 + 0.5 # (NV, 3, H, W) curr_nviews\",\n \"None all_rgb_gt = [] all_rays = [] curr_nviews = nviews[torch.randint(0,\",\n \"pix[..., 0] * H * W + pix[..., 1] *\",\n \"image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord ) #\",\n \"[ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list)\",\n \"is None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx =\",\n \"encoder weights and only train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int,\",\n \"(NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src\",\n \"= all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if\",\n \"= {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) # set the renderer network\",\n \"data, global_step, idx=None): if \\\"images\\\" not in data: return {}\",\n \"alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap\",\n \"using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np = fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np\",\n \"# (NV, 3, H, W) poses = all_poses[obj_idx] # (NV,\",\n \"( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H,\",\n \"2, 3, 1).contiguous().reshape(-1, 3) ) # (NV, H, W, 3)\",\n \"depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse,\",\n \"vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np\",\n \"* self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss\",\n \"max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) /\",\n \"= data[\\\"images\\\"].to(device=device) # (SB, NV, 3, H, W) SB, NV,\",\n \"if c is not None: c = c[batch_idx : batch_idx\",\n \") self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step\",\n \"H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0,\",\n \"= nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest\",\n \") gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3) with torch.no_grad():\",\n \"render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W)\",\n \"4) focal = all_focals[obj_idx] c = None if \\\"c\\\" in\",\n \"vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr =\",\n \"= rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] =\",\n \"eval_step(self, data, global_step): renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train()\",\n \"# (H, W, 8) test_images = images[views_src] # (NS, 3,\",\n \"conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict(\",\n \"dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch, batch):\",\n \"self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine\",\n \"poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is not None else None,\",\n \"# (NV, 4, 4) focal = all_focals[obj_idx] c = None\",\n \"in data: c = data[\\\"c\\\"][obj_idx] if curr_nviews > 1: #\",\n \"H * W + pix[..., 1] * W + pix[...,\",\n \"if not is_train or not self.use_bbox: all_bboxes = None all_rgb_gt\",\n \"images = all_images[obj_idx] # (NV, 3, H, W) poses =\",\n \"dset.z_near self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0 def\",\n \"torch.from_numpy(views_src) # set renderer net to eval mode renderer.eval() source_views\",\n \"and only train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step\",\n \"args.ray_batch_size) pix_inds = pix[..., 0] * H * W +\",\n \"= coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine:\",\n \"type=int, default=4, help=\\\"Object batch size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\",\",\n \"NV, 4) cmin rmin cmax rmax all_focals = data[\\\"focal\\\"] #\",\n \"render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0 loss_dict\",\n \"coarse {} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"],\",\n \"\\\"src\\\")) ) import warnings import trainlib from model import make_model,\",\n \"(SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) #\",\n \"\\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine\",\n \"rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\",\",\n \"all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays =\",\n \"iter\\\", global_step) if not is_train or not self.use_bbox: all_bboxes =\",\n \"= util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3, H,\",\n \"rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss\",\n \"tensorboard logs available in logs/<expname> import sys import os sys.path.insert(\",\n \"test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse\",\n \"in logs/<expname> import sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\",\",\n \"2] else: pix_inds = torch.randint(0, NV * H * W,\",\n \"global_step=global_step) renderer.train() return losses def vis_step(self, data, global_step, idx=None): if\",\n \"psnr} print(\\\"psnr\\\", psnr) # set the renderer network back to\",\n \"+ fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine loss\",\n \"images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H, W) poses =\",\n \": batch_idx + 1] # (1) c = data.get(\\\"c\\\") if\",\n \"default=4, help=\\\"Object batch size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\", \\\"-V\\\", type=str,\",\n \"None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine\",\n \"depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W)\",\n \"8) rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3,\",\n \"H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device) if c is\",\n \"renderer.eval() losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def\",\n \"= test_rays.reshape(1, H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True))\",\n \"\\\"images\\\" not in data: return {} all_images = data[\\\"images\\\"].to(device=device) #\",\n \"> 1: # Somewhat inefficient, don't know better way image_ord[obj_idx]\",\n \"NS, 4, 4) all_bboxes = all_poses = all_images = None\",\n \"all_rgb_gt = [] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews),\",\n \"args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device )\",\n \"rmin cmax rmax all_focals = data[\\\"focal\\\"] # (SB) all_c =\",\n \"using bbox sampling @ iter\\\", global_step) if not is_train or\",\n \") # (NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all\",\n \"None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] *\",\n \"{}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255\",\n \"from render import NeRFRenderer from data import get_split_dataset import util\",\n \"alpha min {}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap\",\n \"3, 1) .cpu() .numpy() .reshape(-1, H, W, 3) ) gt\",\n \"import DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object\",\n \"self.args.checkpoints_path, self.args.name, ) self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0)\",\n \"1: image_ord = torch.randint(0, NV, (SB, 1)) else: image_ord =\",\n \"all_images[obj_idx] # (NV, 3, H, W) poses = all_poses[obj_idx] #\",\n \") # (NV, H, W, 3) if all_bboxes is not\",\n \"torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device) src_images =\",\n \"3) all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord =\",\n \"= render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0\",\n \"dset.z_far, dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if\",\n \"np.hstack(vis_list) vis = vis_coarse if using_fine: print(\\\"f rgb min {}\",\n \"nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest =\",\n \"all_c = data.get(\\\"c\\\") # (SB) if self.use_bbox and global_step >=\",\n \"rgb_fine_np, alpha_fine_cmap, ] vis_fine = np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine))\",\n \"os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import warnings import trainlib from model\",\n \"self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse {} and fine\",\n \"args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset,\",\n \"loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb,\",\n \"= args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def\",\n \"print(\\\"f rgb min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha\",\n \"(SB) all_c = data.get(\\\"c\\\") # (SB) if self.use_bbox and global_step\",\n \"args, conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name,\",\n \"= None if \\\"c\\\" in data: c = data[\\\"c\\\"][obj_idx] if\",\n \"(NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all = (\",\n \"\\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only train MLP\\\",\",\n \"# Training to a set of multiple objects (e.g. ShapeNet\",\n \"alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap\",\n \"* H * W, (args.ray_batch_size,)) rgb_gt = rgb_gt_all[pix_inds] # (ray_batch_size,\",\n \"= [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews ==\",\n \"> 0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W,\",\n \"= util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes =\",\n \"network back to train mode renderer.train() return vis, vals trainer\",\n \"vals = {\\\"psnr\\\": psnr} print(\\\"psnr\\\", psnr) # set the renderer\",\n \"= util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H *\",\n \"if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer = NeRFRenderer.from_conf(conf[\\\"renderer\\\"], lindisp=dset.lindisp,).to( device=device\",\n \"W + pix[..., 2] else: pix_inds = torch.randint(0, NV *\",\n \"train MLP\\\", ) return parser args, conf = util.args.parse_args(extra_args, training=True,\",\n \"(SB) if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False\",\n \"view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) # set\",\n \"] vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine: print(\\\"f\",\n \"vis_coarse if using_fine: print(\\\"f rgb min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max()))\",\n \"want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine)\",\n \"util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near\",\n \"make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\\\"Encoder frozen\\\") net.encoder.eval() renderer\",\n \"= data[\\\"poses\\\"].to(device=device) # (SB, NV, 4, 4) all_bboxes = data.get(\\\"bbox\\\")\",\n \"extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object batch size ('SB')\\\"\",\n \"all_poses = all_images = None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device)\",\n \"= pix[..., 0] * H * W + pix[..., 1]\",\n \"NV, _, H, W = images.shape cam_rays = util.gen_rays( poses,\",\n \") alpha_coarse_cmap = util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) /\",\n \"torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long)\",\n \"poses = all_poses[obj_idx] # (NV, 4, 4) focal = all_focals[obj_idx]\",\n \"if all_c is not None else None, ) render_dict =\",\n \"H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4) focal\",\n \"depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt,\",\n \"range(curr_nviews): view_dest += view_dest >= views_src[vs] views_src = torch.from_numpy(views_src) #\",\n \"(e.g. ShapeNet or DTU) # tensorboard logs available in logs/<expname>\",\n \"make_model, loss from render import NeRFRenderer from data import get_split_dataset\",\n \"extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \\\"images\\\"\",\n \"0] * H * W + pix[..., 1] * W\",\n \"rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse if using_fine: fine_loss\",\n \"else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB):\",\n \"SB, NV, _, H, W = all_images.shape all_poses = data[\\\"poses\\\"].to(device=device)\",\n \"+ 0.5 # (NV, 3, H, W) curr_nviews = nviews[torch.randint(0,\",\n \"inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews,\",\n \"replace=False)) view_dest = np.random.randint(0, NV - curr_nviews) for vs in\",\n \"= coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H, W) depth_fine_np\",\n \"rgb_gt_all = images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1,\",\n \"if \\\"c\\\" in data: c = data[\\\"c\\\"][obj_idx] if curr_nviews >\",\n \"W) SB, NV, _, H, W = all_images.shape all_poses =\",\n \"vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr =\",\n \"idx=None): if \\\"images\\\" not in data: return {} if idx\",\n \"+ 1] # (1) c = data.get(\\\"c\\\") if c is\",\n \"weights and only train MLP\\\", ) return parser args, conf\",\n \"('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and\",\n \"* H * W + pix[..., 1] * W +\",\n \") render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine =\",\n \"= util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list\",\n \"rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item()\",\n \"= util.gen_rays( poses, W, H, focal, self.z_near, self.z_far, c=c )\",\n \"rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals\",\n \"get_split_dataset import util import numpy as np import torch.nn.functional as\",\n \"F import torch from dotmap import DotMap def extra_args(parser): parser.add_argument(\",\n \"self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item()\",\n \") parser.add_argument( \\\"--no_bbox_step\\\", type=int, default=100000, help=\\\"Step to stop using bbox\",\n \"torch.save(renderer.state_dict(), self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \\\"images\\\" not\",\n \"else: print(idx) batch_idx = idx images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV,\",\n \"__init__(self): super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\"\",\n \"alpha min {}, max {}\\\".format( alpha_coarse_np.min(), alpha_coarse_np.max() ) ) alpha_coarse_cmap\",\n \"NV, 4, 4) all_bboxes = data.get(\\\"bbox\\\") # (SB, NV, 4)\",\n \"= fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train: loss.backward()\",\n \"else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr, gt) vals =\",\n \"fine = render_dict.fine using_fine = len(fine) > 0 loss_dict =\",\n \"1) .cpu() .numpy() .reshape(-1, H, W, 3) ) gt =\",\n \"= data.get(\\\"bbox\\\") # (SB, NV, 4) cmin rmin cmax rmax\",\n \"(multiview); put multiple (space delim) to pick randomly per batch\",\n \") self.lambda_coarse = conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda\",\n \"8) test_images = images[views_src] # (NS, 3, H, W) net.encode(\",\n \"render_dict.coarse fine = render_dict.fine using_fine = len(fine) > 0 alpha_coarse_np\",\n \"3, H, W) src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS,\",\n \"render_dict.fine using_fine = len(fine) > 0 loss_dict = {} rgb_loss\",\n \"torch.load(self.renderer_state_path, map_location=device) ) self.z_near = dset.z_near self.z_far = dset.z_far self.use_bbox\",\n \"None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3,\",\n \"in data: return {} if idx is None: batch_idx =\",\n \"import get_split_dataset import util import numpy as np import torch.nn.functional\",\n \"= torch.from_numpy(views_src) # set renderer net to eval mode renderer.eval()\",\n \"W, 3) print(\\\"c rgb min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print(\",\n \"False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path, map_location=device) ) self.z_near\",\n \"action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights and only train MLP\\\", )\",\n \"rgb_loss = rgb_loss * self.lambda_coarse + fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"]\",\n \"else None, ) test_rays = test_rays.reshape(1, H * W, -1)\",\n \"obj_idx in range(SB): if all_bboxes is not None: bboxes =\",\n \"vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ] vis_coarse\",\n \"1.0) print( \\\"lambda coarse {} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) )\",\n \"= all_bboxes[obj_idx] images = all_images[obj_idx] # (NV, 3, H, W)\",\n \"255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views,\",\n \"self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False) if args.resume: if os.path.exists(self.renderer_state_path): renderer.load_state_dict( torch.load(self.renderer_state_path,\",\n \"255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ]\",\n \"c=c ) # (NV, H, W, 8) images_0to1 = images\",\n \"{}, z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far, dset.lindisp) ) net =\",\n \"focal.to(device=device), c=c.to(device=device) if c is not None else None, )\",\n \"vis_coarse = np.hstack(vis_list) vis = vis_coarse if using_fine: print(\\\"f rgb\",\n \"= [] all_rays = [] curr_nviews = nviews[torch.randint(0, len(nviews), ()).item()]\",\n \"set renderer net to eval mode renderer.eval() source_views = (\",\n \"images * 0.5 + 0.5 # (NV, 3, H, W)\",\n \"views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net to eval\",\n \"as F import torch from dotmap import DotMap def extra_args(parser):\",\n \"= np.hstack(vis_list) vis = np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else:\",\n \"self.z_near, self.z_far, c=c ) # (NV, H, W, 8) rgb_gt_all\",\n \"all_poses = data[\\\"poses\\\"].to(device=device) # (SB, NV, 4, 4) all_bboxes =\",\n \"min {}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap =\",\n \"import NeRFRenderer from data import get_split_dataset import util import numpy\",\n \"all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds =\",\n \"dset.lindisp) ) net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc:\",\n \"= c[batch_idx : batch_idx + 1] # (1) NV, _,\",\n \"{}, max {}\\\".format( alpha_fine_np.min(), alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np)\",\n \"bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights\",\n \"conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset,\",\n \"_, H, W = all_images.shape all_poses = data[\\\"poses\\\"].to(device=device) # (SB,\",\n \"train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data,\",\n \"curr_nviews) for vs in range(curr_nviews): view_dest += view_dest >= views_src[vs]\",\n \"Stopped using bbox sampling @ iter\\\", global_step) if not is_train\",\n \"rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np psnr = util.psnr(rgb_psnr,\",\n \") # (NV, H, W, 8) images_0to1 = images *\",\n \"only train MLP\\\", ) return parser args, conf = util.args.parse_args(extra_args,\",\n \"\\\"-B\\\", type=int, default=4, help=\\\"Object batch size ('SB')\\\" ) parser.add_argument( \\\"--nviews\\\",\",\n \"+ pix[..., 2] else: pix_inds = torch.randint(0, NV * H\",\n \"not self.use_bbox: all_bboxes = None all_rgb_gt = [] all_rays =\",\n \"self.renderer_state_path) def calc_losses(self, data, is_train=True, global_step=0): if \\\"images\\\" not in\",\n \"3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W) if using_fine: alpha_fine_np = fine.weights[0].sum(dim=1).cpu().numpy().reshape(H,\",\n \"= torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes\",\n \"util.batched_index_select_nd( all_images, image_ord ) # (SB, NS, 3, H, W)\",\n \"batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx = idx images\",\n \"= list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset,\",\n \"def __init__(self): super().__init__(net, dset, val_dset, args, conf[\\\"train\\\"], device=device) self.renderer_state_path =\",\n \"if using_fine: print(\\\"f rgb min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print(\",\n \"= torch.randint(0, NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews),\",\n \") images_0to1 = images * 0.5 + 0.5 cam_rays =\",\n \"# (NS, 3, H, W) net.encode( test_images.unsqueeze(0), poses[views_src].unsqueeze(0), focal.to(device=device), c=c.to(device=device)\",\n \"+ 0.5 cam_rays = util.gen_rays( poses, W, H, focal, self.z_near,\",\n \"not None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse\",\n \"sys import os sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import\",\n \"poses, W, H, focal, self.z_near, self.z_far, c=c ) # (NV,\",\n \"parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object batch size ('SB')\\\" )\",\n \"DotMap def extra_args(parser): parser.add_argument( \\\"--batch_size\\\", \\\"-B\\\", type=int, default=4, help=\\\"Object batch\",\n \"help=\\\"Freeze encoder weights and only train MLP\\\", ) return parser\",\n \"import trainlib from model import make_model, loss from render import\",\n \"self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse\",\n \"# (NV, 3, H, W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()]\",\n \"image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images\",\n \"device=device) self.renderer_state_path = \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse\",\n \"if all_bboxes is not None: bboxes = all_bboxes[obj_idx] images =\",\n \"/ 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list = [\",\n \"stop using bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze\",\n \"(1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0, NV\",\n \"self.use_bbox = args.no_bbox_step > 0 def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size)\",\n \"get_split_dataset(args.dataset_format, args.datadir) print( \\\"dset z_near {}, z_far {}, lindisp {}\\\".format(dset.z_near,\",\n \"1] * W + pix[..., 2] else: pix_inds = torch.randint(0,\",\n \"not in data: return {} if idx is None: batch_idx\",\n \"W, 3) ) gt = images_0to1[view_dest].permute(1, 2, 0).cpu().numpy().reshape(H, W, 3)\",\n \"don't know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False)\",\n \"if rgb_loss.isnan().any().item()==True: raise() loss_dict[\\\"rc\\\"] = rgb_loss.item() * self.lambda_coarse if using_fine:\",\n \"batch_idx + 1] # (1) NV, _, H, W =\",\n \"torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) # (SB,\",\n \"pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds = pix[..., 0] * H\",\n \"= cam_rays.view(-1, cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays)\",\n \"calc_losses(self, data, is_train=True, global_step=0): if \\\"images\\\" not in data: return\",\n \"put multiple (space delim) to pick randomly per batch ('NV')\\\",\",\n \"bbox sampling @ iter\\\", global_step) if not is_train or not\",\n \"torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes is\",\n \"fine_loss * self.lambda_fine loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine loss =\",\n \"= images.shape cam_rays = util.gen_rays( poses, W, H, focal, self.z_near,\",\n \"ray_batch_size, 8) image_ord = image_ord.to(device) src_images = util.batched_index_select_nd( all_images, image_ord\",\n \"(NV, 4, 4) focal = all_focals[obj_idx] c = None if\",\n \"{} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha min {}, max\",\n \"= np.vstack((vis_coarse, vis_fine)) rgb_psnr = rgb_fine_np else: rgb_psnr = rgb_coarse_np\",\n \"255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np, alpha_coarse_cmap, ]\",\n \"\\\"lambda coarse {} and fine {}\\\".format(self.lambda_coarse, self.lambda_fine) ) self.rgb_coarse_crit =\",\n \"len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews, replace=False)) view_dest = np.random.randint(0,\",\n \"0 loss_dict = {} rgb_loss = self.rgb_coarse_crit(coarse.rgb, all_rgb_gt) if rgb_loss.isnan().any().item()==True:\",\n \"H * W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse =\",\n \"alpha_fine_np.max() ) ) depth_fine_cmap = util.cmap(depth_fine_np) / 255 alpha_fine_cmap =\",\n \"not None: c = c[batch_idx : batch_idx + 1] #\",\n \"- curr_nviews) for vs in range(curr_nviews): view_dest += view_dest >=\",\n \"default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir)\",\n \"cam_rays.shape[-1])[pix_inds].to( device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt =\",\n \"if c is not None else None, ) test_rays =\",\n \"from data import get_split_dataset import util import numpy as np\",\n \"c = data.get(\\\"c\\\") if c is not None: c =\",\n \"None else None, ) test_rays = test_rays.reshape(1, H * W,\",\n \"fine.depth[0].cpu().numpy().reshape(H, W) rgb_fine_np = fine.rgb[0].cpu().numpy().reshape(H, W, 3) print(\\\"c rgb min\",\n \"want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine using_fine = len(fine)\",\n \"util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ =\",\n \"if using_fine: fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss *\",\n \"all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays\",\n \"= render_dict.fine using_fine = len(fine) > 0 loss_dict = {}\",\n \"global_step): return self.calc_losses(data, is_train=True, global_step=global_step) def eval_step(self, data, global_step): renderer.eval()\",\n \"if self.use_bbox and global_step >= args.no_bbox_step: self.use_bbox = False print(\\\">>>\",\n \"curr_nviews), dtype=torch.long) for obj_idx in range(SB): if all_bboxes is not\",\n \"c=all_c.to(device=device) if all_c is not None else None, ) render_dict\",\n \"W, 8) test_images = images[views_src] # (NS, 3, H, W)\",\n \"is not None else None, ) render_dict = DotMap(render_par(all_rays, want_weights=True,))\",\n \"test_rays = cam_rays[view_dest] # (H, W, 8) test_images = images[views_src]\",\n \"help=\\\"Freeze encoder weights and only train MLP\\\", ) parser.add_argument( \\\"--no_bbox_step\\\",\",\n \"loss_dict[\\\"rf\\\"] = fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train:\",\n \"W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3) depth_coarse_np = coarse.depth[0].cpu().numpy().reshape(H, W)\",\n \"self.z_far = dset.z_far self.use_bbox = args.no_bbox_step > 0 def post_batch(self,\",\n \"losses = self.calc_losses(data, is_train=False, global_step=global_step) renderer.train() return losses def vis_step(self,\",\n \"util.cmap(depth_coarse_np) / 255 vis_list = [ *source_views, gt, depth_coarse_cmap, rgb_coarse_np,\",\n \"= [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap, ] vis_fine =\",\n \"\\\"images\\\" not in data: return {} if idx is None:\",\n \"4, 4) focal = data[\\\"focal\\\"][batch_idx : batch_idx + 1] #\",\n \"batch ('NV')\\\", ) parser.add_argument( \\\"--freeze_enc\\\", action=\\\"store_true\\\", default=None, help=\\\"Freeze encoder weights\",\n \"numpy as np import torch.nn.functional as F import torch from\",\n \"rmax all_focals = data[\\\"focal\\\"] # (SB) all_c = data.get(\\\"c\\\") #\",\n \"set the renderer network back to train mode renderer.train() return\",\n \"Parallize render_par = renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class\",\n \"print(idx) batch_idx = idx images = data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3,\",\n \"images.shape cam_rays = util.gen_rays( poses, W, H, focal, self.z_near, self.z_far,\",\n \"{}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha min {}, max {}\\\".format( alpha_fine_np.min(),\",\n \"training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format,\",\n \"NV, (SB, 1)) else: image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for\",\n \"data[\\\"images\\\"][batch_idx].to(device=device) # (NV, 3, H, W) poses = data[\\\"poses\\\"][batch_idx].to(device=device) #\",\n \"in data: return {} all_images = data[\\\"images\\\"].to(device=device) # (SB, NV,\",\n \"data, is_train=True, global_step=0): if \\\"images\\\" not in data: return {}\",\n \"= np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx = idx images =\",\n \"torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) ) images_0to1 = images * 0.5\",\n \"sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"src\\\")) ) import warnings import trainlib\",\n \"self.lambda_fine) ) self.rgb_coarse_crit = loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if\",\n \"()).item()] if curr_nviews == 1: image_ord = torch.randint(0, NV, (SB,\",\n \"not None: bboxes = all_bboxes[obj_idx] images = all_images[obj_idx] # (NV,\",\n \"/ 255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np, alpha_fine_cmap,\",\n \"renderer net to eval mode renderer.eval() source_views = ( images_0to1[views_src]\",\n \"global_step) if not is_train or not self.use_bbox: all_bboxes = None\",\n \"know better way image_ord[obj_idx] = torch.from_numpy( np.random.choice(NV, curr_nviews, replace=False) )\",\n \"loss.get_rgb_loss(conf[\\\"loss.rgb\\\"], True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using\",\n \"all_rays = torch.stack(all_rays) # (SB, ray_batch_size, 8) image_ord = image_ord.to(device)\",\n \"render_dict = DotMap(render_par(all_rays, want_weights=True,)) coarse = render_dict.coarse fine = render_dict.fine\",\n \"is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item() return loss_dict def train_step(self, data,\",\n \"all_focals = data[\\\"focal\\\"] # (SB) all_c = data.get(\\\"c\\\") # (SB)\",\n \"train mode renderer.train() return vis, vals trainer = PixelNeRFTrainer() trainer.start()\",\n \"net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not None\",\n \"# (1) NV, _, H, W = images.shape cam_rays =\",\n \"W = images.shape cam_rays = util.gen_rays( poses, W, H, focal,\",\n \"data[\\\"images\\\"].shape[0]) else: print(idx) batch_idx = idx images = data[\\\"images\\\"][batch_idx].to(device=device) #\",\n \"parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_id[0])\",\n \"(H, W, 8) test_images = images[views_src] # (NS, 3, H,\",\n \"renderer.bind_parallel(net, args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self):\",\n \"# Somewhat inefficient, don't know better way image_ord[obj_idx] = torch.from_numpy(\",\n \"W, -1) render_dict = DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine\",\n \"= data[\\\"poses\\\"][batch_idx].to(device=device) # (NV, 4, 4) focal = data[\\\"focal\\\"][batch_idx :\",\n \"if \\\"images\\\" not in data: return {} if idx is\",\n \"def post_batch(self, epoch, batch): renderer.sched_step(args.batch_size) def extra_save_state(self): torch.save(renderer.state_dict(), self.renderer_state_path) def\",\n \"loss = rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"] = loss.item() return\",\n \"W, H, focal, self.z_near, self.z_far, c=c ) # (NV, H,\",\n \"conf.get_float(\\\"loss.lambda_coarse\\\") self.lambda_fine = conf.get_float(\\\"loss.lambda_fine\\\", 1.0) print( \\\"lambda coarse {} and\",\n \"= loss.item() return loss_dict def train_step(self, data, global_step): return self.calc_losses(data,\",\n \"replace=False) ) images_0to1 = images * 0.5 + 0.5 cam_rays\",\n \"self.renderer_state_path = \\\"%s/%s/_renderer\\\" % ( self.args.checkpoints_path, self.args.name, ) self.lambda_coarse =\",\n \"nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord = torch.randint(0,\",\n \"type=str, default=\\\"1\\\", help=\\\"Number of source views (multiview); put multiple (space\",\n \"util.cmap(depth_fine_np) / 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list =\",\n \"False print(\\\">>> Stopped using bbox sampling @ iter\\\", global_step) if\",\n \"util.cmap(alpha_fine_np) / 255 vis_list = [ *source_views, gt, depth_fine_cmap, rgb_fine_np,\",\n \"W) curr_nviews = nviews[torch.randint(0, len(nviews), (1,)).item()] views_src = np.sort(np.random.choice(NV, curr_nviews,\",\n \"image_ord = torch.empty((SB, curr_nviews), dtype=torch.long) for obj_idx in range(SB): if\",\n \"min {} max {}\\\".format(rgb_coarse_np.min(), rgb_coarse_np.max())) print( \\\"c alpha min {},\",\n \"rgb min {} max {}\\\".format(rgb_fine_np.min(), rgb_fine_np.max())) print( \\\"f alpha min\",\n \"= nviews[torch.randint(0, len(nviews), ()).item()] if curr_nviews == 1: image_ord =\",\n \"fine_loss.item() * self.lambda_fine loss = rgb_loss if is_train: loss.backward() loss_dict[\\\"t\\\"]\",\n \"4) focal = data[\\\"focal\\\"][batch_idx : batch_idx + 1] # (1)\",\n \"DotMap(render_par(test_rays, want_weights=True)) coarse = render_dict.coarse fine = render_dict.fine using_fine =\",\n \"the renderer network back to train mode renderer.train() return vis,\",\n \"= images_0to1 rgb_gt_all = ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3)\",\n \"(NV, H, W, 8) images_0to1 = images * 0.5 +\",\n \"all_bboxes = None all_rgb_gt = [] all_rays = [] curr_nviews\",\n \"(ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt) # (SB, ray_batch_size,\",\n \"(NV, H, W, 3) if all_bboxes is not None: pix\",\n \"0 alpha_coarse_np = coarse.weights[0].sum(dim=-1).cpu().numpy().reshape(H, W) rgb_coarse_np = coarse.rgb[0].cpu().numpy().reshape(H, W, 3)\",\n \">= views_src[vs] views_src = torch.from_numpy(views_src) # set renderer net to\",\n \"print(\\\"using fine loss\\\") fine_loss_conf = conf[\\\"loss.rgb_fine\\\"] self.rgb_fine_crit = loss.get_rgb_loss(fine_loss_conf, False)\",\n \"1: # Somewhat inefficient, don't know better way image_ord[obj_idx] =\",\n \"to train mode renderer.train() return vis, vals trainer = PixelNeRFTrainer()\",\n \"= ( rgb_gt_all.permute(0, 2, 3, 1).contiguous().reshape(-1, 3) ) # (NV,\",\n \"device=device ) # (ray_batch_size, 8) all_rgb_gt.append(rgb_gt) all_rays.append(rays) all_rgb_gt = torch.stack(all_rgb_gt)\",\n \"renderer.eval() source_views = ( images_0to1[views_src] .permute(0, 2, 3, 1) .cpu()\",\n \"c=c.to(device=device) if c is not None else None, ) test_rays\",\n \"view_dest = np.random.randint(0, NV - curr_nviews) for vs in range(curr_nviews):\",\n \"True) fine_loss_conf = conf[\\\"loss.rgb\\\"] if \\\"rgb_fine\\\" in conf[\\\"loss\\\"]: print(\\\"using fine\",\n \"views (multiview); put multiple (space delim) to pick randomly per\",\n \"fine_loss = self.rgb_fine_crit(fine.rgb, all_rgb_gt) rgb_loss = rgb_loss * self.lambda_coarse +\",\n \"if idx is None: batch_idx = np.random.randint(0, data[\\\"images\\\"].shape[0]) else: print(idx)\",\n \"args.datadir) print( \\\"dset z_near {}, z_far {}, lindisp {}\\\".format(dset.z_near, dset.z_far,\",\n \"all_bboxes = all_poses = all_images = None net.encode( src_images, src_poses,\",\n \"MLP\\\", ) return parser args, conf = util.args.parse_args(extra_args, training=True, default_ray_batch_size=128)\",\n \"if all_bboxes is not None: pix = util.bbox_sample(bboxes, args.ray_batch_size) pix_inds\",\n \"all_images, image_ord ) # (SB, NS, 3, H, W) src_poses\",\n \"None net.encode( src_images, src_poses, all_focals.to(device=device), c=all_c.to(device=device) if all_c is not\",\n \"= torch.stack(all_rgb_gt) # (SB, ray_batch_size, 3) all_rays = torch.stack(all_rays) #\",\n \"default=100000, help=\\\"Step to stop using bbox sampling\\\", ) parser.add_argument( \\\"--fixed_test\\\",\",\n \") net = make_model(conf[\\\"model\\\"]).to(device=device) net.stop_encoder_grad = args.freeze_enc if args.freeze_enc: print(\\\"Encoder\",\n \"if curr_nviews == 1: image_ord = torch.randint(0, NV, (SB, 1))\",\n \"args.gpu_id).eval() nviews = list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net,\",\n \"src_poses = util.batched_index_select_nd(all_poses, image_ord) # (SB, NS, 4, 4) all_bboxes\",\n \"views_src = torch.from_numpy(views_src) # set renderer net to eval mode\",\n \"# (SB, NS, 4, 4) all_bboxes = all_poses = all_images\",\n \"/ 255 alpha_fine_cmap = util.cmap(alpha_fine_np) / 255 vis_list = [\",\n \"help=\\\"Number of source views (multiview); put multiple (space delim) to\",\n \"list(map(int, args.nviews.split())) class PixelNeRFTrainer(trainlib.Trainer): def __init__(self): super().__init__(net, dset, val_dset, args,\",\n \"torch.nn.functional as F import torch from dotmap import DotMap def\",\n \"= all_poses = all_images = None net.encode( src_images, src_poses, all_focals.to(device=device),\",\n \".permute(0, 2, 3, 1) .cpu() .numpy() .reshape(-1, H, W, 3)\",\n \"return loss_dict def train_step(self, data, global_step): return self.calc_losses(data, is_train=True, global_step=global_step)\",\n \"# (NV, H, W, 8) rgb_gt_all = images_0to1 rgb_gt_all =\",\n \"psnr) # set the renderer network back to train mode\",\n \"images * 0.5 + 0.5 cam_rays = util.gen_rays( poses, W,\",\n \"= util.cmap(alpha_coarse_np) / 255 depth_coarse_cmap = util.cmap(depth_coarse_np) / 255 vis_list\",\n \"device = util.get_cuda(args.gpu_id[0]) dset, val_dset, _ = get_split_dataset(args.dataset_format, args.datadir) print(\"\n]","isConversational":false}}},{"rowIdx":167,"cells":{"ngram":{"kind":"list like","value":["import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add error","= ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd","return # including summary here for sake of filtering below.","output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text)","project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version","accept this event explicitly to be able # to receive","not isinstance(p, QDialog) and p.parent(): p = p.parent() return p","sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15)","= '', url: str = '', summary: str = '',","if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\"))","project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates if","buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox)","self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available plugins self.worker","+ list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]):","import os import sys from pathlib import Path from typing","= {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url',","buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status)","['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd +=","uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args","self.parent() while not isinstance(p, QDialog) and p.parent(): p = p.parent()","self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if","_on_enabled_checkbox(self, state: int): \"\"\"Called with `state` when checkbox is clicked.\"\"\"","vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0,","True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary)","queue to handle clicks when already processing class Installer: def",") env.insert(\"PYTHONPATH\", combined_paths) # use path of parent process env.insert(","receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md = event.mimeData()","None, parent: QWidget = None, enabled: bool = True, ):","): return # including summary here for sake of filtering","plugin dialog if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname)","import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations","Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout()","QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight |","= standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or","lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0,","# setup process path env = QProcessEnvironment() combined_paths = os.pathsep.join(","self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') ) self.direct_entry_btn","Qt.MatchFixedString) and not plugin_name ): return # including summary here","# including summary here for sake of filtering below. searchable_text","napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import","sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name)","else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def","= QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\")","str = '', summary: str = '', author: str =","QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add error icon","else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch","'', author: str = '', license: str = \"UNKNOWN\", *,","if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class","QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter","buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0)","...\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self)","self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18,","def __init__(self, output_widget: QTextEdit = None): from ...plugins import plugin_manager","self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in","def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False)","* self.installed_list.count(), 10, 10]) # fetch available plugins self.worker =","QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy,","install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if","already_installed = set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): #","''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) #","( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return # including","QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') )","dialog if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta","QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, )","self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] =","if os.path.exists(_packages): packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear()","= QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer)",") self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \"\"\"Called","self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\",","super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info:","including summary here for sake of filtering below. searchable_text =","mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or","in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self,","event): if event.type() == QEvent.DragEnter: # we need to accept","self.working_indicator = QLabel(trans._(\"loading ...\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp)","[user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) # use path of parent","ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from","from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans from","rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"),","item.version latest = project_info.version if parse_version(current) >= parse_version(latest): continue if","TODO: add queue to handle clicks when already processing class","sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy","checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self,","from qtpy.QtWidgets import QApplication app = QApplication([]) w = QtPluginDialog()","'', url: str = '', summary: str = '', author:","self.installed_list.count(), 10, 10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info)","self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide()",") import napari.resources from ...plugins import plugin_manager from ...plugins.pypi import","import parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading import","Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list))","def __init__( self, package_name: str, version: str = '', url:","\"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit =","__init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True)","pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name:","self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox)","'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item,","os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) # use path of","of parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover())","drop file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn =","= getattr( self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda:","widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \"\"\"Filter items to those","i in range(self.count()): item = self.item(i) item.setHidden(item not in shown)","qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget,",") widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \"\"\"Filter items to","list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args","self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self)","6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled)","QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy =","''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins,","for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event)","= QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy","user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def","meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''),","sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout()","..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import","env.insert(\"PYTHONPATH\", combined_paths) # use path of parent process env.insert( \"PATH\",","exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear()","QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical)","def filter(self, text: str): \"\"\"Filter items to those containing `text`.\"\"\"","self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)","10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info):","= None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if","Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show):","installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True","class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer","latest = project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item,","= [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return","QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets","@Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString):","= QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter)","= QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay =","trans._('install by name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"),","handle pip install errors # TODO: add queue to handle","= QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth()","self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info)","parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide()","\" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version","p = p.parent() return p def setup_ui(self, enabled=True): self.v_lay =","in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) #","= \"UNKNOWN\", *, plugin_name: str = None, parent: QWidget =","when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def","installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem(","QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True)","QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close)","str = \"UNKNOWN\", *, plugin_name: str = None, parent: QWidget","= None): from ...plugins import plugin_manager # create install process","or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''),","QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText(","from ...plugins import plugin_manager # create install process self._output_widget =","if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget:","QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary)","plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from","self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if not packages:","Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)","need to accept this event explicitly to be able #","iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc","sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state:","filter(self, text: str): \"\"\"Filter items to those containing `text`.\"\"\" shown","self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox =","-> QDialog: p = self.parent() while not isinstance(p, QDialog) and","import QtPluginErrReporter # TODO: add error icon and handle pip","= QLabel(trans._(\"loading ...\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator","parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda:","-1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred )","\"UNKNOWN\", *, plugin_name: str = None, parent: QWidget = None,","for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest =","self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\")","({count})\", count=count) ) def eventFilter(self, watched, event): if event.type() ==","installed from ...plugins import plugin_manager plugin_manager.discover() # since they might","QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def","''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ),","640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter","eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: # we need","PluginListItem(QFrame): def __init__( self, package_name: str, version: str = '',","str = None, parent: QWidget = None, enabled: bool =","__init__( self, package_name: str, version: str = '', url: str","= '', summary: str = '', author: str = '',","self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name):","self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\"))","QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\")","output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text","'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start()","addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add","items to those containing `text`.\"\"\" shown = self.findItems(text, Qt.MatchContains) for","plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch","def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self,","class Installer: def __init__(self, output_widget: QTextEdit = None): from ...plugins",") sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author","= QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\") mov","shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer()","= QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\")","def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show","in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version if","rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else:","sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self,","import plugin_manager plugin_manager.discover() # since they might not be loaded","if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta = {}","self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"), self) sp","18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by","QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2","self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter )","plugin_name ): return # including summary here for sake of","parent: QWidget = None, enabled: bool = True, ): super().__init__(parent)","filtering below. searchable_text = project_info.name + \" \" + project_info.summary","= QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0,","= QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...')","self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)","import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame,","show): if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show","import Path from typing import Sequence from napari_plugin_engine.dist import standard_metadata","self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1","isinstance(p, QDialog) and p.parent(): p = p.parent() return p def","show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self,","0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred","from pathlib import Path from typing import Sequence from napari_plugin_engine.dist","self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred,","Qt.MatchContains) for i in range(self.count()): item = self.item(i) item.setHidden(item not","QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn)","buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True)","close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show()","= QPushButton( trans._(\"update (v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect(","update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str):","QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie from","self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages = _packages.split()","= project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled,","= QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout","self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import plugin_manager plugin_manager.discover()","QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) #","def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self)","vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter)","QLabel(trans._(\"loading ...\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator =","create install process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable)","return True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show:","'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location',","below. searchable_text = project_info.name + \" \" + project_info.summary item","self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy =","getattr( self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name]))","if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def","mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed,","QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter)","event.accept() if event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls():",">= parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged it","PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet)","load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18))","...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading","10, 10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def","self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p","mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text =","self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count","QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6)","= set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not","initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close)","self) self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn)","= QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter)","self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version if parse_version(current)","self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in","plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str, version: str","else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version',","from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment,","class PluginListItem(QFrame): def __init__( self, package_name: str, version: str =","= QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\"Available Plugins\"))","def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't","add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ):","buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False)","enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10])","= QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight","self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2)","item.outdated = True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\"update","qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog,","= os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) # use path","# already tagged it continue item.outdated = True widg =","not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages]","be able # to receive QDropEvents! event.accept() if event.type() ==","PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot","self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter =","self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy =","env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\",","= QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter)","Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy =","running_as_bundled_app from ...utils.translations import trans from ..qthreading import create_worker from","standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''),","'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for","create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info)","QDialog: p = self.parent() while not isinstance(p, QDialog) and p.parent():","widg = self.itemWidget(item) update_btn = QPushButton( trans._(\"update (v{latest})\", latest=latest), widg","self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"), self)","self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list","if event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls(): files","self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<<","= self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif","event explicitly to be able # to receive QDropEvents! event.accept()","standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess,","self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \"\"\"Called with `state`","self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def",") widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info:","QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label =","*project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall'","widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn)","not be loaded yet already_installed = set() for plugin_name, mod_name,","os import sys from pathlib import Path from typing import","self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd","self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True)","path of parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda:","buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"), self) sp =","self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \"\"\"Filter items","str, version: str = '', url: str = '', summary:","QDropEvents! event.accept() if event.type() == QEvent.Drop: md = event.mimeData() if","handle clicks when already processing class Installer: def __init__(self, output_widget:","{} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''),","def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count)","lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout()","summary here for sake of filtering below. searchable_text = project_info.name","self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self)","by name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"), self)","buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4,","self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide()","== \"__main__\": from qtpy.QtWidgets import QApplication app = QApplication([]) w","add queue to handle clicks when already processing class Installer:","self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0,","trans._(\"Available Plugins ({count})\", count=count) ) def eventFilter(self, watched, event): if","not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer):","enabled=enabled, ) method = getattr( self.installer, 'uninstall' if plugin_name else",") self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred,","_on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if","QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont,","QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0))","2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins","0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0,","event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls(): files =","self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment() combined_paths =","ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest","plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10,","self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0,","QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)","create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from","p.parent(): p = p.parent() return p def setup_ui(self, enabled=True): self.v_lay","self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self)","QDialog) and p.parent(): p = p.parent() return p def setup_ui(self,","self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return # including summary","self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide()","_on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list:","self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0)","plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed:","= self.findItems(text, Qt.MatchContains) for i in range(self.count()): item = self.item(i)","QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4,","sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent","+= [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if","shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()): item =","= ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)","self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count) ) def eventFilter(self, watched,","self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code:","the plugin dialog if plugin_name in ('napari_plugin_engine',): continue if distname:","self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0)","0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter)","normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import","...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from","project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): #","widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo):","QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel,","import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import","qtpy.QtWidgets import QApplication app = QApplication([]) w = QtPluginDialog() w.show()","QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\")","def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget)","name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages)","parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall' if","self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list =","self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url,","= item.version latest = project_info.version if parse_version(current) >= parse_version(latest): continue","if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args =","= QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name)","list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg)","..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import","from ...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info,","rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38)","from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter","*, plugin_name: str = None, parent: QWidget = None, enabled:","icon and handle pip install errors # TODO: add queue","close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version)","self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent() while","= QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout()","if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label)","self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint())","sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd +","self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop","import plugin_manager # create install process self._output_widget = None self.process","def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y']","don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name","class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui()","self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list","exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def","self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True)","update_btn) def filter(self, text: str): \"\"\"Filter items to those containing","_toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<<","self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__(","self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide()","and p.parent(): p = p.parent() return p def setup_ui(self, enabled=True):","= QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth()","self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m',","Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self,","with `state` when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class","self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self,","= QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18)","is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent:","QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary","= self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count) ) def eventFilter(self,","import sys from pathlib import Path from typing import Sequence","self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator =","summary: str = '', author: str = '', license: str","md.hasUrls(): files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return","_handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide)","Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide Status\"))","\"\"\"Called with `state` when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state)","QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button","PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer,","self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip',","Installer: def __init__(self, output_widget: QTextEdit = None): from ...plugins import","def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield)","self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout)","not showing these in the plugin dialog if plugin_name in","self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame):","since they might not be loaded yet already_installed = set()","import QApplication app = QApplication([]) w = QtPluginDialog() w.show() app.exec_()","4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1,","TODO: add error icon and handle pip install errors #","self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p =","duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return","QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins","self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus()","self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def","setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0)","cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list))","plugin_manager # create install process self._output_widget = None self.process =","else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__","self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)","= QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() )","in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else:","self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy =","self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list:","6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox =","= QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths)","self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(","def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current","process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune())","QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch()","import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt,","bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name}","pathlib import Path from typing import Sequence from napari_plugin_engine.dist import","self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if","# we need to accept this event explicitly to be","self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code,","yet already_installed = set() for plugin_name, mod_name, distname in plugin_manager.iter_available():","rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary)","QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout =","napari.resources from ...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo,","= '', license: str = \"UNKNOWN\", *, plugin_name: str =","parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo)","ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter #","# fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if","from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox,","if __name__ == \"__main__\": from qtpy.QtWidgets import QApplication app =","self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy(","self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \"\"\"Called with","if event.type() == QEvent.DragEnter: # we need to accept this","= QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15","for i in range(self.count()): item = self.item(i) item.setHidden(item not in",") self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton()","distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem(","...plugins import plugin_manager plugin_manager.discover() # since they might not be","_on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh()","packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else:","\" \" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version =","QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter =","ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates if (","_packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages","# fetch installed from ...plugins import plugin_manager plugin_manager.discover() # since","QWidget = None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled)","0, 2) self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\")","\"\"\"Filter items to those containing `text`.\"\"\" shown = self.findItems(text, Qt.MatchContains)","sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 =","self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self, packages:","= self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages =","'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self,","self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from","already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self):","from ...utils.translations import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label","\"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def","version: str = '', url: str = '', summary: str","QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont()","Show Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator)","Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self,","# to receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md","md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self, show):","self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget","self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2,","if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name )","self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates","self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator","plugin_name=None, enabled=True ): # don't add duplicates if ( self.findItems(project_info.name,","self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed =","__name__ == \"__main__\": from qtpy.QtWidgets import QApplication app = QApplication([])","+ list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list:","= QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1,","self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \"\"\"Called with `state` when checkbox","self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif =","return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6,","Qt.MatchFixedString): current = item.version latest = project_info.version if parse_version(current) >=","it continue item.outdated = True widg = self.itemWidget(item) update_btn =","= True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\")","__init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done)","showing these in the plugin dialog if plugin_name in ('napari_plugin_engine',):","= QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter","0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)","installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2)","= QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"), self) sp = self.working_indicator.sizePolicy()","self.available_list.clear() # fetch installed from ...plugins import plugin_manager plugin_manager.discover() #","self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter","None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name:","QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay","buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True)",") self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name =","self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count) ) def eventFilter(self, watched, event):","Plugins ({count})\", count=count) ) def eventFilter(self, watched, event): if event.type()","'', license: str = \"UNKNOWN\", *, plugin_name: str = None,","sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy)","packages: Sequence[str] = ()): if not packages: _packages = self.direct_entry_edit.text()","current = item.version latest = project_info.version if parse_version(current) >= parse_version(latest):","= project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'):","self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent /","process path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")]","error icon and handle pip install errors # TODO: add","import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter","self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(","QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1)","item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item","= True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\"update (v{latest})\",","Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show()","from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit,","self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\")","= QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter)","might not be loaded yet already_installed = set() for plugin_name,","clicks when already processing class Installer: def __init__(self, output_widget: QTextEdit","sake of filtering below. searchable_text = project_info.name + \" \"","env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) # use path of parent process","'-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg","import napari.resources from ...plugins import plugin_manager from ...plugins.pypi import (","item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent)","self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else:","in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def","p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1,","self.installer.install(packages) if __name__ == \"__main__\": from qtpy.QtWidgets import QApplication app","setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter)","lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\"Installed","# don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not","self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator)","Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout()","self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0)","...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import","<filename>napari/_qt/dialogs/qt_plugin_dialog.py import os import sys from pathlib import Path from","to accept this event explicitly to be able # to","lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter)","def _on_enabled_checkbox(self, state: int): \"\"\"Called with `state` when checkbox is","refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import plugin_manager","p = self.parent() while not isinstance(p, QDialog) and p.parent(): p","self.itemWidget(item) update_btn = QPushButton( trans._(\"update (v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed,","= QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout",") method = getattr( self.installer, 'uninstall' if plugin_name else 'install'","QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0)","we need to accept this event explicitly to be able","QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources","self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit):","int): \"\"\"Called with `state` when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not","== QEvent.DragEnter: # we need to accept this event explicitly","= output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode()","from typing import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions","self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count()","_install_packages(self, packages: Sequence[str] = ()): if not packages: _packages =","fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name","able # to receive QDropEvents! event.accept() if event.type() == QEvent.Drop:","show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide()","| Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed,","in the plugin dialog if plugin_name in ('napari_plugin_engine',): continue if","self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path","self, package_name: str, version: str = '', url: str =","sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator","continue item.outdated = True widg = self.itemWidget(item) update_btn = QPushButton(","QPushButton( trans._(\"update (v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda:","def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade']","from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app","self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter(","be loaded yet already_installed = set() for plugin_name, mod_name, distname","0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed)","event.type() == QEvent.DragEnter: # we need to accept this event","event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url in md.urls()]","item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name,","self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self)","/ \"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit","self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins","loaded yet already_installed = set() for plugin_name, mod_name, distname in","widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name,","self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080,","'--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix',","user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans","for sake of filtering below. searchable_text = project_info.name + \"","add error icon and handle pip install errors # TODO:","ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''),","QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled)","str = '', url: str = '', summary: str =","meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, )","parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading import create_worker","= QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100)","return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">> Hide","plugin_name: str = None, parent: QWidget = None, enabled: bool","= event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url in","self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall',","self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label)","self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name","containing `text`.\"\"\" shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()):","QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy)","# create install process self._output_widget = None self.process = QProcess()","QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else:","for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self,","QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer =","= self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self,","(v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) )","sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide()","if packages: self.installer.install(packages) if __name__ == \"__main__\": from qtpy.QtWidgets import","self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox","QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self,","parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self,","QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins import","`text`.\"\"\" shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()): item","] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self,","self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install',","self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int):","meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''),","def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide()","self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment()","env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget)","if md.hasUrls(): files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0])","processing class Installer: def __init__(self, output_widget: QTextEdit = None): from","self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal)","tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current =","= str(Path(napari.resources.__file__).parent / \"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov)","p.parent() return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1,","self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 =","QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading","= ()): if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages):","mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list)","== QEvent.Drop: md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile()","self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def","Path from typing import Sequence from napari_plugin_engine.dist import standard_metadata from","when already processing class Installer: def __init__(self, output_widget: QTextEdit =","continue if hasattr(item, 'outdated'): # already tagged it continue item.outdated",") self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget:","= ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget:","setup process path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(),","self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self)","latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3,","_get_dialog(self) -> QDialog: p = self.parent() while not isinstance(p, QDialog)","self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \"\"\"Called with `state` when","sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def","cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'):","['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear()","= QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"),","install errors # TODO: add queue to handle clicks when","mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled","those containing `text`.\"\"\" shown = self.findItems(text, Qt.MatchContains) for i in","meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name,","''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 *","Qt, Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import","+ project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item)","= PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr(","plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname)","= self.parent() while not isinstance(p, QDialog) and p.parent(): p =","if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(),","= '', author: str = '', license: str = \"UNKNOWN\",","QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch()","trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from","( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton,","from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO:","in range(self.count()): item = self.item(i) item.setHidden(item not in shown) class","of filtering below. searchable_text = project_info.name + \" \" +","mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self)","or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn","Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show()","packages: self.installer.install(packages) if __name__ == \"__main__\": from qtpy.QtWidgets import QApplication","font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing","ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(",".qt_plugin_report import QtPluginErrReporter # TODO: add error icon and handle",") def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: #","self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(),","self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed","= Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True)","self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor)","parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged",") from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version,","url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def","sys from pathlib import Path from typing import Sequence from","from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore","): # don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and","self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay","= QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator =","buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False)","pip install errors # TODO: add queue to handle clicks","project_info.name + \" \" + project_info.summary item = QListWidgetItem(searchable_text, parent=self)","not plugin_name ): return # including summary here for sake","QWidget, ) import napari.resources from ...plugins import plugin_manager from ...plugins.pypi","item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version","self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn =","to those containing `text`.\"\"\" shown = self.findItems(text, Qt.MatchContains) for i","QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if","event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show()","= QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button)","def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6)","QVBoxLayout, QWidget, ) import napari.resources from ...plugins import plugin_manager from","-1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self)","= project_info.name + \" \" + project_info.summary item = QListWidgetItem(searchable_text,","meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70","QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins import plugin_manager","None): from ...plugins import plugin_manager # create install process self._output_widget","__init__(self, output_widget: QTextEdit = None): from ...plugins import plugin_manager #","= QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem(","self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self):","= None, parent: QWidget = None, enabled: bool = True,","Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit)","'', summary: str = '', author: str = '', license:","str(Path(napari.resources.__file__).parent / \"loading.gif\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start()","method = getattr( self.installer, 'uninstall' if plugin_name else 'install' )","super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method","sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth()","QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text)","self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status):","): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author)","here for sake of filtering below. searchable_text = project_info.name +","typing import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import","self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary',","QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author)","sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15)","for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing these","enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1","= self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m',","pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args +","if hasattr(item, 'outdated'): # already tagged it continue item.outdated =","self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500)","combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] ) env.insert(\"PYTHONPATH\", combined_paths) # use","2) self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350)","super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\"))","if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages =","'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [","QtPluginErrReporter # TODO: add error icon and handle pip install","= None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup","to be able # to receive QDropEvents! event.accept() if event.type()","0, 2) self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\")","...utils.translations import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import","else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640)","QEvent.DragEnter: # we need to accept this event explicitly to","if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta =","self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)","while not isinstance(p, QDialog) and p.parent(): p = p.parent() return","def _install_packages(self, packages: Sequence[str] = ()): if not packages: _packages","[_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if","# TODO: add queue to handle clicks when already processing","# since they might not be loaded yet already_installed =","`state` when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget):","rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error)","self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn =","if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled)","path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\"PYTHONPATH\")] )","from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report","self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent()","process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready)","str): \"\"\"Filter items to those containing `text`.\"\"\" shown = self.findItems(text,","package_name: str, version: str = '', url: str = '',","4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding,","Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import (","QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info,","sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author =","parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh()","= QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep)","available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in","from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from","self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def","self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() )","set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def","'outdated'): # already tagged it continue item.outdated = True widg","author: str = '', license: str = \"UNKNOWN\", *, plugin_name:","plugin_manager plugin_manager.discover() # since they might not be loaded yet","self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox","self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog:","self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True)","clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget,","self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None):","self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed,","to receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md =","md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url","super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def","project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start()","sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name","if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): # already","mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled,","QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide()","count=count) ) def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter:","item = self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def","count = self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count) ) def","from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs","self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator)","self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show()","meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in","QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env =","QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label =","self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0,","self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0,","self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self):","already tagged it continue item.outdated = True widg = self.itemWidget(item)","self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter","self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\"Available","import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError","= p.parent() return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self)","None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process","meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname","import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir,","lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter)","Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn","self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def","self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"), self)","self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent() while not","self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) #","_packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \"__main__\": from",") self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"),","widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method =","= self.itemWidget(item) update_btn = QPushButton( trans._(\"update (v{latest})\", latest=latest), widg )",") # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available","self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay =","self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self):","plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if","'--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start()","def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer","plugin_manager.iter_available(): # not showing these in the plugin dialog if","self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy","lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter =","license: str = \"UNKNOWN\", *, plugin_name: str = None, parent:","QLabel(self) self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\") mov =","show): if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide()","= QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\"Installed Plugins\"))","self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list =","searchable_text = project_info.name + \" \" + project_info.summary item =","self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\"pip_install_status\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\"loading ...\"),","parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged it continue","combined_paths) # use path of parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\")","), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(),","else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()):","enabled=True ): # don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString)","if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg)","= QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0)","super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\"{package_name} {version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\"))","self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ):","update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self,","buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0,","font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment(","= QPushButton() self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1)","self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0,","QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn)","\"__main__\": from qtpy.QtWidgets import QApplication app = QApplication([]) w =","= create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else:","self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide","these in the plugin dialog if plugin_name in ('napari_plugin_engine',): continue","import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, )","self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"), self)","sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\"small_text\") self.row2.addWidget(self.summary) self.package_author = QLabel(self)","state: int): \"\"\"Called with `state` when checkbox is clicked.\"\"\" plugin_manager.set_blocked(self.plugin_name.text(),","running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ]","# self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available plugins","QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter)","self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\"))","import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem,","self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(","self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label)","install process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels)","import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel","self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep","True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">>","True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\"update (v{latest})\", latest=latest),","from ...plugins import plugin_manager plugin_manager.discover() # since they might not","# not showing these in the plugin dialog if plugin_name","and not plugin_name ): return # including summary here for","mod_name, distname in plugin_manager.iter_available(): # not showing these in the","QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout,","QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() )","sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name =","QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() )","QEvent.Drop: md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for","args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if","url: str = '', summary: str = '', author: str","...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name,","_update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\"Available Plugins ({count})\", count=count) )","Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and","= QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20,","to handle clicks when already processing class Installer: def __init__(self,","''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license',","in plugin_manager.iter_available(): # not showing these in the plugin dialog","QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget,","{version}\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error():","sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy)","Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self)","mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled =","self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo,","self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment() combined_paths","they might not be loaded yet already_installed = set() for","uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2)","= QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self)","self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing |","'--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear()","QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() )","self.error_indicator.setObjectName(\"warning_icon\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary =","continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta =","self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(","sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox)","+ \" \" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version","self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"),","if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]):","_toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\"))","str = '', license: str = \"UNKNOWN\", *, plugin_name: str","self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author)","lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter =","2) self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350)","exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh()",") rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\"close\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open()","def _get_dialog(self) -> QDialog: p = self.parent() while not isinstance(p,",") self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\"\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy","QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show Sorter\"), self)","0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2)","import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter","2, 0, 2) self.avail_label = QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit()","( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages","set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing","self.findItems(text, Qt.MatchContains) for i in range(self.count()): item = self.item(i) item.setHidden(item","if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return #","range(self.count()): item = self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog):","project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg","plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing these in","self.process_error_indicator.setObjectName(\"error_label\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \"loading.gif\") mov = QMovie(load_gif)","QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import","else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def _get_dialog(self) ->","= QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label","plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall' if plugin_name","method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for","output_widget: QTextEdit = None): from ...plugins import plugin_manager # create","plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget:","user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import","= QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth()","self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count =","[ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget:","QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\"small_text\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2)","self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name","QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 =","self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if","packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ ==","sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy)","= QPushButton(trans._(\"<< Show Sorter\"), self) self.close_btn = QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept)","packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages:","self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2,","('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta","self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button =","qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui","str = '', author: str = '', license: str =","files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True","not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer","self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\"warning_icon\")","Status\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\"Show Status\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str]","in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str,","text: str): \"\"\"Filter items to those containing `text`.\"\"\" shown =","2, 0, 2) self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter = QLineEdit()","self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)","state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent)","= QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label","mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install","self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed","self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget","rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name)","from .qt_plugin_report import QtPluginErrReporter # TODO: add error icon and","sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy)","= QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer)","self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') ) self.direct_entry_btn =","self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn)","normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author',","()): if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages","''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name","plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer:","output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self):","QLabel(trans._(\"Available Plugins\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\"search...\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout =","self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def","QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit,","..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add","file...') ) self.direct_entry_btn = QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show","self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show): if","import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent,","self.show_status_btn = QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\"<< Show","lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \"\"\"Filter","QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie","def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import","self.package_author.setText(author) self.action_button.setText(trans._(\"uninstall\")) self.action_button.setObjectName(\"remove_button\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep =","Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from","= [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages)","trans._(\"update (v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()])","QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from","self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\"enable/disable\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth()","| Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy","mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list)","self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 =","# TODO: add error icon and handle pip install errors","project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, )","Sequence[str] = ()): if not packages: _packages = self.direct_entry_edit.text() if","0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2,","= QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env","use path of parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") ) self.process.setProcessEnvironment(env)","vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter =","plugin_manager.discover() # since they might not be loaded yet already_installed","_show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close =","update_btn = QPushButton( trans._(\"update (v{latest})\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)",") update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def","and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd","watched, event): if event.type() == QEvent.DragEnter: # we need to","self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip',","distname in plugin_manager.iter_available(): # not showing these in the plugin","self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\"error_label\")","...plugins import plugin_manager # create install process self._output_widget = None","def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close","napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize,","QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\"Installed Plugins\")) self.installed_filter","mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text","item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg =","fetch installed from ...plugins import plugin_manager plugin_manager.discover() # since they","QPushButton(trans._(\"Install\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\"Show Status\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn","errors # TODO: add queue to handle clicks when already","this event explicitly to be able # to receive QDropEvents!","explicitly to be able # to receive QDropEvents! event.accept() if","text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd =","self.installed_filter.setPlaceholderText(\"search...\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout)","[url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched,","os.path.exists(_packages): packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if","and handle pip install errors # TODO: add queue to","else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\"install\")) self.enabled_checkbox.hide() def","QTextEdit = None): from ...plugins import plugin_manager # create install","in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer =","@Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): #","hasattr(item, 'outdated'): # already tagged it continue item.outdated = True",") self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0)","self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \"__main__\": from qtpy.QtWidgets","self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text =","else: self.show_sorter_btn.setText(trans._(\"<< Show Sorter\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show:","if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear()","self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() #","pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app()","tagged it continue item.outdated = True widg = self.itemWidget(item) update_btn","def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\">> Hide Sorter\")) self.plugin_sorter.show() else:","QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout,","= _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \"__main__\":","enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name)","= QPushButton(trans._(\"Close\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60)","QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter,","plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo)","QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text:","lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\"Available","= installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None,","2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText(","self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\"Hide Status\")) self.stdout_text.show() else:","already processing class Installer: def __init__(self, output_widget: QTextEdit = None):","already_installed.add(distname) meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo(","def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: # we","plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget =","Status\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if not","# use path of parent process env.insert( \"PATH\", QProcessEnvironment.systemEnvironment().value(\"PATH\") )","def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start)","pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str, version:"],"string":"[\n \"import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add error\",\n \"= ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd\",\n \"return # including summary here for sake of filtering below.\",\n \"output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text)\",\n \"project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version\",\n \"accept this event explicitly to be able # to receive\",\n \"not isinstance(p, QDialog) and p.parent(): p = p.parent() return p\",\n \"sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15)\",\n \"= '', url: str = '', summary: str = '',\",\n \"if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\"))\",\n \"project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates if\",\n \"buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox)\",\n \"self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available plugins self.worker\",\n \"+ list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]):\",\n \"import os import sys from pathlib import Path from typing\",\n \"= {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url',\",\n \"buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status)\",\n \"['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd +=\",\n \"uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args\",\n \"self.parent() while not isinstance(p, QDialog) and p.parent(): p = p.parent()\",\n \"self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if\",\n \"_on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with `state` when checkbox is clicked.\\\"\\\"\\\"\",\n \"vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0,\",\n \"True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary)\",\n \"queue to handle clicks when already processing class Installer: def\",\n \") env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path of parent process env.insert(\",\n \"receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md = event.mimeData()\",\n \"None, parent: QWidget = None, enabled: bool = True, ):\",\n \"): return # including summary here for sake of filtering\",\n \"plugin dialog if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname)\",\n \"import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations\",\n \"Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout()\",\n \"QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight |\",\n \"= standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or\",\n \"lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0,\",\n \"# setup process path env = QProcessEnvironment() combined_paths = os.pathsep.join(\",\n \"self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') ) self.direct_entry_btn\",\n \"Qt.MatchFixedString) and not plugin_name ): return # including summary here\",\n \"# including summary here for sake of filtering below. searchable_text\",\n \"napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import\",\n \"sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name)\",\n \"else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def\",\n \"= QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\")\",\n \"str = '', summary: str = '', author: str =\",\n \"QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add error icon\",\n \"else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch\",\n \"'', author: str = '', license: str = \\\"UNKNOWN\\\", *,\",\n \"if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class\",\n \"QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter\",\n \"buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0)\",\n \"...\\\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self)\",\n \"self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18,\",\n \"def __init__(self, output_widget: QTextEdit = None): from ...plugins import plugin_manager\",\n \"self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in\",\n \"def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False)\",\n \"* self.installed_list.count(), 10, 10]) # fetch available plugins self.worker =\",\n \"QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy,\",\n \"install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if\",\n \"already_installed = set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): #\",\n \"''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) #\",\n \"( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return # including\",\n \"QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') )\",\n \"dialog if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta\",\n \"QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, )\",\n \"self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] =\",\n \"if os.path.exists(_packages): packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear()\",\n \"= QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer)\",\n \") self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called\",\n \"self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\",\",\n \"super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info:\",\n \"including summary here for sake of filtering below. searchable_text =\",\n \"mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or\",\n \"in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self,\",\n \"event): if event.type() == QEvent.DragEnter: # we need to accept\",\n \"self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp)\",\n \"[user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path of parent\",\n \"ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from\",\n \"from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans from\",\n \"rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"),\",\n \"item.version latest = project_info.version if parse_version(current) >= parse_version(latest): continue if\",\n \"TODO: add queue to handle clicks when already processing class\",\n \"sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy\",\n \"checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self,\",\n \"from qtpy.QtWidgets import QApplication app = QApplication([]) w = QtPluginDialog()\",\n \"'', url: str = '', summary: str = '', author:\",\n \"self.installed_list.count(), 10, 10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info)\",\n \"self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide()\",\n \") import napari.resources from ...plugins import plugin_manager from ...plugins.pypi import\",\n \"import parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading import\",\n \"Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list))\",\n \"def __init__( self, package_name: str, version: str = '', url:\",\n \"\\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit =\",\n \"__init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True)\",\n \"pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name:\",\n \"self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox)\",\n \"'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item,\",\n \"os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path of\",\n \"of parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover())\",\n \"drop file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn =\",\n \"= getattr( self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda:\",\n \"widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \\\"\\\"\\\"Filter items to those\",\n \"i in range(self.count()): item = self.item(i) item.setHidden(item not in shown)\",\n \"qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget,\",\n \") widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \\\"\\\"\\\"Filter items to\",\n \"list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args\",\n \"self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self)\",\n \"6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled)\",\n \"QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy =\",\n \"''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins,\",\n \"for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event)\",\n \"= QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy\",\n \"user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def\",\n \"meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''),\",\n \"sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout()\",\n \"..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import\",\n \"env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path of parent process env.insert( \\\"PATH\\\",\",\n \"exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear()\",\n \"QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical)\",\n \"def filter(self, text: str): \\\"\\\"\\\"Filter items to those containing `text`.\\\"\\\"\\\"\",\n \"self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\",\n \"10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info):\",\n \"= None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if\",\n \"Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show):\",\n \"installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True\",\n \"class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer\",\n \"latest = project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item,\",\n \"= [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return\",\n \"QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets\",\n \"@Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString):\",\n \"= QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter)\",\n \"= QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay =\",\n \"trans._('install by name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"),\",\n \"handle pip install errors # TODO: add queue to handle\",\n \"= QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth()\",\n \"self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info)\",\n \"parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide()\",\n \"\\\" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version\",\n \"p = p.parent() return p def setup_ui(self, enabled=True): self.v_lay =\",\n \"in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) #\",\n \"= \\\"UNKNOWN\\\", *, plugin_name: str = None, parent: QWidget =\",\n \"when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def\",\n \"installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem(\",\n \"QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True)\",\n \"QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close)\",\n \"str = \\\"UNKNOWN\\\", *, plugin_name: str = None, parent: QWidget\",\n \"= None): from ...plugins import plugin_manager # create install process\",\n \"or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''),\",\n \"QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText(\",\n \"from ...plugins import plugin_manager # create install process self._output_widget =\",\n \"if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget:\",\n \"QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary)\",\n \"plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from\",\n \"self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if not packages:\",\n \"Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)\",\n \"need to accept this event explicitly to be able #\",\n \"iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc\",\n \"sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state:\",\n \"filter(self, text: str): \\\"\\\"\\\"Filter items to those containing `text`.\\\"\\\"\\\" shown\",\n \"self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox =\",\n \"-> QDialog: p = self.parent() while not isinstance(p, QDialog) and\",\n \"import QtPluginErrReporter # TODO: add error icon and handle pip\",\n \"= QLabel(trans._(\\\"loading ...\\\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator\",\n \"parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda:\",\n \"-1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred )\",\n \"\\\"UNKNOWN\\\", *, plugin_name: str = None, parent: QWidget = None,\",\n \"for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest =\",\n \"self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\")\",\n \"({count})\\\", count=count) ) def eventFilter(self, watched, event): if event.type() ==\",\n \"installed from ...plugins import plugin_manager plugin_manager.discover() # since they might\",\n \"QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def\",\n \"''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ),\",\n \"640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter\",\n \"eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: # we need\",\n \"PluginListItem(QFrame): def __init__( self, package_name: str, version: str = '',\",\n \"str = None, parent: QWidget = None, enabled: bool =\",\n \"__init__( self, package_name: str, version: str = '', url: str\",\n \"= '', summary: str = '', author: str = '',\",\n \"self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name):\",\n \"self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\"))\",\n \"QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\")\",\n \"output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text\",\n \"'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start()\",\n \"addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add\",\n \"items to those containing `text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains) for\",\n \"plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch\",\n \"def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self,\",\n \"class Installer: def __init__(self, output_widget: QTextEdit = None): from ...plugins\",\n \") sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author\",\n \"= QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov\",\n \"shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer()\",\n \"= QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\")\",\n \"def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show\",\n \"in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version if\",\n \"rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else:\",\n \"sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self,\",\n \"import plugin_manager plugin_manager.discover() # since they might not be loaded\",\n \"if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta = {}\",\n \"self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self) sp\",\n \"18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by\",\n \"QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2\",\n \"self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter )\",\n \"plugin_name ): return # including summary here for sake of\",\n \"parent: QWidget = None, enabled: bool = True, ): super().__init__(parent)\",\n \"filtering below. searchable_text = project_info.name + \\\" \\\" + project_info.summary\",\n \"= QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0,\",\n \"= QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...')\",\n \"self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)\",\n \"import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame,\",\n \"show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show\",\n \"import Path from typing import Sequence from napari_plugin_engine.dist import standard_metadata\",\n \"self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1\",\n \"isinstance(p, QDialog) and p.parent(): p = p.parent() return p def\",\n \"show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self,\",\n \"0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred\",\n \"from pathlib import Path from typing import Sequence from napari_plugin_engine.dist\",\n \"self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred,\",\n \"Qt.MatchContains) for i in range(self.count()): item = self.item(i) item.setHidden(item not\",\n \"QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn)\",\n \"buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True)\",\n \"close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show()\",\n \"= QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect(\",\n \"update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str):\",\n \"QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie from\",\n \"self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages = _packages.split()\",\n \"= project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled,\",\n \"= QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout\",\n \"self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import plugin_manager plugin_manager.discover()\",\n \"QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) #\",\n \"def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self)\",\n \"vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter)\",\n \"QLabel(trans._(\\\"loading ...\\\"), self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator =\",\n \"create install process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable)\",\n \"return True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show:\",\n \"'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location',\",\n \"below. searchable_text = project_info.name + \\\" \\\" + project_info.summary item\",\n \"self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy =\",\n \"getattr( self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name]))\",\n \"if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def\",\n \"mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed,\",\n \"QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter)\",\n \"event.accept() if event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls():\",\n \">= parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged it\",\n \"PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet)\",\n \"load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18))\",\n \"...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading\",\n \"10, 10]) # fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def\",\n \"self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p\",\n \"mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text =\",\n \"self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count\",\n \"QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6)\",\n \"= set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not\",\n \"initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close)\",\n \"self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn)\",\n \"= QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter)\",\n \"self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version if parse_version(current)\",\n \"self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in\",\n \"plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str, version: str\",\n \"else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version',\",\n \"from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment,\",\n \"class PluginListItem(QFrame): def __init__( self, package_name: str, version: str =\",\n \"= QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\"))\",\n \"def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't\",\n \"add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ):\",\n \"buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False)\",\n \"enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10])\",\n \"= QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight\",\n \"self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2)\",\n \"item.outdated = True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update\",\n \"qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog,\",\n \"= os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path\",\n \"# already tagged it continue item.outdated = True widg =\",\n \"not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages]\",\n \"be able # to receive QDropEvents! event.accept() if event.type() ==\",\n \"PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot\",\n \"self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter =\",\n \"self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy =\",\n \"env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\",\",\n \"= QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter)\",\n \"Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy =\",\n \"running_as_bundled_app from ...utils.translations import trans from ..qthreading import create_worker from\",\n \"standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''),\",\n \"'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for\",\n \"create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info)\",\n \"QDialog: p = self.parent() while not isinstance(p, QDialog) and p.parent():\",\n \"widg = self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg\",\n \"self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self)\",\n \"self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list\",\n \"if event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls(): files\",\n \"self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<<\",\n \"= self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif\",\n \"event explicitly to be able # to receive QDropEvents! event.accept()\",\n \"standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess,\",\n \"self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with `state`\",\n \"self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def\",\n \") widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info:\",\n \"QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label =\",\n \"*project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall'\",\n \"widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn)\",\n \"not be loaded yet already_installed = set() for plugin_name, mod_name,\",\n \"os import sys from pathlib import Path from typing import\",\n \"self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd\",\n \"self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True)\",\n \"path of parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda:\",\n \"buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self) sp =\",\n \"self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \\\"\\\"\\\"Filter items\",\n \"str, version: str = '', url: str = '', summary:\",\n \"QDropEvents! event.accept() if event.type() == QEvent.Drop: md = event.mimeData() if\",\n \"handle clicks when already processing class Installer: def __init__(self, output_widget:\",\n \"{} self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''),\",\n \"def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count)\",\n \"lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout()\",\n \"summary here for sake of filtering below. searchable_text = project_info.name\",\n \"self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self)\",\n \"by name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self)\",\n \"buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4,\",\n \"self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide()\",\n \"== \\\"__main__\\\": from qtpy.QtWidgets import QApplication app = QApplication([]) w\",\n \"add queue to handle clicks when already processing class Installer:\",\n \"self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0,\",\n \"trans._(\\\"Available Plugins ({count})\\\", count=count) ) def eventFilter(self, watched, event): if\",\n \"not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer):\",\n \"enabled=enabled, ) method = getattr( self.installer, 'uninstall' if plugin_name else\",\n \") self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred,\",\n \"_on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if\",\n \"QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont,\",\n \"QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0))\",\n \"2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins\",\n \"0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0,\",\n \"event.type() == QEvent.Drop: md = event.mimeData() if md.hasUrls(): files =\",\n \"self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment() combined_paths =\",\n \"ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest\",\n \"plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(), 10,\",\n \"self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0,\",\n \"QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)\",\n \"create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from\",\n \"p.parent(): p = p.parent() return p def setup_ui(self, enabled=True): self.v_lay\",\n \"self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self)\",\n \"QDialog) and p.parent(): p = p.parent() return p def setup_ui(self,\",\n \"self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return # including summary\",\n \"self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide()\",\n \"_on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list:\",\n \"self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0)\",\n \"plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed:\",\n \"= self.findItems(text, Qt.MatchContains) for i in range(self.count()): item = self.item(i)\",\n \"QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4,\",\n \"sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent\",\n \"+= [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if\",\n \"shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()): item =\",\n \"= ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)\",\n \"self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count) ) def eventFilter(self, watched,\",\n \"self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code:\",\n \"the plugin dialog if plugin_name in ('napari_plugin_engine',): continue if distname:\",\n \"self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0)\",\n \"0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter)\",\n \"normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import\",\n \"...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from\",\n \"project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): #\",\n \"widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo):\",\n \"QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel,\",\n \"import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import\",\n \"qtpy.QtWidgets import QApplication app = QApplication([]) w = QtPluginDialog() w.show()\",\n \"QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\")\",\n \"def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget)\",\n \"name/url, or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages)\",\n \"parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall' if\",\n \"self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list =\",\n \"self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url,\",\n \"= item.version latest = project_info.version if parse_version(current) >= parse_version(latest): continue\",\n \"if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args =\",\n \"= QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name)\",\n \"list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg)\",\n \"..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import\",\n \"from ...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info,\",\n \"rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38)\",\n \"from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter\",\n \"*, plugin_name: str = None, parent: QWidget = None, enabled:\",\n \"icon and handle pip install errors # TODO: add queue\",\n \"close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version)\",\n \"self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent() while\",\n \"= QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout()\",\n \"if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label)\",\n \"self.installer, 'uninstall' if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint())\",\n \"sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd +\",\n \"self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop\",\n \"import plugin_manager # create install process self._output_widget = None self.process\",\n \"def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y']\",\n \"don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name\",\n \"class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui()\",\n \"self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list\",\n \"exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def\",\n \"self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True)\",\n \"update_btn) def filter(self, text: str): \\\"\\\"\\\"Filter items to those containing\",\n \"_toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<<\",\n \"self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__(\",\n \"self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide()\",\n \"and p.parent(): p = p.parent() return p def setup_ui(self, enabled=True):\",\n \"= QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth()\",\n \"self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m',\",\n \"Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self,\",\n \"with `state` when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class\",\n \"self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self,\",\n \"= QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18)\",\n \"is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent:\",\n \"QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary\",\n \"= self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count) ) def eventFilter(self,\",\n \"import sys from pathlib import Path from typing import Sequence\",\n \"self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator =\",\n \"summary: str = '', author: str = '', license: str\",\n \"md.hasUrls(): files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return\",\n \"_handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide)\",\n \"Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\"))\",\n \"\\\"\\\"\\\"Called with `state` when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state)\",\n \"QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button\",\n \"PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer,\",\n \"self._output_widget.clear() self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip',\",\n \"Installer: def __init__(self, output_widget: QTextEdit = None): from ...plugins import\",\n \"def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield)\",\n \"self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout)\",\n \"not showing these in the plugin dialog if plugin_name in\",\n \"self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame):\",\n \"since they might not be loaded yet already_installed = set()\",\n \"import QApplication app = QApplication([]) w = QtPluginDialog() w.show() app.exec_()\",\n \"4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1,\",\n \"TODO: add error icon and handle pip install errors #\",\n \"self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p =\",\n \"duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return\",\n \"QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins\",\n \"self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus()\",\n \"self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def\",\n \"setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0)\",\n \"cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list))\",\n \"plugin_manager # create install process self._output_widget = None self.process =\",\n \"else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__\",\n \"self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)\",\n \"= QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() )\",\n \"in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else:\",\n \"self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy =\",\n \"self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self, pkg_list:\",\n \"6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox =\",\n \"= QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths)\",\n \"self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(\",\n \"def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current\",\n \"process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune())\",\n \"QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch()\",\n \"import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt,\",\n \"bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name}\",\n \"pathlib import Path from typing import Sequence from napari_plugin_engine.dist import\",\n \"self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if\",\n \"# we need to accept this event explicitly to be\",\n \"self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code,\",\n \"yet already_installed = set() for plugin_name, mod_name, distname in plugin_manager.iter_available():\",\n \"rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary)\",\n \"QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout =\",\n \"napari.resources from ...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo,\",\n \"= '', license: str = \\\"UNKNOWN\\\", *, plugin_name: str =\",\n \"parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo)\",\n \"ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter #\",\n \"# fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if\",\n \"from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import ( QCheckBox,\",\n \"if __name__ == \\\"__main__\\\": from qtpy.QtWidgets import QApplication app =\",\n \"self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy(\",\n \"self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with\",\n \"if event.type() == QEvent.DragEnter: # we need to accept this\",\n \"= QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15\",\n \"for i in range(self.count()): item = self.item(i) item.setHidden(item not in\",\n \") self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton()\",\n \"distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem(\",\n \"...plugins import plugin_manager plugin_manager.discover() # since they might not be\",\n \"_on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh()\",\n \"packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else:\",\n \"\\\" \\\" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version =\",\n \"QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter =\",\n \"ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates if (\",\n \"_packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages\",\n \"# fetch installed from ...plugins import plugin_manager plugin_manager.discover() # since\",\n \"QWidget = None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled)\",\n \"0, 2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\")\",\n \"\\\"\\\"\\\"Filter items to those containing `text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains)\",\n \"sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 =\",\n \"self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self, packages:\",\n \"= self.direct_entry_edit.text() if os.path.exists(_packages): packages = [_packages] else: packages =\",\n \"'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self,\",\n \"self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from\",\n \"already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self):\",\n \"from ...utils.translations import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label\",\n \"\\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def\",\n \"version: str = '', url: str = '', summary: str\",\n \"QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont()\",\n \"Show Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator)\",\n \"Installer): super().__init__(parent) self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self,\",\n \"# to receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md\",\n \"md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self, show):\",\n \"self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget\",\n \"self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2,\",\n \"if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name )\",\n \"self, project_info: ProjectInfo, plugin_name=None, enabled=True ): # don't add duplicates\",\n \"self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator\",\n \"plugin_name=None, enabled=True ): # don't add duplicates if ( self.findItems(project_info.name,\",\n \"self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed =\",\n \"__name__ == \\\"__main__\\\": from qtpy.QtWidgets import QApplication app = QApplication([])\",\n \"+ list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg in pkg_list:\",\n \"= QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1,\",\n \"self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with `state` when checkbox\",\n \"self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif =\",\n \"return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6,\",\n \"Qt.MatchFixedString): current = item.version latest = project_info.version if parse_version(current) >=\",\n \"it continue item.outdated = True widg = self.itemWidget(item) update_btn =\",\n \"= True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\")\",\n \"__init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done)\",\n \"showing these in the plugin dialog if plugin_name in ('napari_plugin_engine',):\",\n \"= QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter\",\n \"0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)\",\n \"installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2)\",\n \"= QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"), self) sp = self.working_indicator.sizePolicy()\",\n \"self.available_list.clear() # fetch installed from ...plugins import plugin_manager plugin_manager.discover() #\",\n \"self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter\",\n \"None, enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name:\",\n \"QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay\",\n \"buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True)\",\n \") self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name =\",\n \"self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count) ) def eventFilter(self, watched, event):\",\n \"Plugins ({count})\\\", count=count) ) def eventFilter(self, watched, event): if event.type()\",\n \"'', license: str = \\\"UNKNOWN\\\", *, plugin_name: str = None,\",\n \"sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy)\",\n \"packages: Sequence[str] = ()): if not packages: _packages = self.direct_entry_edit.text()\",\n \"current = item.version latest = project_info.version if parse_version(current) >= parse_version(latest):\",\n \"= project_info.version if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'):\",\n \"self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent /\",\n \"process path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")]\",\n \"error icon and handle pip install errors # TODO: add\",\n \"import create_worker from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter\",\n \"self.plugin_name = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(\",\n \"QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1)\",\n \"item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item\",\n \"= True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update (v{latest})\\\",\",\n \"Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show()\",\n \"from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit,\",\n \"self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\")\",\n \"= QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter)\",\n \"might not be loaded yet already_installed = set() for plugin_name,\",\n \"clicks when already processing class Installer: def __init__(self, output_widget: QTextEdit\",\n \"sake of filtering below. searchable_text = project_info.name + \\\" \\\"\",\n \"env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) # use path of parent process\",\n \"'-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() for pkg\",\n \"import napari.resources from ...plugins import plugin_manager from ...plugins.pypi import (\",\n \"item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent)\",\n \"self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else:\",\n \"in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def\",\n \"p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1,\",\n \"self.installer.install(packages) if __name__ == \\\"__main__\\\": from qtpy.QtWidgets import QApplication app\",\n \"setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter)\",\n \"lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\\\"Installed\",\n \"# don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and not\",\n \"self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator)\",\n \"Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout()\",\n \"self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0)\",\n \"...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import\",\n \"<filename>napari/_qt/dialogs/qt_plugin_dialog.py import os import sys from pathlib import Path from\",\n \"to accept this event explicitly to be able # to\",\n \"lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter)\",\n \"def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with `state` when checkbox is\",\n \"refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import plugin_manager\",\n \"p = self.parent() while not isinstance(p, QDialog) and p.parent(): p\",\n \"self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed,\",\n \"= QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout\",\n \") method = getattr( self.installer, 'uninstall' if plugin_name else 'install'\",\n \"QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0)\",\n \"we need to accept this event explicitly to be able\",\n \"QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources\",\n \"self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit):\",\n \"int): \\\"\\\"\\\"Called with `state` when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not\",\n \"== QEvent.DragEnter: # we need to accept this event explicitly\",\n \"= output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode()\",\n \"from typing import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions\",\n \"self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count()\",\n \"_install_packages(self, packages: Sequence[str] = ()): if not packages: _packages =\",\n \"fetch available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name\",\n \"able # to receive QDropEvents! event.accept() if event.type() == QEvent.Drop:\",\n \"show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide()\",\n \"| Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed,\",\n \"in the plugin dialog if plugin_name in ('napari_plugin_engine',): continue if\",\n \"self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path\",\n \"self, package_name: str, version: str = '', url: str =\",\n \"sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator\",\n \"continue item.outdated = True widg = self.itemWidget(item) update_btn = QPushButton(\",\n \"QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda:\",\n \"def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade']\",\n \"from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app\",\n \"self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter(\",\n \"be loaded yet already_installed = set() for plugin_name, mod_name, distname\",\n \"0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed)\",\n \"event.type() == QEvent.DragEnter: # we need to accept this event\",\n \"event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url in md.urls()]\",\n \"item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name,\",\n \"self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self)\",\n \"/ \\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit\",\n \"self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins\",\n \"loaded yet already_installed = set() for plugin_name, mod_name, distname in\",\n \"widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name,\",\n \"self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080,\",\n \"'--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix',\",\n \"user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import trans\",\n \"for sake of filtering below. searchable_text = project_info.name + \\\"\",\n \"add error icon and handle pip install errors # TODO:\",\n \"ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''),\",\n \"QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled)\",\n \"str = '', url: str = '', summary: str =\",\n \"meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, )\",\n \"parse_version, running_as_bundled_app from ...utils.translations import trans from ..qthreading import create_worker\",\n \"= QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100)\",\n \"return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide\",\n \"plugin_name: str = None, parent: QWidget = None, enabled: bool\",\n \"= event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url in\",\n \"self.process.start() def uninstall(self, pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall',\",\n \"self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label)\",\n \"self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name\",\n \"containing `text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()):\",\n \"QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy)\",\n \"# create install process self._output_widget = None self.process = QProcess()\",\n \"QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else:\",\n \"for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self,\",\n \"QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer =\",\n \"= self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self,\",\n \"(v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) )\",\n \"sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide()\",\n \"if packages: self.installer.install(packages) if __name__ == \\\"__main__\\\": from qtpy.QtWidgets import\",\n \"self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox\",\n \"QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self,\",\n \"parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self,\",\n \"QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins import\",\n \"`text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains) for i in range(self.count()): item\",\n \"] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start() def uninstall(self,\",\n \"self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install',\",\n \"self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int):\",\n \"meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''),\",\n \"def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide()\",\n \"self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment()\",\n \"env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget)\",\n \"if md.hasUrls(): files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0])\",\n \"processing class Installer: def __init__(self, output_widget: QTextEdit = None): from\",\n \"self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal)\",\n \"tag_outdated(self, project_info: ProjectInfo): for item in self.findItems(project_info.name, Qt.MatchFixedString): current =\",\n \"= str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov)\",\n \"p.parent() return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1,\",\n \"self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1 =\",\n \"QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading\",\n \"= ()): if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages):\",\n \"mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list)\",\n \"== QEvent.Drop: md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile()\",\n \"self._output_widget.clear() self.process.start() for pkg in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def\",\n \"Path from typing import Sequence from napari_plugin_engine.dist import standard_metadata from\",\n \"when already processing class Installer: def __init__(self, output_widget: QTextEdit =\",\n \"continue if hasattr(item, 'outdated'): # already tagged it continue item.outdated\",\n \") self.process.setProcessEnvironment(env) self.process.finished.connect(lambda: plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget:\",\n \"= ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget:\",\n \"setup process path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(),\",\n \"self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self)\",\n \"latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3,\",\n \"_get_dialog(self) -> QDialog: p = self.parent() while not isinstance(p, QDialog)\",\n \"self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def _on_enabled_checkbox(self, state: int): \\\"\\\"\\\"Called with `state` when\",\n \"sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2) def\",\n \"cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'):\",\n \"['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if self._output_widget: self._output_widget.clear()\",\n \"= QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"),\",\n \"install errors # TODO: add queue to handle clicks when\",\n \"mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled\",\n \"those containing `text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains) for i in\",\n \"meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name,\",\n \"''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 *\",\n \"Qt, Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import\",\n \"+ project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item)\",\n \"= PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method = getattr(\",\n \"plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname)\",\n \"= self.parent() while not isinstance(p, QDialog) and p.parent(): p =\",\n \"if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(),\",\n \"= '', author: str = '', license: str = \\\"UNKNOWN\\\",\",\n \"QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch()\",\n \"trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel from\",\n \"( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton,\",\n \"from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO:\",\n \"in range(self.count()): item = self.item(i) item.setHidden(item not in shown) class\",\n \"of filtering below. searchable_text = project_info.name + \\\" \\\" +\",\n \"mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self)\",\n \"or drop file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn\",\n \"Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show()\",\n \"packages: self.installer.install(packages) if __name__ == \\\"__main__\\\": from qtpy.QtWidgets import QApplication\",\n \"font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing\",\n \"ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(\",\n \".qt_plugin_report import QtPluginErrReporter # TODO: add error icon and handle\",\n \") def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: #\",\n \"self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(),\",\n \"self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed\",\n \"= Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True)\",\n \"self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor)\",\n \"parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged\",\n \") from ...utils._appdirs import user_plugin_dir, user_site_packages from ...utils.misc import parse_version,\",\n \"url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def\",\n \"sys from pathlib import Path from typing import Sequence from\",\n \"from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore\",\n \"): # don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString) and\",\n \"self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay\",\n \"= QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator =\",\n \"buttonBox.setContentsMargins(0, 0, 4, 0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False)\",\n \"pip install errors # TODO: add queue to handle clicks\",\n \"project_info.name + \\\" \\\" + project_info.summary item = QListWidgetItem(searchable_text, parent=self)\",\n \"not plugin_name ): return # including summary here for sake\",\n \"QWidget, ) import napari.resources from ...plugins import plugin_manager from ...plugins.pypi\",\n \"item in self.findItems(project_info.name, Qt.MatchFixedString): current = item.version latest = project_info.version\",\n \"self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn =\",\n \"to those containing `text`.\\\"\\\"\\\" shown = self.findItems(text, Qt.MatchContains) for i\",\n \"QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if\",\n \"event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show()\",\n \"= QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button)\",\n \"def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6)\",\n \"QVBoxLayout, QWidget, ) import napari.resources from ...plugins import plugin_manager from\",\n \"-1, 6) self.v_lay.setSpacing(0) self.row1 = QHBoxLayout() self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self)\",\n \"= project_info.name + \\\" \\\" + project_info.summary item = QListWidgetItem(searchable_text,\",\n \"meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70\",\n \"QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from ...plugins import plugin_manager\",\n \"None): from ...plugins import plugin_manager # create install process self._output_widget\",\n \"__init__(self, output_widget: QTextEdit = None): from ...plugins import plugin_manager #\",\n \"= QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem(\",\n \"self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self):\",\n \"= None, parent: QWidget = None, enabled: bool = True,\",\n \"Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit)\",\n \"'', summary: str = '', author: str = '', license:\",\n \"str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov = QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start()\",\n \"method = getattr( self.installer, 'uninstall' if plugin_name else 'install' )\",\n \"super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method\",\n \"sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding, QSizePolicy.Preferred ) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth()\",\n \"QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text)\",\n \"self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status):\",\n \"): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author)\",\n \"here for sake of filtering below. searchable_text = project_info.name +\",\n \"typing import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import\",\n \"self.installed_list.addItem( ProjectInfo( normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary',\",\n \"QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author)\",\n \"sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 = QFont() font15.setPointSize(15)\",\n \"for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing these\",\n \"enabled=True): self.v_lay = QVBoxLayout(self) self.v_lay.setContentsMargins(-1, 6, -1, 6) self.v_lay.setSpacing(0) self.row1\",\n \"= self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m',\",\n \"pkg_list: Sequence[str]): args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args +\",\n \"if hasattr(item, 'outdated'): # already tagged it continue item.outdated =\",\n \"self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500)\",\n \"combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] ) env.insert(\\\"PYTHONPATH\\\", combined_paths) # use\",\n \"2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350)\",\n \"super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\"))\",\n \"if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages =\",\n \"'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [\",\n \"QtPluginErrReporter # TODO: add error icon and handle pip install\",\n \"= None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup\",\n \"to be able # to receive QDropEvents! event.accept() if event.type()\",\n \"0, 2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\")\",\n \"...utils.translations import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import\",\n \"else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640)\",\n \"QEvent.DragEnter: # we need to accept this event explicitly to\",\n \"if plugin_name in ('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta =\",\n \"self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)\",\n \"while not isinstance(p, QDialog) and p.parent(): p = p.parent() return\",\n \"def _install_packages(self, packages: Sequence[str] = ()): if not packages: _packages\",\n \"[_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if\",\n \"# TODO: add queue to handle clicks when already processing\",\n \"# since they might not be loaded yet already_installed =\",\n \"`state` when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget):\",\n \"rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error)\",\n \"self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn =\",\n \"if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled)\",\n \"path env = QProcessEnvironment() combined_paths = os.pathsep.join( [user_site_packages(), env.systemEnvironment().value(\\\"PYTHONPATH\\\")] )\",\n \"from ..widgets.qt_eliding_label import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report\",\n \"self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent()\",\n \"process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready)\",\n \"str): \\\"\\\"\\\"Filter items to those containing `text`.\\\"\\\"\\\" shown = self.findItems(text,\",\n \"package_name: str, version: str = '', url: str = '',\",\n \"4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy = QSizePolicy( QSizePolicy.MinimumExpanding,\",\n \"Slot from qtpy.QtGui import QFont, QMovie from qtpy.QtWidgets import (\",\n \"QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg = PluginListItem( *project_info,\",\n \"sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author =\",\n \"parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh()\",\n \"= QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep)\",\n \"available plugins self.worker = create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in\",\n \"from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from\",\n \"self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def\",\n \"self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() )\",\n \"set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def\",\n \"'outdated'): # already tagged it continue item.outdated = True widg\",\n \"author: str = '', license: str = \\\"UNKNOWN\\\", *, plugin_name:\",\n \"plugin_manager plugin_manager.discover() # since they might not be loaded yet\",\n \"self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox\",\n \"self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog:\",\n \"self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit() self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True)\",\n \"clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget,\",\n \"self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None):\",\n \"self.row1.setSpacing(6) self.enabled_checkbox = QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed,\",\n \"to receive QDropEvents! event.accept() if event.type() == QEvent.Drop: md =\",\n \"md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for url\",\n \"super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def\",\n \"project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else: self.available_list.addItem(project_info) self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start()\",\n \"sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name\",\n \"if parse_version(current) >= parse_version(latest): continue if hasattr(item, 'outdated'): # already\",\n \"mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled,\",\n \"QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text = QTextEdit(self.v_splitter) self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide()\",\n \"count=count) ) def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter:\",\n \"item = self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog): def\",\n \"count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count) ) def\",\n \"from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs\",\n \"self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator)\",\n \"self.working_indicator.show() self.process_error_indicator.hide() def _on_installer_done(self, exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show()\",\n \"meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name in\",\n \"QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env =\",\n \"QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label =\",\n \"self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0,\",\n \"self.v_splitter = QSplitter(self.h_splitter) self.v_splitter.setOrientation(Qt.Vertical) self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0,\",\n \"self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self):\",\n \"already tagged it continue item.outdated = True widg = self.itemWidget(item)\",\n \"self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 = QVBoxLayout(self) self.h_splitter\",\n \"self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available\",\n \"import Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError\",\n \"= p.parent() return p def setup_ui(self, enabled=True): self.v_lay = QVBoxLayout(self)\",\n \"None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process\",\n \"meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo( normalized_name(distname\",\n \"import ( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir,\",\n \"lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter)\",\n \"Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn\",\n \"self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def\",\n \"self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self)\",\n \"self.enabled_checkbox.hide() def _get_dialog(self) -> QDialog: p = self.parent() while not\",\n \"self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) #\",\n \"_packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \\\"__main__\\\": from\",\n \") self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"),\",\n \"widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, ) method =\",\n \"= self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg )\",\n \") # self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available\",\n \"self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay =\",\n \"self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self):\",\n \"plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if\",\n \"'--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear() self.process.start()\",\n \"def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent) self.installer = installer\",\n \"plugin_manager.iter_available(): # not showing these in the plugin dialog if\",\n \"self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary = ElidingLabel(parent=self) sizePolicy\",\n \"lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter =\",\n \"license: str = \\\"UNKNOWN\\\", *, plugin_name: str = None, parent:\",\n \"QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov =\",\n \"show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide()\",\n \"= QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\"))\",\n \"self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list =\",\n \"searchable_text = project_info.name + \\\" \\\" + project_info.summary item =\",\n \"self.stdout_text.setReadOnly(True) self.stdout_text.setObjectName(\\\"pip_install_status\\\") self.stdout_text.hide() buttonBox = QHBoxLayout() self.working_indicator = QLabel(trans._(\\\"loading ...\\\"),\",\n \"parse_version(latest): continue if hasattr(item, 'outdated'): # already tagged it continue\",\n \"combined_paths) # use path of parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\")\",\n \"), plugin_name=plugin_name, enabled=plugin_name in plugin_manager.plugins, ) # self.v_splitter.setSizes([70 * self.installed_list.count(),\",\n \"else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()):\",\n \"enabled=True ): # don't add duplicates if ( self.findItems(project_info.name, Qt.MatchFixedString)\",\n \"if plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg)\",\n \"= QCheckBox(self) self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0)\",\n \"super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name) self.package_name.setText(f\\\"{package_name} {version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\"))\",\n \"self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ):\",\n \"update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self,\",\n \"buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn) buttonBox.setContentsMargins(0, 0,\",\n \"font15 = QFont() font15.setPointSize(15) self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment(\",\n \"= QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1)\",\n \"self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0,\",\n \"QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn)\",\n \"\\\"__main__\\\": from qtpy.QtWidgets import QApplication app = QApplication([]) w =\",\n \"= create_worker(iter_napari_plugin_info) def _handle_yield(project_info): if project_info.name in already_installed: self.installed_list.tag_outdated(project_info) else:\",\n \"self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide\",\n \"these in the plugin dialog if plugin_name in ('napari_plugin_engine',): continue\",\n \"import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name, )\",\n \"self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self)\",\n \"sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.summary.sizePolicy().hasHeightForWidth() ) self.summary.setSizePolicy(sizePolicy) self.summary.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.summary) self.package_author = QLabel(self)\",\n \"state: int): \\\"\\\"\\\"Called with `state` when checkbox is clicked.\\\"\\\"\\\" plugin_manager.set_blocked(self.plugin_name.text(),\",\n \"running_as_bundled_app() and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ]\",\n \"# self.v_splitter.setSizes([70 * self.installed_list.count(), 10, 10]) # fetch available plugins\",\n \"QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter)\",\n \"self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\"))\",\n \"import ( QCheckBox, QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem,\",\n \"self.enabled_checkbox.setChecked(enabled) self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(\",\n \"self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.avail_label)\",\n \"install process self._output_widget = None self.process = QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels)\",\n \"import trans from ..qthreading import create_worker from ..widgets.qt_eliding_label import ElidingLabel\",\n \"self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep\",\n \"True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">>\",\n \"True widg = self.itemWidget(item) update_btn = QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest),\",\n \"from ...plugins import plugin_manager plugin_manager.discover() # since they might not\",\n \"# not showing these in the plugin dialog if plugin_name\",\n \"and not plugin_name ): return # including summary here for\",\n \"mod_name, distname in plugin_manager.iter_available(): # not showing these in the\",\n \"QFont, QMovie from qtpy.QtWidgets import ( QCheckBox, QDialog, QFrame, QHBoxLayout,\",\n \"QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() )\",\n \"sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name =\",\n \"QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() )\",\n \"QEvent.Drop: md = event.mimeData() if md.hasUrls(): files = [url.toLocalFile() for\",\n \"args = ['-m', 'pip', 'uninstall', '-y'] self.process.setArguments(args + list(pkg_list)) if\",\n \"url: str = '', summary: str = '', author: str\",\n \"...plugins import plugin_manager from ...plugins.pypi import ( ProjectInfo, iter_napari_plugin_info, normalized_name,\",\n \"_update_count_in_label(self): count = self.available_list.count() self.avail_label.setText( trans._(\\\"Available Plugins ({count})\\\", count=count) )\",\n \"Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and\",\n \"= QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20,\",\n \"to handle clicks when already processing class Installer: def __init__(self,\",\n \"''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author', ''), meta.get('license',\",\n \"in plugin_manager.iter_available(): # not showing these in the plugin dialog\",\n \"QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget,\",\n \"{version}\\\") self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error():\",\n \"sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.action_button.sizePolicy().hasHeightForWidth() ) self.action_button.setSizePolicy(sizePolicy)\",\n \"Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self)\",\n \"mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled =\",\n \"self.installer = installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo,\",\n \"self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env = QProcessEnvironment() combined_paths\",\n \"they might not be loaded yet already_installed = set() for\",\n \"uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2)\",\n \"= QMovie(load_gif) mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self)\",\n \"self.plugin_name.setFont(font15) self.row1.addWidget(self.plugin_name) self.package_name = QLabel(self) self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing |\",\n \"'--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget: self._output_widget.clear()\",\n \"QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() )\",\n \"self.error_indicator.setObjectName(\\\"warning_icon\\\") self.error_indicator.setCursor(Qt.PointingHandCursor) self.error_indicator.hide() self.row2.addWidget(self.error_indicator) self.row2.setContentsMargins(-1, 4, 0, -1) self.summary =\",\n \"continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta =\",\n \"self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(\",\n \"sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox)\",\n \"+ \\\" \\\" + project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version\",\n \"self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"),\",\n \"if self._output_widget: text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]):\",\n \"_toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\"))\",\n \"str = '', license: str = \\\"UNKNOWN\\\", *, plugin_name: str\",\n \"self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author)\",\n \"lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter =\",\n \"2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350)\",\n \"exit_code, exit_status): self.working_indicator.hide() if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh()\",\n \") rep.setWindowFlags(Qt.Sheet) close = QPushButton(trans._(\\\"close\\\"), rep) rep.layout.addWidget(close) rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open()\",\n \"def _get_dialog(self) -> QDialog: p = self.parent() while not isinstance(p,\",\n \") self.enabled_checkbox.setSizePolicy(sizePolicy) self.enabled_checkbox.setMinimumSize(QSize(20, 0)) self.enabled_checkbox.setText(\\\"\\\") self.row1.addWidget(self.enabled_checkbox) self.plugin_name = QLabel(self) sizePolicy\",\n \"QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show Sorter\\\"), self)\",\n \"0) vlay_1.addLayout(buttonBox) self.show_status_btn.setCheckable(True) self.show_status_btn.setChecked(False) self.show_status_btn.toggled.connect(self._toggle_status) self.show_sorter_btn.setCheckable(True) self.show_sorter_btn.setChecked(False) self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2)\",\n \"import ElidingLabel from ..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter\",\n \"2, 0, 2) self.avail_label = QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit()\",\n \"( ProjectInfo, iter_napari_plugin_info, normalized_name, ) from ...utils._appdirs import user_plugin_dir, user_site_packages\",\n \"set() for plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing\",\n \"self.findItems(text, Qt.MatchContains) for i in range(self.count()): item = self.item(i) item.setHidden(item\",\n \"if ( self.findItems(project_info.name, Qt.MatchFixedString) and not plugin_name ): return #\",\n \"range(self.count()): item = self.item(i) item.setHidden(item not in shown) class QtPluginDialog(QDialog):\",\n \"project_info.summary item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg\",\n \"plugin_name, mod_name, distname in plugin_manager.iter_available(): # not showing these in\",\n \"self.process_error_indicator.setObjectName(\\\"error_label\\\") self.process_error_indicator.hide() load_gif = str(Path(napari.resources.__file__).parent / \\\"loading.gif\\\") mov = QMovie(load_gif)\",\n \"QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import\",\n \"else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def _get_dialog(self) ->\",\n \"= QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label\",\n \"plugin_name=plugin_name, enabled=enabled, ) method = getattr( self.installer, 'uninstall' if plugin_name\",\n \"method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo) def tag_outdated(self, project_info: ProjectInfo): for\",\n \"output_widget: QTextEdit = None): from ...plugins import plugin_manager # create\",\n \"plugin_manager.discover()) self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget:\",\n \"user_plugin_dir, user_site_packages from ...utils.misc import parse_version, running_as_bundled_app from ...utils.translations import\",\n \"= QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth()\",\n \"self.show_sorter_btn.toggled.connect(self._toggle_sorter) self.v_splitter.setStretchFactor(1, 2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count =\",\n \"[ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd + list(pkg_list)) if self._output_widget:\",\n \"QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy) self.package_author.setObjectName(\\\"small_text\\\") self.row2.addWidget(self.package_author) self.v_lay.addLayout(self.row2)\",\n \"self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name\",\n \"QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.plugin_name.sizePolicy().hasHeightForWidth() ) self.plugin_name.setSizePolicy(sizePolicy) font15 =\",\n \"self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched, event) def _toggle_sorter(self, show): if\",\n \"packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ ==\",\n \"sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.package_author.sizePolicy().hasHeightForWidth() ) self.package_author.setSizePolicy(sizePolicy)\",\n \"= QPushButton(trans._(\\\"<< Show Sorter\\\"), self) self.close_btn = QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept)\",\n \"packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages:\",\n \"self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list) uninstalled = QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2,\",\n \"('napari_plugin_engine',): continue if distname: already_installed.add(distname) meta = standard_metadata(distname) else: meta\",\n \"self.package_name.setAlignment( Qt.AlignRight | Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button =\",\n \"qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui\",\n \"str = '', author: str = '', license: str =\",\n \"files = [url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True\",\n \"not in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer\",\n \"self.action_button.setSizePolicy(sizePolicy) self.row1.addWidget(self.action_button) self.v_lay.addLayout(self.row1) self.row2 = QHBoxLayout() self.error_indicator = QPushButton() self.error_indicator.setObjectName(\\\"warning_icon\\\")\",\n \"Status\\\")) self.stdout_text.show() else: self.show_status_btn.setText(trans._(\\\"Show Status\\\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str]\",\n \"in pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str,\",\n \"text: str): \\\"\\\"\\\"Filter items to those containing `text`.\\\"\\\"\\\" shown =\",\n \"2, 0, 2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter = QLineEdit()\",\n \"self.row2.addWidget(self.summary) self.package_author = QLabel(self) sizePolicy = QSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0)\",\n \"state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer: Installer): super().__init__(parent)\",\n \"= QWidget(self.v_splitter) lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label\",\n \"mov.setScaledSize(QSize(18, 18)) self.working_indicator.setMovie(mov) mov.start() self.direct_entry_edit = QLineEdit(self) self.direct_entry_edit.installEventFilter(self) self.direct_entry_edit.setPlaceholderText( trans._('install\",\n \"self.v_splitter.setMinimumWidth(500) self.plugin_sorter = QtPluginSorter(parent=self.h_splitter) self.plugin_sorter.layout().setContentsMargins(2, 0, 0, 0) self.plugin_sorter.hide() installed\",\n \"self.process.finished.connect(lambda: plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget\",\n \"rep.plugin_combo.hide() close.clicked.connect(rep.close) rep.open() self.error_indicator.clicked.connect(_show_error) self.error_indicator.show() self.summary.setIndent(18) else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name)\",\n \"from .qt_plugin_report import QtPluginErrReporter # TODO: add error icon and\",\n \"sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth() ) self.enabled_checkbox.setSizePolicy(sizePolicy)\",\n \"= QHBoxLayout() mid_layout.addWidget(self.avail_label) mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer)\",\n \"self.direct_entry_edit.setPlaceholderText( trans._('install by name/url, or drop file...') ) self.direct_entry_btn =\",\n \"self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60) buttonBox.addWidget(self.show_sorter_btn) buttonBox.addWidget(self.close_btn)\",\n \"normalized_name(distname or ''), meta.get('version', ''), meta.get('url', ''), meta.get('summary', ''), meta.get('author',\",\n \"()): if not packages: _packages = self.direct_entry_edit.text() if os.path.exists(_packages): packages\",\n \"''), meta.get('summary', ''), meta.get('author', ''), meta.get('license', ''), ), plugin_name=plugin_name, enabled=plugin_name\",\n \"plugin_manager.set_blocked(self.plugin_name.text(), not state) class QPluginList(QListWidget): def __init__(self, parent: QWidget, installer:\",\n \"output_widget: QTextEdit): if output_widget: self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self):\",\n \"QLabel(trans._(\\\"Available Plugins\\\")) self.avail_filter = QLineEdit() self.avail_filter.setPlaceholderText(\\\"search...\\\") self.avail_filter.setMaximumWidth(350) self.avail_filter.setClearButtonEnabled(True) mid_layout =\",\n \"self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start) self.installer.process.finished.connect(self._on_installer_done) self.refresh() def _on_installer_start(self): self.show_status_btn.setChecked(True) self.working_indicator.show() self.process_error_indicator.hide() def\",\n \"QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit,\",\n \"..widgets.qt_plugin_sorter import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add\",\n \"file...') ) self.direct_entry_btn = QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show\",\n \"self.plugin_sorter.show() else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show): if\",\n \"import standard_metadata from napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent,\",\n \"self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn = QPushButton(trans._(\\\"<< Show\",\n \"lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text: str): \\\"\\\"\\\"Filter\",\n \"QProcess, QProcessEnvironment, QSize, Qt, Slot from qtpy.QtGui import QFont, QMovie\",\n \"def refresh(self): self.installed_list.clear() self.available_list.clear() # fetch installed from ...plugins import\",\n \"self.package_author.setText(author) self.action_button.setText(trans._(\\\"uninstall\\\")) self.action_button.setObjectName(\\\"remove_button\\\") self.enabled_checkbox.setChecked(enabled) if PluginError.get(plugin_name=plugin_name): def _show_error(): rep =\",\n \"Sequence from napari_plugin_engine.dist import standard_metadata from napari_plugin_engine.exceptions import PluginError from\",\n \"= [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages)\",\n \"trans._(\\\"update (v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()])\",\n \"QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout, QWidget, ) import napari.resources from\",\n \"self.enabled_checkbox.stateChanged.connect(self._on_enabled_checkbox) self.enabled_checkbox.setToolTip(trans._(\\\"enable/disable\\\")) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth( self.enabled_checkbox.sizePolicy().hasHeightForWidth()\",\n \"| Qt.AlignTrailing | Qt.AlignVCenter ) self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy\",\n \"mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.installed_list = QPluginList(installed, self.installer) self.installed_filter.textChanged.connect(self.installed_list.filter) lay.addWidget(self.installed_list)\",\n \"self.worker.yielded.connect(_handle_yield) self.worker.finished.connect(self.working_indicator.hide) self.worker.finished.connect(self._update_count_in_label) self.worker.start() def setup_ui(self): self.resize(1080, 640) vlay_1 =\",\n \"# TODO: add error icon and handle pip install errors\",\n \"project_info.version super().addItem(item) widg = PluginListItem( *project_info, parent=self, plugin_name=plugin_name, enabled=enabled, )\",\n \"Sequence[str] = ()): if not packages: _packages = self.direct_entry_edit.text() if\",\n \"0) self.plugin_sorter.hide() installed = QWidget(self.v_splitter) lay = QVBoxLayout(installed) lay.setContentsMargins(0, 2,\",\n \"= QProcess() self.process.setProgram(sys.executable) self.process.setProcessChannelMode(QProcess.MergedChannels) self.process.readyReadStandardOutput.connect(self._on_stdout_ready) # setup process path env\",\n \"use path of parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") ) self.process.setProcessEnvironment(env)\",\n \"vlay_1 = QVBoxLayout(self) self.h_splitter = QSplitter(self) vlay_1.addWidget(self.h_splitter) self.h_splitter.setOrientation(Qt.Horizontal) self.v_splitter =\",\n \"plugin_manager.discover() # since they might not be loaded yet already_installed\",\n \"_show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close =\",\n \"update_btn = QPushButton( trans._(\\\"update (v{latest})\\\", latest=latest), widg ) update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)\",\n \") update_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def\",\n \"and sys.platform.startswith('linux'): cmd += [ '--no-warn-script-location', '--prefix', user_plugin_dir(), ] self.process.setArguments(cmd\",\n \"watched, event): if event.type() == QEvent.DragEnter: # we need to\",\n \"self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd = ['-m', 'pip',\",\n \"distname in plugin_manager.iter_available(): # not showing these in the plugin\",\n \"self) sp = self.working_indicator.sizePolicy() sp.setRetainSizeWhenHidden(True) self.working_indicator.setSizePolicy(sp) self.process_error_indicator = QLabel(self) self.process_error_indicator.setObjectName(\\\"error_label\\\")\",\n \"...plugins import plugin_manager # create install process self._output_widget = None\",\n \"def _show_error(): rep = QtPluginErrReporter( parent=self._get_dialog(), initial_plugin=plugin_name ) rep.setWindowFlags(Qt.Sheet) close\",\n \"napari_plugin_engine.exceptions import PluginError from qtpy.QtCore import QEvent, QProcess, QProcessEnvironment, QSize,\",\n \"QVBoxLayout(installed) lay.setContentsMargins(0, 2, 0, 2) self.installed_label = QLabel(trans._(\\\"Installed Plugins\\\")) self.installed_filter\",\n \"mid_layout.addWidget(self.avail_filter) mid_layout.addStretch() lay.addLayout(mid_layout) self.available_list = QPluginList(uninstalled, self.installer) self.avail_filter.textChanged.connect(self.available_list.filter) lay.addWidget(self.available_list) self.stdout_text\",\n \"item = QListWidgetItem(searchable_text, parent=self) item.version = project_info.version super().addItem(item) widg =\",\n \"fetch installed from ...plugins import plugin_manager plugin_manager.discover() # since they\",\n \"QPushButton(trans._(\\\"Install\\\"), self) self.direct_entry_btn.clicked.connect(self._install_packages) self.show_status_btn = QPushButton(trans._(\\\"Show Status\\\"), self) self.show_status_btn.setFixedWidth(100) self.show_sorter_btn\",\n \"errors # TODO: add queue to handle clicks when already\",\n \"this event explicitly to be able # to receive QDropEvents!\",\n \"explicitly to be able # to receive QDropEvents! event.accept() if\",\n \"text = self.process.readAllStandardOutput().data().decode() self._output_widget.append(text) def install(self, pkg_list: Sequence[str]): cmd =\",\n \"self.installed_filter.setPlaceholderText(\\\"search...\\\") self.installed_filter.setMaximumWidth(350) self.installed_filter.setClearButtonEnabled(True) mid_layout = QHBoxLayout() mid_layout.addWidget(self.installed_label) mid_layout.addWidget(self.installed_filter) mid_layout.addStretch() lay.addLayout(mid_layout)\",\n \"[url.toLocalFile() for url in md.urls()] self.direct_entry_edit.setText(files[0]) return True return super().eventFilter(watched,\",\n \"os.path.exists(_packages): packages = [_packages] else: packages = _packages.split() self.direct_entry_edit.clear() if\",\n \"and handle pip install errors # TODO: add queue to\",\n \"else: self.summary.setIndent(38) else: self.plugin_name.setText(package_name) self.package_name.setText(version) self.summary.setText(summary) self.package_author.setText(author) self.action_button.setText(trans._(\\\"install\\\")) self.enabled_checkbox.hide() def\",\n \"QTextEdit = None): from ...plugins import plugin_manager # create install\",\n \"in shown) class QtPluginDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.installer =\",\n \"@Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None, enabled=True ): #\",\n \"hasattr(item, 'outdated'): # already tagged it continue item.outdated = True\",\n \") self.row1.addWidget(self.package_name) self.action_button = QPushButton(self) sizePolicy = QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0)\",\n \"self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \\\"__main__\\\": from qtpy.QtWidgets\",\n \"self._output_widget = output_widget self.process.setParent(output_widget) def _on_stdout_ready(self): if self._output_widget: text =\",\n \"else: self.show_sorter_btn.setText(trans._(\\\"<< Show Sorter\\\")) self.plugin_sorter.hide() def _toggle_status(self, show): if show:\",\n \"if exit_code: self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear()\",\n \"self.process_error_indicator.show() else: self.show_status_btn.setChecked(False) self.refresh() self.plugin_sorter.refresh() def refresh(self): self.installed_list.clear() self.available_list.clear() #\",\n \"pkg_list: Sequence[str]): cmd = ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app()\",\n \"tagged it continue item.outdated = True widg = self.itemWidget(item) update_btn\",\n \"def _toggle_sorter(self, show): if show: self.show_sorter_btn.setText(trans._(\\\">> Hide Sorter\\\")) self.plugin_sorter.show() else:\",\n \"QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter, QTextEdit, QVBoxLayout,\",\n \"= _packages.split() self.direct_entry_edit.clear() if packages: self.installer.install(packages) if __name__ == \\\"__main__\\\":\",\n \"enabled: bool = True, ): super().__init__(parent) self.setup_ui(enabled) if plugin_name: self.plugin_name.setText(plugin_name)\",\n \"= QPushButton(trans._(\\\"Close\\\"), self) self.close_btn.clicked.connect(self.accept) buttonBox.addWidget(self.show_status_btn) buttonBox.addWidget(self.working_indicator) buttonBox.addWidget(self.direct_entry_edit) buttonBox.addWidget(self.direct_entry_btn) buttonBox.addWidget(self.process_error_indicator) buttonBox.addSpacing(60)\",\n \"QDialog, QFrame, QHBoxLayout, QLabel, QLineEdit, QListWidget, QListWidgetItem, QPushButton, QSizePolicy, QSplitter,\",\n \"plugin_name else 'install' ) widg.action_button.clicked.connect(lambda: method([project_info.name])) item.setSizeHint(widg.sizeHint()) self.setItemWidget(item, widg) @Slot(ProjectInfo)\",\n \"QSizePolicy.Fixed) update_btn.clicked.connect( lambda: self.installer.install([item.text()]) ) widg.row1.insertWidget(3, update_btn) def filter(self, text:\",\n \"lay = QVBoxLayout(uninstalled) lay.setContentsMargins(0, 2, 0, 2) self.avail_label = QLabel(trans._(\\\"Available\",\n \"= installer self.setSortingEnabled(True) @Slot(ProjectInfo) def addItem( self, project_info: ProjectInfo, plugin_name=None,\",\n \"2) self.h_splitter.setStretchFactor(0, 2) self.avail_filter.setFocus() def _update_count_in_label(self): count = self.available_list.count() self.avail_label.setText(\",\n \"self.plugin_sorter.hide() def _toggle_status(self, show): if show: self.show_status_btn.setText(trans._(\\\"Hide Status\\\")) self.stdout_text.show() else:\",\n \"already processing class Installer: def __init__(self, output_widget: QTextEdit = None):\",\n \"already_installed.add(distname) meta = standard_metadata(distname) else: meta = {} self.installed_list.addItem( ProjectInfo(\",\n \"def eventFilter(self, watched, event): if event.type() == QEvent.DragEnter: # we\",\n \"plugin_manager.prune()) self.set_output_widget(output_widget) def set_output_widget(self, output_widget: QTextEdit): if output_widget: self._output_widget =\",\n \"Status\\\")) self.stdout_text.hide() def _install_packages(self, packages: Sequence[str] = ()): if not\",\n \"# use path of parent process env.insert( \\\"PATH\\\", QProcessEnvironment.systemEnvironment().value(\\\"PATH\\\") )\",\n \"def __init__(self, parent=None): super().__init__(parent) self.installer = Installer() self.setup_ui() self.installer.set_output_widget(self.stdout_text) self.installer.process.started.connect(self._on_installer_start)\",\n \"pkg_list: plugin_manager.unregister(pkg) class PluginListItem(QFrame): def __init__( self, package_name: str, version:\"\n]","isConversational":false}}},{"rowIdx":168,"cells":{"ngram":{"kind":"list like","value":["= ('create_partial_webhook_from_id', ) from scarletio import export from ..core import","channel's identifier. Defaults to `0`. Returns ------- webhook : ``Webhook``","given `webhook_id` already exists, then returns that instead. Parameters ----------","a partial webhook from the given parameters. If the webhook",": `int` The identifier number of the webhook. token :","import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \"\"\"","`0`. Returns ------- webhook : ``Webhook`` \"\"\" try: webhook =","------- webhook : ``Webhook`` \"\"\" try: webhook = USERS[webhook_id] except","then returns that instead. Parameters ---------- webhook_id : `int` The","exists, then returns that instead. Parameters ---------- webhook_id : `int`","Optional (Keyword only) The webhook's type. Defaults to `WebhookType.bot`. channel_id","of the webhook. token : `str` The token of the","only) The webhook's channel's identifier. Defaults to `0`. Returns -------","USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type","with the given `webhook_id` already exists, then returns that instead.","Returns ------- webhook : ``Webhook`` \"\"\" try: webhook = USERS[webhook_id]","token of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional","partial webhook from the given parameters. If the webhook with",".preinstanced import WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id,","webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The","`str` The token of the webhook. type_ : ``WebhookType`` =","The token of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`,","channel_id=0): \"\"\" Creates a partial webhook from the given parameters.",": `str` The token of the webhook. type_ : ``WebhookType``","Optional (Keyword only) The webhook's channel's identifier. Defaults to `0`.","webhook's type. Defaults to `WebhookType.bot`. channel_id : `int` = `0`,","\"\"\" Creates a partial webhook from the given parameters. If","If the webhook with the given `webhook_id` already exists, then","create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \"\"\" Creates a partial webhook","webhook with the given `webhook_id` already exists, then returns that","channel_id : `int` = `0`, Optional (Keyword only) The webhook's","= channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token =","webhook. token : `str` The token of the webhook. type_","from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot,","`WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword only) The","token, *, type_=WebhookType.bot, channel_id=0): \"\"\" Creates a partial webhook from","the given `webhook_id` already exists, then returns that instead. Parameters","The webhook's channel's identifier. Defaults to `0`. Returns ------- webhook","..core import USERS from .preinstanced import WebhookType from .webhook import",".webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0):","already exists, then returns that instead. Parameters ---------- webhook_id :",": ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's type.",": ``Webhook`` \"\"\" try: webhook = USERS[webhook_id] except KeyError: webhook","that instead. Parameters ---------- webhook_id : `int` The identifier number","__all__ = ('create_partial_webhook_from_id', ) from scarletio import export from ..core","from the given parameters. If the webhook with the given","webhook from the given parameters. If the webhook with the","from .preinstanced import WebhookType from .webhook import Webhook @export def","= USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id","('create_partial_webhook_from_id', ) from scarletio import export from ..core import USERS","KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_","`WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults to `WebhookType.bot`.","of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword","= Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] =","`webhook_id` already exists, then returns that instead. Parameters ---------- webhook_id","= `WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults to","The identifier number of the webhook. token : `str` The","from scarletio import export from ..core import USERS from .preinstanced",") from scarletio import export from ..core import USERS from","webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id =","= `0`, Optional (Keyword only) The webhook's channel's identifier. Defaults","webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token","``Webhook`` \"\"\" try: webhook = USERS[webhook_id] except KeyError: webhook =","from ..core import USERS from .preinstanced import WebhookType from .webhook","type. Defaults to `WebhookType.bot`. channel_id : `int` = `0`, Optional","---------- webhook_id : `int` The identifier number of the webhook.","identifier number of the webhook. token : `str` The token","the webhook. token : `str` The token of the webhook.","type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's","`0`, Optional (Keyword only) The webhook's channel's identifier. Defaults to","WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *,","returns that instead. Parameters ---------- webhook_id : `int` The identifier","except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type =","webhook's channel's identifier. Defaults to `0`. Returns ------- webhook :","Defaults to `WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword","the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only)","webhook : ``Webhook`` \"\"\" try: webhook = USERS[webhook_id] except KeyError:","Defaults to `0`. Returns ------- webhook : ``Webhook`` \"\"\" try:","scarletio import export from ..core import USERS from .preinstanced import","The webhook's type. Defaults to `WebhookType.bot`. channel_id : `int` =","parameters. If the webhook with the given `webhook_id` already exists,","Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] = webhook","Parameters ---------- webhook_id : `int` The identifier number of the","to `0`. Returns ------- webhook : ``Webhook`` \"\"\" try: webhook","Creates a partial webhook from the given parameters. If the","Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \"\"\" Creates","number of the webhook. token : `str` The token of","(Keyword only) The webhook's channel's identifier. Defaults to `0`. Returns","token : `str` The token of the webhook. type_ :","= type_ USERS[webhook_id] = webhook webhook.token = token return webhook","the webhook with the given `webhook_id` already exists, then returns","import USERS from .preinstanced import WebhookType from .webhook import Webhook","the given parameters. If the webhook with the given `webhook_id`","\"\"\" try: webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id)","webhook_id : `int` The identifier number of the webhook. token","instead. Parameters ---------- webhook_id : `int` The identifier number of","webhook.type = type_ USERS[webhook_id] = webhook webhook.token = token return","only) The webhook's type. Defaults to `WebhookType.bot`. channel_id : `int`","webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id]","def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \"\"\" Creates a partial","USERS from .preinstanced import WebhookType from .webhook import Webhook @export","`int` = `0`, Optional (Keyword only) The webhook's channel's identifier.","given parameters. If the webhook with the given `webhook_id` already","``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults","@export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \"\"\" Creates a","channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token = token","type_=WebhookType.bot, channel_id=0): \"\"\" Creates a partial webhook from the given","identifier. Defaults to `0`. Returns ------- webhook : ``Webhook`` \"\"\"","export from ..core import USERS from .preinstanced import WebhookType from","to `WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword only)","*, type_=WebhookType.bot, channel_id=0): \"\"\" Creates a partial webhook from the","`int` The identifier number of the webhook. token : `str`","try: webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id","(Keyword only) The webhook's type. Defaults to `WebhookType.bot`. channel_id :","import export from ..core import USERS from .preinstanced import WebhookType","import WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token,",": `int` = `0`, Optional (Keyword only) The webhook's channel's"],"string":"[\n \"= ('create_partial_webhook_from_id', ) from scarletio import export from ..core import\",\n \"channel's identifier. Defaults to `0`. Returns ------- webhook : ``Webhook``\",\n \"given `webhook_id` already exists, then returns that instead. Parameters ----------\",\n \"a partial webhook from the given parameters. If the webhook\",\n \": `int` The identifier number of the webhook. token :\",\n \"import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\"\",\n \"`0`. Returns ------- webhook : ``Webhook`` \\\"\\\"\\\" try: webhook =\",\n \"------- webhook : ``Webhook`` \\\"\\\"\\\" try: webhook = USERS[webhook_id] except\",\n \"then returns that instead. Parameters ---------- webhook_id : `int` The\",\n \"exists, then returns that instead. Parameters ---------- webhook_id : `int`\",\n \"Optional (Keyword only) The webhook's type. Defaults to `WebhookType.bot`. channel_id\",\n \"of the webhook. token : `str` The token of the\",\n \"only) The webhook's channel's identifier. Defaults to `0`. Returns -------\",\n \"USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type\",\n \"with the given `webhook_id` already exists, then returns that instead.\",\n \"Returns ------- webhook : ``Webhook`` \\\"\\\"\\\" try: webhook = USERS[webhook_id]\",\n \"token of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional\",\n \"partial webhook from the given parameters. If the webhook with\",\n \".preinstanced import WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id,\",\n \"webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The\",\n \"`str` The token of the webhook. type_ : ``WebhookType`` =\",\n \"The token of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`,\",\n \"channel_id=0): \\\"\\\"\\\" Creates a partial webhook from the given parameters.\",\n \": `str` The token of the webhook. type_ : ``WebhookType``\",\n \"Optional (Keyword only) The webhook's channel's identifier. Defaults to `0`.\",\n \"webhook's type. Defaults to `WebhookType.bot`. channel_id : `int` = `0`,\",\n \"\\\"\\\"\\\" Creates a partial webhook from the given parameters. If\",\n \"If the webhook with the given `webhook_id` already exists, then\",\n \"create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a partial webhook\",\n \"webhook with the given `webhook_id` already exists, then returns that\",\n \"channel_id : `int` = `0`, Optional (Keyword only) The webhook's\",\n \"= channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token =\",\n \"webhook. token : `str` The token of the webhook. type_\",\n \"from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot,\",\n \"`WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword only) The\",\n \"token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a partial webhook from\",\n \"the given `webhook_id` already exists, then returns that instead. Parameters\",\n \"The webhook's channel's identifier. Defaults to `0`. Returns ------- webhook\",\n \"..core import USERS from .preinstanced import WebhookType from .webhook import\",\n \".webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0):\",\n \"already exists, then returns that instead. Parameters ---------- webhook_id :\",\n \": ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's type.\",\n \": ``Webhook`` \\\"\\\"\\\" try: webhook = USERS[webhook_id] except KeyError: webhook\",\n \"that instead. Parameters ---------- webhook_id : `int` The identifier number\",\n \"__all__ = ('create_partial_webhook_from_id', ) from scarletio import export from ..core\",\n \"from the given parameters. If the webhook with the given\",\n \"webhook from the given parameters. If the webhook with the\",\n \"from .preinstanced import WebhookType from .webhook import Webhook @export def\",\n \"= USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id\",\n \"('create_partial_webhook_from_id', ) from scarletio import export from ..core import USERS\",\n \"KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_\",\n \"`WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults to `WebhookType.bot`.\",\n \"of the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword\",\n \"= Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] =\",\n \"`webhook_id` already exists, then returns that instead. Parameters ---------- webhook_id\",\n \"= `WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults to\",\n \"The identifier number of the webhook. token : `str` The\",\n \"from scarletio import export from ..core import USERS from .preinstanced\",\n \") from scarletio import export from ..core import USERS from\",\n \"webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id =\",\n \"= `0`, Optional (Keyword only) The webhook's channel's identifier. Defaults\",\n \"webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token\",\n \"``Webhook`` \\\"\\\"\\\" try: webhook = USERS[webhook_id] except KeyError: webhook =\",\n \"from ..core import USERS from .preinstanced import WebhookType from .webhook\",\n \"type. Defaults to `WebhookType.bot`. channel_id : `int` = `0`, Optional\",\n \"---------- webhook_id : `int` The identifier number of the webhook.\",\n \"identifier number of the webhook. token : `str` The token\",\n \"the webhook. token : `str` The token of the webhook.\",\n \"type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's\",\n \"`0`, Optional (Keyword only) The webhook's channel's identifier. Defaults to\",\n \"WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token, *,\",\n \"returns that instead. Parameters ---------- webhook_id : `int` The identifier\",\n \"except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type =\",\n \"webhook's channel's identifier. Defaults to `0`. Returns ------- webhook :\",\n \"Defaults to `WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword\",\n \"the webhook. type_ : ``WebhookType`` = `WebhookType.bot`, Optional (Keyword only)\",\n \"webhook : ``Webhook`` \\\"\\\"\\\" try: webhook = USERS[webhook_id] except KeyError:\",\n \"Defaults to `0`. Returns ------- webhook : ``Webhook`` \\\"\\\"\\\" try:\",\n \"scarletio import export from ..core import USERS from .preinstanced import\",\n \"The webhook's type. Defaults to `WebhookType.bot`. channel_id : `int` =\",\n \"parameters. If the webhook with the given `webhook_id` already exists,\",\n \"Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id] = webhook\",\n \"Parameters ---------- webhook_id : `int` The identifier number of the\",\n \"to `0`. Returns ------- webhook : ``Webhook`` \\\"\\\"\\\" try: webhook\",\n \"Creates a partial webhook from the given parameters. If the\",\n \"Webhook @export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates\",\n \"number of the webhook. token : `str` The token of\",\n \"(Keyword only) The webhook's channel's identifier. Defaults to `0`. Returns\",\n \"token : `str` The token of the webhook. type_ :\",\n \"= type_ USERS[webhook_id] = webhook webhook.token = token return webhook\",\n \"the webhook with the given `webhook_id` already exists, then returns\",\n \"import USERS from .preinstanced import WebhookType from .webhook import Webhook\",\n \"the given parameters. If the webhook with the given `webhook_id`\",\n \"\\\"\\\"\\\" try: webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id)\",\n \"webhook_id : `int` The identifier number of the webhook. token\",\n \"instead. Parameters ---------- webhook_id : `int` The identifier number of\",\n \"webhook.type = type_ USERS[webhook_id] = webhook webhook.token = token return\",\n \"only) The webhook's type. Defaults to `WebhookType.bot`. channel_id : `int`\",\n \"webhook = Webhook._create_empty(webhook_id) webhook.channel_id = channel_id webhook.type = type_ USERS[webhook_id]\",\n \"def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a partial\",\n \"USERS from .preinstanced import WebhookType from .webhook import Webhook @export\",\n \"`int` = `0`, Optional (Keyword only) The webhook's channel's identifier.\",\n \"given parameters. If the webhook with the given `webhook_id` already\",\n \"``WebhookType`` = `WebhookType.bot`, Optional (Keyword only) The webhook's type. Defaults\",\n \"@export def create_partial_webhook_from_id(webhook_id, token, *, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a\",\n \"channel_id webhook.type = type_ USERS[webhook_id] = webhook webhook.token = token\",\n \"type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a partial webhook from the given\",\n \"identifier. Defaults to `0`. Returns ------- webhook : ``Webhook`` \\\"\\\"\\\"\",\n \"export from ..core import USERS from .preinstanced import WebhookType from\",\n \"to `WebhookType.bot`. channel_id : `int` = `0`, Optional (Keyword only)\",\n \"*, type_=WebhookType.bot, channel_id=0): \\\"\\\"\\\" Creates a partial webhook from the\",\n \"`int` The identifier number of the webhook. token : `str`\",\n \"try: webhook = USERS[webhook_id] except KeyError: webhook = Webhook._create_empty(webhook_id) webhook.channel_id\",\n \"(Keyword only) The webhook's type. Defaults to `WebhookType.bot`. channel_id :\",\n \"import export from ..core import USERS from .preinstanced import WebhookType\",\n \"import WebhookType from .webhook import Webhook @export def create_partial_webhook_from_id(webhook_id, token,\",\n \": `int` = `0`, Optional (Keyword only) The webhook's channel's\"\n]","isConversational":false}}},{"rowIdx":169,"cells":{"ngram":{"kind":"list like","value":["'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2,","def getWordScore(word, n): \"\"\" Returns the score for a word.","is worth 3, D is worth 2, E is worth","0 \"\"\" total_points = 0 for letter in word: total_points","if all n letters are used on the first turn.","total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n:","2, 'e': 1, 'f': 4, 'g': 2, 'h': 4, 'i':","integer (HAND_SIZE; i.e., hand size required for additional points) returns:","1, 'v': 4, 'w': 4, 'x': 8, 'y': 4, 'z':","letters) n: integer (HAND_SIZE; i.e., hand size required for additional","n): \"\"\" Returns the score for a word. Assumes the","if len(word) == n: total_points += 50 return total_points print(getWordScore('waybill',","(HAND_SIZE; i.e., hand size required for additional points) returns: int","8, 'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o':","Returns the score for a word. Assumes the word is","'d': 2, 'e': 1, 'f': 4, 'g': 2, 'h': 4,","'p': 3, 'q': 10, 'r': 1, 's': 1, 't': 1,","'x': 8, 'y': 4, 'z': 10 } def getWordScore(word, n):","on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE;","PLUS 50 points if all n letters are used on","'m': 3, 'n': 1, 'o': 1, 'p': 3, 'q': 10,","are used on the first turn. Letters are scored as","is the sum of the points for letters in the","C is worth 3, D is worth 2, E is","1, 'm': 3, 'n': 1, 'o': 1, 'p': 3, 'q':","'g': 2, 'h': 4, 'i': 1, 'j': 8, 'k': 5,","\"\"\" total_points = 0 for letter in word: total_points +=","turn. Letters are scored as in Scrabble; A is worth","all n letters are used on the first turn. Letters","(see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE; i.e.,","word is a valid word. The score for a word","(lowercase letters) n: integer (HAND_SIZE; i.e., hand size required for","worth 3, C is worth 3, D is worth 2,","0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *=","by the length of the word, PLUS 50 points if","hand size required for additional points) returns: int >= 0","worth 1, and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase","'s': 1, 't': 1, 'u': 1, 'v': 4, 'w': 4,","\"\"\" Returns the score for a word. Assumes the word","for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word)","used on the first turn. Letters are scored as in","'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f': 4,","= { 'a': 1, 'b': 3, 'c': 3, 'd': 2,","total_points = 0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter]","the word is a valid word. The score for a","'z': 10 } def getWordScore(word, n): \"\"\" Returns the score","is a valid word. The score for a word is","word. Assumes the word is a valid word. The score","'e': 1, 'f': 4, 'g': 2, 'h': 4, 'i': 1,","for additional points) returns: int >= 0 \"\"\" total_points =","so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer","len(word) if len(word) == n: total_points += 50 return total_points","Letters are scored as in Scrabble; A is worth 1,","for letters in the word, multiplied by the length of","word, multiplied by the length of the word, PLUS 50","the word, multiplied by the length of the word, PLUS","1, 'p': 3, 'q': 10, 'r': 1, 's': 1, 't':","scored as in Scrabble; A is worth 1, B is","the score for a word. Assumes the word is a","in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word)","worth 2, E is worth 1, and so on (see","} def getWordScore(word, n): \"\"\" Returns the score for a","3, 'q': 10, 'r': 1, 's': 1, 't': 1, 'u':","length of the word, PLUS 50 points if all n","B is worth 3, C is worth 3, D is","50 points if all n letters are used on the","points for letters in the word, multiplied by the length","letters in the word, multiplied by the length of the","sum of the points for letters in the word, multiplied","'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p': 3,","of the points for letters in the word, multiplied by","1, 't': 1, 'u': 1, 'v': 4, 'w': 4, 'x':","10 } def getWordScore(word, n): \"\"\" Returns the score for","is worth 1, B is worth 3, C is worth","total_points *= len(word) if len(word) == n: total_points += 50","letters are used on the first turn. Letters are scored","Scrabble; A is worth 1, B is worth 3, C","3, C is worth 3, D is worth 2, E","on the first turn. Letters are scored as in Scrabble;","word. The score for a word is the sum of","1, 'f': 4, 'g': 2, 'h': 4, 'i': 1, 'j':","The score for a word is the sum of the","word, PLUS 50 points if all n letters are used","in the word, multiplied by the length of the word,","the length of the word, PLUS 50 points if all","8, 'y': 4, 'z': 10 } def getWordScore(word, n): \"\"\"","SCRABBLE_LETTER_VALUES = { 'a': 1, 'b': 3, 'c': 3, 'd':","5, 'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p':","4, 'z': 10 } def getWordScore(word, n): \"\"\" Returns the","'q': 10, 'r': 1, 's': 1, 't': 1, 'u': 1,","2, E is worth 1, and so on (see SCRABBLE_LETTER_VALUES)","word: string (lowercase letters) n: integer (HAND_SIZE; i.e., hand size","word is the sum of the points for letters in","'t': 1, 'u': 1, 'v': 4, 'w': 4, 'x': 8,","i.e., hand size required for additional points) returns: int >=","A is worth 1, B is worth 3, C is","'v': 4, 'w': 4, 'x': 8, 'y': 4, 'z': 10","worth 1, B is worth 3, C is worth 3,","2, 'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l':","score for a word. Assumes the word is a valid","Assumes the word is a valid word. The score for","1, B is worth 3, C is worth 3, D","4, 'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm':","are scored as in Scrabble; A is worth 1, B","required for additional points) returns: int >= 0 \"\"\" total_points","4, 'w': 4, 'x': 8, 'y': 4, 'z': 10 }","1, 'u': 1, 'v': 4, 'w': 4, 'x': 8, 'y':","size required for additional points) returns: int >= 0 \"\"\"","'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l': 1,","score for a word is the sum of the points","the sum of the points for letters in the word,",">= 0 \"\"\" total_points = 0 for letter in word:","len(word) == n: total_points += 50 return total_points print(getWordScore('waybill', 7))","in Scrabble; A is worth 1, B is worth 3,","word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) ==","{ 'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e':","'f': 4, 'g': 2, 'h': 4, 'i': 1, 'j': 8,","first turn. Letters are scored as in Scrabble; A is","points) returns: int >= 0 \"\"\" total_points = 0 for","SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE; i.e., hand","'n': 1, 'o': 1, 'p': 3, 'q': 10, 'r': 1,","4, 'x': 8, 'y': 4, 'z': 10 } def getWordScore(word,","E is worth 1, and so on (see SCRABBLE_LETTER_VALUES) word:","3, 'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g':","n: integer (HAND_SIZE; i.e., hand size required for additional points)","+= SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n: total_points","'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm': 3,","1, 'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n':","3, 'n': 1, 'o': 1, 'p': 3, 'q': 10, 'r':","10, 'r': 1, 's': 1, 't': 1, 'u': 1, 'v':","1, 's': 1, 't': 1, 'u': 1, 'v': 4, 'w':","'u': 1, 'v': 4, 'w': 4, 'x': 8, 'y': 4,","'y': 4, 'z': 10 } def getWordScore(word, n): \"\"\" Returns","SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n: total_points +=","string (lowercase letters) n: integer (HAND_SIZE; i.e., hand size required","'w': 4, 'x': 8, 'y': 4, 'z': 10 } def","int >= 0 \"\"\" total_points = 0 for letter in","and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n:","'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1,","the points for letters in the word, multiplied by the","'o': 1, 'p': 3, 'q': 10, 'r': 1, 's': 1,","additional points) returns: int >= 0 \"\"\" total_points = 0","1, and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters)","the word, PLUS 50 points if all n letters are","valid word. The score for a word is the sum","getWordScore(word, n): \"\"\" Returns the score for a word. Assumes","'r': 1, 's': 1, 't': 1, 'u': 1, 'v': 4,","'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o': 1,","for a word is the sum of the points for","= 0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points","*= len(word) if len(word) == n: total_points += 50 return","is worth 1, and so on (see SCRABBLE_LETTER_VALUES) word: string","4, 'g': 2, 'h': 4, 'i': 1, 'j': 8, 'k':","a valid word. The score for a word is the","of the word, PLUS 50 points if all n letters","as in Scrabble; A is worth 1, B is worth","1, 'o': 1, 'p': 3, 'q': 10, 'r': 1, 's':","3, 'd': 2, 'e': 1, 'f': 4, 'g': 2, 'h':","for a word. Assumes the word is a valid word.","n letters are used on the first turn. Letters are","multiplied by the length of the word, PLUS 50 points","'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n': 1,","is worth 3, C is worth 3, D is worth","is worth 2, E is worth 1, and so on","1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f':","letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if","D is worth 2, E is worth 1, and so","a word is the sum of the points for letters","3, D is worth 2, E is worth 1, and","returns: int >= 0 \"\"\" total_points = 0 for letter","worth 3, D is worth 2, E is worth 1,","a word. Assumes the word is a valid word. The","the first turn. Letters are scored as in Scrabble; A","points if all n letters are used on the first"],"string":"[\n \"'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2,\",\n \"def getWordScore(word, n): \\\"\\\"\\\" Returns the score for a word.\",\n \"is worth 3, D is worth 2, E is worth\",\n \"0 \\\"\\\"\\\" total_points = 0 for letter in word: total_points\",\n \"if all n letters are used on the first turn.\",\n \"total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n:\",\n \"2, 'e': 1, 'f': 4, 'g': 2, 'h': 4, 'i':\",\n \"integer (HAND_SIZE; i.e., hand size required for additional points) returns:\",\n \"1, 'v': 4, 'w': 4, 'x': 8, 'y': 4, 'z':\",\n \"letters) n: integer (HAND_SIZE; i.e., hand size required for additional\",\n \"n): \\\"\\\"\\\" Returns the score for a word. Assumes the\",\n \"if len(word) == n: total_points += 50 return total_points print(getWordScore('waybill',\",\n \"(HAND_SIZE; i.e., hand size required for additional points) returns: int\",\n \"8, 'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o':\",\n \"Returns the score for a word. Assumes the word is\",\n \"'d': 2, 'e': 1, 'f': 4, 'g': 2, 'h': 4,\",\n \"'p': 3, 'q': 10, 'r': 1, 's': 1, 't': 1,\",\n \"'x': 8, 'y': 4, 'z': 10 } def getWordScore(word, n):\",\n \"on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE;\",\n \"PLUS 50 points if all n letters are used on\",\n \"'m': 3, 'n': 1, 'o': 1, 'p': 3, 'q': 10,\",\n \"are used on the first turn. Letters are scored as\",\n \"is the sum of the points for letters in the\",\n \"C is worth 3, D is worth 2, E is\",\n \"1, 'm': 3, 'n': 1, 'o': 1, 'p': 3, 'q':\",\n \"'g': 2, 'h': 4, 'i': 1, 'j': 8, 'k': 5,\",\n \"\\\"\\\"\\\" total_points = 0 for letter in word: total_points +=\",\n \"turn. Letters are scored as in Scrabble; A is worth\",\n \"all n letters are used on the first turn. Letters\",\n \"(see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE; i.e.,\",\n \"word is a valid word. The score for a word\",\n \"(lowercase letters) n: integer (HAND_SIZE; i.e., hand size required for\",\n \"worth 3, C is worth 3, D is worth 2,\",\n \"0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *=\",\n \"by the length of the word, PLUS 50 points if\",\n \"hand size required for additional points) returns: int >= 0\",\n \"worth 1, and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase\",\n \"'s': 1, 't': 1, 'u': 1, 'v': 4, 'w': 4,\",\n \"\\\"\\\"\\\" Returns the score for a word. Assumes the word\",\n \"for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word)\",\n \"used on the first turn. Letters are scored as in\",\n \"'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f': 4,\",\n \"= { 'a': 1, 'b': 3, 'c': 3, 'd': 2,\",\n \"total_points = 0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter]\",\n \"the word is a valid word. The score for a\",\n \"'z': 10 } def getWordScore(word, n): \\\"\\\"\\\" Returns the score\",\n \"is a valid word. The score for a word is\",\n \"word. Assumes the word is a valid word. The score\",\n \"'e': 1, 'f': 4, 'g': 2, 'h': 4, 'i': 1,\",\n \"for additional points) returns: int >= 0 \\\"\\\"\\\" total_points =\",\n \"so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer\",\n \"len(word) if len(word) == n: total_points += 50 return total_points\",\n \"Letters are scored as in Scrabble; A is worth 1,\",\n \"for letters in the word, multiplied by the length of\",\n \"word, multiplied by the length of the word, PLUS 50\",\n \"the word, multiplied by the length of the word, PLUS\",\n \"1, 'p': 3, 'q': 10, 'r': 1, 's': 1, 't':\",\n \"scored as in Scrabble; A is worth 1, B is\",\n \"the score for a word. Assumes the word is a\",\n \"in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word)\",\n \"worth 2, E is worth 1, and so on (see\",\n \"} def getWordScore(word, n): \\\"\\\"\\\" Returns the score for a\",\n \"3, 'q': 10, 'r': 1, 's': 1, 't': 1, 'u':\",\n \"length of the word, PLUS 50 points if all n\",\n \"B is worth 3, C is worth 3, D is\",\n \"50 points if all n letters are used on the\",\n \"points for letters in the word, multiplied by the length\",\n \"letters in the word, multiplied by the length of the\",\n \"sum of the points for letters in the word, multiplied\",\n \"'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p': 3,\",\n \"of the points for letters in the word, multiplied by\",\n \"1, 't': 1, 'u': 1, 'v': 4, 'w': 4, 'x':\",\n \"10 } def getWordScore(word, n): \\\"\\\"\\\" Returns the score for\",\n \"is worth 1, B is worth 3, C is worth\",\n \"total_points *= len(word) if len(word) == n: total_points += 50\",\n \"letters are used on the first turn. Letters are scored\",\n \"Scrabble; A is worth 1, B is worth 3, C\",\n \"3, C is worth 3, D is worth 2, E\",\n \"on the first turn. Letters are scored as in Scrabble;\",\n \"word. The score for a word is the sum of\",\n \"1, 'f': 4, 'g': 2, 'h': 4, 'i': 1, 'j':\",\n \"The score for a word is the sum of the\",\n \"word, PLUS 50 points if all n letters are used\",\n \"in the word, multiplied by the length of the word,\",\n \"the length of the word, PLUS 50 points if all\",\n \"8, 'y': 4, 'z': 10 } def getWordScore(word, n): \\\"\\\"\\\"\",\n \"SCRABBLE_LETTER_VALUES = { 'a': 1, 'b': 3, 'c': 3, 'd':\",\n \"5, 'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p':\",\n \"4, 'z': 10 } def getWordScore(word, n): \\\"\\\"\\\" Returns the\",\n \"'q': 10, 'r': 1, 's': 1, 't': 1, 'u': 1,\",\n \"2, E is worth 1, and so on (see SCRABBLE_LETTER_VALUES)\",\n \"word: string (lowercase letters) n: integer (HAND_SIZE; i.e., hand size\",\n \"word is the sum of the points for letters in\",\n \"'t': 1, 'u': 1, 'v': 4, 'w': 4, 'x': 8,\",\n \"i.e., hand size required for additional points) returns: int >=\",\n \"A is worth 1, B is worth 3, C is\",\n \"'v': 4, 'w': 4, 'x': 8, 'y': 4, 'z': 10\",\n \"worth 1, B is worth 3, C is worth 3,\",\n \"2, 'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l':\",\n \"score for a word. Assumes the word is a valid\",\n \"Assumes the word is a valid word. The score for\",\n \"1, B is worth 3, C is worth 3, D\",\n \"4, 'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm':\",\n \"are scored as in Scrabble; A is worth 1, B\",\n \"required for additional points) returns: int >= 0 \\\"\\\"\\\" total_points\",\n \"4, 'w': 4, 'x': 8, 'y': 4, 'z': 10 }\",\n \"1, 'u': 1, 'v': 4, 'w': 4, 'x': 8, 'y':\",\n \"size required for additional points) returns: int >= 0 \\\"\\\"\\\"\",\n \"'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l': 1,\",\n \"score for a word is the sum of the points\",\n \"the sum of the points for letters in the word,\",\n \">= 0 \\\"\\\"\\\" total_points = 0 for letter in word:\",\n \"len(word) == n: total_points += 50 return total_points print(getWordScore('waybill', 7))\",\n \"in Scrabble; A is worth 1, B is worth 3,\",\n \"word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) ==\",\n \"{ 'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e':\",\n \"'f': 4, 'g': 2, 'h': 4, 'i': 1, 'j': 8,\",\n \"first turn. Letters are scored as in Scrabble; A is\",\n \"points) returns: int >= 0 \\\"\\\"\\\" total_points = 0 for\",\n \"SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE; i.e., hand\",\n \"'n': 1, 'o': 1, 'p': 3, 'q': 10, 'r': 1,\",\n \"4, 'x': 8, 'y': 4, 'z': 10 } def getWordScore(word,\",\n \"E is worth 1, and so on (see SCRABBLE_LETTER_VALUES) word:\",\n \"3, 'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g':\",\n \"n: integer (HAND_SIZE; i.e., hand size required for additional points)\",\n \"+= SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n: total_points\",\n \"'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm': 3,\",\n \"1, 'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n':\",\n \"3, 'n': 1, 'o': 1, 'p': 3, 'q': 10, 'r':\",\n \"10, 'r': 1, 's': 1, 't': 1, 'u': 1, 'v':\",\n \"1, 's': 1, 't': 1, 'u': 1, 'v': 4, 'w':\",\n \"'u': 1, 'v': 4, 'w': 4, 'x': 8, 'y': 4,\",\n \"'y': 4, 'z': 10 } def getWordScore(word, n): \\\"\\\"\\\" Returns\",\n \"SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n: total_points +=\",\n \"string (lowercase letters) n: integer (HAND_SIZE; i.e., hand size required\",\n \"'w': 4, 'x': 8, 'y': 4, 'z': 10 } def\",\n \"int >= 0 \\\"\\\"\\\" total_points = 0 for letter in\",\n \"and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n:\",\n \"'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1,\",\n \"the points for letters in the word, multiplied by the\",\n \"'o': 1, 'p': 3, 'q': 10, 'r': 1, 's': 1,\",\n \"additional points) returns: int >= 0 \\\"\\\"\\\" total_points = 0\",\n \"1, and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters)\",\n \"the word, PLUS 50 points if all n letters are\",\n \"valid word. The score for a word is the sum\",\n \"getWordScore(word, n): \\\"\\\"\\\" Returns the score for a word. Assumes\",\n \"'r': 1, 's': 1, 't': 1, 'u': 1, 'v': 4,\",\n \"'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o': 1,\",\n \"for a word is the sum of the points for\",\n \"= 0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points\",\n \"*= len(word) if len(word) == n: total_points += 50 return\",\n \"is worth 1, and so on (see SCRABBLE_LETTER_VALUES) word: string\",\n \"4, 'g': 2, 'h': 4, 'i': 1, 'j': 8, 'k':\",\n \"a valid word. The score for a word is the\",\n \"of the word, PLUS 50 points if all n letters\",\n \"as in Scrabble; A is worth 1, B is worth\",\n \"1, 'o': 1, 'p': 3, 'q': 10, 'r': 1, 's':\",\n \"3, 'd': 2, 'e': 1, 'f': 4, 'g': 2, 'h':\",\n \"for a word. Assumes the word is a valid word.\",\n \"n letters are used on the first turn. Letters are\",\n \"multiplied by the length of the word, PLUS 50 points\",\n \"'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n': 1,\",\n \"is worth 3, C is worth 3, D is worth\",\n \"is worth 2, E is worth 1, and so on\",\n \"1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f':\",\n \"letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if\",\n \"D is worth 2, E is worth 1, and so\",\n \"a word is the sum of the points for letters\",\n \"3, D is worth 2, E is worth 1, and\",\n \"returns: int >= 0 \\\"\\\"\\\" total_points = 0 for letter\",\n \"worth 3, D is worth 2, E is worth 1,\",\n \"a word. Assumes the word is a valid word. The\",\n \"the first turn. Letters are scored as in Scrabble; A\",\n \"points if all n letters are used on the first\"\n]","isConversational":false}}},{"rowIdx":170,"cells":{"ngram":{"kind":"list like","value":["row in data: f = open(filename+'.tex','r+') old = f.readlines() if","f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv","# Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace('","um certificado para cada nome. for row in data: f","open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def': offset =","old[0][1:4] == 'def': offset = 1 else: offset = 0","Abrir banco de dados para ler nomes. try: con =","% e.args[0] sys.exit(1) finally: if con: con.close() # Gerar um","try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &')","# Gerar um certificado para cada nome. for row in","e: print \"Error %s:\" % e.args[0] sys.exit(1) finally: if con:","<filename>autolatex-master/exemplos_codigo/certificados/certificados.py import os import sys import sqlite3 con = None","data = cur.fetchall() except sqlite3.Error, e: print \"Error %s:\" %","if con: con.close() # Gerar um certificado para cada nome.","Gerar um certificado para cada nome. for row in data:","'def': offset = 1 else: offset = 0 f.seek(0) f.write('\\\\def\\\\name","finally: if con: con.close() # Gerar um certificado para cada","else: offset = 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() #","offset = 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar","in data: f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4]","sqlite3 con = None filename = 'certificado' # Abrir banco","ler nomes. try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select","cur.fetchall() except sqlite3.Error, e: print \"Error %s:\" % e.args[0] sys.exit(1)","data: f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] ==","banco de dados para ler nomes. try: con = sqlite3.connect('math.db')","'+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX not","f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try:","Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf')","'+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX not installed.')","arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open","* from math') data = cur.fetchall() except sqlite3.Error, e: print","f.readlines() if old[0][1:4] == 'def': offset = 1 else: offset","f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf","import sqlite3 con = None filename = 'certificado' # Abrir","= f.readlines() if old[0][1:4] == 'def': offset = 1 else:","os import sys import sqlite3 con = None filename =","para ler nomes. try: con = sqlite3.connect('math.db') cur = con.cursor()","for row in data: f = open(filename+'.tex','r+') old = f.readlines()","f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def':","f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex","= None filename = 'certificado' # Abrir banco de dados","try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from","'+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError:","os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX","= 'certificado' # Abrir banco de dados para ler nomes.","print \"Error %s:\" % e.args[0] sys.exit(1) finally: if con: con.close()","filename = 'certificado' # Abrir banco de dados para ler","sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math') data =","certificado para cada nome. for row in data: f =","import sys import sqlite3 con = None filename = 'certificado'","cur.execute('select * from math') data = cur.fetchall() except sqlite3.Error, e:","con = None filename = 'certificado' # Abrir banco de","= 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo","%s:\" % e.args[0] sys.exit(1) finally: if con: con.close() # Gerar","sys.exit(1) finally: if con: con.close() # Gerar um certificado para","{'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex')","math') data = cur.fetchall() except sqlite3.Error, e: print \"Error %s:\"","con.cursor() cur.execute('select * from math') data = cur.fetchall() except sqlite3.Error,","from math') data = cur.fetchall() except sqlite3.Error, e: print \"Error","de dados para ler nomes. try: con = sqlite3.connect('math.db') cur","para cada nome. for row in data: f = open(filename+'.tex','r+')","dados para ler nomes. try: con = sqlite3.connect('math.db') cur =","= cur.fetchall() except sqlite3.Error, e: print \"Error %s:\" % e.args[0]","nome. for row in data: f = open(filename+'.tex','r+') old =","= 1 else: offset = 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:])","None filename = 'certificado' # Abrir banco de dados para","cur = con.cursor() cur.execute('select * from math') data = cur.fetchall()","0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX","con.close() # Gerar um certificado para cada nome. for row","offset = 1 else: offset = 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n')","nomes. try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select *","= open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def': offset","= sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math') data","# Abrir banco de dados para ler nomes. try: con","\"Error %s:\" % e.args[0] sys.exit(1) finally: if con: con.close() #","import os import sys import sqlite3 con = None filename","os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except","if old[0][1:4] == 'def': offset = 1 else: offset =","old = f.readlines() if old[0][1:4] == 'def': offset = 1","= con.cursor() cur.execute('select * from math') data = cur.fetchall() except","sqlite3.Error, e: print \"Error %s:\" % e.args[0] sys.exit(1) finally: if","except sqlite3.Error, e: print \"Error %s:\" % e.args[0] sys.exit(1) finally:","sys import sqlite3 con = None filename = 'certificado' #","cada nome. for row in data: f = open(filename+'.tex','r+') old","== 'def': offset = 1 else: offset = 0 f.seek(0)","LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf","e.args[0] sys.exit(1) finally: if con: con.close() # Gerar um certificado","con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math')","1 else: offset = 0 f.seek(0) f.write('\\\\def\\\\name {'+row[0]+'}\\n') f.writelines(old[offset:]) f.close()","con: con.close() # Gerar um certificado para cada nome. for","'certificado' # Abrir banco de dados para ler nomes. try:"],"string":"[\n \"row in data: f = open(filename+'.tex','r+') old = f.readlines() if\",\n \"f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv\",\n \"# Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace('\",\n \"um certificado para cada nome. for row in data: f\",\n \"open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def': offset =\",\n \"old[0][1:4] == 'def': offset = 1 else: offset = 0\",\n \"Abrir banco de dados para ler nomes. try: con =\",\n \"% e.args[0] sys.exit(1) finally: if con: con.close() # Gerar um\",\n \"try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &')\",\n \"# Gerar um certificado para cada nome. for row in\",\n \"e: print \\\"Error %s:\\\" % e.args[0] sys.exit(1) finally: if con:\",\n \"<filename>autolatex-master/exemplos_codigo/certificados/certificados.py import os import sys import sqlite3 con = None\",\n \"data = cur.fetchall() except sqlite3.Error, e: print \\\"Error %s:\\\" %\",\n \"if con: con.close() # Gerar um certificado para cada nome.\",\n \"Gerar um certificado para cada nome. for row in data:\",\n \"'def': offset = 1 else: offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name\",\n \"finally: if con: con.close() # Gerar um certificado para cada\",\n \"else: offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() #\",\n \"offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar\",\n \"in data: f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4]\",\n \"sqlite3 con = None filename = 'certificado' # Abrir banco\",\n \"ler nomes. try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select\",\n \"cur.fetchall() except sqlite3.Error, e: print \\\"Error %s:\\\" % e.args[0] sys.exit(1)\",\n \"data: f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] ==\",\n \"banco de dados para ler nomes. try: con = sqlite3.connect('math.db')\",\n \"'+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX not\",\n \"f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try:\",\n \"Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf')\",\n \"'+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX not installed.')\",\n \"arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open\",\n \"* from math') data = cur.fetchall() except sqlite3.Error, e: print\",\n \"f.readlines() if old[0][1:4] == 'def': offset = 1 else: offset\",\n \"f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf\",\n \"import sqlite3 con = None filename = 'certificado' # Abrir\",\n \"= f.readlines() if old[0][1:4] == 'def': offset = 1 else:\",\n \"os import sys import sqlite3 con = None filename =\",\n \"para ler nomes. try: con = sqlite3.connect('math.db') cur = con.cursor()\",\n \"for row in data: f = open(filename+'.tex','r+') old = f.readlines()\",\n \"f = open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def':\",\n \"f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex\",\n \"= None filename = 'certificado' # Abrir banco de dados\",\n \"try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from\",\n \"'+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError:\",\n \"os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except OSError: print('LaTeX\",\n \"= 'certificado' # Abrir banco de dados para ler nomes.\",\n \"print \\\"Error %s:\\\" % e.args[0] sys.exit(1) finally: if con: con.close()\",\n \"filename = 'certificado' # Abrir banco de dados para ler\",\n \"sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math') data =\",\n \"certificado para cada nome. for row in data: f =\",\n \"import sys import sqlite3 con = None filename = 'certificado'\",\n \"cur.execute('select * from math') data = cur.fetchall() except sqlite3.Error, e:\",\n \"con = None filename = 'certificado' # Abrir banco de\",\n \"= 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo\",\n \"%s:\\\" % e.args[0] sys.exit(1) finally: if con: con.close() # Gerar\",\n \"sys.exit(1) finally: if con: con.close() # Gerar um certificado para\",\n \"{'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex')\",\n \"math') data = cur.fetchall() except sqlite3.Error, e: print \\\"Error %s:\\\"\",\n \"con.cursor() cur.execute('select * from math') data = cur.fetchall() except sqlite3.Error,\",\n \"from math') data = cur.fetchall() except sqlite3.Error, e: print \\\"Error\",\n \"de dados para ler nomes. try: con = sqlite3.connect('math.db') cur\",\n \"para cada nome. for row in data: f = open(filename+'.tex','r+')\",\n \"dados para ler nomes. try: con = sqlite3.connect('math.db') cur =\",\n \"= cur.fetchall() except sqlite3.Error, e: print \\\"Error %s:\\\" % e.args[0]\",\n \"nome. for row in data: f = open(filename+'.tex','r+') old =\",\n \"= 1 else: offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:])\",\n \"None filename = 'certificado' # Abrir banco de dados para\",\n \"cur = con.cursor() cur.execute('select * from math') data = cur.fetchall()\",\n \"0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX\",\n \"con.close() # Gerar um certificado para cada nome. for row\",\n \"offset = 1 else: offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n')\",\n \"nomes. try: con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select *\",\n \"= open(filename+'.tex','r+') old = f.readlines() if old[0][1:4] == 'def': offset\",\n \"= sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math') data\",\n \"# Abrir banco de dados para ler nomes. try: con\",\n \"\\\"Error %s:\\\" % e.args[0] sys.exit(1) finally: if con: con.close() #\",\n \"import os import sys import sqlite3 con = None filename\",\n \"os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf &') except\",\n \"if old[0][1:4] == 'def': offset = 1 else: offset =\",\n \"old = f.readlines() if old[0][1:4] == 'def': offset = 1\",\n \"= con.cursor() cur.execute('select * from math') data = cur.fetchall() except\",\n \"sqlite3.Error, e: print \\\"Error %s:\\\" % e.args[0] sys.exit(1) finally: if\",\n \"except sqlite3.Error, e: print \\\"Error %s:\\\" % e.args[0] sys.exit(1) finally:\",\n \"sys import sqlite3 con = None filename = 'certificado' #\",\n \"cada nome. for row in data: f = open(filename+'.tex','r+') old\",\n \"== 'def': offset = 1 else: offset = 0 f.seek(0)\",\n \"LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row[0].replace(' ','_')+'.pdf') #os.system('xdg-open '+filename+'.pdf\",\n \"e.args[0] sys.exit(1) finally: if con: con.close() # Gerar um certificado\",\n \"con = sqlite3.connect('math.db') cur = con.cursor() cur.execute('select * from math')\",\n \"1 else: offset = 0 f.seek(0) f.write('\\\\\\\\def\\\\\\\\name {'+row[0]+'}\\\\n') f.writelines(old[offset:]) f.close()\",\n \"con: con.close() # Gerar um certificado para cada nome. for\",\n \"'certificado' # Abrir banco de dados para ler nomes. try:\"\n]","isConversational":false}}},{"rowIdx":171,"cells":{"ngram":{"kind":"list like","value":["\"r\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes(): print(f\"{chrom}: {f[chrom].get_number_of_chunks()}\")","import Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool):","from pathlib import Path from meth5.meth5 import MetH5File def main(m5file:Path,","import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size)","def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size) as f:","with MetH5File(m5file, \"r\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes():","main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size) as f: for","pathlib import Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int,","MetH5File(m5file, \"r\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes(): print(f\"{chrom}:","<reponame>snajder-r/nanoepiseg from pathlib import Path from meth5.meth5 import MetH5File def","Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with","from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file,","quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size) as f: for chrom in","meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\",","MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size) as","chunk_size:int, quiet:bool): with MetH5File(m5file, \"r\", chunk_size=chunk_size) as f: for chrom"],"string":"[\n \"\\\"r\\\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes(): print(f\\\"{chrom}: {f[chrom].get_number_of_chunks()}\\\")\",\n \"import Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool):\",\n \"from pathlib import Path from meth5.meth5 import MetH5File def main(m5file:Path,\",\n \"import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size)\",\n \"def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f:\",\n \"with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes():\",\n \"main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f: for\",\n \"pathlib import Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int,\",\n \"MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes(): print(f\\\"{chrom}:\",\n \"<reponame>snajder-r/nanoepiseg from pathlib import Path from meth5.meth5 import MetH5File def\",\n \"Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with\",\n \"from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file,\",\n \"quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f: for chrom in\",\n \"meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\",\",\n \"MetH5File def main(m5file:Path, chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as\",\n \"chunk_size:int, quiet:bool): with MetH5File(m5file, \\\"r\\\", chunk_size=chunk_size) as f: for chrom\"\n]","isConversational":false}}},{"rowIdx":172,"cells":{"ngram":{"kind":"list like","value":["for this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir,","quarter = args.quarter # region list (LAEA regions only) valid_region_list","means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data","= dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\"","print(\"Output file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32)","print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except","gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt)","list into array and find mean and std dbarray =","geotiff output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\"","np import sirpy2 as sp2 import argparse from osgeo import","import sirpy2 as sp2 import argparse from osgeo import gdal","parser.parse_args() verbose = args.verbose year = args.year quarter = args.quarter","parser = argparse.ArgumentParser( \"script to make quarterly \" + \"means","dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd =","# make a list of files for this year filepatt","to make quarterly \" + \"means and stdevs of qscat","if i == 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform()","np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds =","== 0: warnmsg = \"No images found for this quarter.\\n\"","\"OND\": list(range(10, 13)), \"JFM\": list(range(1, 4)), \"AMJ\": list(range(4, 7)), }","pr if __name__ == \"__main__\": # set up arguments parser","1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt))","({}) Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\"","gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0)","Ama, etc.)\") parser.add_argument( \"year\", type=int, help=\"Year e.g. 1999 (qscat data","array and find mean and std dbarray = np.ma.stack(db_quarter, axis=2)","1999 (qscat data start in 1999)\" ) args = parser.parse_args()","in monthlist: qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday))","dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean,","globpatt = os.path.join(indir, filepatt) if verbose: print(\"glob pattern: {}\".format(globpatt)) filelist","driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir =","= None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median =","\"JFM\": list(range(1, 4)), \"AMJ\": list(range(4, 7)), } # this allows","std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd","fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn,","quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this quarter","# this allows GDAL to throw Python Exceptions gdal.UseExceptions() def","Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue /","dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj)","\"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\",","db_quarter.append(db_masked) # close datasets a_ds = None # stack list","= gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir = os.path.join(DATADIR,","dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min))","db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image","= np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds","10.0) return pr if __name__ == \"__main__\": # set up","sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE #","dst_ds = None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median","except Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1)","this quarter db_quarter = [] for i, image in enumerate(qlist):","region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file","= dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({})","== NODATA_VALUE # if this is the first image get","Q2M = { \"JAS\": list(range(7, 10)), \"OND\": list(range(10, 13)), \"JFM\":","{}\".format(quarter)) # set data dir indir = os.path.join(DATADIR, \"geotiffs\", region,","10 ** (dbvalue / 10.0) return pr if __name__ ==",") parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\",","\"geotiffs\", region, str(year)) outdir = indir if year == 1999:","a_mask = a_data == NODATA_VALUE # if this is the","dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename)","dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\" Min:","iday)) print(\"{}-{}: {}\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg","output verbosity\", action=\"store_true\", default=False, ) parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\",","\"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for","sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region: {}\".format(region)) print(\"year:","values\" ) parser.add_argument( \"-v\", \"--verbose\", help=\"increase output verbosity\", action=\"store_true\", default=False,","region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file:","srcband = a_ds.GetRasterBand(1) except Exception: print(\"Band ({}) not found\".format(1)) sys.exit(1)","== 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx","= np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\"","dst_filename) if verbose: print(\"Output file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx,","glob.glob(globpatt) qlist = [] for filepath in filelist: fn =","= np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray,","values. import os import sys import glob import numpy as","dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min","dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max =","= gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year, quarter)","set data dir indir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir","default=False, ) parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"),","{ \"JAS\": list(range(7, 10)), \"OND\": list(range(10, 13)), \"JFM\": list(range(1, 4)),","\"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path","7)), } # this allows GDAL to throw Python Exceptions","import os import sys import glob import numpy as np","a_ds = None # stack list into array and find","str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file: {}\".format(dst_path))","for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for","verbosity\", action=\"store_true\", default=False, ) parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\",","if verbose: print(\"glob pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt) qlist =","dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max =","4)), \"AMJ\": list(range(4, 7)), } # this allows GDAL to","= os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\"Unable","add image to db_quarter list db_quarter.append(db_masked) # close datasets a_ds","args.year quarter = args.quarter # region list (LAEA regions only)","qlist = [] for filepath in filelist: fn = os.path.basename(filepath)","to make seasonal means and stddev images of 4-day sig0","for aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region string (e.g. SAm,","monthlist: qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}:","const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region string","dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly","= args.verbose year = args.year quarter = args.quarter # region","dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file for sig0","a list of files for this year filepatt = \"quev-a-{}{}-*.tif\".format(region,","db_quarter = [] for i, image in enumerate(qlist): a_imgpath =","iday = fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose:","= glob.glob(globpatt) qlist = [] for filepath in filelist: fn","default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region","import sys import glob import numpy as np import sirpy2","\"./\" NODATA_VALUE = -9999.0 Q2M = { \"JAS\": list(range(7, 10)),","#!/usr/bin/env python # script to make seasonal means and stddev","# close datasets a_ds = None # stack list into","dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a","as np import sirpy2 as sp2 import argparse from osgeo","__name__ == \"__main__\": # set up arguments parser = argparse.ArgumentParser(","/ 10.0) return pr if __name__ == \"__main__\": # set","= os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file for sig0 means:","sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this quarter db_quarter","verbose: print(\"Output file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1,","throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 **","# set data dir indir = os.path.join(DATADIR, \"geotiffs\", region, str(year))","valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list))","= \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\"glob","dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({}) Mean","for i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try:","print(dbmean.shape) # finally, save as a geotiff output_format = \"GTiff\"","dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter))","dst_ds = None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median","type=int, help=\"Year e.g. 1999 (qscat data start in 1999)\" )","verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}: {}\".format(year, quarter, qlist))","[] for filepath in filelist: fn = os.path.basename(filepath) if verbose:","\"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\", ] region =","\"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\",","indir if year == 1999: year2 = 99 else: year2","list (LAEA regions only) valid_region_list = [ \"Grn\", \"Ala\", \"CAm\",","list(range(7, 10)), \"OND\": list(range(10, 13)), \"JFM\": list(range(1, 4)), \"AMJ\": list(range(4,","Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median))","np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save","a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if","= -9999.0 Q2M = { \"JAS\": list(range(7, 10)), \"OND\": list(range(10,","as a geotiff output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename","means and stddev images of 4-day sig0 # values. import","help=\"Quarter for aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region string (e.g.","and stdevs of qscat dB values\" ) parser.add_argument( \"-v\", \"--verbose\",","print(\"Band ({}) not found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask =","} # this allows GDAL to throw Python Exceptions gdal.UseExceptions()","Exception: print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1)","pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for filepath",") parser.add_argument(\"region\", help=\"BYU region string (e.g. SAm, NAm, Ama, etc.)\")","# loop over images for this quarter db_quarter = []","1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt))","= gdal.Open(a_imgpath) except Exception: print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1) try:","verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year imonth","\"AMJ\": list(range(4, 7)), } # this allows GDAL to throw","Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if","nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE)","dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\" Min:","output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year,","= valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\")","\"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\", ] region = args.region","= \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename =","10)), \"OND\": list(range(10, 13)), \"JFM\": list(range(1, 4)), \"AMJ\": list(range(4, 7)),","i == 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny,","sp2 import argparse from osgeo import gdal DATADIR = \"./\"","numpy as np import sirpy2 as sp2 import argparse from","= \"{:02d}\".format(year - 2000) monthlist = Q2M[quarter] # make a","action=\"store_true\", default=False, ) parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\",","close datasets a_ds = None # stack list into array","<reponame>tmilliman/sir_to_netcdf #!/usr/bin/env python # script to make seasonal means and","-9999.0 Q2M = { \"JAS\": list(range(7, 10)), \"OND\": list(range(10, 13)),","db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0) return pr","len(qlist) == 0: warnmsg = \"No images found for this","projection and geotransform if i == 0: prj = a_ds.GetProjection()","sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter))","mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray,","for standard deviation output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename","os.path.join(indir, filepatt) if verbose: print(\"glob pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt)","region string (e.g. SAm, NAm, Ama, etc.)\") parser.add_argument( \"year\", type=int,","{}\".format(dbmean_median)) # repeat for standard deviation output_format = \"GTiff\" driver","\"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\", ]","print(\" Median: {}\".format(dbmean_median)) # repeat for standard deviation output_format =","NODATA_VALUE # if this is the first image get projection","os import sys import glob import numpy as np import",") parser.add_argument( \"-v\", \"--verbose\", help=\"increase output verbosity\", action=\"store_true\", default=False, )","found for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images","a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter","save as a geotiff output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format)","sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose:","args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not valid.\\n\")","\"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\",","output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename","fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year imonth = fn_dt.month","dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({}) Stdev","= a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data db_masked","a_mask) # add image to db_quarter list db_quarter.append(db_masked) # close","= np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally,","gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data","= dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year))","year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir, filepatt) if","{}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) # set data dir indir","args.verbose year = args.year quarter = args.quarter # region list","(e.g. SAm, NAm, Ama, etc.)\") parser.add_argument( \"year\", type=int, help=\"Year e.g.","quarter, qlist)) if len(qlist) == 0: warnmsg = \"No images","\"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region,","= srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if this","print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max))","DATADIR = \"./\" NODATA_VALUE = -9999.0 Q2M = { \"JAS\":","= { \"JAS\": list(range(7, 10)), \"OND\": list(range(10, 13)), \"JFM\": list(range(1,","dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) #","dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj)","to open {}\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception:","\"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\"glob pattern:","make a list of files for this year filepatt =","in 1999)\" ) args = parser.parse_args() verbose = args.verbose year","repeat for standard deviation output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format)","if verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) # set","from osgeo import gdal DATADIR = \"./\" NODATA_VALUE = -9999.0","arguments parser = argparse.ArgumentParser( \"script to make quarterly \" +","# repeat for standard deviation output_format = \"GTiff\" driver =","up arguments parser = argparse.ArgumentParser( \"script to make quarterly \"","\"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\", ] region","# values. import os import sys import glob import numpy","list db_quarter.append(db_masked) # close datasets a_ds = None # stack","osgeo import gdal DATADIR = \"./\" NODATA_VALUE = -9999.0 Q2M","= \"./\" NODATA_VALUE = -9999.0 Q2M = { \"JAS\": list(range(7,","set up arguments parser = argparse.ArgumentParser( \"script to make quarterly","valid_region_list = [ \"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\",","iyear = fn_dt.year imonth = fn_dt.month iday = fn_dt.day if","{}\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\"Band ({})","= indir if year == 1999: year2 = 99 else:","gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt)","dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region,","loop over images for this quarter db_quarter = [] for","os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\"Unable to","+ \"means and stdevs of qscat dB values\" ) parser.add_argument(","into array and find mean and std dbarray = np.ma.stack(db_quarter,","= argparse.ArgumentParser( \"script to make quarterly \" + \"means and","SAm, NAm, Ama, etc.)\") parser.add_argument( \"year\", type=int, help=\"Year e.g. 1999","dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min =","verbose: print(\"glob pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt) qlist = []","parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\",","image to db_quarter list db_quarter.append(db_masked) # close datasets a_ds =","year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path =","fn_dt.year imonth = fn_dt.month iday = fn_dt.day if imonth in","except Exception: print(\"Band ({}) not found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray()","a geotiff output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename =","to db_quarter list db_quarter.append(db_masked) # close datasets a_ds = None","\"means and stdevs of qscat dB values\" ) parser.add_argument( \"-v\",","[] for i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image)","pr = 10 ** (dbvalue / 10.0) return pr if","driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data)","GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr =","string (e.g. SAm, NAm, Ama, etc.)\") parser.add_argument( \"year\", type=int, help=\"Year","finally, save as a geotiff output_format = \"GTiff\" driver =","= os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir = indir if year","valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region: {}\".format(region))","filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn,","dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region,","print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}: {}\".format(year, quarter, qlist)) if","sig0 # values. import os import sys import glob import","sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\"Band ({}) not","{}\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg = \"No","gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year, quarter) dst_dir","nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation.","if imonth in monthlist: qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear,","= np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\"","({}) Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\" Max: {}\".format(dbstd_max)) print(\"","if __name__ == \"__main__\": # set up arguments parser =","NAm, Ama, etc.)\") parser.add_argument( \"year\", type=int, help=\"Year e.g. 1999 (qscat","# if this is the first image get projection and","Exception: print(\"Band ({}) not found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask","parser.add_argument( \"year\", type=int, help=\"Year e.g. 1999 (qscat data start in","and geotransform if i == 0: prj = a_ds.GetProjection() gt","\"-v\", \"--verbose\", help=\"increase output verbosity\", action=\"store_true\", default=False, ) parser.add_argument( \"-q\",","= [] for filepath in filelist: fn = os.path.basename(filepath) if","{}\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for filepath in","open {}\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\"Band","geotransform if i == 0: prj = a_ds.GetProjection() gt =","args = parser.parse_args() verbose = args.verbose year = args.year quarter","\"No images found for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop","dst_filename) if verbose: print(\"Output file for sig0 means: {}\".format(dst_path)) dst_ds","if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year","list(range(1, 4)), \"AMJ\": list(range(4, 7)), } # this allows GDAL","- 2000) monthlist = Q2M[quarter] # make a list of","print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\" Max: {}\".format(dbstd_max))","os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir = indir if year ==","= np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list db_quarter.append(db_masked)","dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max = dbstd.max()","{}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max","\"Ind\", \"Aus\", \"Ber\", ] region = args.region try: region_index =","data dir indir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir =","db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list","iyear, imonth, iday)) print(\"{}-{}: {}\".format(year, quarter, qlist)) if len(qlist) ==","os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file for sig0 means: {}\".format(dst_path))","for sig0 means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1,","of qscat dB values\" ) parser.add_argument( \"-v\", \"--verbose\", help=\"increase output","# finally, save as a geotiff output_format = \"GTiff\" driver","argparse from osgeo import gdal DATADIR = \"./\" NODATA_VALUE =","if len(qlist) == 0: warnmsg = \"No images found for","sys.exit(1) if verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) #","else: year2 = \"{:02d}\".format(year - 2000) monthlist = Q2M[quarter] #","a_ds = gdal.Open(a_imgpath) except Exception: print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1)","this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir, filepatt)","python # script to make seasonal means and stddev images","sys.exit(0) # loop over images for this quarter db_quarter =","{}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max","print(\"{}-{}: {}\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg =","axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as","\"Aus\", \"Ber\", ] region = args.region try: region_index = valid_region_list.index(region)","= 99 else: year2 = \"{:02d}\".format(year - 2000) monthlist =","dB values\" ) parser.add_argument( \"-v\", \"--verbose\", help=\"increase output verbosity\", action=\"store_true\",","= sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year imonth = fn_dt.month iday","images found for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop over","= \"No images found for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) #","image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\"Unable to open","\"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\",","[ \"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\",","year2 = 99 else: year2 = \"{:02d}\".format(year - 2000) monthlist","\"Ber\", ] region = args.region try: region_index = valid_region_list.index(region) except","of files for this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt","list of files for this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2)","Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) # repeat","import gdal DATADIR = \"./\" NODATA_VALUE = -9999.0 Q2M =","filepath in filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt","= a_ds.GetRasterBand(1) except Exception: print(\"Band ({}) not found\".format(1)) sys.exit(1) a_data","start in 1999)\" ) args = parser.parse_args() verbose = args.verbose","str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file for","if this is the first image get projection and geotransform","filelist = glob.glob(globpatt) qlist = [] for filepath in filelist:","etc.)\") parser.add_argument( \"year\", type=int, help=\"Year e.g. 1999 (qscat data start","4-day sig0 # values. import os import sys import glob","try: srcband = a_ds.GetRasterBand(1) except Exception: print(\"Band ({}) not found\".format(1))","date_flag=\"center\") iyear = fn_dt.year imonth = fn_dt.month iday = fn_dt.day","def db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0) return","a_data == NODATA_VALUE # if this is the first image","= \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year))","quarterly \" + \"means and stdevs of qscat dB values\"","\"year\", type=int, help=\"Year e.g. 1999 (qscat data start in 1999)\"","dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path","\"__main__\": # set up arguments parser = argparse.ArgumentParser( \"script to","first image get projection and geotransform if i == 0:","year = args.year quarter = args.quarter # region list (LAEA","qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}: {}\".format(year,","over images for this quarter db_quarter = [] for i,","outdir = indir if year == 1999: year2 = 99","= \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR,","region, str(year)) outdir = indir if year == 1999: year2","nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE)","\"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\", )","None # stack list into array and find mean and","Median: {}\".format(dbmean_median)) # repeat for standard deviation output_format = \"GTiff\"","find mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean =","as sp2 import argparse from osgeo import gdal DATADIR =","# add image to db_quarter list db_quarter.append(db_masked) # close datasets","= args.year quarter = args.quarter # region list (LAEA regions","verbose = args.verbose year = args.year quarter = args.quarter #","np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list db_quarter.append(db_masked) #","ny, nx = a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data,","glob import numpy as np import sirpy2 as sp2 import","fn = os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\")","Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\" Max: {}\".format(dbstd_max)) print(\" Median: {}\".format(dbstd_median))","not valid.\\n\") sys.stderr.write(\"Valid regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region:","stack list into array and find mean and std dbarray","try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid","try: a_ds = gdal.Open(a_imgpath) except Exception: print(\"Unable to open {}\".format(a_imgpath))","fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None","dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter))","\"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\", \"ChJ\", \"Ind\", \"Aus\",","get projection and geotransform if i == 0: prj =","argparse.ArgumentParser( \"script to make quarterly \" + \"means and stdevs","imonth in monthlist: qlist.append(fn) if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth,","files for this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt =","= 10 ** (dbvalue / 10.0) return pr if __name__","help=\"Year e.g. 1999 (qscat data start in 1999)\" ) args","quarter db_quarter = [] for i, image in enumerate(qlist): a_imgpath","the first image get projection and geotransform if i ==","dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min))","dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min","== 1999: year2 = 99 else: year2 = \"{:02d}\".format(year -","except Exception: print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1) try: srcband =","NODATA_VALUE = -9999.0 Q2M = { \"JAS\": list(range(7, 10)), \"OND\":","to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10","this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this","dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly","year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\"glob pattern: {}\".format(globpatt))","parser.add_argument(\"region\", help=\"BYU region string (e.g. SAm, NAm, Ama, etc.)\") parser.add_argument(","imonth, iday)) print(\"{}-{}: {}\".format(year, quarter, qlist)) if len(qlist) == 0:","seasonal means and stddev images of 4-day sig0 # values.","help=\"BYU region string (e.g. SAm, NAm, Ama, etc.)\") parser.add_argument( \"year\",","= None # stack list into array and find mean","and stddev images of 4-day sig0 # values. import os","sig0 means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32)","= os.path.join(indir, filepatt) if verbose: print(\"glob pattern: {}\".format(globpatt)) filelist =","file for sig0 means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny,","make quarterly \" + \"means and stdevs of qscat dB","print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year imonth =","help=\"increase output verbosity\", action=\"store_true\", default=False, ) parser.add_argument( \"-q\", \"--quarter\", nargs=\"?\",","0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx =","\"geotiffs\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output","a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add","filepatt) if verbose: print(\"glob pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt) qlist","year2 = \"{:02d}\".format(year - 2000) monthlist = Q2M[quarter] # make","= fn_dt.year imonth = fn_dt.month iday = fn_dt.day if imonth","deviation output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region,","for this quarter db_quarter = [] for i, image in","= a_data == NODATA_VALUE # if this is the first","this is the first image get projection and geotransform if","# script to make seasonal means and stddev images of","\"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU","stddev images of 4-day sig0 # values. import os import","args.quarter # region list (LAEA regions only) valid_region_list = [","region = args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\"Region","images of 4-day sig0 # values. import os import sys","import argparse from osgeo import gdal DATADIR = \"./\" NODATA_VALUE","(qscat data start in 1999)\" ) args = parser.parse_args() verbose","a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data =","found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE","= Q2M[quarter] # make a list of files for this","srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if this is","0: warnmsg = \"No images found for this quarter.\\n\" sys.stdout.write(warnmsg)","os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path,","# region list (LAEA regions only) valid_region_list = [ \"Grn\",","None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean)","{}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}: {}\".format(year, quarter, qlist)) if len(qlist)","return pr if __name__ == \"__main__\": # set up arguments","= fn_dt.month iday = fn_dt.day if imonth in monthlist: qlist.append(fn)","i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds","{}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) # repeat for","and find mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean","{}\".format(year)) print(\"quarter: {}\".format(quarter)) # set data dir indir = os.path.join(DATADIR,","np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2)","Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue","import glob import numpy as np import sirpy2 as sp2","(LAEA regions only) valid_region_list = [ \"Grn\", \"Ala\", \"CAm\", \"NAm\",","dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year, quarter) dst_dir =","= a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data","if year == 1999: year2 = 99 else: year2 =",") args = parser.parse_args() verbose = args.verbose year = args.year","db_quarter list db_quarter.append(db_masked) # close datasets a_ds = None #","region list (LAEA regions only) valid_region_list = [ \"Grn\", \"Ala\",","image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds =","warnmsg = \"No images found for this quarter.\\n\" sys.stdout.write(warnmsg) sys.exit(0)","\"--verbose\", help=\"increase output verbosity\", action=\"store_true\", default=False, ) parser.add_argument( \"-q\", \"--quarter\",","verbose: print(\"Output file for sig0 means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path,","# set up arguments parser = argparse.ArgumentParser( \"script to make","images for this quarter db_quarter = [] for i, image","data start in 1999)\" ) args = parser.parse_args() verbose =","qscat dB values\" ) parser.add_argument( \"-v\", \"--verbose\", help=\"increase output verbosity\",","driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data)","a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data db_masked =","print(\" Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) #","and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2)","gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\",","image get projection and geotransform if i == 0: prj","print(\"glob pattern: {}\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for","are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter:","year == 1999: year2 = 99 else: year2 = \"{:02d}\".format(year","list(range(4, 7)), } # this allows GDAL to throw Python","2000) monthlist = Q2M[quarter] # make a list of files","sys import glob import numpy as np import sirpy2 as","make seasonal means and stddev images of 4-day sig0 #","fn_dt.month iday = fn_dt.day if imonth in monthlist: qlist.append(fn) if","qlist)) if len(qlist) == 0: warnmsg = \"No images found","print(\"quarter: {}\".format(quarter)) # set data dir indir = os.path.join(DATADIR, \"geotiffs\",","is the first image get projection and geotransform if i","= dbstd.max() dbstd_median = np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\"","regions are:\\n\") sys.stderr.write(\"{}\\n\".format(valid_region_list)) sys.exit(1) if verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year))","Q2M[quarter] # make a list of files for this year","= os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear","= driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE)","dir indir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir = indir","Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region string (e.g. SAm, NAm, Ama,","\"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\", \"Eur\", \"SAs\",","if verbose: print(\"Output file for sig0 means: {}\".format(dst_path)) dst_ds =","dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file: {}\".format(dst_path)) dst_ds","Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) # repeat for standard deviation","print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) # set data dir indir =","= \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter)","os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose:","(dbvalue / 10.0) return pr if __name__ == \"__main__\": #","import numpy as np import sirpy2 as sp2 import argparse","not found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data ==","\"{:02d}\".format(year - 2000) monthlist = Q2M[quarter] # make a list","np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a geotiff output_format","in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath)","\"ChJ\", \"Ind\", \"Aus\", \"Ber\", ] region = args.region try: region_index","sirpy2 as sp2 import argparse from osgeo import gdal DATADIR","this allows GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue):","datasets a_ds = None # stack list into array and","Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\" Max: {}\".format(dbstd_max)) print(\" Median:","Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\" Max: {}\".format(dbmean_max)) print(\" Median:","= os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if","indir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) outdir = indir if","file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data","gdal.Open(a_imgpath) except Exception: print(\"Unable to open {}\".format(a_imgpath)) sys.exit(1) try: srcband","99 else: year2 = \"{:02d}\".format(year - 2000) monthlist = Q2M[quarter]","\"JAS\": list(range(7, 10)), \"OND\": list(range(10, 13)), \"JFM\": list(range(1, 4)), \"AMJ\":","if verbose: print(\"{}: {}-{}-{}\".format(fn, iyear, imonth, iday)) print(\"{}-{}: {}\".format(year, quarter,","= args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not","np.ma.median(dbmean) print(\"Quarterly ({}) Mean Stats\".format(quarter)) print(\" Min: {}\".format(dbmean_min)) print(\" Max:","list(range(10, 13)), \"JFM\": list(range(1, 4)), \"AMJ\": list(range(4, 7)), } #","np.ma.median(dbstd) print(\"Quarterly ({}) Stdev Stats\".format(quarter)) print(\" Min: {}\".format(dbstd_min)) print(\" Max:","{}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) # repeat for standard deviation output_format","enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except","print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) # set data dir","1999)\" ) args = parser.parse_args() verbose = args.verbose year =","of 4-day sig0 # values. import os import sys import","= np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a geotiff","dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd,","ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt:","dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max = dbmean.max()","quarter) dst_dir = os.path.join(DATADIR, \"geotiffs\", region, str(year)) dst_path = os.path.join(dst_dir,","driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-mean-db-{}-{}.tif\" dst_filename = dst_filename.format(region, year,","allows GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr","a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception:","imonth = fn_dt.month iday = fn_dt.day if imonth in monthlist:","in filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt =","print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min()","\"SAs\", \"ChJ\", \"Ind\", \"Aus\", \"Ber\", ] region = args.region try:","== \"__main__\": # set up arguments parser = argparse.ArgumentParser( \"script","aggregation. Default=JAS\", ) parser.add_argument(\"region\", help=\"BYU region string (e.g. SAm, NAm,","13)), \"JFM\": list(range(1, 4)), \"AMJ\": list(range(4, 7)), } # this","print(\" Max: {}\".format(dbmean_max)) print(\" Median: {}\".format(dbmean_median)) # repeat for standard","= os.path.join(dst_dir, dst_filename) if verbose: print(\"Output file: {}\".format(dst_path)) dst_ds =","\"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\", ) parser.add_argument(\"region\",","# stack list into array and find mean and std","sp2.fn2dt(fn, date_flag=\"center\") iyear = fn_dt.year imonth = fn_dt.month iday =","= None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median =","np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds =","\"script to make quarterly \" + \"means and stdevs of","stdevs of qscat dB values\" ) parser.add_argument( \"-v\", \"--verbose\", help=\"increase","= driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE)","= fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose: print(\"{}:","nx = a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask)","os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\"center\") iyear =","None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd)","choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter for aggregation. Default=JAS\",","filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose:","print(\"Output file for sig0 means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx,","= parser.parse_args() verbose = args.verbose year = args.year quarter =","region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\"Region not valid.\\n\") sys.stderr.write(\"Valid regions","print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min()","str(year)) outdir = indir if year == 1999: year2 =","\"-q\", \"--quarter\", nargs=\"?\", choices=(\"JAS\", \"OND\", \"JFM\", \"AMJ\"), default=\"JAS\", const=\"JAS\", help=\"Quarter","1999: year2 = 99 else: year2 = \"{:02d}\".format(year - 2000)","for filepath in filelist: fn = os.path.basename(filepath) if verbose: print(fn)","= args.quarter # region list (LAEA regions only) valid_region_list =","verbose: print(\"region: {}\".format(region)) print(\"year: {}\".format(year)) print(\"quarter: {}\".format(quarter)) # set data","\"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename = \"{}-quev-std-db-{}-{}.tif\".format(region, year, quarter) dst_dir","script to make seasonal means and stddev images of 4-day","] region = args.region try: region_index = valid_region_list.index(region) except Exception:","({}) not found\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data","= dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\"Quarterly ({})","prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape","= [ \"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\", \"SAf\", \"Sib\",","= a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to","standard deviation output_format = \"GTiff\" driver = gdal.GetDriverByName(output_format) dst_filename =","only) valid_region_list = [ \"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\", \"NAf\",","monthlist = Q2M[quarter] # make a list of files for","= a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) #","regions only) valid_region_list = [ \"Grn\", \"Ala\", \"CAm\", \"NAm\", \"SAm\",","ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt:","e.g. 1999 (qscat data start in 1999)\" ) args =","= [] for i, image in enumerate(qlist): a_imgpath = os.path.join(indir,","= np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds","parser.add_argument( \"-v\", \"--verbose\", help=\"increase output verbosity\", action=\"store_true\", default=False, ) parser.add_argument(","a_ds.GetRasterBand(1) except Exception: print(\"Band ({}) not found\".format(1)) sys.exit(1) a_data =","axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape)","axis=2) print(dbmean.shape) # finally, save as a geotiff output_format =","dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\"gt: {}\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min =","gdal DATADIR = \"./\" NODATA_VALUE = -9999.0 Q2M = {","if verbose: print(\"Output file: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny,","{}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data =","** (dbvalue / 10.0) return pr if __name__ == \"__main__\":","\" + \"means and stdevs of qscat dB values\" )"],"string":"[\n \"for this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir,\",\n \"quarter = args.quarter # region list (LAEA regions only) valid_region_list\",\n \"means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data\",\n \"= dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\"\",\n \"print(\\\"Output file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32)\",\n \"print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except\",\n \"gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt)\",\n \"list into array and find mean and std dbarray =\",\n \"geotiff output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\"\",\n \"np import sirpy2 as sp2 import argparse from osgeo import\",\n \"import sirpy2 as sp2 import argparse from osgeo import gdal\",\n \"parser.parse_args() verbose = args.verbose year = args.year quarter = args.quarter\",\n \"parser = argparse.ArgumentParser( \\\"script to make quarterly \\\" + \\\"means\",\n \"dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd =\",\n \"# make a list of files for this year filepatt\",\n \"to make quarterly \\\" + \\\"means and stdevs of qscat\",\n \"if i == 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform()\",\n \"np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds =\",\n \"== 0: warnmsg = \\\"No images found for this quarter.\\\\n\\\"\",\n \"\\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)), }\",\n \"pr if __name__ == \\\"__main__\\\": # set up arguments parser\",\n \"1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt))\",\n \"({}) Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\"\",\n \"gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0)\",\n \"Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g. 1999 (qscat data\",\n \"array and find mean and std dbarray = np.ma.stack(db_quarter, axis=2)\",\n \"1999 (qscat data start in 1999)\\\" ) args = parser.parse_args()\",\n \"in monthlist: qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday))\",\n \"dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean,\",\n \"globpatt = os.path.join(indir, filepatt) if verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist\",\n \"driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir =\",\n \"= None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median =\",\n \"\\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)), } # this allows\",\n \"std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd\",\n \"fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn,\",\n \"quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this quarter\",\n \"# this allows GDAL to throw Python Exceptions gdal.UseExceptions() def\",\n \"Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue /\",\n \"dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj)\",\n \"\\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\",\",\n \"db_quarter.append(db_masked) # close datasets a_ds = None # stack list\",\n \"= gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir = os.path.join(DATADIR,\",\n \"dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min))\",\n \"db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image\",\n \"= np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds\",\n \"10.0) return pr if __name__ == \\\"__main__\\\": # set up\",\n \"sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE #\",\n \"dst_ds = None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median\",\n \"except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1)\",\n \"this quarter db_quarter = [] for i, image in enumerate(qlist):\",\n \"region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file\",\n \"= dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({})\",\n \"== NODATA_VALUE # if this is the first image get\",\n \"Q2M = { \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\":\",\n \"{}\\\".format(quarter)) # set data dir indir = os.path.join(DATADIR, \\\"geotiffs\\\", region,\",\n \"10 ** (dbvalue / 10.0) return pr if __name__ ==\",\n \") parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\",\",\n \"\\\"geotiffs\\\", region, str(year)) outdir = indir if year == 1999:\",\n \"a_mask = a_data == NODATA_VALUE # if this is the\",\n \"dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename)\",\n \"dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\" Min:\",\n \"iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg\",\n \"output verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\",\",\n \"\\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for\",\n \"sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year:\",\n \"values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False,\",\n \"region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file:\",\n \"srcband = a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1)\",\n \"== 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx\",\n \"= np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\"\",\n \"dst_filename) if verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx,\",\n \"glob.glob(globpatt) qlist = [] for filepath in filelist: fn =\",\n \"= np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray,\",\n \"values. import os import sys import glob import numpy as\",\n \"dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min\",\n \"dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max =\",\n \"= gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year, quarter)\",\n \"set data dir indir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir\",\n \"default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"),\",\n \"{ \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)),\",\n \"\\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path\",\n \"7)), } # this allows GDAL to throw Python Exceptions\",\n \"import os import sys import glob import numpy as np\",\n \"a_ds = None # stack list into array and find\",\n \"str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file: {}\\\".format(dst_path))\",\n \"for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for\",\n \"verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\",\",\n \"if verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist =\",\n \"dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max =\",\n \"4)), \\\"AMJ\\\": list(range(4, 7)), } # this allows GDAL to\",\n \"= os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\\\"Unable\",\n \"add image to db_quarter list db_quarter.append(db_masked) # close datasets a_ds\",\n \"args.year quarter = args.quarter # region list (LAEA regions only)\",\n \"qlist = [] for filepath in filelist: fn = os.path.basename(filepath)\",\n \"to make seasonal means and stddev images of 4-day sig0\",\n \"for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g. SAm,\",\n \"monthlist: qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}:\",\n \"const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region string\",\n \"dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly\",\n \"= args.verbose year = args.year quarter = args.quarter # region\",\n \"dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file for sig0\",\n \"a list of files for this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region,\",\n \"db_quarter = [] for i, image in enumerate(qlist): a_imgpath =\",\n \"iday = fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose:\",\n \"= glob.glob(globpatt) qlist = [] for filepath in filelist: fn\",\n \"default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region\",\n \"import sys import glob import numpy as np import sirpy2\",\n \"\\\"./\\\" NODATA_VALUE = -9999.0 Q2M = { \\\"JAS\\\": list(range(7, 10)),\",\n \"#!/usr/bin/env python # script to make seasonal means and stddev\",\n \"# close datasets a_ds = None # stack list into\",\n \"dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a\",\n \"as np import sirpy2 as sp2 import argparse from osgeo\",\n \"__name__ == \\\"__main__\\\": # set up arguments parser = argparse.ArgumentParser(\",\n \"/ 10.0) return pr if __name__ == \\\"__main__\\\": # set\",\n \"= os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file for sig0 means:\",\n \"sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this quarter db_quarter\",\n \"verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1,\",\n \"throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 **\",\n \"# set data dir indir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year))\",\n \"valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list))\",\n \"= \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\\\"glob\",\n \"dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean\",\n \"for i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try:\",\n \"print(dbmean.shape) # finally, save as a geotiff output_format = \\\"GTiff\\\"\",\n \"dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter))\",\n \"dst_ds = None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median\",\n \"type=int, help=\\\"Year e.g. 1999 (qscat data start in 1999)\\\" )\",\n \"verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist))\",\n \"[] for filepath in filelist: fn = os.path.basename(filepath) if verbose:\",\n \"\\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region =\",\n \"\\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\",\",\n \"indir if year == 1999: year2 = 99 else: year2\",\n \"list (LAEA regions only) valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\",\",\n \"list(range(7, 10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4,\",\n \"Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median))\",\n \"np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save\",\n \"a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if\",\n \"= -9999.0 Q2M = { \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10,\",\n \"as a geotiff output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename\",\n \"means and stddev images of 4-day sig0 # values. import\",\n \"help=\\\"Quarter for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g.\",\n \"and stdevs of qscat dB values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\",\",\n \"print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask =\",\n \"} # this allows GDAL to throw Python Exceptions gdal.UseExceptions()\",\n \"Exception: print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1)\",\n \"pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for filepath\",\n \") parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g. SAm, NAm, Ama, etc.)\\\")\",\n \"# loop over images for this quarter db_quarter = []\",\n \"1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt))\",\n \"= gdal.Open(a_imgpath) except Exception: print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1) try:\",\n \"verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year imonth\",\n \"\\\"AMJ\\\": list(range(4, 7)), } # this allows GDAL to throw\",\n \"Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if\",\n \"nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE)\",\n \"dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\" Min:\",\n \"output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year,\",\n \"= valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\")\",\n \"\\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region = args.region\",\n \"= \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename =\",\n \"10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)),\",\n \"i == 0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny,\",\n \"sp2 import argparse from osgeo import gdal DATADIR = \\\"./\\\"\",\n \"numpy as np import sirpy2 as sp2 import argparse from\",\n \"= \\\"{:02d}\\\".format(year - 2000) monthlist = Q2M[quarter] # make a\",\n \"action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\",\",\n \"close datasets a_ds = None # stack list into array\",\n \"<reponame>tmilliman/sir_to_netcdf #!/usr/bin/env python # script to make seasonal means and\",\n \"-9999.0 Q2M = { \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10, 13)),\",\n \"db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0) return pr\",\n \"len(qlist) == 0: warnmsg = \\\"No images found for this\",\n \"projection and geotransform if i == 0: prj = a_ds.GetProjection()\",\n \"sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter))\",\n \"mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray,\",\n \"for standard deviation output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename\",\n \"os.path.join(indir, filepatt) if verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt)\",\n \"region string (e.g. SAm, NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int,\",\n \"{}\\\".format(dbmean_median)) # repeat for standard deviation output_format = \\\"GTiff\\\" driver\",\n \"\\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ]\",\n \"print(\\\" Median: {}\\\".format(dbmean_median)) # repeat for standard deviation output_format =\",\n \"NODATA_VALUE # if this is the first image get projection\",\n \"os import sys import glob import numpy as np import\",\n \") parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False, )\",\n \"found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images\",\n \"a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter\",\n \"save as a geotiff output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format)\",\n \"sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose:\",\n \"args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\")\",\n \"\\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\",\",\n \"output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename\",\n \"fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year imonth = fn_dt.month\",\n \"dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev\",\n \"= a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data db_masked\",\n \"a_mask) # add image to db_quarter list db_quarter.append(db_masked) # close\",\n \"= np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally,\",\n \"gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data\",\n \"= dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year))\",\n \"year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir, filepatt) if\",\n \"{}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set data dir indir\",\n \"args.verbose year = args.year quarter = args.quarter # region list\",\n \"(e.g. SAm, NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g.\",\n \"quarter, qlist)) if len(qlist) == 0: warnmsg = \\\"No images\",\n \"\\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region,\",\n \"= srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if this\",\n \"print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max))\",\n \"DATADIR = \\\"./\\\" NODATA_VALUE = -9999.0 Q2M = { \\\"JAS\\\":\",\n \"= { \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1,\",\n \"dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) #\",\n \"dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj)\",\n \"to open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception:\",\n \"\\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\\\"glob pattern:\",\n \"make a list of files for this year filepatt =\",\n \"in 1999)\\\" ) args = parser.parse_args() verbose = args.verbose year\",\n \"repeat for standard deviation output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format)\",\n \"if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set\",\n \"from osgeo import gdal DATADIR = \\\"./\\\" NODATA_VALUE = -9999.0\",\n \"arguments parser = argparse.ArgumentParser( \\\"script to make quarterly \\\" +\",\n \"# repeat for standard deviation output_format = \\\"GTiff\\\" driver =\",\n \"up arguments parser = argparse.ArgumentParser( \\\"script to make quarterly \\\"\",\n \"\\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region\",\n \"# values. import os import sys import glob import numpy\",\n \"list db_quarter.append(db_masked) # close datasets a_ds = None # stack\",\n \"osgeo import gdal DATADIR = \\\"./\\\" NODATA_VALUE = -9999.0 Q2M\",\n \"= \\\"./\\\" NODATA_VALUE = -9999.0 Q2M = { \\\"JAS\\\": list(range(7,\",\n \"set up arguments parser = argparse.ArgumentParser( \\\"script to make quarterly\",\n \"valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\",\",\n \"iyear = fn_dt.year imonth = fn_dt.month iday = fn_dt.day if\",\n \"{}\\\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({})\",\n \"= indir if year == 1999: year2 = 99 else:\",\n \"gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt)\",\n \"dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region,\",\n \"loop over images for this quarter db_quarter = [] for\",\n \"os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\\\"Unable to\",\n \"+ \\\"means and stdevs of qscat dB values\\\" ) parser.add_argument(\",\n \"into array and find mean and std dbarray = np.ma.stack(db_quarter,\",\n \"= argparse.ArgumentParser( \\\"script to make quarterly \\\" + \\\"means and\",\n \"SAm, NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g. 1999\",\n \"dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min =\",\n \"verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist = []\",\n \"parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\",\",\n \"image to db_quarter list db_quarter.append(db_masked) # close datasets a_ds =\",\n \"year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path =\",\n \"fn_dt.year imonth = fn_dt.month iday = fn_dt.day if imonth in\",\n \"except Exception: print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray()\",\n \"a geotiff output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename =\",\n \"to db_quarter list db_quarter.append(db_masked) # close datasets a_ds = None\",\n \"\\\"means and stdevs of qscat dB values\\\" ) parser.add_argument( \\\"-v\\\",\",\n \"[] for i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image)\",\n \"pr = 10 ** (dbvalue / 10.0) return pr if\",\n \"driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data)\",\n \"GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr =\",\n \"string (e.g. SAm, NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year\",\n \"finally, save as a geotiff output_format = \\\"GTiff\\\" driver =\",\n \"= os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir = indir if year\",\n \"valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region))\",\n \"filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn,\",\n \"dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region,\",\n \"print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if\",\n \"sig0 # values. import os import sys import glob import\",\n \"sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({}) not\",\n \"{}\\\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg = \\\"No\",\n \"gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year, quarter) dst_dir\",\n \"nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation.\",\n \"if imonth in monthlist: qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear,\",\n \"= np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\"\",\n \"({}) Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\" Max: {}\\\".format(dbstd_max)) print(\\\"\",\n \"if __name__ == \\\"__main__\\\": # set up arguments parser =\",\n \"NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g. 1999 (qscat\",\n \"# if this is the first image get projection and\",\n \"Exception: print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask\",\n \"parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g. 1999 (qscat data start in\",\n \"and geotransform if i == 0: prj = a_ds.GetProjection() gt\",\n \"\\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\",\",\n \"= [] for filepath in filelist: fn = os.path.basename(filepath) if\",\n \"{}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for filepath in\",\n \"open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband = a_ds.GetRasterBand(1) except Exception: print(\\\"Band\",\n \"geotransform if i == 0: prj = a_ds.GetProjection() gt =\",\n \"args = parser.parse_args() verbose = args.verbose year = args.year quarter\",\n \"\\\"No images found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop\",\n \"dst_filename) if verbose: print(\\\"Output file for sig0 means: {}\\\".format(dst_path)) dst_ds\",\n \"if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year\",\n \"list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)), } # this allows GDAL\",\n \"- 2000) monthlist = Q2M[quarter] # make a list of\",\n \"print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\" Max: {}\\\".format(dbstd_max))\",\n \"os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir = indir if year ==\",\n \"= np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list db_quarter.append(db_masked)\",\n \"dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max = dbstd.max()\",\n \"{}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min() dbstd_max\",\n \"\\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region = args.region try: region_index =\",\n \"data dir indir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir =\",\n \"db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list\",\n \"iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if len(qlist) ==\",\n \"os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file for sig0 means: {}\\\".format(dst_path))\",\n \"for sig0 means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1,\",\n \"of qscat dB values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output\",\n \"# finally, save as a geotiff output_format = \\\"GTiff\\\" driver\",\n \"argparse from osgeo import gdal DATADIR = \\\"./\\\" NODATA_VALUE =\",\n \"if len(qlist) == 0: warnmsg = \\\"No images found for\",\n \"sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) #\",\n \"else: year2 = \\\"{:02d}\\\".format(year - 2000) monthlist = Q2M[quarter] #\",\n \"a_ds = gdal.Open(a_imgpath) except Exception: print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1)\",\n \"this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir, filepatt)\",\n \"python # script to make seasonal means and stddev images\",\n \"sys.exit(0) # loop over images for this quarter db_quarter =\",\n \"{}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max\",\n \"print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if len(qlist) == 0: warnmsg =\",\n \"axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as\",\n \"\\\"Aus\\\", \\\"Ber\\\", ] region = args.region try: region_index = valid_region_list.index(region)\",\n \"= 99 else: year2 = \\\"{:02d}\\\".format(year - 2000) monthlist =\",\n \"dB values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\",\",\n \"= sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year imonth = fn_dt.month iday\",\n \"images found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop over\",\n \"= \\\"No images found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) #\",\n \"image) try: a_ds = gdal.Open(a_imgpath) except Exception: print(\\\"Unable to open\",\n \"\\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\",\",\n \"[ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\",\",\n \"year2 = 99 else: year2 = \\\"{:02d}\\\".format(year - 2000) monthlist\",\n \"\\\"Ber\\\", ] region = args.region try: region_index = valid_region_list.index(region) except\",\n \"of files for this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt\",\n \"list of files for this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2)\",\n \"Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) # repeat\",\n \"import gdal DATADIR = \\\"./\\\" NODATA_VALUE = -9999.0 Q2M =\",\n \"filepath in filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt\",\n \"= a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1) a_data\",\n \"start in 1999)\\\" ) args = parser.parse_args() verbose = args.verbose\",\n \"str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file for\",\n \"if this is the first image get projection and geotransform\",\n \"filelist = glob.glob(globpatt) qlist = [] for filepath in filelist:\",\n \"etc.)\\\") parser.add_argument( \\\"year\\\", type=int, help=\\\"Year e.g. 1999 (qscat data start\",\n \"4-day sig0 # values. import os import sys import glob\",\n \"try: srcband = a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({}) not found\\\".format(1))\",\n \"date_flag=\\\"center\\\") iyear = fn_dt.year imonth = fn_dt.month iday = fn_dt.day\",\n \"def db2pr(dbvalue): pr = 10 ** (dbvalue / 10.0) return\",\n \"a_data == NODATA_VALUE # if this is the first image\",\n \"= \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year))\",\n \"quarterly \\\" + \\\"means and stdevs of qscat dB values\\\"\",\n \"\\\"year\\\", type=int, help=\\\"Year e.g. 1999 (qscat data start in 1999)\\\"\",\n \"dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path\",\n \"\\\"__main__\\\": # set up arguments parser = argparse.ArgumentParser( \\\"script to\",\n \"first image get projection and geotransform if i == 0:\",\n \"year = args.year quarter = args.quarter # region list (LAEA\",\n \"qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year,\",\n \"over images for this quarter db_quarter = [] for i,\",\n \"outdir = indir if year == 1999: year2 = 99\",\n \"= \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year, quarter) dst_dir = os.path.join(DATADIR,\",\n \"region, str(year)) outdir = indir if year == 1999: year2\",\n \"nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE)\",\n \"\\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\", )\",\n \"None # stack list into array and find mean and\",\n \"Median: {}\\\".format(dbmean_median)) # repeat for standard deviation output_format = \\\"GTiff\\\"\",\n \"find mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean =\",\n \"as sp2 import argparse from osgeo import gdal DATADIR =\",\n \"# add image to db_quarter list db_quarter.append(db_masked) # close datasets\",\n \"= args.year quarter = args.quarter # region list (LAEA regions\",\n \"verbose = args.verbose year = args.year quarter = args.quarter #\",\n \"np.ma.MaskedArray(db_data, a_mask) # add image to db_quarter list db_quarter.append(db_masked) #\",\n \"ny, nx = a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data,\",\n \"glob import numpy as np import sirpy2 as sp2 import\",\n \"fn = os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\")\",\n \"Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\" Max: {}\\\".format(dbstd_max)) print(\\\" Median: {}\\\".format(dbstd_median))\",\n \"not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region:\",\n \"stack list into array and find mean and std dbarray\",\n \"try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid\",\n \"try: a_ds = gdal.Open(a_imgpath) except Exception: print(\\\"Unable to open {}\\\".format(a_imgpath))\",\n \"fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None\",\n \"dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter))\",\n \"\\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\",\",\n \"get projection and geotransform if i == 0: prj =\",\n \"argparse.ArgumentParser( \\\"script to make quarterly \\\" + \\\"means and stdevs\",\n \"imonth in monthlist: qlist.append(fn) if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth,\",\n \"files for this year filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt =\",\n \"= 10 ** (dbvalue / 10.0) return pr if __name__\",\n \"help=\\\"Year e.g. 1999 (qscat data start in 1999)\\\" ) args\",\n \"quarter db_quarter = [] for i, image in enumerate(qlist): a_imgpath\",\n \"the first image get projection and geotransform if i ==\",\n \"dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min))\",\n \"dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min\",\n \"== 1999: year2 = 99 else: year2 = \\\"{:02d}\\\".format(year -\",\n \"except Exception: print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband =\",\n \"NODATA_VALUE = -9999.0 Q2M = { \\\"JAS\\\": list(range(7, 10)), \\\"OND\\\":\",\n \"to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10\",\n \"this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0) # loop over images for this\",\n \"dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly\",\n \"year2) globpatt = os.path.join(indir, filepatt) if verbose: print(\\\"glob pattern: {}\\\".format(globpatt))\",\n \"parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g. SAm, NAm, Ama, etc.)\\\") parser.add_argument(\",\n \"imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if len(qlist) == 0:\",\n \"seasonal means and stddev images of 4-day sig0 # values.\",\n \"help=\\\"BYU region string (e.g. SAm, NAm, Ama, etc.)\\\") parser.add_argument( \\\"year\\\",\",\n \"= None # stack list into array and find mean\",\n \"and stddev images of 4-day sig0 # values. import os\",\n \"sig0 means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32)\",\n \"= os.path.join(indir, filepatt) if verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist =\",\n \"file for sig0 means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny,\",\n \"make quarterly \\\" + \\\"means and stdevs of qscat dB\",\n \"print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year imonth =\",\n \"help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\",\",\n \"0: prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx =\",\n \"\\\"geotiffs\\\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output\",\n \"a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add\",\n \"filepatt) if verbose: print(\\\"glob pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist\",\n \"year2 = \\\"{:02d}\\\".format(year - 2000) monthlist = Q2M[quarter] # make\",\n \"= fn_dt.year imonth = fn_dt.month iday = fn_dt.day if imonth\",\n \"deviation output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region,\",\n \"for this quarter db_quarter = [] for i, image in\",\n \"= a_data == NODATA_VALUE # if this is the first\",\n \"this is the first image get projection and geotransform if\",\n \"# script to make seasonal means and stddev images of\",\n \"\\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU\",\n \"stddev images of 4-day sig0 # values. import os import\",\n \"args.quarter # region list (LAEA regions only) valid_region_list = [\",\n \"region = args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region\",\n \"images of 4-day sig0 # values. import os import sys\",\n \"import argparse from osgeo import gdal DATADIR = \\\"./\\\" NODATA_VALUE\",\n \"(qscat data start in 1999)\\\" ) args = parser.parse_args() verbose\",\n \"a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data =\",\n \"found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE\",\n \"= Q2M[quarter] # make a list of files for this\",\n \"srcband.ReadAsArray() a_mask = a_data == NODATA_VALUE # if this is\",\n \"0: warnmsg = \\\"No images found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg)\",\n \"os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path,\",\n \"# region list (LAEA regions only) valid_region_list = [ \\\"Grn\\\",\",\n \"None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean)\",\n \"{}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter, qlist)) if len(qlist)\",\n \"return pr if __name__ == \\\"__main__\\\": # set up arguments\",\n \"= fn_dt.month iday = fn_dt.day if imonth in monthlist: qlist.append(fn)\",\n \"i, image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds\",\n \"{}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) # repeat for\",\n \"and find mean and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean\",\n \"{}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set data dir indir = os.path.join(DATADIR,\",\n \"np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2)\",\n \"Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr = 10 ** (dbvalue\",\n \"import glob import numpy as np import sirpy2 as sp2\",\n \"(LAEA regions only) valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\",\",\n \"dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year, quarter) dst_dir =\",\n \"= a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape db_data\",\n \"if year == 1999: year2 = 99 else: year2 =\",\n \") args = parser.parse_args() verbose = args.verbose year = args.year\",\n \"db_quarter list db_quarter.append(db_masked) # close datasets a_ds = None #\",\n \"region list (LAEA regions only) valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\",\",\n \"image in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds =\",\n \"warnmsg = \\\"No images found for this quarter.\\\\n\\\" sys.stdout.write(warnmsg) sys.exit(0)\",\n \"\\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument( \\\"-q\\\", \\\"--quarter\\\",\",\n \"verbose: print(\\\"Output file for sig0 means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path,\",\n \"# set up arguments parser = argparse.ArgumentParser( \\\"script to make\",\n \"images for this quarter db_quarter = [] for i, image\",\n \"data start in 1999)\\\" ) args = parser.parse_args() verbose =\",\n \"qscat dB values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\",\",\n \"driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data)\",\n \"a_ds.GetGeoTransform() ny, nx = a_data.shape db_data = a_data db_masked =\",\n \"print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) #\",\n \"and std dbarray = np.ma.stack(db_quarter, axis=2) dbmean = np.ma.mean(dbarray, axis=2)\",\n \"gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\",\",\n \"image get projection and geotransform if i == 0: prj\",\n \"print(\\\"glob pattern: {}\\\".format(globpatt)) filelist = glob.glob(globpatt) qlist = [] for\",\n \"are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter:\",\n \"year == 1999: year2 = 99 else: year2 = \\\"{:02d}\\\".format(year\",\n \"list(range(4, 7)), } # this allows GDAL to throw Python\",\n \"2000) monthlist = Q2M[quarter] # make a list of files\",\n \"sys import glob import numpy as np import sirpy2 as\",\n \"make seasonal means and stddev images of 4-day sig0 #\",\n \"fn_dt.month iday = fn_dt.day if imonth in monthlist: qlist.append(fn) if\",\n \"qlist)) if len(qlist) == 0: warnmsg = \\\"No images found\",\n \"print(\\\"quarter: {}\\\".format(quarter)) # set data dir indir = os.path.join(DATADIR, \\\"geotiffs\\\",\",\n \"is the first image get projection and geotransform if i\",\n \"= dbstd.max() dbstd_median = np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\"\",\n \"regions are:\\\\n\\\") sys.stderr.write(\\\"{}\\\\n\\\".format(valid_region_list)) sys.exit(1) if verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year))\",\n \"Q2M[quarter] # make a list of files for this year\",\n \"= os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear\",\n \"= driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE)\",\n \"dir indir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir = indir\",\n \"Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g. SAm, NAm, Ama,\",\n \"\\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\", \\\"Eur\\\", \\\"SAs\\\",\",\n \"if verbose: print(\\\"Output file for sig0 means: {}\\\".format(dst_path)) dst_ds =\",\n \"dst_path = os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds\",\n \"Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) # repeat for standard deviation\",\n \"print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set data dir indir =\",\n \"= \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter)\",\n \"os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if verbose:\",\n \"(dbvalue / 10.0) return pr if __name__ == \\\"__main__\\\": #\",\n \"import numpy as np import sirpy2 as sp2 import argparse\",\n \"not found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data ==\",\n \"\\\"{:02d}\\\".format(year - 2000) monthlist = Q2M[quarter] # make a list\",\n \"np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a geotiff output_format\",\n \"in enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath)\",\n \"\\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region = args.region try: region_index\",\n \"sirpy2 as sp2 import argparse from osgeo import gdal DATADIR\",\n \"this allows GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue):\",\n \"datasets a_ds = None # stack list into array and\",\n \"Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\" Max: {}\\\".format(dbstd_max)) print(\\\" Median:\",\n \"Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median:\",\n \"= os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path = os.path.join(dst_dir, dst_filename) if\",\n \"indir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) outdir = indir if\",\n \"file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data\",\n \"gdal.Open(a_imgpath) except Exception: print(\\\"Unable to open {}\\\".format(a_imgpath)) sys.exit(1) try: srcband\",\n \"99 else: year2 = \\\"{:02d}\\\".format(year - 2000) monthlist = Q2M[quarter]\",\n \"\\\"JAS\\\": list(range(7, 10)), \\\"OND\\\": list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\":\",\n \"if verbose: print(\\\"{}: {}-{}-{}\\\".format(fn, iyear, imonth, iday)) print(\\\"{}-{}: {}\\\".format(year, quarter,\",\n \"= args.region try: region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not\",\n \"np.ma.median(dbmean) print(\\\"Quarterly ({}) Mean Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbmean_min)) print(\\\" Max:\",\n \"list(range(10, 13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)), } #\",\n \"np.ma.median(dbstd) print(\\\"Quarterly ({}) Stdev Stats\\\".format(quarter)) print(\\\" Min: {}\\\".format(dbstd_min)) print(\\\" Max:\",\n \"{}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) # repeat for standard deviation output_format\",\n \"enumerate(qlist): a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except\",\n \"print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set data dir\",\n \"1999)\\\" ) args = parser.parse_args() verbose = args.verbose year =\",\n \"of 4-day sig0 # values. import os import sys import\",\n \"= np.ma.std(dbarray, axis=2) print(dbmean.shape) # finally, save as a geotiff\",\n \"dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd,\",\n \"ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt:\",\n \"dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min() dbmean_max = dbmean.max()\",\n \"quarter) dst_dir = os.path.join(DATADIR, \\\"geotiffs\\\", region, str(year)) dst_path = os.path.join(dst_dir,\",\n \"driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-mean-db-{}-{}.tif\\\" dst_filename = dst_filename.format(region, year,\",\n \"allows GDAL to throw Python Exceptions gdal.UseExceptions() def db2pr(dbvalue): pr\",\n \"a_imgpath = os.path.join(indir, image) try: a_ds = gdal.Open(a_imgpath) except Exception:\",\n \"imonth = fn_dt.month iday = fn_dt.day if imonth in monthlist:\",\n \"in filelist: fn = os.path.basename(filepath) if verbose: print(fn) fn_dt =\",\n \"print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min = dbmean.min()\",\n \"\\\"SAs\\\", \\\"ChJ\\\", \\\"Ind\\\", \\\"Aus\\\", \\\"Ber\\\", ] region = args.region try:\",\n \"== \\\"__main__\\\": # set up arguments parser = argparse.ArgumentParser( \\\"script\",\n \"aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\", help=\\\"BYU region string (e.g. SAm, NAm,\",\n \"13)), \\\"JFM\\\": list(range(1, 4)), \\\"AMJ\\\": list(range(4, 7)), } # this\",\n \"print(\\\" Max: {}\\\".format(dbmean_max)) print(\\\" Median: {}\\\".format(dbmean_median)) # repeat for standard\",\n \"= os.path.join(dst_dir, dst_filename) if verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds =\",\n \"\\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\", ) parser.add_argument(\\\"region\\\",\",\n \"# stack list into array and find mean and std\",\n \"sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear = fn_dt.year imonth = fn_dt.month iday =\",\n \"= None dbmean_min = dbmean.min() dbmean_max = dbmean.max() dbmean_median =\",\n \"np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds =\",\n \"\\\"script to make quarterly \\\" + \\\"means and stdevs of\",\n \"stdevs of qscat dB values\\\" ) parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase\",\n \"= driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE)\",\n \"= fn_dt.day if imonth in monthlist: qlist.append(fn) if verbose: print(\\\"{}:\",\n \"nx = a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask)\",\n \"os.path.basename(filepath) if verbose: print(fn) fn_dt = sp2.fn2dt(fn, date_flag=\\\"center\\\") iyear =\",\n \"None dbstd_min = dbstd.min() dbstd_max = dbstd.max() dbstd_median = np.ma.median(dbstd)\",\n \"choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter for aggregation. Default=JAS\\\",\",\n \"filepatt = \\\"quev-a-{}{}-*.tif\\\".format(region, year2) globpatt = os.path.join(indir, filepatt) if verbose:\",\n \"print(\\\"Output file for sig0 means: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx,\",\n \"= parser.parse_args() verbose = args.verbose year = args.year quarter =\",\n \"region_index = valid_region_list.index(region) except Exception: sys.stderr.write(\\\"Region not valid.\\\\n\\\") sys.stderr.write(\\\"Valid regions\",\n \"print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbstd_min = dbstd.min()\",\n \"str(year)) outdir = indir if year == 1999: year2 =\",\n \"\\\"-q\\\", \\\"--quarter\\\", nargs=\\\"?\\\", choices=(\\\"JAS\\\", \\\"OND\\\", \\\"JFM\\\", \\\"AMJ\\\"), default=\\\"JAS\\\", const=\\\"JAS\\\", help=\\\"Quarter\",\n \"1999: year2 = 99 else: year2 = \\\"{:02d}\\\".format(year - 2000)\",\n \"for filepath in filelist: fn = os.path.basename(filepath) if verbose: print(fn)\",\n \"= args.quarter # region list (LAEA regions only) valid_region_list =\",\n \"verbose: print(\\\"region: {}\\\".format(region)) print(\\\"year: {}\\\".format(year)) print(\\\"quarter: {}\\\".format(quarter)) # set data\",\n \"\\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename = \\\"{}-quev-std-db-{}-{}.tif\\\".format(region, year, quarter) dst_dir\",\n \"script to make seasonal means and stddev images of 4-day\",\n \"] region = args.region try: region_index = valid_region_list.index(region) except Exception:\",\n \"({}) not found\\\".format(1)) sys.exit(1) a_data = srcband.ReadAsArray() a_mask = a_data\",\n \"= dbmean.min() dbmean_max = dbmean.max() dbmean_median = np.ma.median(dbmean) print(\\\"Quarterly ({})\",\n \"prj = a_ds.GetProjection() gt = a_ds.GetGeoTransform() ny, nx = a_data.shape\",\n \"= [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\", \\\"SAf\\\", \\\"Sib\\\",\",\n \"= a_data db_masked = np.ma.MaskedArray(db_data, a_mask) # add image to\",\n \"standard deviation output_format = \\\"GTiff\\\" driver = gdal.GetDriverByName(output_format) dst_filename =\",\n \"only) valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\", \\\"NAf\\\",\",\n \"monthlist = Q2M[quarter] # make a list of files for\",\n \"= a_data.shape db_data = a_data db_masked = np.ma.MaskedArray(db_data, a_mask) #\",\n \"regions only) valid_region_list = [ \\\"Grn\\\", \\\"Ala\\\", \\\"CAm\\\", \\\"NAm\\\", \\\"SAm\\\",\",\n \"ny, 1, gdal.GDT_Float32) dst_data = np.ma.filled(dbmean, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt:\",\n \"e.g. 1999 (qscat data start in 1999)\\\" ) args =\",\n \"= [] for i, image in enumerate(qlist): a_imgpath = os.path.join(indir,\",\n \"= np.ma.filled(dbstd, fill_value=NODATA_VALUE) dst_ds.GetRasterBand(1).WriteArray(dst_data) dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds\",\n \"parser.add_argument( \\\"-v\\\", \\\"--verbose\\\", help=\\\"increase output verbosity\\\", action=\\\"store_true\\\", default=False, ) parser.add_argument(\",\n \"a_ds.GetRasterBand(1) except Exception: print(\\\"Band ({}) not found\\\".format(1)) sys.exit(1) a_data =\",\n \"axis=2) dbmean = np.ma.mean(dbarray, axis=2) dbstd = np.ma.std(dbarray, axis=2) print(dbmean.shape)\",\n \"axis=2) print(dbmean.shape) # finally, save as a geotiff output_format =\",\n \"dst_ds.GetRasterBand(1).SetNoDataValue(NODATA_VALUE) print(\\\"gt: {}\\\".format(gt)) dst_ds.SetGeoTransform(gt) dst_ds.SetProjection(prj) dst_ds = None dbmean_min =\",\n \"gdal DATADIR = \\\"./\\\" NODATA_VALUE = -9999.0 Q2M = {\",\n \"if verbose: print(\\\"Output file: {}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny,\",\n \"{}\\\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data =\",\n \"** (dbvalue / 10.0) return pr if __name__ == \\\"__main__\\\":\",\n \"\\\" + \\\"means and stdevs of qscat dB values\\\" )\"\n]","isConversational":false}}},{"rowIdx":173,"cells":{"ngram":{"kind":"list like","value":["', end='') termo += razão cont += 1 print('Pausa') mais","cont += 1 print('Pausa') mais = int(input('Quantos termos você quer","priemiro termo da PA: ')) razão = int(input('Digite a razão","a razão da PA: ')) termo = primeiro cont =","while mais != 0: total += mais while cont <=","= int(input('Digite a razão da PA: ')) termo = primeiro","usar a mais? ')) print(f'a progressão foi finalizada com {total}","= 0 mais = 10 while mais != 0: total","da PA: ')) termo = primeiro cont = 1 total","primeiro = int(input('Digite o priemiro termo da PA: ')) razão","o priemiro termo da PA: ')) razão = int(input('Digite a","+= 1 print('Pausa') mais = int(input('Quantos termos você quer usar","total = 0 mais = 10 while mais != 0:","total += mais while cont <= total: print(f'{termo} ', end='')","termo += razão cont += 1 print('Pausa') mais = int(input('Quantos","')) termo = primeiro cont = 1 total = 0","print(f'{termo} ', end='') termo += razão cont += 1 print('Pausa')","end='') termo += razão cont += 1 print('Pausa') mais =","int(input('Digite o priemiro termo da PA: ')) razão = int(input('Digite","quer usar a mais? ')) print(f'a progressão foi finalizada com","mais while cont <= total: print(f'{termo} ', end='') termo +=","razão cont += 1 print('Pausa') mais = int(input('Quantos termos você","mais != 0: total += mais while cont <= total:","!= 0: total += mais while cont <= total: print(f'{termo}","while cont <= total: print(f'{termo} ', end='') termo += razão","= int(input('Quantos termos você quer usar a mais? ')) print(f'a","mais = int(input('Quantos termos você quer usar a mais? '))","razão da PA: ')) termo = primeiro cont = 1","1 total = 0 mais = 10 while mais !=","int(input('Digite a razão da PA: ')) termo = primeiro cont","= primeiro cont = 1 total = 0 mais =","+= mais while cont <= total: print(f'{termo} ', end='') termo","termo da PA: ')) razão = int(input('Digite a razão da","0: total += mais while cont <= total: print(f'{termo} ',","da PA: ')) razão = int(input('Digite a razão da PA:","0 mais = 10 while mais != 0: total +=","+= razão cont += 1 print('Pausa') mais = int(input('Quantos termos","termo = primeiro cont = 1 total = 0 mais","primeiro cont = 1 total = 0 mais = 10","= int(input('Digite o priemiro termo da PA: ')) razão =","PA: ')) razão = int(input('Digite a razão da PA: '))","= 10 while mais != 0: total += mais while","10 while mais != 0: total += mais while cont","cont <= total: print(f'{termo} ', end='') termo += razão cont","int(input('Quantos termos você quer usar a mais? ')) print(f'a progressão","total: print(f'{termo} ', end='') termo += razão cont += 1","<= total: print(f'{termo} ', end='') termo += razão cont +=","PA: ')) termo = primeiro cont = 1 total =","mais? ')) print(f'a progressão foi finalizada com {total} termos mostrados')","1 print('Pausa') mais = int(input('Quantos termos você quer usar a","termos você quer usar a mais? ')) print(f'a progressão foi","= 1 total = 0 mais = 10 while mais","você quer usar a mais? ')) print(f'a progressão foi finalizada","razão = int(input('Digite a razão da PA: ')) termo =","mais = 10 while mais != 0: total += mais","print('Pausa') mais = int(input('Quantos termos você quer usar a mais?","cont = 1 total = 0 mais = 10 while","a mais? ')) print(f'a progressão foi finalizada com {total} termos","')) razão = int(input('Digite a razão da PA: ')) termo"],"string":"[\n \"', end='') termo += razão cont += 1 print('Pausa') mais\",\n \"cont += 1 print('Pausa') mais = int(input('Quantos termos você quer\",\n \"priemiro termo da PA: ')) razão = int(input('Digite a razão\",\n \"a razão da PA: ')) termo = primeiro cont =\",\n \"while mais != 0: total += mais while cont <=\",\n \"= int(input('Digite a razão da PA: ')) termo = primeiro\",\n \"usar a mais? ')) print(f'a progressão foi finalizada com {total}\",\n \"= 0 mais = 10 while mais != 0: total\",\n \"da PA: ')) termo = primeiro cont = 1 total\",\n \"primeiro = int(input('Digite o priemiro termo da PA: ')) razão\",\n \"o priemiro termo da PA: ')) razão = int(input('Digite a\",\n \"+= 1 print('Pausa') mais = int(input('Quantos termos você quer usar\",\n \"total = 0 mais = 10 while mais != 0:\",\n \"total += mais while cont <= total: print(f'{termo} ', end='')\",\n \"termo += razão cont += 1 print('Pausa') mais = int(input('Quantos\",\n \"')) termo = primeiro cont = 1 total = 0\",\n \"print(f'{termo} ', end='') termo += razão cont += 1 print('Pausa')\",\n \"end='') termo += razão cont += 1 print('Pausa') mais =\",\n \"int(input('Digite o priemiro termo da PA: ')) razão = int(input('Digite\",\n \"quer usar a mais? ')) print(f'a progressão foi finalizada com\",\n \"mais while cont <= total: print(f'{termo} ', end='') termo +=\",\n \"razão cont += 1 print('Pausa') mais = int(input('Quantos termos você\",\n \"mais != 0: total += mais while cont <= total:\",\n \"!= 0: total += mais while cont <= total: print(f'{termo}\",\n \"while cont <= total: print(f'{termo} ', end='') termo += razão\",\n \"= int(input('Quantos termos você quer usar a mais? ')) print(f'a\",\n \"mais = int(input('Quantos termos você quer usar a mais? '))\",\n \"razão da PA: ')) termo = primeiro cont = 1\",\n \"1 total = 0 mais = 10 while mais !=\",\n \"int(input('Digite a razão da PA: ')) termo = primeiro cont\",\n \"= primeiro cont = 1 total = 0 mais =\",\n \"+= mais while cont <= total: print(f'{termo} ', end='') termo\",\n \"termo da PA: ')) razão = int(input('Digite a razão da\",\n \"0: total += mais while cont <= total: print(f'{termo} ',\",\n \"da PA: ')) razão = int(input('Digite a razão da PA:\",\n \"0 mais = 10 while mais != 0: total +=\",\n \"+= razão cont += 1 print('Pausa') mais = int(input('Quantos termos\",\n \"termo = primeiro cont = 1 total = 0 mais\",\n \"primeiro cont = 1 total = 0 mais = 10\",\n \"= int(input('Digite o priemiro termo da PA: ')) razão =\",\n \"PA: ')) razão = int(input('Digite a razão da PA: '))\",\n \"= 10 while mais != 0: total += mais while\",\n \"10 while mais != 0: total += mais while cont\",\n \"cont <= total: print(f'{termo} ', end='') termo += razão cont\",\n \"int(input('Quantos termos você quer usar a mais? ')) print(f'a progressão\",\n \"total: print(f'{termo} ', end='') termo += razão cont += 1\",\n \"<= total: print(f'{termo} ', end='') termo += razão cont +=\",\n \"PA: ')) termo = primeiro cont = 1 total =\",\n \"mais? ')) print(f'a progressão foi finalizada com {total} termos mostrados')\",\n \"1 print('Pausa') mais = int(input('Quantos termos você quer usar a\",\n \"termos você quer usar a mais? ')) print(f'a progressão foi\",\n \"= 1 total = 0 mais = 10 while mais\",\n \"você quer usar a mais? ')) print(f'a progressão foi finalizada\",\n \"razão = int(input('Digite a razão da PA: ')) termo =\",\n \"mais = 10 while mais != 0: total += mais\",\n \"print('Pausa') mais = int(input('Quantos termos você quer usar a mais?\",\n \"cont = 1 total = 0 mais = 10 while\",\n \"a mais? ')) print(f'a progressão foi finalizada com {total} termos\",\n \"')) razão = int(input('Digite a razão da PA: ')) termo\"\n]","isConversational":false}}},{"rowIdx":174,"cells":{"ngram":{"kind":"list like","value":["- handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values","\\'{results[value.name]}\\'' ) else: error = f'ObjectHandler - invalid value type","results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler",") else: error = f'ObjectHandler - invalid value type ({type(value)}).","element is not None: self.logger.info( f'ObjectHandler - element found: \\'{QName(element).localname}\\'","- values found for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0] if","results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query,","results[value.name] is None: self.logger.info( f'ObjectHandler - no value found for","else: self.logger.info( f'ObjectHandler - value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' )","QName from typing import Union, List, Any from tqdm import","DataObject, node: Element = None) -> Union[list, dict]: is_not_root =","Element = None) -> Union[list, dict]: is_not_root = True if","is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle","{len(values)}' ) return values[0] if len(values) == 1 else values","= True if node is None: is_not_root = False node","DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0] if len(values) == 1 else","self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler -","parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self,","handle DataObject(\\'{data_object.name}\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for","found for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler - values found for","self.logger.info( f'ObjectHandler - no value found for DataValue(\\'{value.name}\\')') else: self.logger.info(","ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler =","- invalid value type ({type(value)}). Value must be of type","- value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else: error =","elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is","Element, QName from typing import Union, List, Any from tqdm","from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser):","= [] elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm(","== 1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node:","typing import Union, List, Any from tqdm import tqdm import","__init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def","import Union, List, Any from tqdm import tqdm import logging","node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name]","-> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path,","not values: self.logger.warning( f'ObjectHandler - no values found for DataObject(\\'{data_object.name}\\')')","invalid value type ({type(value)}). Value must be of type DataObject","None) -> Union[list, dict]: is_not_root = True if node is","f'ObjectHandler - invalid value type ({type(value)}). Value must be of","is not None: self.logger.info( f'ObjectHandler - element found: \\'{QName(element).localname}\\' at","= logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node:","from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object","import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self,","{n_fmt:>4}/{total_fmt}\"): if element is not None: self.logger.info( f'ObjectHandler - element","DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is not None:","None: self.logger.info( f'ObjectHandler - element found: \\'{QName(element).localname}\\' at line {element.sourceline","else: self.logger.info( f'ObjectHandler - values found for DataObject(\\'{data_object.name}\\'): {len(values)}' )","1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element)","at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not","import Element, QName from typing import Union, List, Any from","self.logger.info( f'ObjectHandler - values found for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return","if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue):","found: \\'{QName(element).localname}\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element))","import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger =","element)) if not values: self.logger.warning( f'ObjectHandler - no values found","isn't allowed on DataValue return None if element is None:","# DataQuery.Reference isn't allowed on DataValue return None if element","self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')')","- 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler","def handle(self, data_object: DataObject, node: Element = None) -> Union[list,","if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else:","element in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"):","for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\'{value.name}\\') =","Value must be of type DataObject or DataValue' self.logger.error(error) raise","import tqdm import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler","lxml.etree import Element, QName from typing import Union, List, Any","= None) -> Union[list, dict]: is_not_root = True if node","{element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning(","- no values found for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler -","AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object:","import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject","-> dict: results = {} for value in values: if","not None: self.logger.info( f'ObjectHandler - element found: \\'{QName(element).localname}\\' at line",") values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler - no","f'ObjectHandler - no values found for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler","type ({type(value)}). Value must be of type DataObject or DataValue'","len(values) == 1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]],","None: self.logger.info( f'ObjectHandler - no value found for DataValue(\\'{value.name}\\')') else:","f'ObjectHandler - no value found for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler","|{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is not None: self.logger.info( f'ObjectHandler -","= self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')',","= PathHandler(parser) def handle(self, data_object: DataObject, node: Element = None)","self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject,","be of type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return","DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger()","node) else: # DataQuery.Reference isn't allowed on DataValue return None","bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is not None: self.logger.info( f'ObjectHandler","query: DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if","element found: \\'{QName(element).localname}\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values,","arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def","from lxml.etree import Element, QName from typing import Union, List,","DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info(","value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else: error = f'ObjectHandler","arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import","node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't","self.logger.warning( f'ObjectHandler - no values found for DataObject(\\'{data_object.name}\\')') else: self.logger.info(","self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference","Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element =","def __handle_value(self, query: DataQuery, node: Element) -> Any: if isinstance(query.path,","values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler - no values","- [root] handle DataObject(\\'{data_object.name}\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path,","values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for element in","element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on","Any from tqdm import tqdm import logging from arxml_data_extractor.handler import","no value found for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler - value","node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element","from tqdm import tqdm import logging from arxml_data_extractor.handler import value_handler","values found for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0] if len(values)","else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values = [] elements","value type ({type(value)}). Value must be of type DataObject or","desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is not","self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query: DataQuery, node:","DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference:","else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed","arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import","Union, List, Any from tqdm import tqdm import logging from","DataObject(\\'{data_object.name}\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for element","from typing import Union, List, Any from tqdm import tqdm","Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node)","__handle_value(self, query: DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath):","DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element =","= \\'{results[value.name]}\\'' ) else: error = f'ObjectHandler - invalid value","values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict:","arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger","= self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on DataValue","query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node)","in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if","DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class","- element found: \\'{QName(element).localname}\\' at line {element.sourceline - 1}' )","({type(value)}). Value must be of type DataObject or DataValue' self.logger.error(error)","results def __handle_value(self, query: DataQuery, node: Element) -> Any: if","raise TypeError(error) return results def __handle_value(self, query: DataQuery, node: Element)","data_object: DataObject, node: Element = None) -> Union[list, dict]: is_not_root","-> Union[list, dict]: is_not_root = True if node is None:","is None: self.logger.info( f'ObjectHandler - no value found for DataValue(\\'{value.name}\\')')","<reponame>Brokdar/ArxmlDataExtractor from lxml.etree import Element, QName from typing import Union,","if element is not None: self.logger.info( f'ObjectHandler - element found:","\\'{QName(element).localname}\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if","value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from","True if node is None: is_not_root = False node =","import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery","DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self,","element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else:","if len(values) == 1 else values def __handle_values(self, values: List[Union[DataValue,","values: List[Union[DataValue, DataObject]], node: Element) -> dict: results = {}","1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler -","DataQuery.Reference isn't allowed on DataValue return None if element is","results = {} for value in values: if isinstance(value, DataObject):","if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root]","self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler - no","value found for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler - value found:","isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None:","DataObject]], node: Element) -> dict: results = {} for value","error = f'ObjectHandler - invalid value type ({type(value)}). Value must","self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root,","arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import","values found for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler - values found","on DataValue return None if element is None: return None","no values found for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler - values","self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on DataValue return","PathHandler(parser) def handle(self, data_object: DataObject, node: Element = None) ->","for DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler - values found for DataObject(\\'{data_object.name}\\'):","of type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results","node) for element in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}%","elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is","{} for value in values: if isinstance(value, DataObject): results[value.name] =","handle(self, data_object: DataObject, node: Element = None) -> Union[list, dict]:","values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value,","found for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0] if len(values) ==","DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser:","[root] handle DataObject(\\'{data_object.name}\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node)","List[Union[DataValue, DataObject]], node: Element) -> dict: results = {} for","DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\''","allowed on DataValue return None if element is None: return","None: is_not_root = False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler","False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')')","else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) ->","found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else: error = f'ObjectHandler -","AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from","= self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler","for element in tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}|","if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath,","[] elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements,","= self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler -","if results[value.name] is None: self.logger.info( f'ObjectHandler - no value found","tqdm( elements, desc=f'Handle DataObject(\\'{data_object.name}\\')', disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element","return values[0] if len(values) == 1 else values def __handle_values(self,","value in values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node)","or DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query:","from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value","tqdm import tqdm import logging from arxml_data_extractor.handler import value_handler from","class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler","from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser","line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values:","values: self.logger.warning( f'ObjectHandler - no values found for DataObject(\\'{data_object.name}\\')') else:","def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser)","node: Element = None) -> Union[list, dict]: is_not_root = True","__handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict: results =","= {} for value in values: if isinstance(value, DataObject): results[value.name]","self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if","DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query: DataQuery,","None if element is None: return None return value_handler.handle(query, element)","import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue","PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from","is_not_root = False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler -","f'ObjectHandler - value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else: error","must be of type DataObject or DataValue' self.logger.error(error) raise TypeError(error)","List, Any from tqdm import tqdm import logging from arxml_data_extractor.handler","DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name] =","Union[list, dict]: is_not_root = True if node is None: is_not_root","isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name]","elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle","for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0] if len(values) == 1","DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values = []","found for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\'{value.name}\\')","return results def __handle_value(self, query: DataQuery, node: Element) -> Any:","DataObject(\\'{data_object.name}\\')') else: self.logger.info( f'ObjectHandler - values found for DataObject(\\'{data_object.name}\\'): {len(values)}'","self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node: Element =","= f'ObjectHandler - invalid value type ({type(value)}). Value must be","else: error = f'ObjectHandler - invalid value type ({type(value)}). Value","Element) -> dict: results = {} for value in values:","self.logger.info( f'ObjectHandler - value found: DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else:","disable=is_not_root, bar_format=\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\"): if element is not None: self.logger.info(","f'ObjectHandler - element found: \\'{QName(element).localname}\\' at line {element.sourceline - 1}'",") return values[0] if len(values) == 1 else values def","node: Element) -> dict: results = {} for value in","is_not_root = True if node is None: is_not_root = False","self.logger.info( f'ObjectHandler - element found: \\'{QName(element).localname}\\' at line {element.sourceline -","self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values = [] elements =","for value in values: if isinstance(value, DataObject): results[value.name] = self.handle(value,","DataValue return None if element is None: return None return","handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values =","- no value found for DataValue(\\'{value.name}\\')') else: self.logger.info( f'ObjectHandler -","= False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle","f'ObjectHandler - values found for DataObject(\\'{data_object.name}\\'): {len(values)}' ) return values[0]","= self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: #","dict: results = {} for value in values: if isinstance(value,","tqdm import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import","logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from","from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query","= self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node)","values[0] if len(values) == 1 else values def __handle_values(self, values:","dict]: is_not_root = True if node is None: is_not_root =","in values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif","TypeError(error) return results def __handle_value(self, query: DataQuery, node: Element) ->","node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\'{data_object.name}\\')') else:","is None: is_not_root = False node = self.path_handler.parser.root if is_not_root:","else: # DataQuery.Reference isn't allowed on DataValue return None if","import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler","def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict: results","if not values: self.logger.warning( f'ObjectHandler - no values found for","node) if results[value.name] is None: self.logger.info( f'ObjectHandler - no value","from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler():","arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import","import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser","logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node: Element","if node is None: is_not_root = False node = self.path_handler.parser.root","node is None: is_not_root = False node = self.path_handler.parser.root if","DataValue(\\'{value.name}\\') = \\'{results[value.name]}\\'' ) else: error = f'ObjectHandler - invalid","isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element","return None if element is None: return None return value_handler.handle(query,","type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results def"],"string":"[\n \"- handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')') values\",\n \"\\\\'{results[value.name]}\\\\'' ) else: error = f'ObjectHandler - invalid value type\",\n \"results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler\",\n \") else: error = f'ObjectHandler - invalid value type ({type(value)}).\",\n \"element is not None: self.logger.info( f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\'\",\n \"- values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0] if\",\n \"results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query,\",\n \"results[value.name] is None: self.logger.info( f'ObjectHandler - no value found for\",\n \"else: self.logger.info( f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' )\",\n \"QName from typing import Union, List, Any from tqdm import\",\n \"DataObject, node: Element = None) -> Union[list, dict]: is_not_root =\",\n \"Element = None) -> Union[list, dict]: is_not_root = True if\",\n \"is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root] handle\",\n \"{len(values)}' ) return values[0] if len(values) == 1 else values\",\n \"= True if node is None: is_not_root = False node\",\n \"DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0] if len(values) == 1 else\",\n \"self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler -\",\n \"parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self,\",\n \"handle DataObject(\\\\'{data_object.name}\\\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for\",\n \"found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler - values found for\",\n \"self.logger.info( f'ObjectHandler - no value found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info(\",\n \"ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler =\",\n \"- invalid value type ({type(value)}). Value must be of type\",\n \"- value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else: error =\",\n \"elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is\",\n \"Element, QName from typing import Union, List, Any from tqdm\",\n \"from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser):\",\n \"= [] elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm(\",\n \"== 1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node:\",\n \"typing import Union, List, Any from tqdm import tqdm import\",\n \"__init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def\",\n \"import Union, List, Any from tqdm import tqdm import logging\",\n \"node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name]\",\n \"-> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path,\",\n \"not values: self.logger.warning( f'ObjectHandler - no values found for DataObject(\\\\'{data_object.name}\\\\')')\",\n \"invalid value type ({type(value)}). Value must be of type DataObject\",\n \"None) -> Union[list, dict]: is_not_root = True if node is\",\n \"f'ObjectHandler - invalid value type ({type(value)}). Value must be of\",\n \"is not None: self.logger.info( f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\' at\",\n \"= logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node:\",\n \"from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object\",\n \"import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self,\",\n \"{n_fmt:>4}/{total_fmt}\\\"): if element is not None: self.logger.info( f'ObjectHandler - element\",\n \"DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is not None:\",\n \"None: self.logger.info( f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline\",\n \"else: self.logger.info( f'ObjectHandler - values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' )\",\n \"1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element)\",\n \"at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not\",\n \"import Element, QName from typing import Union, List, Any from\",\n \"self.logger.info( f'ObjectHandler - values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return\",\n \"if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue):\",\n \"found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element))\",\n \"import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger =\",\n \"element)) if not values: self.logger.warning( f'ObjectHandler - no values found\",\n \"isn't allowed on DataValue return None if element is None:\",\n \"# DataQuery.Reference isn't allowed on DataValue return None if element\",\n \"self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')')\",\n \"- 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler\",\n \"def handle(self, data_object: DataObject, node: Element = None) -> Union[list,\",\n \"if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else:\",\n \"element in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"):\",\n \"for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\') =\",\n \"Value must be of type DataObject or DataValue' self.logger.error(error) raise\",\n \"import tqdm import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler\",\n \"lxml.etree import Element, QName from typing import Union, List, Any\",\n \"= None) -> Union[list, dict]: is_not_root = True if node\",\n \"{element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning(\",\n \"- no values found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler -\",\n \"AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object:\",\n \"import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject\",\n \"-> dict: results = {} for value in values: if\",\n \"not None: self.logger.info( f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\' at line\",\n \") values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler - no\",\n \"f'ObjectHandler - no values found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler\",\n \"type ({type(value)}). Value must be of type DataObject or DataValue'\",\n \"len(values) == 1 else values def __handle_values(self, values: List[Union[DataValue, DataObject]],\",\n \"None: self.logger.info( f'ObjectHandler - no value found for DataValue(\\\\'{value.name}\\\\')') else:\",\n \"f'ObjectHandler - no value found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler\",\n \"|{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is not None: self.logger.info( f'ObjectHandler -\",\n \"= self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')',\",\n \"= PathHandler(parser) def handle(self, data_object: DataObject, node: Element = None)\",\n \"self.logger = logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject,\",\n \"be of type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return\",\n \"DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger()\",\n \"node) else: # DataQuery.Reference isn't allowed on DataValue return None\",\n \"bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is not None: self.logger.info( f'ObjectHandler\",\n \"query: DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if\",\n \"element found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values,\",\n \"arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def\",\n \"from lxml.etree import Element, QName from typing import Union, List,\",\n \"DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info(\",\n \"value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else: error = f'ObjectHandler\",\n \"arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import\",\n \"node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't\",\n \"self.logger.warning( f'ObjectHandler - no values found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(\",\n \"self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference\",\n \"Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element =\",\n \"def __handle_value(self, query: DataQuery, node: Element) -> Any: if isinstance(query.path,\",\n \"values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler - no values\",\n \"- [root] handle DataObject(\\\\'{data_object.name}\\\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path,\",\n \"values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for element in\",\n \"element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on\",\n \"Any from tqdm import tqdm import logging from arxml_data_extractor.handler import\",\n \"no value found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler - value\",\n \"node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element\",\n \"from tqdm import tqdm import logging from arxml_data_extractor.handler import value_handler\",\n \"values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0] if len(values)\",\n \"else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')') values = [] elements\",\n \"value type ({type(value)}). Value must be of type DataObject or\",\n \"desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is not\",\n \"self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query: DataQuery, node:\",\n \"DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference:\",\n \"else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed\",\n \"arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import\",\n \"Union, List, Any from tqdm import tqdm import logging from\",\n \"DataObject(\\\\'{data_object.name}\\\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node) for element\",\n \"from typing import Union, List, Any from tqdm import tqdm\",\n \"Any: if isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node)\",\n \"__handle_value(self, query: DataQuery, node: Element) -> Any: if isinstance(query.path, DataQuery.XPath):\",\n \"DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element =\",\n \"= \\\\'{results[value.name]}\\\\'' ) else: error = f'ObjectHandler - invalid value\",\n \"values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict:\",\n \"arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger\",\n \"= self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on DataValue\",\n \"query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node)\",\n \"in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if\",\n \"DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class\",\n \"- element found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline - 1}' )\",\n \"({type(value)}). Value must be of type DataObject or DataValue' self.logger.error(error)\",\n \"results def __handle_value(self, query: DataQuery, node: Element) -> Any: if\",\n \"raise TypeError(error) return results def __handle_value(self, query: DataQuery, node: Element)\",\n \"data_object: DataObject, node: Element = None) -> Union[list, dict]: is_not_root\",\n \"-> Union[list, dict]: is_not_root = True if node is None:\",\n \"is None: self.logger.info( f'ObjectHandler - no value found for DataValue(\\\\'{value.name}\\\\')')\",\n \"<reponame>Brokdar/ArxmlDataExtractor from lxml.etree import Element, QName from typing import Union,\",\n \"if element is not None: self.logger.info( f'ObjectHandler - element found:\",\n \"\\\\'{QName(element).localname}\\\\' at line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if\",\n \"value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from\",\n \"True if node is None: is_not_root = False node =\",\n \"import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery\",\n \"DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self,\",\n \"element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else:\",\n \"if len(values) == 1 else values def __handle_values(self, values: List[Union[DataValue,\",\n \"values: List[Union[DataValue, DataObject]], node: Element) -> dict: results = {}\",\n \"1}' ) values.append(self.__handle_values(data_object.values, element)) if not values: self.logger.warning( f'ObjectHandler -\",\n \"DataQuery.Reference isn't allowed on DataValue return None if element is\",\n \"results = {} for value in values: if isinstance(value, DataObject):\",\n \"if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root]\",\n \"self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler - no\",\n \"value found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler - value found:\",\n \"isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if results[value.name] is None:\",\n \"DataObject]], node: Element) -> dict: results = {} for value\",\n \"error = f'ObjectHandler - invalid value type ({type(value)}). Value must\",\n \"self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root,\",\n \"arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import\",\n \"values found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler - values found\",\n \"on DataValue return None if element is None: return None\",\n \"no values found for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler - values\",\n \"self.path_handler.element_by_xpath(query.path.xpath, node) else: # DataQuery.Reference isn't allowed on DataValue return\",\n \"PathHandler(parser) def handle(self, data_object: DataObject, node: Element = None) ->\",\n \"for DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler - values found for DataObject(\\\\'{data_object.name}\\\\'):\",\n \"of type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results\",\n \"node) for element in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}%\",\n \"elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is\",\n \"{} for value in values: if isinstance(value, DataObject): results[value.name] =\",\n \"handle(self, data_object: DataObject, node: Element = None) -> Union[list, dict]:\",\n \"values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value,\",\n \"found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0] if len(values) ==\",\n \"DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler(): def __init__(self, parser:\",\n \"[root] handle DataObject(\\\\'{data_object.name}\\\\')') values = [] elements = self.path_handler.elements_by_path(data_object.path, node)\",\n \"List[Union[DataValue, DataObject]], node: Element) -> dict: results = {} for\",\n \"DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\''\",\n \"allowed on DataValue return None if element is None: return\",\n \"None: is_not_root = False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler\",\n \"False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')')\",\n \"else values def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) ->\",\n \"found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else: error = f'ObjectHandler -\",\n \"AsrParser from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from\",\n \"= self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler\",\n \"for element in tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}|\",\n \"if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath,\",\n \"[] elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements,\",\n \"= self.__handle_value(value.query, node) if results[value.name] is None: self.logger.info( f'ObjectHandler -\",\n \"if results[value.name] is None: self.logger.info( f'ObjectHandler - no value found\",\n \"tqdm( elements, desc=f'Handle DataObject(\\\\'{data_object.name}\\\\')', disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element\",\n \"return values[0] if len(values) == 1 else values def __handle_values(self,\",\n \"value in values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node)\",\n \"or DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query:\",\n \"from arxml_data_extractor.query.data_query import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value\",\n \"tqdm import tqdm import logging from arxml_data_extractor.handler import value_handler from\",\n \"class ObjectHandler(): def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler\",\n \"from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser\",\n \"line {element.sourceline - 1}' ) values.append(self.__handle_values(data_object.values, element)) if not values:\",\n \"values: self.logger.warning( f'ObjectHandler - no values found for DataObject(\\\\'{data_object.name}\\\\')') else:\",\n \"def __init__(self, parser: AsrParser): self.logger = logging.getLogger() self.path_handler = PathHandler(parser)\",\n \"node: Element = None) -> Union[list, dict]: is_not_root = True\",\n \"__handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict: results =\",\n \"= {} for value in values: if isinstance(value, DataObject): results[value.name]\",\n \"self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node) if\",\n \"DataValue' self.logger.error(error) raise TypeError(error) return results def __handle_value(self, query: DataQuery,\",\n \"None if element is None: return None return value_handler.handle(query, element)\",\n \"import DataQuery from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue\",\n \"PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query import DataQuery from\",\n \"is_not_root = False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler -\",\n \"f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else: error\",\n \"must be of type DataObject or DataValue' self.logger.error(error) raise TypeError(error)\",\n \"List, Any from tqdm import tqdm import logging from arxml_data_extractor.handler\",\n \"DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name] =\",\n \"Union[list, dict]: is_not_root = True if node is None: is_not_root\",\n \"isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif isinstance(value, DataValue): results[value.name]\",\n \"elements = self.path_handler.elements_by_path(data_object.path, node) for element in tqdm( elements, desc=f'Handle\",\n \"for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0] if len(values) == 1\",\n \"DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')') values = []\",\n \"found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\')\",\n \"return results def __handle_value(self, query: DataQuery, node: Element) -> Any:\",\n \"DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info( f'ObjectHandler - values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}'\",\n \"self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node: Element =\",\n \"= f'ObjectHandler - invalid value type ({type(value)}). Value must be\",\n \"else: error = f'ObjectHandler - invalid value type ({type(value)}). Value\",\n \"Element) -> dict: results = {} for value in values:\",\n \"self.logger.info( f'ObjectHandler - value found: DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else:\",\n \"disable=is_not_root, bar_format=\\\"{desc:<70}{percentage:3.0f}% |{bar:70}| {n_fmt:>4}/{total_fmt}\\\"): if element is not None: self.logger.info(\",\n \"f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline - 1}'\",\n \") return values[0] if len(values) == 1 else values def\",\n \"node: Element) -> dict: results = {} for value in\",\n \"is_not_root = True if node is None: is_not_root = False\",\n \"self.logger.info( f'ObjectHandler - element found: \\\\'{QName(element).localname}\\\\' at line {element.sourceline -\",\n \"self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')') values = [] elements =\",\n \"for value in values: if isinstance(value, DataObject): results[value.name] = self.handle(value,\",\n \"DataValue return None if element is None: return None return\",\n \"handle DataObject(\\\\'{data_object.name}\\\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\\\'{data_object.name}\\\\')') values =\",\n \"- no value found for DataValue(\\\\'{value.name}\\\\')') else: self.logger.info( f'ObjectHandler -\",\n \"= False node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle\",\n \"f'ObjectHandler - values found for DataObject(\\\\'{data_object.name}\\\\'): {len(values)}' ) return values[0]\",\n \"= self.path_handler.element_by_inline_ref(query.path, node) else: element = self.path_handler.element_by_xpath(query.path.xpath, node) else: #\",\n \"dict: results = {} for value in values: if isinstance(value,\",\n \"tqdm import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import\",\n \"logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from\",\n \"from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser from arxml_data_extractor.query.data_query\",\n \"= self.handle(value, node) elif isinstance(value, DataValue): results[value.name] = self.__handle_value(value.query, node)\",\n \"values[0] if len(values) == 1 else values def __handle_values(self, values:\",\n \"dict]: is_not_root = True if node is None: is_not_root =\",\n \"in values: if isinstance(value, DataObject): results[value.name] = self.handle(value, node) elif\",\n \"TypeError(error) return results def __handle_value(self, query: DataQuery, node: Element) ->\",\n \"node = self.path_handler.parser.root if is_not_root: self.logger.info(f'ObjectHandler - handle DataObject(\\\\'{data_object.name}\\\\')') else:\",\n \"is None: is_not_root = False node = self.path_handler.parser.root if is_not_root:\",\n \"else: # DataQuery.Reference isn't allowed on DataValue return None if\",\n \"import logging from arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler\",\n \"def __handle_values(self, values: List[Union[DataValue, DataObject]], node: Element) -> dict: results\",\n \"if not values: self.logger.warning( f'ObjectHandler - no values found for\",\n \"node) if results[value.name] is None: self.logger.info( f'ObjectHandler - no value\",\n \"from arxml_data_extractor.query.data_object import DataObject from arxml_data_extractor.query.data_value import DataValue class ObjectHandler():\",\n \"arxml_data_extractor.handler import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import\",\n \"import value_handler from arxml_data_extractor.handler.path_handler import PathHandler from arxml_data_extractor.asr.asr_parser import AsrParser\",\n \"logging.getLogger() self.path_handler = PathHandler(parser) def handle(self, data_object: DataObject, node: Element\",\n \"if node is None: is_not_root = False node = self.path_handler.parser.root\",\n \"node is None: is_not_root = False node = self.path_handler.parser.root if\",\n \"DataValue(\\\\'{value.name}\\\\') = \\\\'{results[value.name]}\\\\'' ) else: error = f'ObjectHandler - invalid\",\n \"isinstance(query.path, DataQuery.XPath): if query.path.is_reference: element = self.path_handler.element_by_inline_ref(query.path, node) else: element\",\n \"return None if element is None: return None return value_handler.handle(query,\",\n \"type DataObject or DataValue' self.logger.error(error) raise TypeError(error) return results def\"\n]","isConversational":false}}},{"rowIdx":175,"cells":{"ngram":{"kind":"list like","value":["MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO","\"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS, } ) return [create_trace_dict]","substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS","the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is","notice shall be included in all copies or substantial portions","data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list)","get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} } \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id,","\"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])),","create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise","create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS, }","\"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} } \"\"\" % (","str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus,","( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type):","OTHER DEALINGS IN THE SOFTWARE. \"\"\" from copy import deepcopy","2021 THL A29 Limited, a Tencent company. All rights reserved.","\"\"\" Tencent is pleased to support the open source community","\"\"\" from copy import deepcopy from datamanage.pro import exceptions as","utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\":","Software without restriction, including without limitation the rights to use,","MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import (","IN THE SOFTWARE. \"\"\" from copy import deepcopy from datamanage.pro","dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\":","query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id})","NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS","copies of the Software, and to permit persons to whom","hereby granted, free of charge, to any person obtaining a","rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License.","to deal in the Software without restriction, including without limitation","OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE","THL A29 Limited, a Tencent company. All rights reserved. BK-BASE","\"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'],","Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is","% ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value,","portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\",","= MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True ) create_info_ret =","utf-8 -*- \"\"\" Tencent is pleased to support the open","source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021",":rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" { get_dataset_create_info(func:","DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,","DEALINGS IN THE SOFTWARE. \"\"\" from copy import deepcopy from","FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR","# -*- coding: utf-8 -*- \"\"\" Tencent is pleased to","modify, merge, publish, distribute, sublicense, and/or sell copies of the","ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE","create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update(","persons to whom the Software is furnished to do so,","limitation the rights to use, copy, modify, merge, publish, distribute,","subject to the following conditions: The above copyright notice and","OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH","= query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id':","of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT","datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict","License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted,","create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']),","蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a Tencent","蓝鲸基础平台 is licensed under the MIT License. License for BK-BASE","a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed","NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A","raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and","Software is furnished to do so, subject to the following","Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF","import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType,","sell copies of the Software, and to permit persons to","included in all copies or substantial portions of the Software.","DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息","copy, modify, merge, publish, distribute, sublicense, and/or sell copies of","for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of","MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info']","furnished to do so, subject to the following conditions: The","or substantial portions of the Software. THE SOFTWARE IS PROVIDED","EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR","BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a","EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES","publish, distribute, sublicense, and/or sell copies of the Software, and","\"Software\"), to deal in the Software without restriction, including without","IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS","datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def","\"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS,","be included in all copies or substantial portions of the","dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type,","if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) #","to use, copy, modify, merge, publish, distribute, sublicense, and/or sell",":return: 数据创建相关信息 :rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\"","the following conditions: The above copyright notice and this permission","files (the \"Software\"), to deal in the Software without restriction,","ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF","} \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search(","the rights to use, copy, modify, merge, publish, distribute, sublicense,","software and associated documentation files (the \"Software\"), to deal in","notice and this permission notice shall be included in all","is hereby granted, free of charge, to any person obtaining","dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息","Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under",":param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list","to the following conditions: The above copyright notice and this","conditions: The above copyright notice and this permission notice shall","the Software without restriction, including without limitation the rights to","THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND","BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License for","and/or sell copies of the Software, and to permit persons","to support the open source community by making BK-BASE 蓝鲸基础平台","permit persons to whom the Software is furnished to do","as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local,","do so, subject to the following conditions: The above copyright","any person obtaining a copy of this software and associated","import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id,","WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN","WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT","THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE","# 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at}","THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,","copy of this software and associated documentation files (the \"Software\"),","FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL","including without limitation the rights to use, copy, modify, merge,","OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR","DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param","CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS","数据类型 :return: 数据创建相关信息 :rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement =","IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER","ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not","= deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'],","\"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype:","without limitation the rights to use, copy, modify, merge, publish,","= \"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} } \"\"\" %","restriction, including without limitation the rights to use, copy, modify,","数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \"\"\" #","to permit persons to whom the Software is furnished to","OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF","Copyright (C) 2021 THL A29 Limited, a Tencent company. All","data_set_create_info_statement = \"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} } \"\"\"","OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR","OR OTHER DEALINGS IN THE SOFTWARE. \"\"\" from copy import","deal in the Software without restriction, including without limitation the","list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict =","BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR","\"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by","ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO","community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL","2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'],","distribute, sublicense, and/or sell copies of the Software, and to","PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR","from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from","the Software, and to permit persons to whom the Software","and associated documentation files (the \"Software\"), to deal in the","the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY","to whom the Software is furnished to do so, subject","FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT","BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of charge,","DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\":","WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT","License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free","TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE","{ \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\":","licensed under the MIT License. License for BK-BASE 蓝鲸基础平台: --------------------------------------------------------------------","this software and associated documentation files (the \"Software\"), to deal","of the Software, and to permit persons to whom the","all copies or substantial portions of the Software. THE SOFTWARE","-*- coding: utf-8 -*- \"\"\" Tencent is pleased to support","(the \"Software\"), to deal in the Software without restriction, including","merge, publish, distribute, sublicense, and/or sell copies of the Software,","All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT","so, subject to the following conditions: The above copyright notice","PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR","charge, to any person obtaining a copy of this software","to do so, subject to the following conditions: The above","WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.","following conditions: The above copyright notice and this permission notice","THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY","\"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\"backend_type\":","USE OR OTHER DEALINGS IN THE SOFTWARE. \"\"\" from copy","LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,","in the Software without restriction, including without limitation the rights","permission notice shall be included in all copies or substantial","import deepcopy from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api","available. Copyright (C) 2021 THL A29 Limited, a Tencent company.","\"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value,","# 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\":","SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,","making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited,","BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER","-------------------------------------------------------------------- Permission is hereby granted, free of charge, to any","INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS","list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" { get_dataset_create_info(func: eq(%s,","HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,","copyright notice and this permission notice shall be included in","dataset_id, ) query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True","MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby",") query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True )","OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \"\"\"","exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import","not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息","and to permit persons to whom the Software is furnished","copies or substantial portions of the Software. THE SOFTWARE IS","OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED","SOFTWARE. \"\"\" from copy import deepcopy from datamanage.pro import exceptions",") create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret):","IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,","SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY","dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime","the open source community by making BK-BASE 蓝鲸基础平台 available. Copyright","whom the Software is furnished to do so, subject to","\"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS,","AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT","reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License","datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from","in all copies or substantial portions of the Software. THE","obtaining a copy of this software and associated documentation files","dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement","-*- \"\"\" Tencent is pleased to support the open source","dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\":","LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN","and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT)","of this software and associated documentation files (the \"Software\"), to","OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR","a copy of this software and associated documentation files (the","OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE","from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS,","sublicense, and/or sell copies of the Software, and to permit","LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR","\"%s\")){created_by created_at} } \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result","DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\": utc_to_local(create_info_ret[0]['created_at']), \"created_by\": create_info_ret[0]['created_by'], \"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\":","OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN","is licensed under the MIT License. License for BK-BASE 蓝鲸基础平台:","CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF","this permission notice shall be included in all copies or","utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType,","datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT,","CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN","above copyright notice and this permission notice shall be included","A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE","deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"created_at\":","DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS, } ) return","\"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,","数据创建相关信息 :rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" {","A29 Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台","coding: utf-8 -*- \"\"\" Tencent is pleased to support the","IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING","KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE","is furnished to do so, subject to the following conditions:","ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id","to any person obtaining a copy of this software and","raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( {","Tencent is pleased to support the open source community by","(isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict","shall be included in all copies or substantial portions of","person obtaining a copy of this software and associated documentation","query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True ) create_info_ret","FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN","1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \"\"\" { get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} }","(C) 2021 THL A29 Limited, a Tencent company. All rights","and this permission notice shall be included in all copies","copy import deepcopy from datamanage.pro import exceptions as dm_pro_errors from","created_at} } \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result =","\"show_type\": DataTraceShowType.DISPLAY.value, \"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS, } )","AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT","\"statement\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret,","OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE","free of charge, to any person obtaining a copy of","IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE","IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,","OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT","Software, and to permit persons to whom the Software is","the Software is furnished to do so, subject to the","from copy import deepcopy from datamanage.pro import exceptions as dm_pro_errors","SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.","rights to use, copy, modify, merge, publish, distribute, sublicense, and/or","eq(%s, \"%s\")){created_by created_at} } \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, )","company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the","DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param","蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of charge, to","get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return:","def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型","documentation files (the \"Software\"), to deal in the Software without","is pleased to support the open source community by making","( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\":","create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \"sub_type\": dataset_type, \"sub_type_alias\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \"description\":","by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29","without restriction, including without limitation the rights to use, copy,","under the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission","TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION","pleased to support the open source community by making BK-BASE","from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi","COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER","DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id:","dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \"\"\"","{ get_dataset_create_info(func: eq(%s, \"%s\")){created_by created_at} } \"\"\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'],","NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE","use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies","import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time","OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR","granted, free of charge, to any person obtaining a copy","from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, )","support the open source community by making BK-BASE 蓝鲸基础平台 available.","THE SOFTWARE. \"\"\" from copy import deepcopy from datamanage.pro import","of charge, to any person obtaining a copy of this","PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS","deepcopy from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import","Permission is hereby granted, free of charge, to any person",":param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \"\"\" # 1)从dgraph中获取数据创建相关信息","The above copyright notice and this permission notice shall be","DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement},","{\"backend_type\": ComplexSearchBackendType.DGRAPH.value, \"statement\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if","import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import","associated documentation files (the \"Software\"), to deal in the Software",") def get_dataset_create_info(dataset_id, dataset_type): \"\"\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type:","ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION","open source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C)","THE USE OR OTHER DEALINGS IN THE SOFTWARE. \"\"\" from","AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES","WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING"],"string":"[\n \"MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO\",\n \"\\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\": DataTraceFinishStatus.STATUS_ALIAS, } ) return [create_trace_dict]\",\n \"substantial portions of the Software. THE SOFTWARE IS PROVIDED \\\"AS\",\n \"the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is\",\n \"notice shall be included in all copies or substantial portions\",\n \"data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list)\",\n \"get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id,\",\n \"\\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])),\",\n \"create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise\",\n \"create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\": DataTraceFinishStatus.STATUS_ALIAS, }\",\n \"\\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" % (\",\n \"str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus,\",\n \"( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type):\",\n \"OTHER DEALINGS IN THE SOFTWARE. \\\"\\\"\\\" from copy import deepcopy\",\n \"2021 THL A29 Limited, a Tencent company. All rights reserved.\",\n \"\\\"\\\"\\\" Tencent is pleased to support the open source community\",\n \"\\\"\\\"\\\" from copy import deepcopy from datamanage.pro import exceptions as\",\n \"utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\":\",\n \"Software without restriction, including without limitation the rights to use,\",\n \"MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import (\",\n \"IN THE SOFTWARE. \\\"\\\"\\\" from copy import deepcopy from datamanage.pro\",\n \"dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\":\",\n \"query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id})\",\n \"NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\",\n \"copies of the Software, and to permit persons to whom\",\n \"hereby granted, free of charge, to any person obtaining a\",\n \"rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License.\",\n \"to deal in the Software without restriction, including without limitation\",\n \"OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE\",\n \"THL A29 Limited, a Tencent company. All rights reserved. BK-BASE\",\n \"\\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'],\",\n \"Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is\",\n \"% ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value,\",\n \"portions of the Software. THE SOFTWARE IS PROVIDED \\\"AS IS\\\",\",\n \"= MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret =\",\n \"utf-8 -*- \\\"\\\"\\\" Tencent is pleased to support the open\",\n \"source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021\",\n \":rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func:\",\n \"DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\",\n \"DEALINGS IN THE SOFTWARE. \\\"\\\"\\\" from copy import deepcopy from\",\n \"FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" Tencent is pleased to\",\n \"modify, merge, publish, distribute, sublicense, and/or sell copies of the\",\n \"ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE\",\n \"create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update(\",\n \"persons to whom the Software is furnished to do so,\",\n \"limitation the rights to use, copy, modify, merge, publish, distribute,\",\n \"subject to the following conditions: The above copyright notice and\",\n \"OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH\",\n \"= query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id':\",\n \"of the Software. THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT\",\n \"datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict\",\n \"License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted,\",\n \"create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']),\",\n \"蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a Tencent\",\n \"蓝鲸基础平台 is licensed under the MIT License. License for BK-BASE\",\n \"a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed\",\n \"NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A\",\n \"raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and\",\n \"Software is furnished to do so, subject to the following\",\n \"Software. THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF\",\n \"import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType,\",\n \"sell copies of the Software, and to permit persons to\",\n \"included in all copies or substantial portions of the Software.\",\n \"DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息\",\n \"copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\",\n \"for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of\",\n \"MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info']\",\n \"furnished to do so, subject to the following conditions: The\",\n \"or substantial portions of the Software. THE SOFTWARE IS PROVIDED\",\n \"EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR\",\n \"BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a\",\n \"EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\",\n \"publish, distribute, sublicense, and/or sell copies of the Software, and\",\n \"\\\"Software\\\"), to deal in the Software without restriction, including without\",\n \"IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\",\n \"datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def\",\n \"\\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS,\",\n \"be included in all copies or substantial portions of the\",\n \"dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type,\",\n \"if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) #\",\n \"to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\",\n \":return: 数据创建相关信息 :rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\"\",\n \"the following conditions: The above copyright notice and this permission\",\n \"files (the \\\"Software\\\"), to deal in the Software without restriction,\",\n \"ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF\",\n \"} \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search(\",\n \"the rights to use, copy, modify, merge, publish, distribute, sublicense,\",\n \"software and associated documentation files (the \\\"Software\\\"), to deal in\",\n \"notice and this permission notice shall be included in all\",\n \"is hereby granted, free of charge, to any person obtaining\",\n \"dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息\",\n \"Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under\",\n \":param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list\",\n \"to the following conditions: The above copyright notice and this\",\n \"conditions: The above copyright notice and this permission notice shall\",\n \"the Software without restriction, including without limitation the rights to\",\n \"THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\",\n \"BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License for\",\n \"and/or sell copies of the Software, and to permit persons\",\n \"to support the open source community by making BK-BASE 蓝鲸基础平台\",\n \"permit persons to whom the Software is furnished to do\",\n \"as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local,\",\n \"do so, subject to the following conditions: The above copyright\",\n \"any person obtaining a copy of this software and associated\",\n \"import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id,\",\n \"WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\",\n \"WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT\",\n \"THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\",\n \"# 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at}\",\n \"THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\",\n \"copy of this software and associated documentation files (the \\\"Software\\\"),\",\n \"FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\",\n \"including without limitation the rights to use, copy, modify, merge,\",\n \"OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\",\n \"DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param\",\n \"CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\",\n \"数据类型 :return: 数据创建相关信息 :rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement =\",\n \"IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\",\n \"ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not\",\n \"= deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'],\",\n \"\\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype:\",\n \"without limitation the rights to use, copy, modify, merge, publish,\",\n \"= \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" %\",\n \"restriction, including without limitation the rights to use, copy, modify,\",\n \"数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \\\"\\\"\\\" #\",\n \"to permit persons to whom the Software is furnished to\",\n \"OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\",\n \"Copyright (C) 2021 THL A29 Limited, a Tencent company. All\",\n \"data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\"\",\n \"OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR\",\n \"OR OTHER DEALINGS IN THE SOFTWARE. \\\"\\\"\\\" from copy import\",\n \"deal in the Software without restriction, including without limitation the\",\n \"list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict =\",\n \"BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR\",\n \"\\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by\",\n \"ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\",\n \"community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL\",\n \"2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'],\",\n \"distribute, sublicense, and/or sell copies of the Software, and to\",\n \"PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\",\n \"from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from\",\n \"the Software, and to permit persons to whom the Software\",\n \"and associated documentation files (the \\\"Software\\\"), to deal in the\",\n \"the Software. THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY\",\n \"to whom the Software is furnished to do so, subject\",\n \"FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT\",\n \"BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of charge,\",\n \"DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\":\",\n \"WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT\",\n \"License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free\",\n \"TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE\",\n \"{ \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\":\",\n \"licensed under the MIT License. License for BK-BASE 蓝鲸基础平台: --------------------------------------------------------------------\",\n \"this software and associated documentation files (the \\\"Software\\\"), to deal\",\n \"of the Software, and to permit persons to whom the\",\n \"all copies or substantial portions of the Software. THE SOFTWARE\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" Tencent is pleased to support\",\n \"(the \\\"Software\\\"), to deal in the Software without restriction, including\",\n \"merge, publish, distribute, sublicense, and/or sell copies of the Software,\",\n \"All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT\",\n \"so, subject to the following conditions: The above copyright notice\",\n \"PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\",\n \"charge, to any person obtaining a copy of this software\",\n \"to do so, subject to the following conditions: The above\",\n \"WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\",\n \"following conditions: The above copyright notice and this permission notice\",\n \"THE SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY\",\n \"\\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\\\"backend_type\\\":\",\n \"USE OR OTHER DEALINGS IN THE SOFTWARE. \\\"\\\"\\\" from copy\",\n \"LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\",\n \"in the Software without restriction, including without limitation the rights\",\n \"permission notice shall be included in all copies or substantial\",\n \"import deepcopy from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api\",\n \"available. Copyright (C) 2021 THL A29 Limited, a Tencent company.\",\n \"\\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value,\",\n \"# 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\":\",\n \"SOFTWARE IS PROVIDED \\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND,\",\n \"making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited,\",\n \"BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\",\n \"-------------------------------------------------------------------- Permission is hereby granted, free of charge, to any\",\n \"INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\",\n \"list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s,\",\n \"HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\",\n \"copyright notice and this permission notice shall be included in\",\n \"dataset_id, ) query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True\",\n \"MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby\",\n \") query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True )\",\n \"OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \\\"\\\"\\\"\",\n \"exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import\",\n \"not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息\",\n \"and to permit persons to whom the Software is furnished\",\n \"copies or substantial portions of the Software. THE SOFTWARE IS\",\n \"OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\",\n \"SOFTWARE. \\\"\\\"\\\" from copy import deepcopy from datamanage.pro import exceptions\",\n \") create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret):\",\n \"IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\",\n \"SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\",\n \"dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime\",\n \"the open source community by making BK-BASE 蓝鲸基础平台 available. Copyright\",\n \"whom the Software is furnished to do so, subject to\",\n \"\\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\": DataTraceFinishStatus.STATUS_ALIAS,\",\n \"AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\",\n \"reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License\",\n \"datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from\",\n \"in all copies or substantial portions of the Software. THE\",\n \"obtaining a copy of this software and associated documentation files\",\n \"dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement\",\n \"-*- \\\"\\\"\\\" Tencent is pleased to support the open source\",\n \"dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\":\",\n \"LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN\",\n \"and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT)\",\n \"of this software and associated documentation files (the \\\"Software\\\"), to\",\n \"OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR\",\n \"a copy of this software and associated documentation files (the\",\n \"OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE\",\n \"from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS,\",\n \"sublicense, and/or sell copies of the Software, and to permit\",\n \"LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR\",\n \"\\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result\",\n \"DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\": utc_to_local(create_info_ret[0]['created_at']), \\\"created_by\\\": create_info_ret[0]['created_by'], \\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\":\",\n \"OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN\",\n \"is licensed under the MIT License. License for BK-BASE 蓝鲸基础平台:\",\n \"CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\",\n \"this permission notice shall be included in all copies or\",\n \"utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType,\",\n \"datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT,\",\n \"CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\",\n \"above copyright notice and this permission notice shall be included\",\n \"A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\",\n \"deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"created_at\\\":\",\n \"DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\": DataTraceFinishStatus.STATUS_ALIAS, } ) return\",\n \"\\\"AS IS\\\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,\",\n \"数据创建相关信息 :rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" {\",\n \"A29 Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台\",\n \"coding: utf-8 -*- \\\"\\\"\\\" Tencent is pleased to support the\",\n \"IS\\\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING\",\n \"KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE\",\n \"is furnished to do so, subject to the following conditions:\",\n \"ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id\",\n \"to any person obtaining a copy of this software and\",\n \"raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( {\",\n \"Tencent is pleased to support the open source community by\",\n \"(isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict\",\n \"shall be included in all copies or substantial portions of\",\n \"person obtaining a copy of this software and associated documentation\",\n \"query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret\",\n \"FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\",\n \"1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = \\\"\\\"\\\" { get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} }\",\n \"(C) 2021 THL A29 Limited, a Tencent company. All rights\",\n \"and this permission notice shall be included in all copies\",\n \"copy import deepcopy from datamanage.pro import exceptions as dm_pro_errors from\",\n \"created_at} } \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result =\",\n \"\\\"show_type\\\": DataTraceShowType.DISPLAY.value, \\\"datetime\\\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \\\"status\\\": DataTraceFinishStatus.STATUS, \\\"status_alias\\\": DataTraceFinishStatus.STATUS_ALIAS, } )\",\n \"AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT\",\n \"\\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret,\",\n \"OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\",\n \"free of charge, to any person obtaining a copy of\",\n \"IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE\",\n \"IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\",\n \"OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT\",\n \"Software, and to permit persons to whom the Software is\",\n \"the Software is furnished to do so, subject to the\",\n \"from copy import deepcopy from datamanage.pro import exceptions as dm_pro_errors\",\n \"SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\",\n \"rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\",\n \"eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, )\",\n \"company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the\",\n \"DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param\",\n \"蓝鲸基础平台: -------------------------------------------------------------------- Permission is hereby granted, free of charge, to\",\n \"get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return:\",\n \"def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型\",\n \"documentation files (the \\\"Software\\\"), to deal in the Software without\",\n \"is pleased to support the open source community by making\",\n \"( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\":\",\n \"create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { \\\"sub_type\\\": dataset_type, \\\"sub_type_alias\\\": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], \\\"description\\\":\",\n \"by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29\",\n \"without restriction, including without limitation the rights to use, copy,\",\n \"under the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- Permission\",\n \"TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\",\n \"pleased to support the open source community by making BK-BASE\",\n \"from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi\",\n \"COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\",\n \"DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id:\",\n \"dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \\\"\\\"\\\"\",\n \"{ get_dataset_create_info(func: eq(%s, \\\"%s\\\")){created_by created_at} } \\\"\\\"\\\" % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'],\",\n \"NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE\",\n \"use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\",\n \"import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time\",\n \"OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR\",\n \"granted, free of charge, to any person obtaining a copy\",\n \"from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, )\",\n \"support the open source community by making BK-BASE 蓝鲸基础平台 available.\",\n \"THE SOFTWARE. \\\"\\\"\\\" from copy import deepcopy from datamanage.pro import\",\n \"of charge, to any person obtaining a copy of this\",\n \"PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS\",\n \"deepcopy from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import\",\n \"Permission is hereby granted, free of charge, to any person\",\n \":param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list \\\"\\\"\\\" # 1)从dgraph中获取数据创建相关信息\",\n \"The above copyright notice and this permission notice shall be\",\n \"DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement},\",\n \"{\\\"backend_type\\\": ComplexSearchBackendType.DGRAPH.value, \\\"statement\\\": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if\",\n \"import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import\",\n \"associated documentation files (the \\\"Software\\\"), to deal in the Software\",\n \") def get_dataset_create_info(dataset_id, dataset_type): \\\"\\\"\\\"获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type:\",\n \"ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\",\n \"open source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C)\",\n \"THE USE OR OTHER DEALINGS IN THE SOFTWARE. \\\"\\\"\\\" from\",\n \"AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES\",\n \"WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\"\n]","isConversational":false}}},{"rowIdx":176,"cells":{"ngram":{"kind":"list like","value":["Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import multiprocessing from","alldocs if doc.split == 'test'] #doc_list = alldocs[:] # for","# # with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm)","f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') #","split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra","window=5 (both sides) approximates paper's 10-word total window size #","# # # Is there a better pattern to use?","continue # # with open('/'.join([dirname, output]), 'w') as n: #","import os import os.path import requests import tarfile import sys","t_clean = t_clean.replace('\\n', ' ') # t_clean = re.sub(r'[^\\x00-\\x7F]+',' ',","'' # output = fol.replace('/', '-') + '.txt' # #","split, sentiment)) # #train_docs = [doc for doc in alldocs","') # t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # # for","# alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as","# Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM","sentiment)) # #train_docs = [doc for doc in alldocs if","# # # Replace breaks with spaces # norm_text =","# temp += \"\\n\" # # temp_norm = normalize_text(temp) #","by sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) #","'*.txt'])) # # for txt in txt_files: # with open(txt,","# # if len(temp_norm) == 1: # continue # #","t_clean = t.read() # # t_clean = t_clean.replace('\\n', ' ')","len(temp_norm) == 1: # continue # # with open('/'.join([dirname, output]),","tokens[1:] # tags = [line_no] # `tags = [tokens[0]]` would","gensim.models.doc2vec import TaggedDocument from collections import namedtuple from gensim.models import","# PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0,","' ') # # temp += t_clean # # temp","requests import tarfile import sys import re import gensim from","enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec)","to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # #","= t_clean.replace(c, ' ') # # temp += t_clean #","= [chr(0x85)] # t_clean = t.read() # # t_clean =","alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f:","punctuation/symbols from words #def normalize_text(text): # norm_text = text.lower() #","# split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k","serves as template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) #","in txt_files: # with open(txt, 'r') as t: # control_chars","10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5,","negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100,","for txt in txt_files: # with open(txt, 'r') as t:","tarfile import sys import re import gensim from gensim.models.doc2vec import","[doc for doc in alldocs if doc.split == 'train'] #test_docs","[doc for doc in alldocs if doc.split == 'test'] #doc_list","os.path import requests import tarfile import sys import re import","\"\\n\" # # temp_norm = normalize_text(temp) # # if len(temp_norm)","'\"', ',', '(', ')', '!', '?', ';', ':']: # norm_text","= '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to","Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] #","= [doc for doc in alldocs if doc.split == 'train']","normalize_text(text): # norm_text = text.lower() # # # Replace breaks","')', '!', '?', ';', ':']: # norm_text = norm_text.replace(char, '","unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc","25k train, 25k test, 25k extra # sentiment = [1.0,","[chr(0x85)] # t_clean = t.read() # # t_clean = t_clean.replace('\\n',","= [] # will hold all docs in original order","multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow","# PV-DM/concat requires one special NULL word so it serves","in ['.', '\"', ',', '(', ')', '!', '?', ';', ':']:","norm_text = text.lower() # # # Replace breaks with spaces","from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec","pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # #","None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown","# Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), #","'-') + '.txt' # # # Is there a better","= [tokens[0]]` would also work at extra memory cost #","Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average","cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test,","Create a document vector list and save it #doc_vec_list =","import glob import os import os.path import requests import tarfile","with open(txt, 'r') as t: # control_chars = [chr(0x85)] #","= pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for","tags, split, sentiment)) # #train_docs = [doc for doc in","will hold all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt'))","normalize_text(temp) # # if len(temp_norm) == 1: # continue #","breaks with spaces # norm_text = norm_text.replace('<br />', ' ')","from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname =","OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys)","from words #def normalize_text(text): # norm_text = text.lower() # #","{1}\\n\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags","#locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to lower-case and","25k extra # sentiment = [1.0, 0.0, 1.0, 0.0, None,","#with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx, line","' + char + ' ') # # return norm_text","and save it #doc_vec_list = [x.docvecs for x in simple_models]","pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \"this","in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1],","line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words =","# Pad punctuation with spaces on both sides # for","'test'] #doc_list = alldocs[:] # for reshuffling per pass #","'/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to lower-case","# for char in ['.', '\"', ',', '(', ')', '!',","in folders: # temp = '' # output = fol.replace('/',","import gensim.models.doc2vec from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec","# temp += t_clean # # temp += \"\\n\" #","spaces # norm_text = norm_text.replace('<br />', ' ') # #","to lower-case and strip punctuation/symbols from words #def normalize_text(text): #","for idx, line in enumerate(alldata.splitlines()): # num_line = \"_*{0} {1}\\n\".format(idx,","a document vector list and save it #doc_vec_list = [x.docvecs","[] # will hold all docs in original order #with","namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from collections import","len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as","[tokens[0]]` would also work at extra memory cost # split","gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow otherwise\" #","import locale import glob import os import os.path import requests","Is there a better pattern to use? # txt_files =","# ## Create a document vector list and save it","train, 25k test, 25k extra # sentiment = [1.0, 0.0,","use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for","size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW #","text to lower-case and strip punctuation/symbols from words #def normalize_text(text):","for char in ['.', '\"', ',', '(', ')', '!', '?',","line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words","alldocs if doc.split == 'train'] #test_docs = [doc for doc","# with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) #","# num_line = \"_*{0} {1}\\n\".format(idx, line) # f.write(num_line) # #SentimentDocument","order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line","alldata: # for line_no, line in enumerate(alldata): # tokens =","# for idx, line in enumerate(alldata.splitlines()): # num_line = \"_*{0}","# tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags","in alldocs if doc.split == 'train'] #test_docs = [doc for","NULL word so it serves as template #for model in","as template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name","# return norm_text # # ## Concat and normalize test/train","word so it serves as template #for model in simple_models[1:]:","as f: # for idx, line in enumerate(alldata.splitlines()): # num_line","gensim from gensim.models.doc2vec import TaggedDocument from collections import namedtuple from","open(txt, 'r') as t: # control_chars = [chr(0x85)] # t_clean","# ## Concat and normalize test/train data #folders = os.listdir(dirname)","locale import glob import os import os.path import requests import","a better pattern to use? # txt_files = glob.glob('/'.join([dirname, fol,","12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) #","= ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create","enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] #","0.0, 1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K pos,","alldocs[:] # for reshuffling per pass # #cores = multiprocessing.cpu_count()","for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc']","and normalize test/train data #folders = os.listdir(dirname) #alldata = ''","of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one","# control_chars = [chr(0x85)] # t_clean = t.read() # #","# Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #]","idx, line in enumerate(alldata.splitlines()): # num_line = \"_*{0} {1}\\n\".format(idx, line)","# # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores),","= os.listdir(dirname) #alldata = '' # #for fol in folders:","negative=5, hs=0, min_count=2, workers=cores), #] # ## speed setup by","total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0,","= glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in txt_files:","25k test, 25k extra # sentiment = [1.0, 0.0, 1.0,","hs=0, min_count=2, workers=cores), #] # ## speed setup by sharing","= [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500] #","c in control_chars: # t_clean = t_clean.replace(c, ' ') #","t_clean # # temp += \"\\n\" # # temp_norm =","+= \"\\n\" # # temp_norm = normalize_text(temp) # # if","# # temp_norm = normalize_text(temp) # # if len(temp_norm) ==","folders: # temp = '' # output = fol.replace('/', '-')","sides # for char in ['.', '\"', ',', '(', ')',","> -1, \"this will be painfully slow otherwise\" # #simple_models","for doc in alldocs if doc.split == 'train'] #test_docs =","there a better pattern to use? # txt_files = glob.glob('/'.join([dirname,","# # t_clean = t_clean.replace('\\n', ' ') # t_clean =","# # temp += \"\\n\" # # temp_norm = normalize_text(temp)","None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown #","then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs =","for c in control_chars: # t_clean = t_clean.replace(c, ' ')","None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words,","i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in","spaces on both sides # for char in ['.', '\"',","min_count=2, workers=cores), #] # ## speed setup by sharing results","pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x","workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2,","in alldocs if doc.split == 'test'] #doc_list = alldocs[:] #","from collections import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec","= norm_text.replace(char, ' ' + char + ' ') #","re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # # for c in control_chars: #","#models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ##","txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in","= ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector list","results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires","it #doc_vec_list = [x.docvecs for x in simple_models] docvecs =","+ char + ' ') # # return norm_text #","n.write(temp_norm) # # alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']),","re import gensim from gensim.models.doc2vec import TaggedDocument from collections import","temp_norm = normalize_text(temp) # # if len(temp_norm) == 1: #","# #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx,","= norm_text.replace('<br />', ' ') # # # Pad punctuation","size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ## speed","both sides # for char in ['.', '\"', ',', '(',","doc in alldocs if doc.split == 'train'] #test_docs = [doc","# 25k train, 25k test, 25k extra # sentiment =","# for reshuffling per pass # #cores = multiprocessing.cpu_count() #assert","glob import os import os.path import requests import tarfile import","f: # for idx, line in enumerate(alldata.splitlines()): # num_line =","#with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line in","' ') # # # Pad punctuation with spaces on","= [x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb'))","- window=5 (both sides) approximates paper's 10-word total window size","PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word total","#alldata = '' # #for fol in folders: # temp","pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment))","for line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() #","pattern to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) #","data #folders = os.listdir(dirname) #alldata = '' # #for fol","reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ##","model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL","= normalize_text(temp) # # if len(temp_norm) == 1: # continue","as f: for vec in x: f.write(vec) f.write(\"\\n\") # pickle.dump(models_by_name,","# n.write(temp_norm) # # alldata += temp_norm # #with open('/'.join([dirname,","+= t_clean # # temp += \"\\n\" # # temp_norm","text.lower() # # # Replace breaks with spaces # norm_text","## speed setup by sharing results of 1st model's vocabulary","import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL,","= OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm'] =","for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs)","for reshuffling per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION","ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector list and","Convert text to lower-case and strip punctuation/symbols from words #def","template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name =","'alldata-id.txt')) as alldata: # for line_no, line in enumerate(alldata): #","open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec) f.write(\"\\n\") #","glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in txt_files: #","# # PV-DM w/concatenation - window=5 (both sides) approximates paper's","import gensim from gensim.models.doc2vec import TaggedDocument from collections import namedtuple","neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs","# txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt","gensim.models import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import","# for line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split()","# PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), #","# f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment')","= [line_no] # `tags = [tokens[0]]` would also work at","approximates paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100,","in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:]","for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec","' ' + char + ' ') # # return","# # Pad punctuation with spaces on both sides #","= t_clean.replace('\\n', ' ') # t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean)","docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0]","' ') # # return norm_text # # ## Concat","= '' # output = fol.replace('/', '-') + '.txt' #","'!', '?', ';', ':']: # norm_text = norm_text.replace(char, ' '","# `tags = [tokens[0]]` would also work at extra memory","t_clean = t_clean.replace(c, ' ') # # temp += t_clean","'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in","# temp_norm = normalize_text(temp) # # if len(temp_norm) == 1:","import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from collections","'words tags split sentiment') # #alldocs = [] # will","with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) # #","# for txt in txt_files: # with open(txt, 'r') as","'C') # # ## Convert text to lower-case and strip","import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') #","print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w')","# # for txt in txt_files: # with open(txt, 'r')","'alldata-id.txt']), 'w') as f: # for idx, line in enumerate(alldata.splitlines()):","= tokens[1:] # tags = [line_no] # `tags = [tokens[0]]`","0.0, None, None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2","# ## Convert text to lower-case and strip punctuation/symbols from","# words = tokens[1:] # tags = [line_no] # `tags","if doc.split == 'test'] #doc_list = alldocs[:] # for reshuffling","line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split","on both sides # for char in ['.', '\"', ',',","tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags =","norm_text.replace(char, ' ' + char + ' ') # #","collections import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from","# temp = '' # output = fol.replace('/', '-') +","#def normalize_text(text): # norm_text = text.lower() # # # Replace","== 1: # continue # # with open('/'.join([dirname, output]), 'w')","= gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags = [line_no]","Concat and normalize test/train data #folders = os.listdir(dirname) #alldata =","temp += t_clean # # temp += \"\\n\" # #","simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print","sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat","with spaces # norm_text = norm_text.replace('<br />', ' ') #","split sentiment') # #alldocs = [] # will hold all","PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2,","OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1],","# # ## Convert text to lower-case and strip punctuation/symbols","# #for fol in folders: # temp = '' #","in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for","docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for","control_chars = [chr(0x85)] # t_clean = t.read() # # t_clean","# # for c in control_chars: # t_clean = t_clean.replace(c,","t_clean.replace('\\n', ' ') # t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) #","+ '.txt' # # # Is there a better pattern","'?', ';', ':']: # norm_text = norm_text.replace(char, ' ' +","from gensim.models.doc2vec import TaggedDocument from collections import namedtuple from gensim.models","+ ' ') # # return norm_text # # ##","## Concat and normalize test/train data #folders = os.listdir(dirname) #alldata","import os.path import requests import tarfile import sys import re","multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname","for doc in alldocs if doc.split == 'test'] #doc_list =","# #train_docs = [doc for doc in alldocs if doc.split","tags split sentiment') # #alldocs = [] # will hold","min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0,","', t_clean) # # for c in control_chars: # t_clean","[x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print","`tags = [tokens[0]]` would also work at extra memory cost","ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a","# norm_text = norm_text.replace(char, ' ' + char + '","# t_clean = t.read() # # t_clean = t_clean.replace('\\n', '","# #models_by_name = OrderedDict((str(model), model) for model in simple_models) #","simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document","= '' # #for fol in folders: # temp =","# t_clean = t_clean.replace('\\n', ' ') # t_clean = re.sub(r'[^\\x00-\\x7F]+','","alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc for doc","model) for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]])","PV-DM/concat requires one special NULL word so it serves as","[12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split,","import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\")","painfully slow otherwise\" # #simple_models = [ # # PV-DM","gensim.models.doc2vec from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import","print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs):","# #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will","# #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs","norm_text = norm_text.replace('<br />', ' ') # # # Pad","sides) approximates paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1,","namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs = [] #","print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f:","= namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs = []","#models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector","document vector list and save it #doc_vec_list = [x.docvecs for","#cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be","reshuffling per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION >","import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import multiprocessing","# # temp += t_clean # # temp += \"\\n\"","workers=cores), #] # ## speed setup by sharing results of","#] # ## speed setup by sharing results of 1st","# # ## Concat and normalize test/train data #folders =","hold all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as","'w') as n: # n.write(temp_norm) # # alldata += temp_norm","# #alldocs = [] # will hold all docs in","# PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word","from gensim.models import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict","#models_by_name = OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm']","words #def normalize_text(text): # norm_text = text.lower() # # #","char in ['.', '\"', ',', '(', ')', '!', '?', ';',","'r') as t: # control_chars = [chr(0x85)] # t_clean =","min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100,","with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec) f.write(\"\\n\")","return norm_text # # ## Concat and normalize test/train data","collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import","fol in folders: # temp = '' # output =","output]), 'w') as n: # n.write(temp_norm) # # alldata +=","t_clean) # # for c in control_chars: # t_clean =","scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so","# ## speed setup by sharing results of 1st model's","# for c in control_chars: # t_clean = t_clean.replace(c, '","all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata:","as alldata: # for line_no, line in enumerate(alldata): # tokens","test, 25k extra # sentiment = [1.0, 0.0, 1.0, 0.0,","per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1,","requires one special NULL word so it serves as template","for vec in x: f.write(vec) f.write(\"\\n\") # pickle.dump(models_by_name, open('model.p', 'wb'))","special NULL word so it serves as template #for model","# norm_text = text.lower() # # # Replace breaks with","= text.lower() # # # Replace breaks with spaces #","') # # temp += t_clean # # temp +=","t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # # for c in","setup by sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs)","os import os.path import requests import tarfile import sys import","in enumerate(alldata.splitlines()): # num_line = \"_*{0} {1}\\n\".format(idx, line) # f.write(num_line)","import re import gensim from gensim.models.doc2vec import TaggedDocument from collections","# Is there a better pattern to use? # txt_files","import TaggedDocument from collections import namedtuple from gensim.models import Doc2Vec","char + ' ') # # return norm_text # #","simple_models[0]]) # ## Create a document vector list and save","size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores),","1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K pos, 12.5K","import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle","t: # control_chars = [chr(0x85)] # t_clean = t.read() #","len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with","None, None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then","# with open(txt, 'r') as t: # control_chars = [chr(0x85)]","';', ':']: # norm_text = norm_text.replace(char, ' ' + char","negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1,","= multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully","model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model)","# will hold all docs in original order #with open(os.path.join(dirname,","list and save it #doc_vec_list = [x.docvecs for x in","output = fol.replace('/', '-') + '.txt' # # # Is","also work at extra memory cost # split = ['train','test','extra','extra'][line_no//25000]","'w') as f: # for idx, line in enumerate(alldata.splitlines()): #","size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average #","os.listdir(dirname) #alldata = '' # #for fol in folders: #","paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5,","= [doc for doc in alldocs if doc.split == 'test']","# #simple_models = [ # # PV-DM w/concatenation - window=5","',', '(', ')', '!', '?', ';', ':']: # norm_text =","#alldocs = [] # will hold all docs in original","[line_no] # `tags = [tokens[0]]` would also work at extra","#assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow otherwise\"","window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0,","x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print","(both sides) approximates paper's 10-word total window size # Doc2Vec(dm=1,","control_chars: # t_clean = t_clean.replace(c, ' ') # # temp","# sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None,","#dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text","open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) # # alldata","otherwise\" # #simple_models = [ # # PV-DM w/concatenation -","simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]])","\"_*{0} {1}\\n\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words","it serves as template #for model in simple_models[1:]: # model.reset_from(simple_models[0])","strip punctuation/symbols from words #def normalize_text(text): # norm_text = text.lower()","tags = [line_no] # `tags = [tokens[0]]` would also work","[ # # PV-DM w/concatenation - window=5 (both sides) approximates","norm_text.replace('<br />', ' ') # # # Pad punctuation with","doc in alldocs if doc.split == 'test'] #doc_list = alldocs[:]","'' # #for fol in folders: # temp = ''","## Convert text to lower-case and strip punctuation/symbols from words","temp += \"\\n\" # # temp_norm = normalize_text(temp) # #","if len(temp_norm) == 1: # continue # # with open('/'.join([dirname,","['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra # sentiment","fol, '*.txt'])) # # for txt in txt_files: # with","== 'test'] #doc_list = alldocs[:] # for reshuffling per pass","') # # return norm_text # # ## Concat and","#simple_models = [ # # PV-DM w/concatenation - window=5 (both","temp = '' # output = fol.replace('/', '-') + '.txt'","#doc_vec_list = [x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p',","doc.split == 'test'] #doc_list = alldocs[:] # for reshuffling per","= fol.replace('/', '-') + '.txt' # # # Is there","import requests import tarfile import sys import re import gensim","punctuation with spaces on both sides # for char in","\"this will be painfully slow otherwise\" # #simple_models = [","txt_files: # with open(txt, 'r') as t: # control_chars =","memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k","num_line = \"_*{0} {1}\\n\".format(idx, line) # f.write(num_line) # #SentimentDocument =","t_clean.replace(c, ' ') # # temp += t_clean # #","at extra memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k","import sys import re import gensim from gensim.models.doc2vec import TaggedDocument","# # Is there a better pattern to use? #","= alldocs[:] # for reshuffling per pass # #cores =","# t_clean = t_clean.replace(c, ' ') # # temp +=","Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # #","= re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # # for c in control_chars:","in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for","window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2,","#for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model),","as n: # n.write(temp_norm) # # alldata += temp_norm #","# # Replace breaks with spaces # norm_text = norm_text.replace('<br","f: for vec in x: f.write(vec) f.write(\"\\n\") # pickle.dump(models_by_name, open('model.p',","# continue # # with open('/'.join([dirname, output]), 'w') as n:","gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data'","# model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model in","vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word","line in enumerate(alldata.splitlines()): # num_line = \"_*{0} {1}\\n\".format(idx, line) #","# norm_text = norm_text.replace('<br />', ' ') # # #","in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0])","gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags = [line_no] #","fol.replace('/', '-') + '.txt' # # # Is there a","w/concatenation - window=5 (both sides) approximates paper's 10-word total window","model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model in simple_models)","# # return norm_text # # ## Concat and normalize","norm_text = norm_text.replace(char, ' ' + char + ' ')","words = tokens[1:] # tags = [line_no] # `tags =","enumerate(alldata.splitlines()): # num_line = \"_*{0} {1}\\n\".format(idx, line) # f.write(num_line) #","lower-case and strip punctuation/symbols from words #def normalize_text(text): # norm_text","1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special","') # # # Pad punctuation with spaces on both","t.read() # # t_clean = t_clean.replace('\\n', ' ') # t_clean","# # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5,","one special NULL word so it serves as template #for","extra # sentiment = [1.0, 0.0, 1.0, 0.0, None, None,","== 'train'] #test_docs = [doc for doc in alldocs if","'train'] #test_docs = [doc for doc in alldocs if doc.split","n: # n.write(temp_norm) # # alldata += temp_norm # #with","w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores),","in control_chars: # t_clean = t_clean.replace(c, ' ') # #","better pattern to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt']))","['.', '\"', ',', '(', ')', '!', '?', ';', ':']: #","#doc_list = alldocs[:] # for reshuffling per pass # #cores","extra memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train,","sys import re import gensim from gensim.models.doc2vec import TaggedDocument from","TaggedDocument from collections import namedtuple from gensim.models import Doc2Vec import","# Replace breaks with spaces # norm_text = norm_text.replace('<br />',","# output = fol.replace('/', '-') + '.txt' # # #","save it #doc_vec_list = [x.docvecs for x in simple_models] docvecs","= t.read() # # t_clean = t_clean.replace('\\n', ' ') #","= ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra #","'.txt' # # # Is there a better pattern to","#folders = os.listdir(dirname) #alldata = '' # #for fol in","PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # #","normalize test/train data #folders = os.listdir(dirname) #alldata = '' #","be painfully slow otherwise\" # #simple_models = [ # #","will be painfully slow otherwise\" # #simple_models = [ #","temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for","docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: #","open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx, line in","#SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs =","doc.split == 'train'] #test_docs = [doc for doc in alldocs","with spaces on both sides # for char in ['.',","txt in txt_files: # with open(txt, 'r') as t: #","sentiment') # #alldocs = [] # will hold all docs","original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no,","= [ # # PV-DM w/concatenation - window=5 (both sides)","#simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so it","test/train data #folders = os.listdir(dirname) #alldata = '' # #for","':']: # norm_text = norm_text.replace(char, ' ' + char +","would also work at extra memory cost # split =","/>', ' ') # # # Pad punctuation with spaces","in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x:","workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10,","norm_text # # ## Concat and normalize test/train data #folders","vector list and save it #doc_vec_list = [x.docvecs for x","model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] =","import tarfile import sys import re import gensim from gensim.models.doc2vec","dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ##","#for fol in folders: # temp = '' # output","# tags = [line_no] # `tags = [tokens[0]]` would also","[1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K","Replace breaks with spaces # norm_text = norm_text.replace('<br />', '","work at extra memory cost # split = ['train','test','extra','extra'][line_no//25000] #","hs=0, min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1,","# alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc for","-1, \"this will be painfully slow otherwise\" # #simple_models =","# t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # # for c","window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ## speed setup","# # # Pad punctuation with spaces on both sides","+= temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: #","ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C')","# if len(temp_norm) == 1: # continue # # with","if doc.split == 'train'] #test_docs = [doc for doc in","= \"_*{0} {1}\\n\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument',","'(', ')', '!', '?', ';', ':']: # norm_text = norm_text.replace(char,","sys.setdefaultencoding(\"utf-8\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert","as t: # control_chars = [chr(0x85)] # t_clean = t.read()","sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500]","so it serves as template #for model in simple_models[1:]: #","# #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) #","and strip punctuation/symbols from words #def normalize_text(text): # norm_text =","' ') # t_clean = re.sub(r'[^\\x00-\\x7F]+',' ', t_clean) # #","Pad punctuation with spaces on both sides # for char","#train_docs = [doc for doc in alldocs if doc.split ==","hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5,","simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model","# [12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags,","# # alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w')","slow otherwise\" # #simple_models = [ # # PV-DM w/concatenation","## Create a document vector list and save it #doc_vec_list","#test_docs = [doc for doc in alldocs if doc.split ==","dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW","speed setup by sharing results of 1st model's vocabulary scan","1: # continue # # with open('/'.join([dirname, output]), 'w') as","open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line in enumerate(alldata):"],"string":"[\n \"Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import multiprocessing from\",\n \"alldocs if doc.split == 'test'] #doc_list = alldocs[:] # for\",\n \"# # with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm)\",\n \"f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') #\",\n \"split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra\",\n \"window=5 (both sides) approximates paper's 10-word total window size #\",\n \"# # # Is there a better pattern to use?\",\n \"continue # # with open('/'.join([dirname, output]), 'w') as n: #\",\n \"import os import os.path import requests import tarfile import sys\",\n \"t_clean = t_clean.replace('\\\\n', ' ') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ',\",\n \"'' # output = fol.replace('/', '-') + '.txt' # #\",\n \"split, sentiment)) # #train_docs = [doc for doc in alldocs\",\n \"') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # # for\",\n \"# alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as\",\n \"# Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM\",\n \"sentiment)) # #train_docs = [doc for doc in alldocs if\",\n \"# # # Replace breaks with spaces # norm_text =\",\n \"# temp += \\\"\\\\n\\\" # # temp_norm = normalize_text(temp) #\",\n \"by sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) #\",\n \"'*.txt'])) # # for txt in txt_files: # with open(txt,\",\n \"# # if len(temp_norm) == 1: # continue # #\",\n \"t_clean = t.read() # # t_clean = t_clean.replace('\\\\n', ' ')\",\n \"len(temp_norm) == 1: # continue # # with open('/'.join([dirname, output]),\",\n \"tokens[1:] # tags = [line_no] # `tags = [tokens[0]]` would\",\n \"gensim.models.doc2vec import TaggedDocument from collections import namedtuple from gensim.models import\",\n \"# PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0,\",\n \"' ') # # temp += t_clean # # temp\",\n \"requests import tarfile import sys import re import gensim from\",\n \"enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec)\",\n \"to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # #\",\n \"= t_clean.replace(c, ' ') # # temp += t_clean #\",\n \"= [chr(0x85)] # t_clean = t.read() # # t_clean =\",\n \"alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f:\",\n \"punctuation/symbols from words #def normalize_text(text): # norm_text = text.lower() #\",\n \"# split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k\",\n \"serves as template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) #\",\n \"in txt_files: # with open(txt, 'r') as t: # control_chars\",\n \"10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5,\",\n \"negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100,\",\n \"for txt in txt_files: # with open(txt, 'r') as t:\",\n \"tarfile import sys import re import gensim from gensim.models.doc2vec import\",\n \"[doc for doc in alldocs if doc.split == 'train'] #test_docs\",\n \"[doc for doc in alldocs if doc.split == 'test'] #doc_list\",\n \"os.path import requests import tarfile import sys import re import\",\n \"\\\"\\\\n\\\" # # temp_norm = normalize_text(temp) # # if len(temp_norm)\",\n \"'\\\"', ',', '(', ')', '!', '?', ';', ':']: # norm_text\",\n \"= '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to\",\n \"Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] #\",\n \"= [doc for doc in alldocs if doc.split == 'train']\",\n \"normalize_text(text): # norm_text = text.lower() # # # Replace breaks\",\n \"')', '!', '?', ';', ':']: # norm_text = norm_text.replace(char, '\",\n \"unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc\",\n \"25k train, 25k test, 25k extra # sentiment = [1.0,\",\n \"[chr(0x85)] # t_clean = t.read() # # t_clean = t_clean.replace('\\\\n',\",\n \"= [] # will hold all docs in original order\",\n \"multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will be painfully slow\",\n \"# PV-DM/concat requires one special NULL word so it serves\",\n \"in ['.', '\\\"', ',', '(', ')', '!', '?', ';', ':']:\",\n \"norm_text = text.lower() # # # Replace breaks with spaces\",\n \"from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec\",\n \"pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # #\",\n \"None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown\",\n \"# Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), #\",\n \"'-') + '.txt' # # # Is there a better\",\n \"= [tokens[0]]` would also work at extra memory cost #\",\n \"Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average\",\n \"cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test,\",\n \"Create a document vector list and save it #doc_vec_list =\",\n \"import glob import os import os.path import requests import tarfile\",\n \"with open(txt, 'r') as t: # control_chars = [chr(0x85)] #\",\n \"= pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for\",\n \"tags, split, sentiment)) # #train_docs = [doc for doc in\",\n \"will hold all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt'))\",\n \"normalize_text(temp) # # if len(temp_norm) == 1: # continue #\",\n \"breaks with spaces # norm_text = norm_text.replace('<br />', ' ')\",\n \"from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname =\",\n \"OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys)\",\n \"from words #def normalize_text(text): # norm_text = text.lower() # #\",\n \"{1}\\\\n\\\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags\",\n \"#locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to lower-case and\",\n \"25k extra # sentiment = [1.0, 0.0, 1.0, 0.0, None,\",\n \"#with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx, line\",\n \"' + char + ' ') # # return norm_text\",\n \"and save it #doc_vec_list = [x.docvecs for x in simple_models]\",\n \"pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this\",\n \"in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1],\",\n \"line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words =\",\n \"# Pad punctuation with spaces on both sides # for\",\n \"'test'] #doc_list = alldocs[:] # for reshuffling per pass #\",\n \"'/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text to lower-case\",\n \"# for char in ['.', '\\\"', ',', '(', ')', '!',\",\n \"in folders: # temp = '' # output = fol.replace('/',\",\n \"import gensim.models.doc2vec from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec\",\n \"# temp += t_clean # # temp += \\\"\\\\n\\\" #\",\n \"spaces # norm_text = norm_text.replace('<br />', ' ') # #\",\n \"to lower-case and strip punctuation/symbols from words #def normalize_text(text): #\",\n \"for idx, line in enumerate(alldata.splitlines()): # num_line = \\\"_*{0} {1}\\\\n\\\".format(idx,\",\n \"a document vector list and save it #doc_vec_list = [x.docvecs\",\n \"[] # will hold all docs in original order #with\",\n \"namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from collections import\",\n \"len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as\",\n \"[tokens[0]]` would also work at extra memory cost # split\",\n \"gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will be painfully slow otherwise\\\" #\",\n \"import locale import glob import os import os.path import requests\",\n \"Is there a better pattern to use? # txt_files =\",\n \"# ## Create a document vector list and save it\",\n \"train, 25k test, 25k extra # sentiment = [1.0, 0.0,\",\n \"use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for\",\n \"size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW #\",\n \"text to lower-case and strip punctuation/symbols from words #def normalize_text(text):\",\n \"for char in ['.', '\\\"', ',', '(', ')', '!', '?',\",\n \"line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words\",\n \"alldocs if doc.split == 'train'] #test_docs = [doc for doc\",\n \"# with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) #\",\n \"# num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line) # #SentimentDocument\",\n \"order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line\",\n \"alldata: # for line_no, line in enumerate(alldata): # tokens =\",\n \"# for idx, line in enumerate(alldata.splitlines()): # num_line = \\\"_*{0}\",\n \"# tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags\",\n \"in alldocs if doc.split == 'train'] #test_docs = [doc for\",\n \"NULL word so it serves as template #for model in\",\n \"as template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name\",\n \"# return norm_text # # ## Concat and normalize test/train\",\n \"word so it serves as template #for model in simple_models[1:]:\",\n \"as f: # for idx, line in enumerate(alldata.splitlines()): # num_line\",\n \"gensim from gensim.models.doc2vec import TaggedDocument from collections import namedtuple from\",\n \"open(txt, 'r') as t: # control_chars = [chr(0x85)] # t_clean\",\n \"# ## Concat and normalize test/train data #folders = os.listdir(dirname)\",\n \"locale import glob import os import os.path import requests import\",\n \"a better pattern to use? # txt_files = glob.glob('/'.join([dirname, fol,\",\n \"12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) #\",\n \"= ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create\",\n \"enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] #\",\n \"0.0, 1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K pos,\",\n \"alldocs[:] # for reshuffling per pass # #cores = multiprocessing.cpu_count()\",\n \"for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc']\",\n \"and normalize test/train data #folders = os.listdir(dirname) #alldata = ''\",\n \"of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one\",\n \"# control_chars = [chr(0x85)] # t_clean = t.read() # #\",\n \"# Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #]\",\n \"idx, line in enumerate(alldata.splitlines()): # num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line)\",\n \"# # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores),\",\n \"= os.listdir(dirname) #alldata = '' # #for fol in folders:\",\n \"negative=5, hs=0, min_count=2, workers=cores), #] # ## speed setup by\",\n \"total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0,\",\n \"= glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in txt_files:\",\n \"25k test, 25k extra # sentiment = [1.0, 0.0, 1.0,\",\n \"hs=0, min_count=2, workers=cores), #] # ## speed setup by sharing\",\n \"= [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500] #\",\n \"c in control_chars: # t_clean = t_clean.replace(c, ' ') #\",\n \"t_clean # # temp += \\\"\\\\n\\\" # # temp_norm =\",\n \"+= \\\"\\\\n\\\" # # temp_norm = normalize_text(temp) # # if\",\n \"# # temp_norm = normalize_text(temp) # # if len(temp_norm) ==\",\n \"folders: # temp = '' # output = fol.replace('/', '-')\",\n \"sides # for char in ['.', '\\\"', ',', '(', ')',\",\n \"> -1, \\\"this will be painfully slow otherwise\\\" # #simple_models\",\n \"for doc in alldocs if doc.split == 'train'] #test_docs =\",\n \"there a better pattern to use? # txt_files = glob.glob('/'.join([dirname,\",\n \"# # t_clean = t_clean.replace('\\\\n', ' ') # t_clean =\",\n \"# # temp += \\\"\\\\n\\\" # # temp_norm = normalize_text(temp)\",\n \"None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown #\",\n \"then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs =\",\n \"for c in control_chars: # t_clean = t_clean.replace(c, ' ')\",\n \"None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words,\",\n \"i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in\",\n \"spaces on both sides # for char in ['.', '\\\"',\",\n \"min_count=2, workers=cores), #] # ## speed setup by sharing results\",\n \"pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x\",\n \"workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2,\",\n \"in alldocs if doc.split == 'test'] #doc_list = alldocs[:] #\",\n \"from collections import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec\",\n \"= norm_text.replace(char, ' ' + char + ' ') #\",\n \"re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # # for c in control_chars: #\",\n \"#models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ##\",\n \"txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in\",\n \"= ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector list\",\n \"results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires\",\n \"it #doc_vec_list = [x.docvecs for x in simple_models] docvecs =\",\n \"+ char + ' ') # # return norm_text #\",\n \"n.write(temp_norm) # # alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']),\",\n \"re import gensim from gensim.models.doc2vec import TaggedDocument from collections import\",\n \"temp_norm = normalize_text(temp) # # if len(temp_norm) == 1: #\",\n \"# #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx,\",\n \"= norm_text.replace('<br />', ' ') # # # Pad punctuation\",\n \"size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ## speed\",\n \"both sides # for char in ['.', '\\\"', ',', '(',\",\n \"doc in alldocs if doc.split == 'train'] #test_docs = [doc\",\n \"# 25k train, 25k test, 25k extra # sentiment =\",\n \"# for reshuffling per pass # #cores = multiprocessing.cpu_count() #assert\",\n \"glob import os import os.path import requests import tarfile import\",\n \"f: # for idx, line in enumerate(alldata.splitlines()): # num_line =\",\n \"#with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line in\",\n \"' ') # # # Pad punctuation with spaces on\",\n \"= [x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb'))\",\n \"- window=5 (both sides) approximates paper's 10-word total window size\",\n \"PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word total\",\n \"#alldata = '' # #for fol in folders: # temp\",\n \"pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment))\",\n \"for line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() #\",\n \"pattern to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) #\",\n \"data #folders = os.listdir(dirname) #alldata = '' # #for fol\",\n \"reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ##\",\n \"model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL\",\n \"= normalize_text(temp) # # if len(temp_norm) == 1: # continue\",\n \"as f: for vec in x: f.write(vec) f.write(\\\"\\\\n\\\") # pickle.dump(models_by_name,\",\n \"# n.write(temp_norm) # # alldata += temp_norm # #with open('/'.join([dirname,\",\n \"+= t_clean # # temp += \\\"\\\\n\\\" # # temp_norm\",\n \"text.lower() # # # Replace breaks with spaces # norm_text\",\n \"## speed setup by sharing results of 1st model's vocabulary\",\n \"import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL,\",\n \"= OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm'] =\",\n \"for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs)\",\n \"for reshuffling per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION\",\n \"ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector list and\",\n \"Convert text to lower-case and strip punctuation/symbols from words #def\",\n \"template #for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name =\",\n \"'alldata-id.txt')) as alldata: # for line_no, line in enumerate(alldata): #\",\n \"open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec) f.write(\\\"\\\\n\\\") #\",\n \"glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt in txt_files: #\",\n \"# # PV-DM w/concatenation - window=5 (both sides) approximates paper's\",\n \"import gensim from gensim.models.doc2vec import TaggedDocument from collections import namedtuple\",\n \"neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs\",\n \"# txt_files = glob.glob('/'.join([dirname, fol, '*.txt'])) # # for txt\",\n \"gensim.models import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import\",\n \"# for line_no, line in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split()\",\n \"# PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), #\",\n \"# f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment')\",\n \"= [line_no] # `tags = [tokens[0]]` would also work at\",\n \"approximates paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100,\",\n \"in enumerate(alldata): # tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:]\",\n \"for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec\",\n \"' ' + char + ' ') # # return\",\n \"# # Pad punctuation with spaces on both sides #\",\n \"= t_clean.replace('\\\\n', ' ') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean)\",\n \"docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0]\",\n \"' ') # # return norm_text # # ## Concat\",\n \"= '' # output = fol.replace('/', '-') + '.txt' #\",\n \"'!', '?', ';', ':']: # norm_text = norm_text.replace(char, ' '\",\n \"# `tags = [tokens[0]]` would also work at extra memory\",\n \"t_clean = t_clean.replace(c, ' ') # # temp += t_clean\",\n \"'rb')) print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in\",\n \"# temp_norm = normalize_text(temp) # # if len(temp_norm) == 1:\",\n \"import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from collections\",\n \"'words tags split sentiment') # #alldocs = [] # will\",\n \"with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) # #\",\n \"# for txt in txt_files: # with open(txt, 'r') as\",\n \"'C') # # ## Convert text to lower-case and strip\",\n \"import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') #\",\n \"print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w')\",\n \"# # for txt in txt_files: # with open(txt, 'r')\",\n \"'alldata-id.txt']), 'w') as f: # for idx, line in enumerate(alldata.splitlines()):\",\n \"= tokens[1:] # tags = [line_no] # `tags = [tokens[0]]`\",\n \"0.0, None, None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2\",\n \"# ## Convert text to lower-case and strip punctuation/symbols from\",\n \"# words = tokens[1:] # tags = [line_no] # `tags\",\n \"if doc.split == 'test'] #doc_list = alldocs[:] # for reshuffling\",\n \"line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words tags split\",\n \"on both sides # for char in ['.', '\\\"', ',',\",\n \"tokens = gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags =\",\n \"norm_text.replace(char, ' ' + char + ' ') # #\",\n \"collections import namedtuple from gensim.models import Doc2Vec import gensim.models.doc2vec from\",\n \"# temp = '' # output = fol.replace('/', '-') +\",\n \"#def normalize_text(text): # norm_text = text.lower() # # # Replace\",\n \"== 1: # continue # # with open('/'.join([dirname, output]), 'w')\",\n \"= gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags = [line_no]\",\n \"Concat and normalize test/train data #folders = os.listdir(dirname) #alldata =\",\n \"temp += t_clean # # temp += \\\"\\\\n\\\" # #\",\n \"simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0]) print\",\n \"sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat\",\n \"with spaces # norm_text = norm_text.replace('<br />', ' ') #\",\n \"split sentiment') # #alldocs = [] # will hold all\",\n \"PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2,\",\n \"OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1],\",\n \"# # ## Convert text to lower-case and strip punctuation/symbols\",\n \"# #for fol in folders: # temp = '' #\",\n \"in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for\",\n \"docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for\",\n \"control_chars = [chr(0x85)] # t_clean = t.read() # # t_clean\",\n \"# # for c in control_chars: # t_clean = t_clean.replace(c,\",\n \"t_clean.replace('\\\\n', ' ') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) #\",\n \"+ '.txt' # # # Is there a better pattern\",\n \"'?', ';', ':']: # norm_text = norm_text.replace(char, ' ' +\",\n \"from gensim.models.doc2vec import TaggedDocument from collections import namedtuple from gensim.models\",\n \"+ ' ') # # return norm_text # # ##\",\n \"## Concat and normalize test/train data #folders = os.listdir(dirname) #alldata\",\n \"import os.path import requests import tarfile import sys import re\",\n \"multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname\",\n \"for doc in alldocs if doc.split == 'test'] #doc_list =\",\n \"# #train_docs = [doc for doc in alldocs if doc.split\",\n \"tags split sentiment') # #alldocs = [] # will hold\",\n \"min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0,\",\n \"', t_clean) # # for c in control_chars: # t_clean\",\n \"[x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print\",\n \"`tags = [tokens[0]]` would also work at extra memory cost\",\n \"ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a\",\n \"# norm_text = norm_text.replace(char, ' ' + char + '\",\n \"# t_clean = t.read() # # t_clean = t_clean.replace('\\\\n', '\",\n \"# #models_by_name = OrderedDict((str(model), model) for model in simple_models) #\",\n \"simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document\",\n \"= '' # #for fol in folders: # temp =\",\n \"# t_clean = t_clean.replace('\\\\n', ' ') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+','\",\n \"alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc for doc\",\n \"model) for model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]])\",\n \"PV-DM/concat requires one special NULL word so it serves as\",\n \"[12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags, split,\",\n \"import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\")\",\n \"painfully slow otherwise\\\" # #simple_models = [ # # PV-DM\",\n \"gensim.models.doc2vec from collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import\",\n \"print len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs):\",\n \"# #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will\",\n \"# #SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs\",\n \"norm_text = norm_text.replace('<br />', ' ') # # # Pad\",\n \"sides) approximates paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1,\",\n \"namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs = [] #\",\n \"print docvecs[0] for i,x in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f:\",\n \"= namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs = []\",\n \"#models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) # ## Create a document vector\",\n \"document vector list and save it #doc_vec_list = [x.docvecs for\",\n \"#cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will be\",\n \"reshuffling per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION >\",\n \"import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import multiprocessing\",\n \"# # temp += t_clean # # temp += \\\"\\\\n\\\"\",\n \"workers=cores), #] # ## speed setup by sharing results of\",\n \"#] # ## speed setup by sharing results of 1st\",\n \"# # ## Concat and normalize test/train data #folders =\",\n \"hold all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as\",\n \"'w') as n: # n.write(temp_norm) # # alldata += temp_norm\",\n \"# #alldocs = [] # will hold all docs in\",\n \"# PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word\",\n \"from gensim.models import Doc2Vec import gensim.models.doc2vec from collections import OrderedDict\",\n \"#models_by_name = OrderedDict((str(model), model) for model in simple_models) # #models_by_name['dbow+dmm']\",\n \"words #def normalize_text(text): # norm_text = text.lower() # # #\",\n \"char in ['.', '\\\"', ',', '(', ')', '!', '?', ';',\",\n \"'r') as t: # control_chars = [chr(0x85)] # t_clean =\",\n \"min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100,\",\n \"with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x: f.write(vec) f.write(\\\"\\\\n\\\")\",\n \"return norm_text # # ## Concat and normalize test/train data\",\n \"collections import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import\",\n \"fol in folders: # temp = '' # output =\",\n \"output]), 'w') as n: # n.write(temp_norm) # # alldata +=\",\n \"t_clean) # # for c in control_chars: # t_clean =\",\n \"scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so\",\n \"# ## speed setup by sharing results of 1st model's\",\n \"# for c in control_chars: # t_clean = t_clean.replace(c, '\",\n \"all docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata:\",\n \"as alldata: # for line_no, line in enumerate(alldata): # tokens\",\n \"test, 25k extra # sentiment = [1.0, 0.0, 1.0, 0.0,\",\n \"per pass # #cores = multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1,\",\n \"requires one special NULL word so it serves as template\",\n \"for vec in x: f.write(vec) f.write(\\\"\\\\n\\\") # pickle.dump(models_by_name, open('model.p', 'wb'))\",\n \"special NULL word so it serves as template #for model\",\n \"# norm_text = text.lower() # # # Replace breaks with\",\n \"= text.lower() # # # Replace breaks with spaces #\",\n \"') # # temp += t_clean # # temp +=\",\n \"t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # # for c in\",\n \"setup by sharing results of 1st model's vocabulary scan #simple_models[0].build_vocab(alldocs)\",\n \"os import os.path import requests import tarfile import sys import\",\n \"in enumerate(alldata.splitlines()): # num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line)\",\n \"import re import gensim from gensim.models.doc2vec import TaggedDocument from collections\",\n \"# Is there a better pattern to use? # txt_files\",\n \"import TaggedDocument from collections import namedtuple from gensim.models import Doc2Vec\",\n \"char + ' ') # # return norm_text # #\",\n \"simple_models[0]]) # ## Create a document vector list and save\",\n \"size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores),\",\n \"1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K pos, 12.5K\",\n \"import OrderedDict import multiprocessing from gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle\",\n \"t: # control_chars = [chr(0x85)] # t_clean = t.read() #\",\n \"len(docvecs) print len(docvecs[0]) print docvecs[0] for i,x in enumerate(docvecs): with\",\n \"None, None, None, None][line_no//12500] # [12.5K pos, 12.5K neg]*2 then\",\n \"# with open(txt, 'r') as t: # control_chars = [chr(0x85)]\",\n \"';', ':']: # norm_text = norm_text.replace(char, ' ' + char\",\n \"negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1,\",\n \"= multiprocessing.cpu_count() #assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will be painfully\",\n \"model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model)\",\n \"# will hold all docs in original order #with open(os.path.join(dirname,\",\n \"list and save it #doc_vec_list = [x.docvecs for x in\",\n \"output = fol.replace('/', '-') + '.txt' # # # Is\",\n \"also work at extra memory cost # split = ['train','test','extra','extra'][line_no//25000]\",\n \"'w') as f: # for idx, line in enumerate(alldata.splitlines()): #\",\n \"size=100, negative=5, hs=0, min_count=2, workers=cores), # # PV-DM w/average #\",\n \"os.listdir(dirname) #alldata = '' # #for fol in folders: #\",\n \"paper's 10-word total window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5,\",\n \"= [doc for doc in alldocs if doc.split == 'test']\",\n \"# #simple_models = [ # # PV-DM w/concatenation - window=5\",\n \"',', '(', ')', '!', '?', ';', ':']: # norm_text =\",\n \"#alldocs = [] # will hold all docs in original\",\n \"[line_no] # `tags = [tokens[0]]` would also work at extra\",\n \"#assert gensim.models.doc2vec.FAST_VERSION > -1, \\\"this will be painfully slow otherwise\\\"\",\n \"window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0,\",\n \"x in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print\",\n \"(both sides) approximates paper's 10-word total window size # Doc2Vec(dm=1,\",\n \"control_chars: # t_clean = t_clean.replace(c, ' ') # # temp\",\n \"# sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None,\",\n \"#dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert text\",\n \"open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm) # # alldata\",\n \"otherwise\\\" # #simple_models = [ # # PV-DM w/concatenation -\",\n \"simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]])\",\n \"\\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument', 'words\",\n \"it serves as template #for model in simple_models[1:]: # model.reset_from(simple_models[0])\",\n \"strip punctuation/symbols from words #def normalize_text(text): # norm_text = text.lower()\",\n \"tags = [line_no] # `tags = [tokens[0]]` would also work\",\n \"[ # # PV-DM w/concatenation - window=5 (both sides) approximates\",\n \"norm_text.replace('<br />', ' ') # # # Pad punctuation with\",\n \"doc in alldocs if doc.split == 'test'] #doc_list = alldocs[:]\",\n \"'' # #for fol in folders: # temp = ''\",\n \"## Convert text to lower-case and strip punctuation/symbols from words\",\n \"temp += \\\"\\\\n\\\" # # temp_norm = normalize_text(temp) # #\",\n \"if len(temp_norm) == 1: # continue # # with open('/'.join([dirname,\",\n \"['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra # sentiment\",\n \"fol, '*.txt'])) # # for txt in txt_files: # with\",\n \"== 'test'] #doc_list = alldocs[:] # for reshuffling per pass\",\n \"') # # return norm_text # # ## Concat and\",\n \"#simple_models = [ # # PV-DM w/concatenation - window=5 (both\",\n \"temp = '' # output = fol.replace('/', '-') + '.txt'\",\n \"#doc_vec_list = [x.docvecs for x in simple_models] docvecs = pickle.load(open('docvecs.p',\",\n \"doc.split == 'test'] #doc_list = alldocs[:] # for reshuffling per\",\n \"= fol.replace('/', '-') + '.txt' # # # Is there\",\n \"import requests import tarfile import sys import re import gensim\",\n \"punctuation with spaces on both sides # for char in\",\n \"\\\"this will be painfully slow otherwise\\\" # #simple_models = [\",\n \"txt_files: # with open(txt, 'r') as t: # control_chars =\",\n \"memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train, 25k\",\n \"num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line) # #SentimentDocument =\",\n \"t_clean.replace(c, ' ') # # temp += t_clean # #\",\n \"at extra memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k\",\n \"import sys import re import gensim from gensim.models.doc2vec import TaggedDocument\",\n \"# # Is there a better pattern to use? #\",\n \"= alldocs[:] # for reshuffling per pass # #cores =\",\n \"# t_clean = t_clean.replace(c, ' ') # # temp +=\",\n \"Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # #\",\n \"= re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # # for c in control_chars:\",\n \"in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for\",\n \"window size # Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2,\",\n \"#for model in simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model),\",\n \"as n: # n.write(temp_norm) # # alldata += temp_norm #\",\n \"# # Replace breaks with spaces # norm_text = norm_text.replace('<br\",\n \"f: for vec in x: f.write(vec) f.write(\\\"\\\\n\\\") # pickle.dump(models_by_name, open('model.p',\",\n \"# continue # # with open('/'.join([dirname, output]), 'w') as n:\",\n \"gensim.test.test_doc2vec import ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data'\",\n \"# model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model in\",\n \"vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word\",\n \"line in enumerate(alldata.splitlines()): # num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line) #\",\n \"# norm_text = norm_text.replace('<br />', ' ') # # #\",\n \"in simple_models] docvecs = pickle.load(open('docvecs.p', 'rb')) print len(docvecs) print len(docvecs[0])\",\n \"gensim.utils.to_unicode(line).split() # words = tokens[1:] # tags = [line_no] #\",\n \"fol.replace('/', '-') + '.txt' # # # Is there a\",\n \"w/concatenation - window=5 (both sides) approximates paper's 10-word total window\",\n \"model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model in simple_models)\",\n \"# # return norm_text # # ## Concat and normalize\",\n \"norm_text = norm_text.replace(char, ' ' + char + ' ')\",\n \"words = tokens[1:] # tags = [line_no] # `tags =\",\n \"enumerate(alldata.splitlines()): # num_line = \\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line) #\",\n \"lower-case and strip punctuation/symbols from words #def normalize_text(text): # norm_text\",\n \"1st model's vocabulary scan #simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special\",\n \"') # # # Pad punctuation with spaces on both\",\n \"t.read() # # t_clean = t_clean.replace('\\\\n', ' ') # t_clean\",\n \"# # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5,\",\n \"one special NULL word so it serves as template #for\",\n \"extra # sentiment = [1.0, 0.0, 1.0, 0.0, None, None,\",\n \"== 'train'] #test_docs = [doc for doc in alldocs if\",\n \"'train'] #test_docs = [doc for doc in alldocs if doc.split\",\n \"n: # n.write(temp_norm) # # alldata += temp_norm # #with\",\n \"w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores),\",\n \"in control_chars: # t_clean = t_clean.replace(c, ' ') # #\",\n \"better pattern to use? # txt_files = glob.glob('/'.join([dirname, fol, '*.txt']))\",\n \"['.', '\\\"', ',', '(', ')', '!', '?', ';', ':']: #\",\n \"#doc_list = alldocs[:] # for reshuffling per pass # #cores\",\n \"extra memory cost # split = ['train','test','extra','extra'][line_no//25000] # 25k train,\",\n \"sys import re import gensim from gensim.models.doc2vec import TaggedDocument from\",\n \"TaggedDocument from collections import namedtuple from gensim.models import Doc2Vec import\",\n \"# Replace breaks with spaces # norm_text = norm_text.replace('<br />',\",\n \"# output = fol.replace('/', '-') + '.txt' # # #\",\n \"save it #doc_vec_list = [x.docvecs for x in simple_models] docvecs\",\n \"= t.read() # # t_clean = t_clean.replace('\\\\n', ' ') #\",\n \"= ['train','test','extra','extra'][line_no//25000] # 25k train, 25k test, 25k extra #\",\n \"'.txt' # # # Is there a better pattern to\",\n \"#folders = os.listdir(dirname) #alldata = '' # #for fol in\",\n \"PV-DBOW # Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores), # #\",\n \"normalize test/train data #folders = os.listdir(dirname) #alldata = '' #\",\n \"be painfully slow otherwise\\\" # #simple_models = [ # #\",\n \"will be painfully slow otherwise\\\" # #simple_models = [ #\",\n \"temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for\",\n \"docs in original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: #\",\n \"open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: # for idx, line in\",\n \"#SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment') # #alldocs =\",\n \"doc.split == 'train'] #test_docs = [doc for doc in alldocs\",\n \"with spaces on both sides # for char in ['.',\",\n \"txt in txt_files: # with open(txt, 'r') as t: #\",\n \"sentiment') # #alldocs = [] # will hold all docs\",\n \"original order #with open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no,\",\n \"= [ # # PV-DM w/concatenation - window=5 (both sides)\",\n \"#simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so it\",\n \"test/train data #folders = os.listdir(dirname) #alldata = '' # #for\",\n \"':']: # norm_text = norm_text.replace(char, ' ' + char +\",\n \"would also work at extra memory cost # split =\",\n \"/>', ' ') # # # Pad punctuation with spaces\",\n \"in enumerate(docvecs): with open('docvecs_'+str(i)+'.txt','w') as f: for vec in x:\",\n \"workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1, size=100, window=10,\",\n \"norm_text # # ## Concat and normalize test/train data #folders\",\n \"vector list and save it #doc_vec_list = [x.docvecs for x\",\n \"model in simple_models) # #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] =\",\n \"import tarfile import sys import re import gensim from gensim.models.doc2vec\",\n \"dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ##\",\n \"#for fol in folders: # temp = '' # output\",\n \"# tags = [line_no] # `tags = [tokens[0]]` would also\",\n \"[1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500] # [12.5K\",\n \"Replace breaks with spaces # norm_text = norm_text.replace('<br />', '\",\n \"work at extra memory cost # split = ['train','test','extra','extra'][line_no//25000] #\",\n \"hs=0, min_count=2, workers=cores), # # PV-DM w/average # Doc2Vec(dm=1, dm_mean=1,\",\n \"# alldocs.append(SentimentDocument(words, tags, split, sentiment)) # #train_docs = [doc for\",\n \"-1, \\\"this will be painfully slow otherwise\\\" # #simple_models =\",\n \"# t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # # for c\",\n \"window=10, negative=5, hs=0, min_count=2, workers=cores), #] # ## speed setup\",\n \"# # # Pad punctuation with spaces on both sides\",\n \"+= temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w') as f: #\",\n \"ConcatenatedDoc2Vec import pickle reload(sys) sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C')\",\n \"# if len(temp_norm) == 1: # continue # # with\",\n \"if doc.split == 'train'] #test_docs = [doc for doc in\",\n \"= \\\"_*{0} {1}\\\\n\\\".format(idx, line) # f.write(num_line) # #SentimentDocument = namedtuple('SentimentDocument',\",\n \"'(', ')', '!', '?', ';', ':']: # norm_text = norm_text.replace(char,\",\n \"sys.setdefaultencoding(\\\"utf-8\\\") #dirname = '/scratch/ap4608/judge_data' #locale.setlocale(locale.LC_ALL, 'C') # # ## Convert\",\n \"as t: # control_chars = [chr(0x85)] # t_clean = t.read()\",\n \"sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//12500]\",\n \"so it serves as template #for model in simple_models[1:]: #\",\n \"# #models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]]) #models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]]) #\",\n \"and strip punctuation/symbols from words #def normalize_text(text): # norm_text =\",\n \"' ') # t_clean = re.sub(r'[^\\\\x00-\\\\x7F]+',' ', t_clean) # #\",\n \"Pad punctuation with spaces on both sides # for char\",\n \"#train_docs = [doc for doc in alldocs if doc.split ==\",\n \"hs=0, min_count=2, workers=cores), # # PV-DBOW # Doc2Vec(dm=0, size=100, negative=5,\",\n \"simple_models[1:]: # model.reset_from(simple_models[0]) # #models_by_name = OrderedDict((str(model), model) for model\",\n \"# [12.5K pos, 12.5K neg]*2 then unknown # alldocs.append(SentimentDocument(words, tags,\",\n \"# # alldata += temp_norm # #with open('/'.join([dirname, 'alldata-id.txt']), 'w')\",\n \"slow otherwise\\\" # #simple_models = [ # # PV-DM w/concatenation\",\n \"## Create a document vector list and save it #doc_vec_list\",\n \"#test_docs = [doc for doc in alldocs if doc.split ==\",\n \"dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores), # # PV-DBOW\",\n \"speed setup by sharing results of 1st model's vocabulary scan\",\n \"1: # continue # # with open('/'.join([dirname, output]), 'w') as\",\n \"open(os.path.join(dirname, 'alldata-id.txt')) as alldata: # for line_no, line in enumerate(alldata):\"\n]","isConversational":false}}},{"rowIdx":177,"cells":{"ngram":{"kind":"list like","value":["range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__': unittest.main()","<gh_stars>1-10 import unittest from caravan.task import Task from caravan.tables import","[Task.create(\"echo %d\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5)","unittest from caravan.task import Task from caravan.tables import Tables class","for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks =","test_find(self): tasks = [Task.create(\"echo %d\" % i) for i in","def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t =","= [Task.create(\"echo %d\" % i) for i in range(10)] self.assertEqual(Task.all(),","[Task.create(\"echo %d\" % i) for i in range(10)] self.assertEqual(Task.all(), tasks)","1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0, 2.0, 3.0],","i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\"echo","import Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear()","Task(1234, \"echo hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo","for i in range(10): t = Task.create(\"echo %d\" % i)","self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self):","TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t","t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0)","import Task from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self):","3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111)","= [Task.create(\"echo %d\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(),","3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in","False) self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0, 2.0, 3.0], 0, 3,","%d\" % i) for i in range(10)] self.assertEqual(Task.all(), tasks) def","range(10): t = Task.create(\"echo %d\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(),","in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\"echo %d\"","test_task(self): t = Task(1234, \"echo hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(),","%d\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def","from caravan.task import Task from caravan.tables import Tables class TestRun(unittest.TestCase):","in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__':","tasks) def test_find(self): tasks = [Task.create(\"echo %d\" % i) for","Tables.get() self.t.clear() def test_task(self): t = Task(1234, \"echo hello world\")","t = Task(1234, \"echo hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False)","def test_task(self): t = Task(1234, \"echo hello world\") self.assertEqual(t.id(), 1234)","i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\"echo","= Task(1234, \"echo hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(),","%d\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5),","% i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self):","setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t = Task(1234,","self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0, 2.0, 3.0], 0,","222) def test_create(self): for i in range(10): t = Task.create(\"echo","hello world\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished())","caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get()","Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def","import unittest from caravan.task import Task from caravan.tables import Tables","Task from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t","self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\"echo %d\"","test_all(self): tasks = [Task.create(\"echo %d\" % i) for i in","class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self):","from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t =","\"echo hello world\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222)","world\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(),","= Task.create(\"echo %d\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()),","111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(),","self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\"echo %d\" % i)","self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10): t =","self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i","self.t = Tables.get() self.t.clear() def test_task(self): t = Task(1234, \"echo","3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3)","= Tables.get() self.t.clear() def test_task(self): t = Task(1234, \"echo hello","0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(),","i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks","caravan.task import Task from caravan.tables import Tables class TestRun(unittest.TestCase): def","i in range(10): t = Task.create(\"echo %d\" % i) self.assertEqual(t.id(),","def test_find(self): tasks = [Task.create(\"echo %d\" % i) for i","for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__","self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks =","% i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5])","self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111,","Task.create(\"echo %d\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10)","hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello world\")","world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0,","i) for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks","False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\"echo %d\" %","t = Task.create(\"echo %d\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False)","tasks = [Task.create(\"echo %d\" % i) for i in range(10)]","self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello world\") t._store_result([1.0, 2.0,","self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\"echo %d\" % i)","def test_all(self): tasks = [Task.create(\"echo %d\" % i) for i","2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(),","test_create(self): for i in range(10): t = Task.create(\"echo %d\" %","self.t.clear() def test_task(self): t = Task(1234, \"echo hello world\") self.assertEqual(t.id(),","i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ ==","222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222)","in range(10): t = Task.create(\"echo %d\" % i) self.assertEqual(t.id(), i)","i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if","\"echo hello world\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \"echo hello","range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\"echo %d\" %","0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for","self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10):","10) def test_all(self): tasks = [Task.create(\"echo %d\" % i) for","111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10): t","% i) for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self):","self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def","def test_create(self): for i in range(10): t = Task.create(\"echo %d\""],"string":"[\n \"range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__': unittest.main()\",\n \"<gh_stars>1-10 import unittest from caravan.task import Task from caravan.tables import\",\n \"[Task.create(\\\"echo %d\\\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5)\",\n \"unittest from caravan.task import Task from caravan.tables import Tables class\",\n \"for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks =\",\n \"test_find(self): tasks = [Task.create(\\\"echo %d\\\" % i) for i in\",\n \"def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t =\",\n \"= [Task.create(\\\"echo %d\\\" % i) for i in range(10)] self.assertEqual(Task.all(),\",\n \"[Task.create(\\\"echo %d\\\" % i) for i in range(10)] self.assertEqual(Task.all(), tasks)\",\n \"1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0, 2.0, 3.0],\",\n \"i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\\\"echo\",\n \"import Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear()\",\n \"Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo\",\n \"for i in range(10): t = Task.create(\\\"echo %d\\\" % i)\",\n \"self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self):\",\n \"TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t\",\n \"t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0)\",\n \"import Task from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self):\",\n \"3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111)\",\n \"= [Task.create(\\\"echo %d\\\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(),\",\n \"3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in\",\n \"False) self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0, 2.0, 3.0], 0, 3,\",\n \"%d\\\" % i) for i in range(10)] self.assertEqual(Task.all(), tasks) def\",\n \"range(10): t = Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(),\",\n \"in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\\\"echo %d\\\"\",\n \"test_task(self): t = Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(),\",\n \"%d\\\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def\",\n \"from caravan.task import Task from caravan.tables import Tables class TestRun(unittest.TestCase):\",\n \"in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__':\",\n \"tasks) def test_find(self): tasks = [Task.create(\\\"echo %d\\\" % i) for\",\n \"Tables.get() self.t.clear() def test_task(self): t = Task(1234, \\\"echo hello world\\\")\",\n \"t = Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False)\",\n \"def test_task(self): t = Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(), 1234)\",\n \"i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\\\"echo\",\n \"= Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(),\",\n \"%d\\\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5),\",\n \"% i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self):\",\n \"setUp(self): self.t = Tables.get() self.t.clear() def test_task(self): t = Task(1234,\",\n \"self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0, 2.0, 3.0], 0,\",\n \"222) def test_create(self): for i in range(10): t = Task.create(\\\"echo\",\n \"hello world\\\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished())\",\n \"caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get()\",\n \"Tables class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def\",\n \"import unittest from caravan.task import Task from caravan.tables import Tables\",\n \"Task from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t\",\n \"self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\\\"echo %d\\\"\",\n \"test_all(self): tasks = [Task.create(\\\"echo %d\\\" % i) for i in\",\n \"class TestRun(unittest.TestCase): def setUp(self): self.t = Tables.get() self.t.clear() def test_task(self):\",\n \"from caravan.tables import Tables class TestRun(unittest.TestCase): def setUp(self): self.t =\",\n \"\\\"echo hello world\\\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222)\",\n \"world\\\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(),\",\n \"= Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()),\",\n \"111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(),\",\n \"self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\\\"echo %d\\\" % i)\",\n \"self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10): t =\",\n \"self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i\",\n \"self.t = Tables.get() self.t.clear() def test_task(self): t = Task(1234, \\\"echo\",\n \"3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3)\",\n \"= Tables.get() self.t.clear() def test_task(self): t = Task(1234, \\\"echo hello\",\n \"0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(),\",\n \"i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks\",\n \"caravan.task import Task from caravan.tables import Tables class TestRun(unittest.TestCase): def\",\n \"i in range(10): t = Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(),\",\n \"def test_find(self): tasks = [Task.create(\\\"echo %d\\\" % i) for i\",\n \"for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__\",\n \"self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks =\",\n \"% i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5])\",\n \"self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0, 2.0, 3.0], 0, 3, 111,\",\n \"Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False) self.assertEqual(len(Task.all()), 10)\",\n \"hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello world\\\")\",\n \"world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0,\",\n \"i) for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks\",\n \"False) self.assertEqual(len(Task.all()), 10) def test_all(self): tasks = [Task.create(\\\"echo %d\\\" %\",\n \"t = Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(), i) self.assertEqual(t.is_finished(), False)\",\n \"tasks = [Task.create(\\\"echo %d\\\" % i) for i in range(10)]\",\n \"self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello world\\\") t._store_result([1.0, 2.0,\",\n \"self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\\\"echo %d\\\" % i)\",\n \"def test_all(self): tasks = [Task.create(\\\"echo %d\\\" % i) for i\",\n \"2.0, 3.0], 0, 3, 111, 222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(),\",\n \"test_create(self): for i in range(10): t = Task.create(\\\"echo %d\\\" %\",\n \"self.t.clear() def test_task(self): t = Task(1234, \\\"echo hello world\\\") self.assertEqual(t.id(),\",\n \"i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ ==\",\n \"222) self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222)\",\n \"in range(10): t = Task.create(\\\"echo %d\\\" % i) self.assertEqual(t.id(), i)\",\n \"i) for i in range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if\",\n \"\\\"echo hello world\\\") self.assertEqual(t.id(), 1234) self.assertEqual(t.is_finished(), False) self.assertEqual(t.command(), \\\"echo hello\",\n \"range(10)] self.assertEqual(Task.all(), tasks) def test_find(self): tasks = [Task.create(\\\"echo %d\\\" %\",\n \"0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for\",\n \"self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10):\",\n \"10) def test_all(self): tasks = [Task.create(\\\"echo %d\\\" % i) for\",\n \"111) self.assertEqual(t.finish_at(), 222) def test_create(self): for i in range(10): t\",\n \"% i) for i in range(10)] self.assertEqual(Task.all(), tasks) def test_find(self):\",\n \"self.assertTrue(t.is_finished()) self.assertEqual(t.rc(), 0) self.assertEqual(t.rank(), 3) self.assertEqual(t.start_at(), 111) self.assertEqual(t.finish_at(), 222) def\",\n \"def test_create(self): for i in range(10): t = Task.create(\\\"echo %d\\\"\"\n]","isConversational":false}}},{"rowIdx":178,"cells":{"ngram":{"kind":"list like","value":["self.get(\"allowed_domains\", default=None) if allowed_domains is not None: return allowed_domains.split(',') def","self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return","argument is missing: %s' % name, type='argument_required') else: return default","is None and adblock_rules is None: # skip validation return","data, max_timeout): self.data = data self.max_timeout = max_timeout @classmethod def","Initialize options from a Twisted Request. \"\"\" # 1. GET","get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str): content_types =","har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\" Validate size string in","except ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description': \"Can't","list of strings\", ) return save_args def get_load_args(self): load_args =","def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def","not have a body\") return body def get_render_all(self, wait=None): result","def get_render_all(self, wait=None): result = self._get_bool(\"render_all\", False) if result ==","(isinstance(el, (list, tuple)) and len(el) == 2 and string_only): self.raise_error(","list): self.raise_error( argument=\"save_args\", description=\"'save_args' should be either a comma-separated \"","= self.get(\"engine\", default=\"webkit\", type=str) if engine not in {\"webkit\", \"chromium\"}:","= self.data.get(name) if value is not None: if type is","in headers: string_only = all(isinstance(e, str) for e in el)","size strings. Does not special-case ``'full'`` viewport. Raises ``ValueError`` if","from splash.utils import to_bytes, path_join_secure from splash.errors import BadOption class","a JSON array of \" \"(name, value) pairs or a","should be either a JSON object with \" \"argument hashes","map(int, size_str.split('x')) except ValueError: raise ValueError(\"Invalid viewport format: %s\" %","a list of strings\", ) return save_args def get_load_args(self): load_args","options from a Twisted Request. \"\"\" # 1. GET /","max=range[1], value=value) return value elif default is self._REQUIRED: self.raise_error(name, 'Required","isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self):","'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path):","# 2. application/json POST data if b'application/json' in content_type: try:","return value elif default is self._REQUIRED: self.raise_error(name, 'Required argument is","'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path),","type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods","defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h","dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\",","value=value) return value elif default is self._REQUIRED: self.raise_error(name, 'Required argument","argument='headers', description=\"'headers' must be either a JSON array of \"","as e: self.raise_error(\"viewport\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None):","def get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if","self.raise_error( argument=\"load_args\", description=\"'load_args' should be either a JSON object with","if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def","\"Unknown render engine {}\".format(engine)) if browser_engines_enabled is not None: if","def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self):","self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return {","'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(),","return self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method =","request should not have a body\") return body def get_render_all(self,","= adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\", \"Invalid filter names: %s\"","type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name,","def load_cached_args(self, cache): load_args = self.get_load_args() for name, key in","render full webpage\") else: try: validate_size_str(viewport) except ValueError as e:","import to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object): \"\"\"","\"\"\" Return a list of argument names from load_args which","\"\"\" Initialize options from a Twisted Request. \"\"\" # 1.","and adblock_rules is None: # skip validation return filter_names if","content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[]) if","@classmethod def fromrequest(cls, request, max_timeout): \"\"\" Initialize options from a","not all(isinstance(a, str) for a in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args'","} def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality':","request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST data if","defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return","params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \"\"\" Initialize","get_proxy(self): return self.get(\"proxy\", default=None) def get_js_source(self): return self.get(\"js_source\", default=None) def","wait=None): result = self._get_bool(\"render_all\", False) if result == 1 and","value is not None: if type is not None: try:","is out of the allowed range', min=range[0], max=range[1], value=value) return","argument names\", ) # JSON array if not all(isinstance(a, str)","defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\", type=str) if","JSON object\" ) return headers def get_save_args(self): save_args = self.get(\"save_args\",","self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\", default=None) if not","return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are","self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout,","el in headers: string_only = all(isinstance(e, str) for e in","a JSON array with argument names\", ) # JSON array","validate_size_str(viewport) except ValueError as e: self.raise_error(\"viewport\", str(e)) return viewport def","'lua': self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(), 'height':","if value is not None: if type is not None:","pairs or a JSON object\" ) return headers def get_save_args(self):","not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method)) return","return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method =","= self.get(\"http_method\", \"GET\") if method.upper() not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\",","type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED): return self.get(name,","POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in","js_profile: return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles","profile_dir def get_headers(self): headers = self.get(\"headers\", default=None, type=None) if headers","= self.get(\"load_args\", default=None, type=None) if load_args is None: return {}","value is not a \" \"semicolon-separated list of name=hash pairs\"","is None: self.raise_error('js', 'Javascript profiles are not enabled on server')","for name, key in (load_args or {}).items(): self.data[name] = cache[key]","ValueError as e: # security check fails print(e) self.raise_error('js', 'Javascript","not js_profile: return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript","get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value","scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\" %","\"filters\", \"Invalid filter names: %s\" % (filter_names,) ) if adblock_rules","profiles are not enabled on server') try: profile_dir = path_join_secure(js_profiles_path,","self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(),","validate both viewport and window size strings. Does not special-case","def get_http2(self): engine = self.get_engine() if self.get_engine() == \"webkit\": default","validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area =","a list of (name, key) pairs. \"\"\" save_args = self.get_save_args()","validation return filter_names if not filter_names: return filter_names if pool","= self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(',')))","return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\",","object\" ) return headers def get_save_args(self): save_args = self.get(\"save_args\", default=None,","self.data = data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument,","h = map(int, size_str.split('x')) except ValueError: raise ValueError(\"Invalid viewport format:","[self.data.get(name) for name in save_args] keys = cache.add_many(save_values) return list(zip(save_args,","the allowed range', min=range[0], max=range[1], value=value) return value elif default","self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(),","render engine {}\".format(engine)) if browser_engines_enabled is not None: if engine","Does not special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes","in el) if not (isinstance(el, (list, tuple)) and len(el) ==","b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json","is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' % name,","save_args = self.get(\"save_args\", default=None, type=None) if save_args is None: return","webpage\") return result def get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path):","def get_height(self): return self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self):","engine = self.get_engine() if self.get_engine() == \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED","(list, tuple)) and len(el) == 2 and string_only): self.raise_error( argument='headers',",") return scale_method def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0,","description=\"'load_args' string value is not a \" \"semicolon-separated list of","default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name,","\"Unsupported HTTP method {}\".format(method)) return method def get_body(self): body =","not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\"'headers' must be","def get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG),","jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR),","get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() }","load_cached_args(self, cache): load_args = self.get_load_args() for name, key in (load_args","Can be used to validate both viewport and window size","if engine not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine","filter_names: return filter_names if pool is not None: adblock_rules =","except ValueError as e: self.raise_error(\"viewport\", str(e)) return viewport def get_filters(self,","\"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine)) if browser_engines_enabled is not","save_args = self.get_save_args() save_values = [self.data.get(name) for name in save_args]","self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params =","profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile","from a Twisted Request. \"\"\" # 1. GET / POST","or a semicolon-separated list \" \"of name=hash pairs\" ) return","\"string or a JSON array with argument names\", ) #","defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\" Validate size string in WxH","get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default, type=float, range=(0,","def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self):","\"Invalid filter names: %s\" % (filter_names,) ) if adblock_rules is","be used to validate both viewport and window size strings.","content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({","description=\"'save_args' should be either a comma-separated \" \"string or a","2. application/json POST data if b'application/json' in content_type: try: content","self.raise_error(name, 'Required argument is missing: %s' % name, type='argument_required') else:","str): try: load_args = dict( kv.split(\"=\", 1) for kv in","e: # security check fails print(e) self.raise_error('js', 'Javascript profile does","= defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w,","0: self.raise_error(\"render_all\", \"Pass non-zero 'wait' to render full webpage\") return","% (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\",","not a \" \"semicolon-separated list of name=hash pairs\" ) if","max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except","None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*']) if","to validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area","self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media':","pairs\" ) if not isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should","self.raise_error(name, 'Argument is out of the allowed range', min=range[0], max=range[1],","if not isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args' should be either","import os import json from splash import defaults from splash.utils","scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method","= type(value) except ValueError: msg = \"Argument %r has a","'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return {","self.get('filters', '') filter_names = [f for f in filter_names.split(',') if","self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default,","a in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should be a list","argument names from load_args which can't be loaded \"\"\" return","raise_error(cls, argument, description, type='bad_argument', **kwargs): params = { 'type': type,","engine not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine))","in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method)) return method","content_type: request.content.seek(0) # 2. application/json POST data if b'application/json' in","return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self):","}) # 3. js_source from application/javascript POST requests if b'application/javascript'","'description': description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request,","isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should be either a JSON","'invalid_json', 'description': \"Can't decode JSON\", 'message': str(e), }) # 3.",") return load_args def get_viewport(self, wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE)","area=%d) is out of range (%dx%d, area=%d)\" % (w, h,","isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\"'headers' must be either","\"Pass non-zero 'wait' to render full webpage\") else: try: validate_size_str(viewport)","if type is not None: try: value = type(value) except","range (%dx%d, area=%d)\" % (w, h, w * h, max_width,","tuple, dict)): self.raise_error( argument='headers', description=\"'headers' must be either a JSON","\"\"\" # 1. GET / POST data data = {key.decode('utf-8'):","isinstance(load_args, str): try: load_args = dict( kv.split(\"=\", 1) for kv","load_args is None: return {} if isinstance(load_args, str): try: load_args","(name,) self.raise_error(name, msg, required_type=type.__name__) if range is not None and","GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key,","or {}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str,","save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args'","allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\" % scale_method, allowed=allowed_scale_methods, received=scale_method,","def get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\", default=None) if not js_profile:","strings. Does not special-case ``'full'`` viewport. Raises ``ValueError`` if anything","application/javascript POST requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8')","'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(),","body def get_render_all(self, wait=None): result = self._get_bool(\"render_all\", False) if result","is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\",","wait = self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait':","'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(),","type=None) if save_args is None: return [] if isinstance(save_args, str):","scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return self.get(\"quality\",","content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e:","return filter_names if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules","self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not in","max_timeout): self.data = data self.max_timeout = max_timeout @classmethod def raise_error(cls,","= {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict(","defaults.VIEWPORT_SIZE) if viewport == 'full': if wait == 0: self.raise_error(\"viewport\",","used to validate both viewport and window size strings. Does","if wait == 0: self.raise_error(\"viewport\", \"Pass non-zero 'wait' to render","return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(),","if isinstance(url, bytes): url = url.decode('utf8') return url def get_uid(self):","content_types = self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str): content_types = list(filter(None,","description, type='bad_argument', **kwargs): params = { 'type': type, 'argument': argument,","names: %s\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains","% size_str) else: if not ((0 < w <= max_width)","key, values in request.args.items()} if request.method == b'POST': content_type =","does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does","\"filters\", \"Invalid filter names: %s\" % (unknown_filters,) ) return filter_names","self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\", None,","description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout):","0: self.raise_error(\"viewport\", \"Pass non-zero 'wait' to render full webpage\") else:","self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine)) if browser_engines_enabled is not None:","get_baseurl(self): return self._get_url(\"baseurl\", default=None) def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float,","if anything goes wrong. :param size_str: string to validate \"\"\"","to render a response. \"\"\" _REQUIRED = object() def __init__(self,","defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\",","\" \"(name, value) pairs or a JSON object\" ) return","= \"Argument %r has a wrong type\" % (name,) self.raise_error(name,","get_height(self): return self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method","or a JSON object\" ) return headers def get_save_args(self): save_args","= save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args' should","'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params","def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default, type=float,","name, default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url, bytes):","return self.get(\"proxy\", default=None) def get_js_source(self): return self.get(\"js_source\", default=None) def get_width(self):","not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not","png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY),","self.raise_error(\"viewport\", \"Pass non-zero 'wait' to render full webpage\") else: try:","if not js_profile: return js_profile if js_profiles_path is None: self.raise_error('js',","get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\", default=None) if not js_profile: return","and len(el) == 2 and string_only): self.raise_error( argument='headers', description=\"'headers' must","defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT)","\"\"\" Options that control how to render a response. \"\"\"","range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\",","request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) #","self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body':","method = self.get(\"http_method\", \"GET\") if method.upper() not in [\"POST\", \"GET\"]:","of strings\", ) return save_args def get_load_args(self): load_args = self.get(\"load_args\",","response. \"\"\" _REQUIRED = object() def __init__(self, data, max_timeout): self.data","= url.decode('utf8') return url def get_uid(self): return self.get('uid') def get_url(self):",") def validate_size_str(size_str): \"\"\" Validate size string in WxH format.","has a wrong type\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if","def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\", default=None)","\"GET\") if method.upper() not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP","params def get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\",","'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method':","# 3. js_source from application/javascript POST requests if b'application/javascript' in","self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params()","try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e: #","default=None, type=None) if load_args is None: return {} if isinstance(load_args,","if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return","None: return {} if isinstance(load_args, str): try: load_args = dict(","\"of name=hash pairs\" ) return load_args def get_viewport(self, wait=None): viewport","= self.get(name, default, type=None) if isinstance(url, bytes): url = url.decode('utf8')",") except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string value is not","f] if pool is None and adblock_rules is None: #","None and adblock_rules is None: # skip validation return filter_names","a JSON object\" ) if isinstance(headers, (list, tuple)): for el","coding: utf-8 -*- import os import json from splash import","if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] =","raise ValueError(\"Viewport (%dx%d, area=%d) is out of range (%dx%d, area=%d)\"","to_bytes) method = self.get(\"http_method\", \"GET\").upper() if method == 'GET' and","self.get('uid') def get_url(self): return self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\", default=None)","get_url(self): return self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\", default=None) def get_wait(self):","defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster',","self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all':","defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def","params = { 'type': type, 'argument': argument, 'description': description }","adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error(","def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str): content_types","w, h = map(int, size_str.split('x')) except ValueError: raise ValueError(\"Invalid viewport","defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str):","self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED)","if not filter_names: return filter_names if pool is not None:","'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return","method == 'GET' and body: self.raise_error(\"body\", \"GET request should not","1 and wait == 0: self.raise_error(\"render_all\", \"Pass non-zero 'wait' to","in (load_args or {}).items(): self.data[name] = cache[key] def get(self, name,","unknown_filters: self.raise_error( \"filters\", \"Invalid filter names: %s\" % (unknown_filters,) )","try: w, h = map(int, size_str.split('x')) except ValueError: raise ValueError(\"Invalid","description=\"'save_args' should be a list of strings\", ) return save_args","load_args.split(';') ) except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string value is","'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), }","'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(),","of \" \"(name, value) pairs or a JSON object\" )","def get_viewport(self, wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport ==","get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\", type=str) if engine not","array if not all(isinstance(a, str) for a in save_args): self.raise_error(","def __init__(self, data, max_timeout): self.data = data self.max_timeout = max_timeout","fromrequest(cls, request, max_timeout): \"\"\" Initialize options from a Twisted Request.","get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return","type=None) if headers is None: return headers if not isinstance(headers,","either a JSON array of \" \"(name, value) pairs or","get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params())","if isinstance(save_args, str): # comma-separated string save_args = save_args.split(',') if","ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description': \"Can't decode","\" \"(name, value) pairs or a JSON object\" ) if","in WxH format. Can be used to validate both viewport","value <= range[1]): self.raise_error(name, 'Argument is out of the allowed","get_width(self): return self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return","filter names: %s\" % (filter_names,) ) if adblock_rules is not","value elif default is self._REQUIRED: self.raise_error(name, 'Required argument is missing:","if viewport == 'full': if wait == 0: self.raise_error(\"viewport\", \"Pass","return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\", default=None) def get_js_source(self):","h < max_area)): raise ValueError(\"Viewport (%dx%d, area=%d) is out of","= ['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method',","self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait == 0:","None and not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument","save_args def get_load_args(self): load_args = self.get(\"load_args\", default=None, type=None) if load_args","_REQUIRED = object() def __init__(self, data, max_timeout): self.data = data","= self.get(\"body\", None, to_bytes) method = self.get(\"http_method\", \"GET\").upper() if method","return params def get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES),","a Twisted Request. \"\"\" # 1. GET / POST data","\"Pass non-zero 'wait' to render full webpage\") return result def","if browser_engines_enabled is not None: if engine not in browser_engines_enabled:",") return headers def get_save_args(self): save_args = self.get(\"save_args\", default=None, type=None)","if not all(isinstance(a, str) for a in save_args): self.raise_error( argument=\"save_args\",","all values to cache. Return a list of (name, key)","argument=\"save_args\", description=\"'save_args' should be either a comma-separated \" \"string or","either a comma-separated \" \"string or a JSON array with","} def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method':","self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params","defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\",","self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\", type=str)","= defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except ValueError:","that control how to render a response. \"\"\" _REQUIRED =","['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid","js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(),","result = self._get_bool(\"render_all\", False) if result == 1 and wait","params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return","hashes or a semicolon-separated list \" \"of name=hash pairs\" )","import defaults from splash.utils import to_bytes, path_join_secure from splash.errors import","<= max_width) and (0 < h <= max_heigth) and (w","return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types,","= { 'type': type, 'argument': argument, 'description': description } params.update(kwargs)","request.content.seek(0) # 2. application/json POST data if b'application/json' in content_type:","{'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\"html\",","self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images':","import json from splash import defaults from splash.utils import to_bytes,","for a in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should be a","not None: if type is not None: try: value =","exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist')","defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait()","def fromrequest(cls, request, max_timeout): \"\"\" Initialize options from a Twisted","bytes): url = url.decode('utf8') return url def get_uid(self): return self.get('uid')","def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()}","cache): \"\"\" Return a list of argument names from load_args","path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security check fails","default=None) if allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self):","type=str) if engine not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render","wait == 0: self.raise_error(\"viewport\", \"Pass non-zero 'wait' to render full","url = self.get(name, default, type=None) if isinstance(url, bytes): url =","can't be loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\"","== 0: self.raise_error(\"render_all\", \"Pass non-zero 'wait' to render full webpage\")","None: self.raise_error( \"filters\", \"Invalid filter names: %s\" % (filter_names,) )","return self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED):","self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\"scale_method\",","None: # skip validation return filter_names if not filter_names: return","if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\"'headers' must","= self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait,","adblock_rules is None: # skip validation return filter_names if not","pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f for","``'full'`` viewport. Raises ``ValueError`` if anything goes wrong. :param size_str:","webpage\") else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\"viewport\", str(e))","type\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range is not","argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls,","\"Can't decode JSON\", 'message': str(e), }) # 3. js_source from","history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\" Validate size","(%dx%d, area=%d) is out of range (%dx%d, area=%d)\" % (w,","def raise_error(cls, argument, description, type='bad_argument', **kwargs): params = { 'type':","= {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if request.method","range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\", None, type=int, range=(1, defaults.MAX_HEIGTH))","isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args' should be either a comma-separated","if not isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should be either",":param size_str: string to validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth","% name, type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED):","= self.get_engine() if self.get_engine() == \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else:","'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait),","defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\" Validate","cache): \"\"\" Process save_args and put all values to cache.","in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as","cache): load_args = self.get_load_args() for name, key in (load_args or","JSON object with \" \"argument hashes or a semicolon-separated list","\" \"string or a JSON array with argument names\", )","return save_args def get_load_args(self): load_args = self.get(\"load_args\", default=None, type=None) if","engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def","name in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def","scale_method def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def","description=\"'headers' must be either a JSON array of \" \"(name,","max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h =","self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def","utf-8 -*- import os import json from splash import defaults","= cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value =","\"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\" Process save_args and","out of the allowed range', min=range[0], max=range[1], value=value) return value","if method.upper() not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method","all(isinstance(a, str) for a in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should","argument='scale_method', description=\"Invalid 'scale_method': %s\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return","except ValueError: raise ValueError(\"Invalid viewport format: %s\" % size_str) else:","viewport == 'full': if wait == 0: self.raise_error(\"viewport\", \"Pass non-zero","\" \"argument hashes or a semicolon-separated list \" \"of name=hash","self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value","return url def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\"url\")",") if isinstance(headers, (list, tuple)): for el in headers: string_only","comma-separated \" \"string or a JSON array with argument names\",","{}\".format(method)) return method def get_body(self): body = self.get(\"body\", None, to_bytes)","'Javascript profiles are not enabled on server') try: profile_dir =","return default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int,","enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError","in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self,","range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6))","string_only): self.raise_error( argument='headers', description=\"'headers' must be either a JSON array","if pool is None and adblock_rules is None: # skip","max_timeout) def get_expired_args(self, cache): \"\"\" Return a list of argument","argument=\"save_args\", description=\"'save_args' should be a list of strings\", ) return","either a JSON object with \" \"argument hashes or a","None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \"filters\",","\"\"\" save_args = self.get_save_args() save_values = [self.data.get(name) for name in","1e6)) def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\",","cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args()","%s\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains =","size_str: string to validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth =","def get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\",","string in WxH format. Can be used to validate both","object with \" \"argument hashes or a semicolon-separated list \"","<= value <= range[1]): self.raise_error(name, 'Argument is out of the","a comma-separated \" \"string or a JSON array with argument","<= max_heigth) and (w * h < max_area)): raise ValueError(\"Viewport","not special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes wrong.","= request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def","raise BadOption({ 'type': 'invalid_json', 'description': \"Can't decode JSON\", 'message': str(e),","body\") return body def get_render_all(self, wait=None): result = self._get_bool(\"render_all\", False)","from splash.errors import BadOption class RenderOptions(object): \"\"\" Options that control","(list, tuple)): for el in headers: string_only = all(isinstance(e, str)","is not a \" \"semicolon-separated list of name=hash pairs\" )","None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\", \"Invalid filter","console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\"","and body: self.raise_error(\"body\", \"GET request should not have a body\")","return engine def get_http2(self): engine = self.get_engine() if self.get_engine() ==","return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\"response_body\",","if adblock_rules is None: self.raise_error( \"filters\", \"Invalid filter names: %s\"","{}\".format(engine)) return engine def get_http2(self): engine = self.get_engine() if self.get_engine()","None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD)","if result == 1 and wait == 0: self.raise_error(\"render_all\", \"Pass","= self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait ==","if self.get_engine() == \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine","have a body\") return body def get_render_all(self, wait=None): result =","default=None, type=None) if headers is None: return headers if not","def get_js_source(self): return self.get(\"js_source\", default=None) def get_width(self): return self.get(\"width\", None,","'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self):","script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def","how to render a response. \"\"\" _REQUIRED = object() def","is not None: if type is not None: try: value","default=None) def get_js_source(self): return self.get(\"js_source\", default=None) def get_width(self): return self.get(\"width\",","if isinstance(headers, (list, tuple)): for el in headers: string_only =","name=hash pairs\" ) if not isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args'","== 'full': if wait == 0: self.raise_error(\"viewport\", \"Pass non-zero 'wait'","and (0 < h <= max_heigth) and (w * h","None: try: value = type(value) except ValueError: msg = \"Argument","% scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return","load_args = self.get_load_args() for name, key in (load_args or {}).items():","required_type=type.__name__) if range is not None and not (range[0] <=","cls(data, max_timeout) def get_expired_args(self, cache): \"\"\" Return a list of","if headers is None: return headers if not isinstance(headers, (list,","description=\"'load_args' should be either a JSON object with \" \"argument","self.get(\"engine\", default=\"webkit\", type=str) if engine not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\",","self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source':","special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes wrong. :param","type is not None: try: value = type(value) except ValueError:","range', min=range[0], max=range[1], value=value) return value elif default is self._REQUIRED:","with argument names\", ) # JSON array if not all(isinstance(a,","self.raise_error('js', 'Javascript profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js',","list of (name, key) pairs. \"\"\" save_args = self.get_save_args() save_values","None: self.raise_error('js', 'Javascript profiles are not enabled on server') try:",") return save_args def get_load_args(self): load_args = self.get(\"load_args\", default=None, type=None)","size_str) else: if not ((0 < w <= max_width) and","[f for f in filter_names.split(',') if f] if pool is",") if not isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should be","name, key in (load_args or {}).items(): self.data[name] = cache[key] def","js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not enabled on","headers: string_only = all(isinstance(e, str) for e in el) if","format: %s\" % size_str) else: if not ((0 < w","raise ValueError(\"Invalid viewport format: %s\" % size_str) else: if not","return content_types def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None):","-*- import os import json from splash import defaults from","and put all values to cache. Return a list of","of the allowed range', min=range[0], max=range[1], value=value) return value elif","= defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED","or a JSON object\" ) if isinstance(headers, (list, tuple)): for","save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args' should be","json from splash import defaults from splash.utils import to_bytes, path_join_secure","'message': str(e), }) # 3. js_source from application/javascript POST requests","max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try:","save_values = [self.data.get(name) for name in save_args] keys = cache.add_many(save_values)","defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\", defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), )","js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not","# 1. GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8')","url.decode('utf8') return url def get_uid(self): return self.get('uid') def get_url(self): return","goes wrong. :param size_str: string to validate \"\"\" max_width =","return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\" Process save_args and put","except ValueError: msg = \"Argument %r has a wrong type\"","type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self):","non-zero 'wait' to render full webpage\") return result def get_lua_source(self):","or a JSON array with argument names\", ) # JSON","string_only = all(isinstance(e, str) for e in el) if not","values[0].decode('utf-8') for key, values in request.args.items()} if request.method == b'POST':","os import json from splash import defaults from splash.utils import","get_body(self): body = self.get(\"body\", None, to_bytes) method = self.get(\"http_method\", \"GET\").upper()","\"\"\" Process save_args and put all values to cache. Return","return headers if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers',","filter_names if not filter_names: return filter_names if pool is not","pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is","in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should be a list of","browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine)) return engine def get_http2(self):","get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\",","Return a list of (name, key) pairs. \"\"\" save_args =","should be either a comma-separated \" \"string or a JSON","(0 < h <= max_heigth) and (w * h <","area=%d)\" % (w, h, w * h, max_width, max_heigth, max_area))","RenderOptions(object): \"\"\" Options that control how to render a response.","argument=\"load_args\", description=\"'load_args' should be either a JSON object with \"","'type': type, 'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params)","from splash import defaults from splash.utils import to_bytes, path_join_secure from","@classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params = {","both viewport and window size strings. Does not special-case ``'full'``","== b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2.","dict)): self.raise_error( argument='headers', description=\"'headers' must be either a JSON array","save_args and put all values to cache. Return a list","method def get_body(self): body = self.get(\"body\", None, to_bytes) method =","adblock_rules is None: self.raise_error( \"filters\", \"Invalid filter names: %s\" %","allowed range', min=range[0], max=range[1], value=value) return value elif default is","os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return profile_dir def","def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names =",") return filter_names def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None) if","= request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type':","splash.errors import BadOption class RenderOptions(object): \"\"\" Options that control how","min=range[0], max=range[1], value=value) return value elif default is self._REQUIRED: self.raise_error(name,","= defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path): wait =","range=None): value = self.data.get(name) if value is not None: if","received=scale_method, ) return scale_method def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int,","default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url':","default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float,","el) if not (isinstance(el, (list, tuple)) and len(el) == 2","of (name, key) pairs. \"\"\" save_args = self.get_save_args() save_values =","list of name=hash pairs\" ) if not isinstance(load_args, dict): self.raise_error(","if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\"","def validate_size_str(size_str): \"\"\" Validate size string in WxH format. Can","viewport. Raises ``ValueError`` if anything goes wrong. :param size_str: string","max_timeout): \"\"\" Initialize options from a Twisted Request. \"\"\" #","of argument names from load_args which can't be loaded \"\"\"","defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\",","else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\",","msg, required_type=type.__name__) if range is not None and not (range[0]","browser_engines_enabled is not None: if engine not in browser_engines_enabled: self.raise_error(\"engine\",","JSON array with argument names\", ) # JSON array if","== 0: self.raise_error(\"viewport\", \"Pass non-zero 'wait' to render full webpage\")","is missing: %s' % name, type='argument_required') else: return default def","body = self.get(\"body\", None, to_bytes) method = self.get(\"http_method\", \"GET\").upper() if","return self._get_url(\"baseurl\", default=None) def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0,","def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str): content_types","self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def","for f in filter_names.split(',') if f] if pool is None","get_render_all(self, wait=None): result = self._get_bool(\"render_all\", False) if result == 1","not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\" % scale_method,","to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object): \"\"\" Options","not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\", \"Invalid","return filter_names def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None) if allowed_domains","list \" \"of name=hash pairs\" ) return load_args def get_viewport(self,","self.raise_error( argument='headers', description=\"'headers' must be either a JSON array of","render a response. \"\"\" _REQUIRED = object() def __init__(self, data,","return load_args def get_viewport(self, wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if","if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters:","default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url, bytes): url","defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\", default=None) def get_js_source(self): return self.get(\"js_source\",","'') filter_names = [f for f in filter_names.split(',') if f]","\"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA","get_expired_args(self, cache): \"\"\" Return a list of argument names from","return self.get(\"js_source\", default=None) def get_width(self): return self.get(\"width\", None, type=int, range=(1,","name=hash pairs\" ) return load_args def get_viewport(self, wait=None): viewport =","self.raise_error(\"viewport\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names =","get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self):","params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\",","import BadOption class RenderOptions(object): \"\"\" Options that control how to","\"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method)) return method def get_body(self):","* h < max_area)): raise ValueError(\"Viewport (%dx%d, area=%d) is out","string to validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH","tuple)) and len(el) == 2 and string_only): self.raise_error( argument='headers', description=\"'headers'","== 'GET' and body: self.raise_error(\"body\", \"GET request should not have","%s\" % size_str) else: if not ((0 < w <=","array of \" \"(name, value) pairs or a JSON object\"","defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not in allowed_scale_methods:","POST requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0)","value) pairs or a JSON object\" ) return headers def","for el in headers: string_only = all(isinstance(e, str) for e","get_http2(self): engine = self.get_engine() if self.get_engine() == \"webkit\": default =","get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\", default=None)","to render full webpage\") return result def get_lua_source(self): return self.get(\"lua_source\")","allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY,","keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for name, key","HTTP method {}\".format(method)) return method def get_body(self): body = self.get(\"body\",","headers if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\"'headers'","get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f","is None: return headers if not isinstance(headers, (list, tuple, dict)):","headers = self.get(\"headers\", default=None, type=None) if headers is None: return","return self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\", default=None) def get_wait(self): return","pairs. \"\"\" save_args = self.get_save_args() save_values = [self.data.get(name) for name","# JSON array if not all(isinstance(a, str) for a in","pool is None and adblock_rules is None: # skip validation","'description': \"Can't decode JSON\", 'message': str(e), }) # 3. js_source","not exist') return profile_dir def get_headers(self): headers = self.get(\"headers\", default=None,","if isinstance(load_args, str): try: load_args = dict( kv.split(\"=\", 1) for","[\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method)) return method def","range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods =","wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers':","comma-separated string save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error(","ValueError as e: self.raise_error(\"viewport\", str(e)) return viewport def get_filters(self, pool=None,","which can't be loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache):","str): content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return","content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types =","default=None) def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def","self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url","= list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\",","return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self):","not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return profile_dir","content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data,","self.data.get(name) if value is not None: if type is not","render full webpage\") return result def get_lua_source(self): return self.get(\"lua_source\") def","\" \"of name=hash pairs\" ) return load_args def get_viewport(self, wait=None):","return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\"js\", default=None) if","key in (load_args or {}).items(): self.data[name] = cache[key] def get(self,","argument, description, type='bad_argument', **kwargs): params = { 'type': type, 'argument':","on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as","self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML),","= cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args =","save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache):","default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value is not","(range[0] <= value <= range[1]): self.raise_error(name, 'Argument is out of","and not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument is","range[1]): self.raise_error(name, 'Argument is out of the allowed range', min=range[0],","def get_http_method(self): method = self.get(\"http_method\", \"GET\") if method.upper() not in","{ 'type': type, 'argument': argument, 'description': description } params.update(kwargs) raise","Return a list of argument names from load_args which can't","for e in el) if not (isinstance(el, (list, tuple)) and","if load_args is None: return {} if isinstance(load_args, str): try:","unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\", \"Invalid filter names:","self.get_engine() == \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine ==","engine = self.get(\"engine\", default=\"webkit\", type=str) if engine not in {\"webkit\",","self.raise_error(\"body\", \"GET request should not have a body\") return body","viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names","if f] if pool is None and adblock_rules is None:","\"\"\" Validate size string in WxH format. Can be used","{}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str, range=None):","'Javascript profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript","max_heigth) and (w * h < max_area)): raise ValueError(\"Viewport (%dx%d,","self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs):","strings\", ) return save_args def get_load_args(self): load_args = self.get(\"load_args\", default=None,","def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self):","for kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args'","Options that control how to render a response. \"\"\" _REQUIRED","default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types","-*- coding: utf-8 -*- import os import json from splash","None: return [] if isinstance(save_args, str): # comma-separated string save_args","data if b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content))","= self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(',')))","content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST","\"GET\").upper() if method == 'GET' and body: self.raise_error(\"body\", \"GET request","in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return","non-zero 'wait' to render full webpage\") else: try: validate_size_str(viewport) except","= self.get('filters', '') filter_names = [f for f in filter_names.split(',')","def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine =","{ 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport':","be either a comma-separated \" \"string or a JSON array","type(value) except ValueError: msg = \"Argument %r has a wrong","self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return","viewport format: %s\" % size_str) else: if not ((0 <","< h <= max_heigth) and (w * h < max_area)):","allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str):","id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \"\"\" Return a","'scale_method': %s\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def","request, max_timeout): \"\"\" Initialize options from a Twisted Request. \"\"\"","self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return","get_viewport(self, wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport == 'full':","== 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def get_common_params(self,","as e: raise BadOption({ 'type': 'invalid_json', 'description': \"Can't decode JSON\",","%s\" % (filter_names,) ) if adblock_rules is not None: unknown_filters","JSON\", 'message': str(e), }) # 3. js_source from application/javascript POST","not isinstance(load_args, dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should be either a","html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT),","``ValueError`` if anything goes wrong. :param size_str: string to validate","'Required argument is missing: %s' % name, type='argument_required') else: return","self.get(\"headers\", default=None, type=None) if headers is None: return headers if","type=None) if load_args is None: return {} if isinstance(load_args, str):","(%dx%d, area=%d)\" % (w, h, w * h, max_width, max_heigth,","JSON array of \" \"(name, value) pairs or a JSON","key) pairs. \"\"\" save_args = self.get_save_args() save_values = [self.data.get(name) for","'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod def","load_args = self.get(\"load_args\", default=None, type=None) if load_args is None: return","\"semicolon-separated list of name=hash pairs\" ) if not isinstance(load_args, dict):","value = type(value) except ValueError: msg = \"Argument %r has","self.raise_error( argument=\"save_args\", description=\"'save_args' should be either a comma-separated \" \"string","'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua':","pairs or a JSON object\" ) if isinstance(headers, (list, tuple)):","if content_type: request.content.seek(0) # 2. application/json POST data if b'application/json'","list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for name,","False) if result == 1 and wait == 0: self.raise_error(\"render_all\",","string value is not a \" \"semicolon-separated list of name=hash","content_types def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str):","tuple)): for el in headers: string_only = all(isinstance(e, str) for","type, 'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod","js_source from application/javascript POST requests if b'application/javascript' in content_type: data['js_source']","= dict( kv.split(\"=\", 1) for kv in load_args.split(';') ) except","self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), #","msg = \"Argument %r has a wrong type\" % (name,)","Request. \"\"\" # 1. GET / POST data data =","<reponame>tashidexiaoL/splashnew # -*- coding: utf-8 -*- import os import json","def get_proxy(self): return self.get(\"proxy\", default=None) def get_js_source(self): return self.get(\"js_source\", default=None)","validate_size_str(size_str): \"\"\" Validate size string in WxH format. Can be","self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return { 'width':","{}\".format(engine)) if browser_engines_enabled is not None: if engine not in","fails print(e) self.raise_error('js', 'Javascript profile does not exist') if not","return scale_method def get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100))","except ValueError as e: # security check fails print(e) self.raise_error('js',","filter_names.split(',') if f] if pool is None and adblock_rules is","not None and not (range[0] <= value <= range[1]): self.raise_error(name,","= min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default, type=float, range=(0, self.max_timeout)) def","cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\" Process save_args and put all","3. js_source from application/javascript POST requests if b'application/javascript' in content_type:","None, to_bytes) method = self.get(\"http_method\", \"GET\").upper() if method == 'GET'","get_js_source(self): return self.get(\"js_source\", default=None) def get_width(self): return self.get(\"width\", None, type=int,","check fails print(e) self.raise_error('js', 'Javascript profile does not exist') if","if allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types","content_types = self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None,","self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\" % scale_method, allowed=allowed_scale_methods, received=scale_method, )","'wait' to render full webpage\") else: try: validate_size_str(viewport) except ValueError","self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout':","render engine {}\".format(engine)) return engine def get_http2(self): engine = self.get_engine()","def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1))","def _get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None) if","return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default =","result == 1 and wait == 0: self.raise_error(\"render_all\", \"Pass non-zero","isinstance(save_args, str): # comma-separated string save_args = save_args.split(',') if not","elif default is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s'","= self.get(\"save_args\", default=None, type=None) if save_args is None: return []","[] if isinstance(save_args, str): # comma-separated string save_args = save_args.split(',')","and (w * h < max_area)): raise ValueError(\"Viewport (%dx%d, area=%d)","cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name)","Twisted Request. \"\"\" # 1. GET / POST data data","be either a JSON array of \" \"(name, value) pairs","wrong type\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range is","self.raise_error( argument=\"save_args\", description=\"'save_args' should be a list of strings\", )","def get_url(self): return self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\", default=None) def","self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\", default=None) def get_js_source(self): return","a body\") return body def get_render_all(self, wait=None): result = self._get_bool(\"render_all\",","data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout)","get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\"proxy\", default=None) def","content_types def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine","should be a list of strings\", ) return save_args def","(load_args or {}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED,","keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args","self.get(\"js\", default=None) if not js_profile: return js_profile if js_profiles_path is","not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error(","get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types, str): content_types =","engine {}\".format(engine)) return engine def get_http2(self): engine = self.get_engine() if","else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\"viewport\", str(e)) return","f in filter_names.split(',') if f] if pool is None and","self.get(\"http_method\", \"GET\").upper() if method == 'GET' and body: self.raise_error(\"body\", \"GET","Process save_args and put all values to cache. Return a","ValueError(\"Viewport (%dx%d, area=%d) is out of range (%dx%d, area=%d)\" %","value) pairs or a JSON object\" ) if isinstance(headers, (list,","a list of argument names from load_args which can't be","2 and string_only): self.raise_error( argument='headers', description=\"'headers' must be either a","kv.split(\"=\", 1) for kv in load_args.split(';') ) except ValueError: self.raise_error(","try: value = type(value) except ValueError: msg = \"Argument %r","JSON array if not all(isinstance(a, str) for a in save_args):","'Argument is out of the allowed range', min=range[0], max=range[1], value=value)","\"\"\" _REQUIRED = object() def __init__(self, data, max_timeout): self.data =","names from load_args which can't be loaded \"\"\" return cache.get_missing(self.get_load_args().items())","BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \"\"\" Initialize options from","self._get_url(\"baseurl\", default=None) def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout()))","def get_engine(self, browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\", type=str) if engine","e in el) if not (isinstance(el, (list, tuple)) and len(el)","dict( kv.split(\"=\", 1) for kv in load_args.split(';') ) except ValueError:","} params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \"\"\"","defaults.SHOW_HISTORY), har=self._get_bool(\"har\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \"\"\" Validate size string","return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG),","= self.get_load_args() for name, key in (load_args or {}).items(): self.data[name]","js_profile) except ValueError as e: # security check fails print(e)","be loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\" Process","h <= max_heigth) and (w * h < max_area)): raise","self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' % name, type='argument_required')","str): content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types","server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e:","if method == 'GET' and body: self.raise_error(\"body\", \"GET request should","filter_names if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if","BadOption class RenderOptions(object): \"\"\" Options that control how to render","for name in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys))","load_args def get_viewport(self, wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport","full webpage\") else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\"viewport\",","return profile_dir def get_headers(self): headers = self.get(\"headers\", default=None, type=None) if","name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def _get_url(self,","profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security","data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if","if not (isinstance(el, (list, tuple)) and len(el) == 2 and","default=None, type=None) if save_args is None: return [] if isinstance(save_args,","data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description':","isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self):","url def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\"url\") def","in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine)) return engine def","value = self.data.get(name) if value is not None: if type","((0 < w <= max_width) and (0 < h <=","return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '')","if engine not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine))","data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()}","request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self, cache):","and wait == 0: self.raise_error(\"render_all\", \"Pass non-zero 'wait' to render","# 'lua': self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(),","{\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine)) if browser_engines_enabled is","self.get(\"timeout\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT,","default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default =","to validate both viewport and window size strings. Does not","default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path): wait","self.raise_error( \"filters\", \"Invalid filter names: %s\" % (filter_names,) ) if","allowed_scale_methods = ['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error(","default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types","isinstance(headers, (list, tuple)): for el in headers: string_only = all(isinstance(e,","content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self,","isinstance(url, bytes): url = url.decode('utf8') return url def get_uid(self): return","cache. Return a list of (name, key) pairs. \"\"\" save_args",") if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if","is not None: if engine not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled","self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method = self.get(\"http_method\",","ValueError: msg = \"Argument %r has a wrong type\" %","defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x'))","w <= max_width) and (0 < h <= max_heigth) and","path_join_secure from splash.errors import BadOption class RenderOptions(object): \"\"\" Options that","defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def","def get_body(self): body = self.get(\"body\", None, to_bytes) method = self.get(\"http_method\",","(filter_names,) ) if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names)","ValueError: raise ValueError(\"Invalid viewport format: %s\" % size_str) else: if","profile does not exist') return profile_dir def get_headers(self): headers =","allowed_domains = self.get(\"allowed_domains\", default=None) if allowed_domains is not None: return","self.raise_error( argument=\"load_args\", description=\"'load_args' string value is not a \" \"semicolon-separated","names: %s\" % (filter_names,) ) if adblock_rules is not None:","from load_args which can't be loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def","def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\"url\") def get_baseurl(self):","in allowed_scale_methods: self.raise_error( argument='scale_method', description=\"Invalid 'scale_method': %s\" % scale_method, allowed=allowed_scale_methods,","data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \"\"\"","not isinstance(save_args, list): self.raise_error( argument=\"save_args\", description=\"'save_args' should be either a","filter_names def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None) if allowed_domains is","self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return","wrong. :param size_str: string to validate \"\"\" max_width = defaults.VIEWPORT_MAX_WIDTH","does not exist') return profile_dir def get_headers(self): headers = self.get(\"headers\",","if save_args is None: return [] if isinstance(save_args, str): #","b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError","= list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED)","= pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \"filters\", \"Invalid filter","js_profile = self.get(\"js\", default=None) if not js_profile: return js_profile if","return [] if isinstance(save_args, str): # comma-separated string save_args =","# skip validation return filter_names if not filter_names: return filter_names","= self.get_save_args() save_values = [self.data.get(name) for name in save_args] keys","raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \"\"\" Initialize options","= [self.data.get(name) for name in save_args] keys = cache.add_many(save_values) return","self.raise_error(name, msg, required_type=type.__name__) if range is not None and not","'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait),","return cls(data, max_timeout) def get_expired_args(self, cache): \"\"\" Return a list","not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument is out","not ((0 < w <= max_width) and (0 < h","not None: try: value = type(value) except ValueError: msg =","is out of range (%dx%d, area=%d)\" % (w, h, w","as e: # security check fails print(e) self.raise_error('js', 'Javascript profile","'wait' to render full webpage\") return result def get_lua_source(self): return","get_quality(self): return self.get(\"quality\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method","_get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url,","{ 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self):","\" \"semicolon-separated list of name=hash pairs\" ) if not isinstance(load_args,","get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if","\"Invalid filter names: %s\" % (unknown_filters,) ) return filter_names def","class RenderOptions(object): \"\"\" Options that control how to render a","== 1 and wait == 0: self.raise_error(\"render_all\", \"Pass non-zero 'wait'","return {} if isinstance(load_args, str): try: load_args = dict( kv.split(\"=\",","= data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description,","try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise","full webpage\") return result def get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self,","%s' % name, type='argument_required') else: return default def _get_bool(self, name,","b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request)","type=None) if isinstance(url, bytes): url = url.decode('utf8') return url def","defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method = self.get(\"http_method\", \"GET\")","self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method':","else: if not ((0 < w <= max_width) and (0","if request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0)","self.get(\"js_source\", default=None) def get_width(self): return self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH))","JSON object\" ) if isinstance(headers, (list, tuple)): for el in","allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types =","headers def get_save_args(self): save_args = self.get(\"save_args\", default=None, type=None) if save_args","/ POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values","def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None) if allowed_domains is not","engine not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine)) return","= id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \"\"\" Return","str): # comma-separated string save_args = save_args.split(',') if not isinstance(save_args,","return filter_names if not filter_names: return filter_names if pool is","\"Argument %r has a wrong type\" % (name,) self.raise_error(name, msg,","type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\", defaults.RESOURCE_TIMEOUT, type=float, range=(0,","self.raise_error(\"render_all\", \"Pass non-zero 'wait' to render full webpage\") return result","def get_headers(self): headers = self.get(\"headers\", default=None, type=None) if headers is","should not have a body\") return body def get_render_all(self, wait=None):","security check fails print(e) self.raise_error('js', 'Javascript profile does not exist')","window size strings. Does not special-case ``'full'`` viewport. Raises ``ValueError``","method.upper() not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method))","< max_area)): raise ValueError(\"Viewport (%dx%d, area=%d) is out of range","1) for kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\"load_args\",","default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url =","max_area)): raise ValueError(\"Viewport (%dx%d, area=%d) is out of range (%dx%d,","default is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' %","is None: # skip validation return filter_names if not filter_names:","names\", ) # JSON array if not all(isinstance(a, str) for","def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method()","None: if type is not None: try: value = type(value)","# -*- coding: utf-8 -*- import os import json from","POST data if b'application/json' in content_type: try: content = request.content.read().decode('utf-8')","range=(0, 1e6)) def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return","application/json POST data if b'application/json' in content_type: try: content =","pairs\" ) return load_args def get_viewport(self, wait=None): viewport = self.get(\"viewport\",","viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait","anything goes wrong. :param size_str: string to validate \"\"\" max_width","missing: %s' % name, type='argument_required') else: return default def _get_bool(self,","max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params =","is None: self.raise_error( \"filters\", \"Invalid filter names: %s\" % (filter_names,)","pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \"filters\", \"Invalid filter names:","not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine)) return engine","get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(), 'baseurl':","return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def","url = url.decode('utf8') return url def get_uid(self): return self.get('uid') def","1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None)","self.raise_error( \"filters\", \"Invalid filter names: %s\" % (unknown_filters,) ) return","of name=hash pairs\" ) if not isinstance(load_args, dict): self.raise_error( argument=\"load_args\",","name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value is","'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def","self.get(\"load_args\", default=None, type=None) if load_args is None: return {} if","# security check fails print(e) self.raise_error('js', 'Javascript profile does not","get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def","None: if engine not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render engine","{} if isinstance(load_args, str): try: load_args = dict( kv.split(\"=\", 1)","e: raise BadOption({ 'type': 'invalid_json', 'description': \"Can't decode JSON\", 'message':","js_profiles_path): js_profile = self.get(\"js\", default=None) if not js_profile: return js_profile","save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should be a list of strings\",","'full': if wait == 0: self.raise_error(\"viewport\", \"Pass non-zero 'wait' to","str(e), }) # 3. js_source from application/javascript POST requests if","not enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except","self.get(name, default, type=None) if isinstance(url, bytes): url = url.decode('utf8') return","== \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium'","control how to render a response. \"\"\" _REQUIRED = object()","return self.get('uid') def get_url(self): return self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\",","== 2 and string_only): self.raise_error( argument='headers', description=\"'headers' must be either","(unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None)","min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self):","(w * h < max_area)): raise ValueError(\"Viewport (%dx%d, area=%d) is","= request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST data","default, type=None) if isinstance(url, bytes): url = url.decode('utf8') return url","splash.utils import to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object):","is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None:","'type': 'invalid_json', 'description': \"Can't decode JSON\", 'message': str(e), }) #","loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \"\"\" Process save_args","self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self):","of range (%dx%d, area=%d)\" % (w, h, w * h,","WxH format. Can be used to validate both viewport and","if b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except","self.get_load_args() for name, key in (load_args or {}).items(): self.data[name] =","engine {}\".format(engine)) if browser_engines_enabled is not None: if engine not","request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self,","try: load_args = dict( kv.split(\"=\", 1) for kv in load_args.split(';')","= self.get(\"js\", default=None) if not js_profile: return js_profile if js_profiles_path","{key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if request.method ==","= max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params","decode JSON\", 'message': str(e), }) # 3. js_source from application/javascript","list of argument names from load_args which can't be loaded","method = self.get(\"http_method\", \"GET\").upper() if method == 'GET' and body:","(name, key) pairs. \"\"\" save_args = self.get_save_args() save_values = [self.data.get(name)","# comma-separated string save_args = save_args.split(',') if not isinstance(save_args, list):","default=None) if not js_profile: return js_profile if js_profiles_path is None:","exist') return profile_dir def get_headers(self): headers = self.get(\"headers\", default=None, type=None)","get_headers(self): headers = self.get(\"headers\", default=None, type=None) if headers is None:","get_allowed_domains(self): allowed_domains = self.get(\"allowed_domains\", default=None) if allowed_domains is not None:","self.get(\"body\", None, to_bytes) method = self.get(\"http_method\", \"GET\").upper() if method ==","a semicolon-separated list \" \"of name=hash pairs\" ) return load_args","if range is not None and not (range[0] <= value","to cache. Return a list of (name, key) pairs. \"\"\"","defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE), history=self._get_bool(\"history\",","engine def get_http2(self): engine = self.get_engine() if self.get_engine() == \"webkit\":","filter_names = self.get('filters', '') filter_names = [f for f in","object() def __init__(self, data, max_timeout): self.data = data self.max_timeout =","def get_expired_args(self, cache): \"\"\" Return a list of argument names","self.raise_error(\"engine\", \"Disabled render engine {}\".format(engine)) return engine def get_http2(self): engine","return method def get_body(self): body = self.get(\"body\", None, to_bytes) method","= self.get(\"http_method\", \"GET\").upper() if method == 'GET' and body: self.raise_error(\"body\",","\"(name, value) pairs or a JSON object\" ) if isinstance(headers,","self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2':","is None: return {} if isinstance(load_args, str): try: load_args =","list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[])","Raises ``ValueError`` if anything goes wrong. :param size_str: string to","get_include_params(self): return dict( html=self._get_bool(\"html\", defaults.DO_HTML), iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\",","**kwargs): params = { 'type': type, 'argument': argument, 'description': description","adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \"filters\", \"Invalid","= path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security check","'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(),","= self.get(\"allowed_domains\", default=None) if allowed_domains is not None: return allowed_domains.split(',')","defaults.TIMEOUT) return self.get(\"timeout\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return","= self._get_bool(\"render_all\", False) if result == 1 and wait ==","except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string value is not a","save_args_to_cache(self, cache): \"\"\" Process save_args and put all values to","if unknown_filters: self.raise_error( \"filters\", \"Invalid filter names: %s\" % (unknown_filters,)","(list, tuple, dict)): self.raise_error( argument='headers', description=\"'headers' must be either a","try: validate_size_str(viewport) except ValueError as e: self.raise_error(\"viewport\", str(e)) return viewport","'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(),","kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string","result def get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile =","__init__(self, data, max_timeout): self.data = data self.max_timeout = max_timeout @classmethod","get_http_method(self): method = self.get(\"http_method\", \"GET\") if method.upper() not in [\"POST\",","be either a JSON object with \" \"argument hashes or","adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\", \"Invalid filter names: %s\" %","request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type:","semicolon-separated list \" \"of name=hash pairs\" ) return load_args def","return body def get_render_all(self, wait=None): result = self._get_bool(\"render_all\", False) if","argument=\"load_args\", description=\"'load_args' string value is not a \" \"semicolon-separated list","str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters',","defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except ValueError: raise","skip validation return filter_names if not filter_names: return filter_names if","def get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(),","data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument',","in filter_names.split(',') if f] if pool is None and adblock_rules","type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return","return self.get(\"timeout\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\"resource_timeout\",","= defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int,","requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid']","type='bad_argument', **kwargs): params = { 'type': type, 'argument': argument, 'description':","from application/javascript POST requests if b'application/javascript' in content_type: data['js_source'] =","get_save_args(self): save_args = self.get(\"save_args\", default=None, type=None) if save_args is None:","type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\", None, type=int, range=(1,","load_args which can't be loaded \"\"\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self,","to render full webpage\") else: try: validate_size_str(viewport) except ValueError as","% (filter_names,) ) if adblock_rules is not None: unknown_filters =","len(el) == 2 and string_only): self.raise_error( argument='headers', description=\"'headers' must be","\"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default","= all(isinstance(e, str) for e in el) if not (isinstance(el,","method {}\".format(method)) return method def get_body(self): body = self.get(\"body\", None,","must be either a JSON array of \" \"(name, value)","None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\", None, type=int,","object\" ) if isinstance(headers, (list, tuple)): for el in headers:","a JSON object\" ) return headers def get_save_args(self): save_args =","def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES)","not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine)) if","and window size strings. Does not special-case ``'full'`` viewport. Raises","get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\"url\") def get_baseurl(self): return","'Javascript profile does not exist') return profile_dir def get_headers(self): headers","= self.get(\"headers\", default=None, type=None) if headers is None: return headers","type=int, range=(0, 100)) def get_http_method(self): method = self.get(\"http_method\", \"GET\") if","in request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type') if","str) for e in el) if not (isinstance(el, (list, tuple))","array with argument names\", ) # JSON array if not","return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return","self.raise_error('js', 'Javascript profile does not exist') return profile_dir def get_headers(self):","def get_baseurl(self): return self._get_url(\"baseurl\", default=None) def get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME,","self.get(\"http_method\", \"GET\") if method.upper() not in [\"POST\", \"GET\"]: self.raise_error(\"http_method\", \"Unsupported","get_load_args(self): load_args = self.get(\"load_args\", default=None, type=None) if load_args is None:","list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\"html5_media\", defaults.HTML5_MEDIA_ENABLED) def","browser_engines_enabled=None): engine = self.get(\"engine\", default=\"webkit\", type=str) if engine not in","if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not enabled","description=\"Invalid 'scale_method': %s\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method","self.get(\"save_args\", default=None, type=None) if save_args is None: return [] if","\"GET request should not have a body\") return body def","all(isinstance(e, str) for e in el) if not (isinstance(el, (list,","BadOption({ 'type': 'invalid_json', 'description': \"Can't decode JSON\", 'message': str(e), })","get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\", defaults.AUTOLOAD_IMAGES) def","filter_names = [f for f in filter_names.split(',') if f] if","= self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not","not filter_names: return filter_names if pool is not None: adblock_rules","format. Can be used to validate both viewport and window","< w <= max_width) and (0 < h <= max_heigth)","size_str.split('x')) except ValueError: raise ValueError(\"Invalid viewport format: %s\" % size_str)","be a list of strings\", ) return save_args def get_load_args(self):","values in request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type')","put all values to cache. Return a list of (name,","self.raise_error('js', 'Javascript profiles are not enabled on server') try: profile_dir","save_args is None: return [] if isinstance(save_args, str): # comma-separated","def get_scale_method(self): scale_method = self.get(\"scale_method\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector']","name, type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED): return","self.get_save_args() save_values = [self.data.get(name) for name in save_args] keys =","\"Disabled render engine {}\".format(engine)) return engine def get_http2(self): engine =","is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \"filters\",","self.get(\"allowed_content_types\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return","not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\"allowed_content_types\", default=['*'])","and string_only): self.raise_error( argument='headers', description=\"'headers' must be either a JSON","size string in WxH format. Can be used to validate","self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy':","adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f for f","wait=None): viewport = self.get(\"viewport\", defaults.VIEWPORT_SIZE) if viewport == 'full': if","default=\"webkit\", type=str) if engine not in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown","'GET' and body: self.raise_error(\"body\", \"GET request should not have a","Validate size string in WxH format. Can be used to","range=(0, 100)) def get_http_method(self): method = self.get(\"http_method\", \"GET\") if method.upper()","get_wait(self): return self.get(\"wait\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default","request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type': 'invalid_json',","range is not None and not (range[0] <= value <=","\"(name, value) pairs or a JSON object\" ) return headers","is None: return [] if isinstance(save_args, str): # comma-separated string","dict): self.raise_error( argument=\"load_args\", description=\"'load_args' should be either a JSON object","def get_width(self): return self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self):","values to cache. Return a list of (name, key) pairs.","None: return headers if not isinstance(headers, (list, tuple, dict)): self.raise_error(","self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile':","ValueError(\"Invalid viewport format: %s\" % size_str) else: if not ((0","100)) def get_http_method(self): method = self.get(\"http_method\", \"GET\") if method.upper() not","1. GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for","'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(),","<= range[1]): self.raise_error(name, 'Argument is out of the allowed range',","out of range (%dx%d, area=%d)\" % (w, h, w *","%s\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self):","'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(),",") # JSON array if not all(isinstance(a, str) for a","defaults from splash.utils import to_bytes, path_join_secure from splash.errors import BadOption","a JSON object with \" \"argument hashes or a semicolon-separated","self._get_url(\"url\") def get_baseurl(self): return self._get_url(\"baseurl\", default=None) def get_wait(self): return self.get(\"wait\",","defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\"images\",","return headers def get_save_args(self): save_args = self.get(\"save_args\", default=None, type=None) if","else: return default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default,","are not enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile)","= [f for f in filter_names.split(',') if f] if pool","return self._get_bool(\"http2\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return","def save_args_to_cache(self, cache): \"\"\" Process save_args and put all values","is not None: try: value = type(value) except ValueError: msg","filter names: %s\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self):","headers is None: return headers if not isinstance(headers, (list, tuple,","is not None and not (range[0] <= value <= range[1]):","self.get(\"proxy\", default=None) def get_js_source(self): return self.get(\"js_source\", default=None) def get_width(self): return","wait == 0: self.raise_error(\"render_all\", \"Pass non-zero 'wait' to render full","a response. \"\"\" _REQUIRED = object() def __init__(self, data, max_timeout):","str) for a in save_args): self.raise_error( argument=\"save_args\", description=\"'save_args' should be","content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\"html5_media\",","self.raise_error(\"http_method\", \"Unsupported HTTP method {}\".format(method)) return method def get_body(self): body","in {\"webkit\", \"chromium\"}: self.raise_error(\"engine\", \"Unknown render engine {}\".format(engine)) if browser_engines_enabled","return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for","_get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def","not None: if engine not in browser_engines_enabled: self.raise_error(\"engine\", \"Disabled render","def get_save_args(self): save_args = self.get(\"save_args\", default=None, type=None) if save_args is","for key, values in request.args.items()} if request.method == b'POST': content_type","= object() def __init__(self, data, max_timeout): self.data = data self.max_timeout","\"argument hashes or a semicolon-separated list \" \"of name=hash pairs\"","def get_response_body(self): return self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED)","default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0,","print(e) self.raise_error('js', 'Javascript profile does not exist') if not os.path.isdir(profile_dir):","a \" \"semicolon-separated list of name=hash pairs\" ) if not","viewport and window size strings. Does not special-case ``'full'`` viewport.","if not ((0 < w <= max_width) and (0 <","load_args = dict( kv.split(\"=\", 1) for kv in load_args.split(';') )","not (isinstance(el, (list, tuple)) and len(el) == 2 and string_only):","body: self.raise_error(\"body\", \"GET request should not have a body\") return","if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules","iframes=self._get_bool(\"iframes\", defaults.DO_IFRAMES), png=self._get_bool(\"png\", defaults.DO_PNG), jpeg=self._get_bool(\"jpeg\", defaults.DO_JPEG), script=self._get_bool(\"script\", defaults.SHOW_SCRIPT), console=self._get_bool(\"console\", defaults.SHOW_CONSOLE),","% (name,) self.raise_error(name, msg, required_type=type.__name__) if range is not None","default=None) def get_width(self): return self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH)) def","= map(int, size_str.split('x')) except ValueError: raise ValueError(\"Invalid viewport format: %s\"","string save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\"save_args\",","in load_args.split(';') ) except ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string value","return content_types def get_forbidden_content_types(self): content_types = self.get(\"forbidden_content_types\", default=[]) if isinstance(content_types,","self._get_bool(\"render_all\", False) if result == 1 and wait == 0:","%r has a wrong type\" % (name,) self.raise_error(name, msg, required_type=type.__name__)","with \" \"argument hashes or a semicolon-separated list \" \"of","assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\"http2\", default)","def get_load_args(self): load_args = self.get(\"load_args\", default=None, type=None) if load_args is","e: self.raise_error(\"viewport\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names","range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name, default,","self.get_engine() if self.get_engine() == \"webkit\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert","return self.get(\"width\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\"height\",","max_width) and (0 < h <= max_heigth) and (w *","type=str, range=None): value = self.data.get(name) if value is not None:","default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\"timeout\", default, type=float, range=(0, self.max_timeout))","splash import defaults from splash.utils import to_bytes, path_join_secure from splash.errors","return result def get_lua_source(self): return self.get(\"lua_source\") def get_js_profile(self, js_profiles_path): js_profile","a wrong type\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range","ValueError: self.raise_error( argument=\"load_args\", description=\"'load_args' string value is not a \""],"string":"[\n \"self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is not None: return allowed_domains.split(',') def\",\n \"self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return\",\n \"argument is missing: %s' % name, type='argument_required') else: return default\",\n \"is None and adblock_rules is None: # skip validation return\",\n \"data, max_timeout): self.data = data self.max_timeout = max_timeout @classmethod def\",\n \"Initialize options from a Twisted Request. \\\"\\\"\\\" # 1. GET\",\n \"get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str): content_types =\",\n \"har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\" Validate size string in\",\n \"except ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description': \\\"Can't\",\n \"list of strings\\\", ) return save_args def get_load_args(self): load_args =\",\n \"def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def\",\n \"not have a body\\\") return body def get_render_all(self, wait=None): result\",\n \"def get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\", False) if result ==\",\n \"(isinstance(el, (list, tuple)) and len(el) == 2 and string_only): self.raise_error(\",\n \"list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be either a comma-separated \\\"\",\n \"= self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine not in {\\\"webkit\\\", \\\"chromium\\\"}:\",\n \"= self.data.get(name) if value is not None: if type is\",\n \"in headers: string_only = all(isinstance(e, str) for e in el)\",\n \"size strings. Does not special-case ``'full'`` viewport. Raises ``ValueError`` if\",\n \"from splash.utils import to_bytes, path_join_secure from splash.errors import BadOption class\",\n \"a JSON array of \\\" \\\"(name, value) pairs or a\",\n \"should be either a JSON object with \\\" \\\"argument hashes\",\n \"map(int, size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid viewport format: %s\\\" %\",\n \"a list of strings\\\", ) return save_args def get_load_args(self): load_args\",\n \"options from a Twisted Request. \\\"\\\"\\\" # 1. GET /\",\n \"max=range[1], value=value) return value elif default is self._REQUIRED: self.raise_error(name, 'Required\",\n \"isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self):\",\n \"'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path):\",\n \"# 2. application/json POST data if b'application/json' in content_type: try:\",\n \"return value elif default is self._REQUIRED: self.raise_error(name, 'Required argument is\",\n \"'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path),\",\n \"type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods\",\n \"defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h\",\n \"dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\",\",\n \"value=value) return value elif default is self._REQUIRED: self.raise_error(name, 'Required argument\",\n \"argument='headers', description=\\\"'headers' must be either a JSON array of \\\"\",\n \"as e: self.raise_error(\\\"viewport\\\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None):\",\n \"def get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if\",\n \"self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be either a JSON object with\",\n \"if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def\",\n \"\\\"Unknown render engine {}\\\".format(engine)) if browser_engines_enabled is not None: if\",\n \"def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self):\",\n \"self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return {\",\n \"'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(),\",\n \"return self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method =\",\n \"request should not have a body\\\") return body def get_render_all(self,\",\n \"= adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\"\",\n \"type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name,\",\n \"def load_cached_args(self, cache): load_args = self.get_load_args() for name, key in\",\n \"render full webpage\\\") else: try: validate_size_str(viewport) except ValueError as e:\",\n \"import to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" Return a list of argument names from load_args which\",\n \"\\\"\\\"\\\" Initialize options from a Twisted Request. \\\"\\\"\\\" # 1.\",\n \"and adblock_rules is None: # skip validation return filter_names if\",\n \"content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if\",\n \"@classmethod def fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize options from a\",\n \"not all(isinstance(a, str) for a in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args'\",\n \"} def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality':\",\n \"request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST data if\",\n \"defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return\",\n \"params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize\",\n \"get_proxy(self): return self.get(\\\"proxy\\\", default=None) def get_js_source(self): return self.get(\\\"js_source\\\", default=None) def\",\n \"wait=None): result = self._get_bool(\\\"render_all\\\", False) if result == 1 and\",\n \"value is not None: if type is not None: try:\",\n \"is out of the allowed range', min=range[0], max=range[1], value=value) return\",\n \"argument names\\\", ) # JSON array if not all(isinstance(a, str)\",\n \"defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if\",\n \"JSON object\\\" ) return headers def get_save_args(self): save_args = self.get(\\\"save_args\\\",\",\n \"self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\", default=None) if not\",\n \"return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are\",\n \"self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout,\",\n \"el in headers: string_only = all(isinstance(e, str) for e in\",\n \"a JSON array with argument names\\\", ) # JSON array\",\n \"validate_size_str(viewport) except ValueError as e: self.raise_error(\\\"viewport\\\", str(e)) return viewport def\",\n \"'lua': self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(), 'height':\",\n \"if value is not None: if type is not None:\",\n \"pairs or a JSON object\\\" ) return headers def get_save_args(self):\",\n \"not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method)) return\",\n \"return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method =\",\n \"= self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\",\",\n \"type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED): return self.get(name,\",\n \"POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in\",\n \"js_profile: return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles\",\n \"profile_dir def get_headers(self): headers = self.get(\\\"headers\\\", default=None, type=None) if headers\",\n \"= self.get(\\\"load_args\\\", default=None, type=None) if load_args is None: return {}\",\n \"value is not a \\\" \\\"semicolon-separated list of name=hash pairs\\\"\",\n \"is None: self.raise_error('js', 'Javascript profiles are not enabled on server')\",\n \"for name, key in (load_args or {}).items(): self.data[name] = cache[key]\",\n \"ValueError as e: # security check fails print(e) self.raise_error('js', 'Javascript\",\n \"not js_profile: return js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript\",\n \"get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value\",\n \"scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" %\",\n \"\\\"filters\\\", \\\"Invalid filter names: %s\\\" % (filter_names,) ) if adblock_rules\",\n \"profiles are not enabled on server') try: profile_dir = path_join_secure(js_profiles_path,\",\n \"self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(),\",\n \"validate both viewport and window size strings. Does not special-case\",\n \"def get_http2(self): engine = self.get_engine() if self.get_engine() == \\\"webkit\\\": default\",\n \"validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area =\",\n \"a list of (name, key) pairs. \\\"\\\"\\\" save_args = self.get_save_args()\",\n \"validation return filter_names if not filter_names: return filter_names if pool\",\n \"= self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(',')))\",\n \"return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\",\",\n \"object\\\" ) return headers def get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None,\",\n \"self.data = data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument,\",\n \"h = map(int, size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid viewport format:\",\n \"[self.data.get(name) for name in save_args] keys = cache.add_many(save_values) return list(zip(save_args,\",\n \"the allowed range', min=range[0], max=range[1], value=value) return value elif default\",\n \"self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(),\",\n \"render engine {}\\\".format(engine)) if browser_engines_enabled is not None: if engine\",\n \"Does not special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes\",\n \"in el) if not (isinstance(el, (list, tuple)) and len(el) ==\",\n \"b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json\",\n \"is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' % name,\",\n \"save_args = self.get(\\\"save_args\\\", default=None, type=None) if save_args is None: return\",\n \"webpage\\\") return result def get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path):\",\n \"def get_height(self): return self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self):\",\n \"engine = self.get_engine() if self.get_engine() == \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED\",\n \"(list, tuple)) and len(el) == 2 and string_only): self.raise_error( argument='headers',\",\n \") return scale_method def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0,\",\n \"description=\\\"'load_args' string value is not a \\\" \\\"semicolon-separated list of\",\n \"default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name,\",\n \"\\\"Unsupported HTTP method {}\\\".format(method)) return method def get_body(self): body =\",\n \"not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers' must be\",\n \"def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG),\",\n \"jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR),\",\n \"get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() }\",\n \"load_cached_args(self, cache): load_args = self.get_load_args() for name, key in (load_args\",\n \"Can be used to validate both viewport and window size\",\n \"if engine not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine\",\n \"filter_names: return filter_names if pool is not None: adblock_rules =\",\n \"except ValueError as e: self.raise_error(\\\"viewport\\\", str(e)) return viewport def get_filters(self,\",\n \"\\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine)) if browser_engines_enabled is not\",\n \"save_args = self.get_save_args() save_values = [self.data.get(name) for name in save_args]\",\n \"self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params =\",\n \"profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile\",\n \"from a Twisted Request. \\\"\\\"\\\" # 1. GET / POST\",\n \"or a semicolon-separated list \\\" \\\"of name=hash pairs\\\" ) return\",\n \"\\\"string or a JSON array with argument names\\\", ) #\",\n \"defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\" Validate size string in WxH\",\n \"get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float, range=(0,\",\n \"def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self):\",\n \"\\\"Invalid filter names: %s\\\" % (filter_names,) ) if adblock_rules is\",\n \"be used to validate both viewport and window size strings.\",\n \"content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({\",\n \"description=\\\"'save_args' should be either a comma-separated \\\" \\\"string or a\",\n \"2. application/json POST data if b'application/json' in content_type: try: content\",\n \"self.raise_error(name, 'Required argument is missing: %s' % name, type='argument_required') else:\",\n \"str): try: load_args = dict( kv.split(\\\"=\\\", 1) for kv in\",\n \"e: # security check fails print(e) self.raise_error('js', 'Javascript profile does\",\n \"= defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w,\",\n \"0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to render full webpage\\\") return\",\n \"% (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\",\",\n \"not a \\\" \\\"semicolon-separated list of name=hash pairs\\\" ) if\",\n \"max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except\",\n \"None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if\",\n \"to validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area\",\n \"self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media':\",\n \"pairs\\\" ) if not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should\",\n \"self.raise_error(name, 'Argument is out of the allowed range', min=range[0], max=range[1],\",\n \"if not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be either\",\n \"import os import json from splash import defaults from splash.utils\",\n \"scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method\",\n \"= type(value) except ValueError: msg = \\\"Argument %r has a\",\n \"'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return {\",\n \"self.get('filters', '') filter_names = [f for f in filter_names.split(',') if\",\n \"self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default,\",\n \"a in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be a list\",\n \"argument names from load_args which can't be loaded \\\"\\\"\\\" return\",\n \"raise_error(cls, argument, description, type='bad_argument', **kwargs): params = { 'type': type,\",\n \"engine not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine))\",\n \"in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method)) return method\",\n \"content_type: request.content.seek(0) # 2. application/json POST data if b'application/json' in\",\n \"return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self):\",\n \"}) # 3. js_source from application/javascript POST requests if b'application/javascript'\",\n \"'description': description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request,\",\n \"isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be either a JSON\",\n \"'invalid_json', 'description': \\\"Can't decode JSON\\\", 'message': str(e), }) # 3.\",\n \") return load_args def get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE)\",\n \"area=%d) is out of range (%dx%d, area=%d)\\\" % (w, h,\",\n \"isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers' must be either\",\n \"\\\"Pass non-zero 'wait' to render full webpage\\\") else: try: validate_size_str(viewport)\",\n \"if type is not None: try: value = type(value) except\",\n \"range (%dx%d, area=%d)\\\" % (w, h, w * h, max_width,\",\n \"tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers' must be either a JSON\",\n \"\\\"\\\"\\\" # 1. GET / POST data data = {key.decode('utf-8'):\",\n \"isinstance(load_args, str): try: load_args = dict( kv.split(\\\"=\\\", 1) for kv\",\n \"load_args is None: return {} if isinstance(load_args, str): try: load_args\",\n \"(name,) self.raise_error(name, msg, required_type=type.__name__) if range is not None and\",\n \"GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key,\",\n \"or {}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str,\",\n \"save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args'\",\n \"allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method,\",\n \"def get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\", default=None) if not js_profile:\",\n \"strings. Does not special-case ``'full'`` viewport. Raises ``ValueError`` if anything\",\n \"application/javascript POST requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8')\",\n \"'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(),\",\n \"body def get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\", False) if result\",\n \"is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\",\",\n \"wait = self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait':\",\n \"'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(),\",\n \"type=None) if save_args is None: return [] if isinstance(save_args, str):\",\n \"scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return self.get(\\\"quality\\\",\",\n \"content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e:\",\n \"return filter_names if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules\",\n \"self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not in\",\n \"max_timeout): self.data = data self.max_timeout = max_timeout @classmethod def raise_error(cls,\",\n \"= {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict(\",\n \"defaults.VIEWPORT_SIZE) if viewport == 'full': if wait == 0: self.raise_error(\\\"viewport\\\",\",\n \"used to validate both viewport and window size strings. Does\",\n \"if wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to render\",\n \"return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(),\",\n \"if isinstance(url, bytes): url = url.decode('utf8') return url def get_uid(self):\",\n \"content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str): content_types = list(filter(None,\",\n \"description, type='bad_argument', **kwargs): params = { 'type': type, 'argument': argument,\",\n \"names: %s\\\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains\",\n \"% size_str) else: if not ((0 < w <= max_width)\",\n \"key, values in request.args.items()} if request.method == b'POST': content_type =\",\n \"does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does\",\n \"\\\"filters\\\", \\\"Invalid filter names: %s\\\" % (unknown_filters,) ) return filter_names\",\n \"self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\", None,\",\n \"description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout):\",\n \"0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to render full webpage\\\") else:\",\n \"self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine)) if browser_engines_enabled is not None:\",\n \"get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float,\",\n \"if anything goes wrong. :param size_str: string to validate \\\"\\\"\\\"\",\n \"to render a response. \\\"\\\"\\\" _REQUIRED = object() def __init__(self,\",\n \"defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\",\",\n \"\\\" \\\"(name, value) pairs or a JSON object\\\" ) return\",\n \"= \\\"Argument %r has a wrong type\\\" % (name,) self.raise_error(name,\",\n \"get_height(self): return self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method\",\n \"or a JSON object\\\" ) return headers def get_save_args(self): save_args\",\n \"= save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should\",\n \"'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params\",\n \"def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float,\",\n \"name, default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url, bytes):\",\n \"return self.get(\\\"proxy\\\", default=None) def get_js_source(self): return self.get(\\\"js_source\\\", default=None) def get_width(self):\",\n \"not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not\",\n \"png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY),\",\n \"self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to render full webpage\\\") else: try:\",\n \"if not js_profile: return js_profile if js_profiles_path is None: self.raise_error('js',\",\n \"get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\", default=None) if not js_profile: return\",\n \"and len(el) == 2 and string_only): self.raise_error( argument='headers', description=\\\"'headers' must\",\n \"defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT)\",\n \"\\\"\\\"\\\" Options that control how to render a response. \\\"\\\"\\\"\",\n \"range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\",\",\n \"request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) #\",\n \"self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body':\",\n \"method = self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not in [\\\"POST\\\", \\\"GET\\\"]:\",\n \"of strings\\\", ) return save_args def get_load_args(self): load_args = self.get(\\\"load_args\\\",\",\n \"response. \\\"\\\"\\\" _REQUIRED = object() def __init__(self, data, max_timeout): self.data\",\n \"= url.decode('utf8') return url def get_uid(self): return self.get('uid') def get_url(self):\",\n \") def validate_size_str(size_str): \\\"\\\"\\\" Validate size string in WxH format.\",\n \"has a wrong type\\\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if\",\n \"def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\", default=None)\",\n \"\\\"GET\\\") if method.upper() not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP\",\n \"params def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\",\",\n \"'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method':\",\n \"# 3. js_source from application/javascript POST requests if b'application/javascript' in\",\n \"self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params()\",\n \"try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e: #\",\n \"default=None, type=None) if load_args is None: return {} if isinstance(load_args,\",\n \"if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return\",\n \"None: return {} if isinstance(load_args, str): try: load_args = dict(\",\n \"\\\"of name=hash pairs\\\" ) return load_args def get_viewport(self, wait=None): viewport\",\n \"= self.get(name, default, type=None) if isinstance(url, bytes): url = url.decode('utf8')\",\n \") except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value is not\",\n \"f] if pool is None and adblock_rules is None: #\",\n \"None and adblock_rules is None: # skip validation return filter_names\",\n \"a JSON object\\\" ) if isinstance(headers, (list, tuple)): for el\",\n \"coding: utf-8 -*- import os import json from splash import\",\n \"if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] =\",\n \"raise ValueError(\\\"Viewport (%dx%d, area=%d) is out of range (%dx%d, area=%d)\\\"\",\n \"to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method == 'GET' and\",\n \"self.get('uid') def get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None)\",\n \"get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self):\",\n \"defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster',\",\n \"self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all':\",\n \"defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def\",\n \"params = { 'type': type, 'argument': argument, 'description': description }\",\n \"adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error(\",\n \"def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str): content_types\",\n \"w, h = map(int, size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid viewport\",\n \"defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str):\",\n \"self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED)\",\n \"if not filter_names: return filter_names if pool is not None:\",\n \"'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return\",\n \"method == 'GET' and body: self.raise_error(\\\"body\\\", \\\"GET request should not\",\n \"1 and wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to\",\n \"in (load_args or {}).items(): self.data[name] = cache[key] def get(self, name,\",\n \"unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (unknown_filters,) )\",\n \"try: w, h = map(int, size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid\",\n \"description=\\\"'save_args' should be a list of strings\\\", ) return save_args\",\n \"load_args.split(';') ) except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value is\",\n \"'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), }\",\n \"'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(),\",\n \"of \\\" \\\"(name, value) pairs or a JSON object\\\" )\",\n \"def get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport ==\",\n \"get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine not\",\n \"array if not all(isinstance(a, str) for a in save_args): self.raise_error(\",\n \"def __init__(self, data, max_timeout): self.data = data self.max_timeout = max_timeout\",\n \"fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize options from a Twisted Request.\",\n \"get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return\",\n \"type=None) if headers is None: return headers if not isinstance(headers,\",\n \"either a JSON array of \\\" \\\"(name, value) pairs or\",\n \"get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params())\",\n \"if isinstance(save_args, str): # comma-separated string save_args = save_args.split(',') if\",\n \"ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description': \\\"Can't decode\",\n \"\\\" \\\"(name, value) pairs or a JSON object\\\" ) if\",\n \"in WxH format. Can be used to validate both viewport\",\n \"value <= range[1]): self.raise_error(name, 'Argument is out of the allowed\",\n \"get_width(self): return self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return\",\n \"filter names: %s\\\" % (filter_names,) ) if adblock_rules is not\",\n \"value elif default is self._REQUIRED: self.raise_error(name, 'Required argument is missing:\",\n \"if viewport == 'full': if wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass\",\n \"return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\", default=None) def get_js_source(self):\",\n \"h < max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d) is out of\",\n \"= ['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method',\",\n \"self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait == 0:\",\n \"None and not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument\",\n \"save_args def get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None, type=None) if load_args\",\n \"_REQUIRED = object() def __init__(self, data, max_timeout): self.data = data\",\n \"= self.get(\\\"body\\\", None, to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method\",\n \"return params def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES),\",\n \"a Twisted Request. \\\"\\\"\\\" # 1. GET / POST data\",\n \"\\\"Pass non-zero 'wait' to render full webpage\\\") return result def\",\n \"if browser_engines_enabled is not None: if engine not in browser_engines_enabled:\",\n \") return headers def get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None, type=None)\",\n \"if not all(isinstance(a, str) for a in save_args): self.raise_error( argument=\\\"save_args\\\",\",\n \"all values to cache. Return a list of (name, key)\",\n \"argument=\\\"save_args\\\", description=\\\"'save_args' should be either a comma-separated \\\" \\\"string or\",\n \"either a comma-separated \\\" \\\"string or a JSON array with\",\n \"} def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method':\",\n \"self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params\",\n \"defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\",\",\n \"self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str)\",\n \"= defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except ValueError:\",\n \"that control how to render a response. \\\"\\\"\\\" _REQUIRED =\",\n \"['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid\",\n \"js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(),\",\n \"result = self._get_bool(\\\"render_all\\\", False) if result == 1 and wait\",\n \"params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return\",\n \"hashes or a semicolon-separated list \\\" \\\"of name=hash pairs\\\" )\",\n \"import defaults from splash.utils import to_bytes, path_join_secure from splash.errors import\",\n \"<= max_width) and (0 < h <= max_heigth) and (w\",\n \"return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types,\",\n \"= { 'type': type, 'argument': argument, 'description': description } params.update(kwargs)\",\n \"request.content.seek(0) # 2. application/json POST data if b'application/json' in content_type:\",\n \"{'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\",\",\n \"self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images':\",\n \"import json from splash import defaults from splash.utils import to_bytes,\",\n \"for a in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be a\",\n \"not None: if type is not None: try: value =\",\n \"exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist')\",\n \"defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait()\",\n \"def fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize options from a Twisted\",\n \"bytes): url = url.decode('utf8') return url def get_uid(self): return self.get('uid')\",\n \"def get_png_params(self): return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()}\",\n \"cache): \\\"\\\"\\\" Return a list of argument names from load_args\",\n \"path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security check fails\",\n \"default=None) if allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self):\",\n \"type=str) if engine not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render\",\n \"wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to render full\",\n \"url = self.get(name, default, type=None) if isinstance(url, bytes): url =\",\n \"can't be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\"\",\n \"== 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to render full webpage\\\")\",\n \"None: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (filter_names,) )\",\n \"return self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED):\",\n \"self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\",\",\n \"None: # skip validation return filter_names if not filter_names: return\",\n \"if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers' must\",\n \"= self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait,\",\n \"adblock_rules is None: # skip validation return filter_names if not\",\n \"pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f for\",\n \"``'full'`` viewport. Raises ``ValueError`` if anything goes wrong. :param size_str:\",\n \"webpage\\\") else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\\\"viewport\\\", str(e))\",\n \"type\\\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range is not\",\n \"argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls,\",\n \"\\\"Can't decode JSON\\\", 'message': str(e), }) # 3. js_source from\",\n \"history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\" Validate size\",\n \"(%dx%d, area=%d) is out of range (%dx%d, area=%d)\\\" % (w,\",\n \"def raise_error(cls, argument, description, type='bad_argument', **kwargs): params = { 'type':\",\n \"= {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if request.method\",\n \"range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\", None, type=int, range=(1, defaults.MAX_HEIGTH))\",\n \"isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be either a comma-separated\",\n \"if not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be either\",\n \":param size_str: string to validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth\",\n \"% name, type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED):\",\n \"= self.get_engine() if self.get_engine() == \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else:\",\n \"'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait),\",\n \"defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\" Validate\",\n \"cache): \\\"\\\"\\\" Process save_args and put all values to cache.\",\n \"in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as\",\n \"cache): load_args = self.get_load_args() for name, key in (load_args or\",\n \"JSON object with \\\" \\\"argument hashes or a semicolon-separated list\",\n \"\\\" \\\"string or a JSON array with argument names\\\", )\",\n \"return save_args def get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None, type=None) if\",\n \"engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def\",\n \"name in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def\",\n \"scale_method def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def\",\n \"description=\\\"'headers' must be either a JSON array of \\\" \\\"(name,\",\n \"max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h =\",\n \"self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def\",\n \"utf-8 -*- import os import json from splash import defaults\",\n \"= cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value =\",\n \"\\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args and\",\n \"out of the allowed range', min=range[0], max=range[1], value=value) return value\",\n \"if method.upper() not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method\",\n \"all(isinstance(a, str) for a in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should\",\n \"argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return\",\n \"except ValueError: raise ValueError(\\\"Invalid viewport format: %s\\\" % size_str) else:\",\n \"viewport == 'full': if wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero\",\n \"\\\" \\\"argument hashes or a semicolon-separated list \\\" \\\"of name=hash\",\n \"self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value\",\n \"return url def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\\\"url\\\")\",\n \") if isinstance(headers, (list, tuple)): for el in headers: string_only\",\n \"comma-separated \\\" \\\"string or a JSON array with argument names\\\",\",\n \"{}\\\".format(method)) return method def get_body(self): body = self.get(\\\"body\\\", None, to_bytes)\",\n \"'Javascript profiles are not enabled on server') try: profile_dir =\",\n \"return default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int,\",\n \"enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError\",\n \"in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self,\",\n \"range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6))\",\n \"string_only): self.raise_error( argument='headers', description=\\\"'headers' must be either a JSON array\",\n \"if pool is None and adblock_rules is None: # skip\",\n \"max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\" Return a list of argument\",\n \"argument=\\\"save_args\\\", description=\\\"'save_args' should be a list of strings\\\", ) return\",\n \"either a JSON object with \\\" \\\"argument hashes or a\",\n \"None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \\\"filters\\\",\",\n \"\\\"\\\"\\\" save_args = self.get_save_args() save_values = [self.data.get(name) for name in\",\n \"1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\",\",\n \"cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args()\",\n \"%s\\\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains =\",\n \"size_str: string to validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth =\",\n \"def get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\",\",\n \"string in WxH format. Can be used to validate both\",\n \"object with \\\" \\\"argument hashes or a semicolon-separated list \\\"\",\n \"<= value <= range[1]): self.raise_error(name, 'Argument is out of the\",\n \"a comma-separated \\\" \\\"string or a JSON array with argument\",\n \"<= max_heigth) and (w * h < max_area)): raise ValueError(\\\"Viewport\",\n \"not special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes wrong.\",\n \"= request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def\",\n \"raise BadOption({ 'type': 'invalid_json', 'description': \\\"Can't decode JSON\\\", 'message': str(e),\",\n \"body\\\") return body def get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\", False)\",\n \"from splash.errors import BadOption class RenderOptions(object): \\\"\\\"\\\" Options that control\",\n \"(list, tuple)): for el in headers: string_only = all(isinstance(e, str)\",\n \"is not a \\\" \\\"semicolon-separated list of name=hash pairs\\\" )\",\n \"None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter\",\n \"console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\"\",\n \"and body: self.raise_error(\\\"body\\\", \\\"GET request should not have a body\\\")\",\n \"return engine def get_http2(self): engine = self.get_engine() if self.get_engine() ==\",\n \"return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\",\",\n \"if adblock_rules is None: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\"\",\n \"{}\\\".format(engine)) return engine def get_http2(self): engine = self.get_engine() if self.get_engine()\",\n \"None, type=int, range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD)\",\n \"if result == 1 and wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass\",\n \"= self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait ==\",\n \"if self.get_engine() == \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine\",\n \"have a body\\\") return body def get_render_all(self, wait=None): result =\",\n \"default=None, type=None) if headers is None: return headers if not\",\n \"def get_js_source(self): return self.get(\\\"js_source\\\", default=None) def get_width(self): return self.get(\\\"width\\\", None,\",\n \"'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self):\",\n \"script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def\",\n \"how to render a response. \\\"\\\"\\\" _REQUIRED = object() def\",\n \"is not None: if type is not None: try: value\",\n \"default=None) def get_js_source(self): return self.get(\\\"js_source\\\", default=None) def get_width(self): return self.get(\\\"width\\\",\",\n \"if isinstance(headers, (list, tuple)): for el in headers: string_only =\",\n \"name=hash pairs\\\" ) if not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args'\",\n \"== 'full': if wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait'\",\n \"and (0 < h <= max_heigth) and (w * h\",\n \"None: try: value = type(value) except ValueError: msg = \\\"Argument\",\n \"% scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return\",\n \"load_args = self.get_load_args() for name, key in (load_args or {}).items():\",\n \"required_type=type.__name__) if range is not None and not (range[0] <=\",\n \"cls(data, max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\" Return a list of\",\n \"if headers is None: return headers if not isinstance(headers, (list,\",\n \"description=\\\"'load_args' should be either a JSON object with \\\" \\\"argument\",\n \"self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\",\",\n \"self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source':\",\n \"special-case ``'full'`` viewport. Raises ``ValueError`` if anything goes wrong. :param\",\n \"type is not None: try: value = type(value) except ValueError:\",\n \"range', min=range[0], max=range[1], value=value) return value elif default is self._REQUIRED:\",\n \"with argument names\\\", ) # JSON array if not all(isinstance(a,\",\n \"self.raise_error('js', 'Javascript profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js',\",\n \"list of (name, key) pairs. \\\"\\\"\\\" save_args = self.get_save_args() save_values\",\n \"None: self.raise_error('js', 'Javascript profiles are not enabled on server') try:\",\n \") return save_args def get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None, type=None)\",\n \"size_str) else: if not ((0 < w <= max_width) and\",\n \"[f for f in filter_names.split(',') if f] if pool is\",\n \") if not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be\",\n \"name, key in (load_args or {}).items(): self.data[name] = cache[key] def\",\n \"js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not enabled on\",\n \"headers: string_only = all(isinstance(e, str) for e in el) if\",\n \"format: %s\\\" % size_str) else: if not ((0 < w\",\n \"raise ValueError(\\\"Invalid viewport format: %s\\\" % size_str) else: if not\",\n \"return content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None):\",\n \"-*- import os import json from splash import defaults from\",\n \"and put all values to cache. Return a list of\",\n \"of the allowed range', min=range[0], max=range[1], value=value) return value elif\",\n \"= defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED\",\n \"or a JSON object\\\" ) if isinstance(headers, (list, tuple)): for\",\n \"save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be\",\n \"json from splash import defaults from splash.utils import to_bytes, path_join_secure\",\n \"'message': str(e), }) # 3. js_source from application/javascript POST requests\",\n \"max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try:\",\n \"save_values = [self.data.get(name) for name in save_args] keys = cache.add_many(save_values)\",\n \"defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\", defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), )\",\n \"js_profile if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not\",\n \"# 1. GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8')\",\n \"url.decode('utf8') return url def get_uid(self): return self.get('uid') def get_url(self): return\",\n \"goes wrong. :param size_str: string to validate \\\"\\\"\\\" max_width =\",\n \"return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args and put\",\n \"except ValueError: msg = \\\"Argument %r has a wrong type\\\"\",\n \"type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self):\",\n \"non-zero 'wait' to render full webpage\\\") return result def get_lua_source(self):\",\n \"or a JSON array with argument names\\\", ) # JSON\",\n \"string_only = all(isinstance(e, str) for e in el) if not\",\n \"values[0].decode('utf-8') for key, values in request.args.items()} if request.method == b'POST':\",\n \"os import json from splash import defaults from splash.utils import\",\n \"get_body(self): body = self.get(\\\"body\\\", None, to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper()\",\n \"\\\"\\\"\\\" Process save_args and put all values to cache. Return\",\n \"return headers if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers',\",\n \"filter_names if not filter_names: return filter_names if pool is not\",\n \"pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is\",\n \"in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be a list of\",\n \"browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine)) return engine def get_http2(self):\",\n \"get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\",\",\n \"Return a list of (name, key) pairs. \\\"\\\"\\\" save_args =\",\n \"should be either a comma-separated \\\" \\\"string or a JSON\",\n \"(0 < h <= max_heigth) and (w * h <\",\n \"area=%d)\\\" % (w, h, w * h, max_width, max_heigth, max_area))\",\n \"RenderOptions(object): \\\"\\\"\\\" Options that control how to render a response.\",\n \"argument=\\\"load_args\\\", description=\\\"'load_args' should be either a JSON object with \\\"\",\n \"'type': type, 'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params)\",\n \"from splash import defaults from splash.utils import to_bytes, path_join_secure from\",\n \"@classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params = {\",\n \"both viewport and window size strings. Does not special-case ``'full'``\",\n \"== b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2.\",\n \"dict)): self.raise_error( argument='headers', description=\\\"'headers' must be either a JSON array\",\n \"save_args and put all values to cache. Return a list\",\n \"method def get_body(self): body = self.get(\\\"body\\\", None, to_bytes) method =\",\n \"adblock_rules is None: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" %\",\n \"allowed range', min=range[0], max=range[1], value=value) return value elif default is\",\n \"os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return profile_dir def\",\n \"def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names =\",\n \") return filter_names def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if\",\n \"= request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type':\",\n \"splash.errors import BadOption class RenderOptions(object): \\\"\\\"\\\" Options that control how\",\n \"min=range[0], max=range[1], value=value) return value elif default is self._REQUIRED: self.raise_error(name,\",\n \"= defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path): wait =\",\n \"range=None): value = self.data.get(name) if value is not None: if\",\n \"received=scale_method, ) return scale_method def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int,\",\n \"default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url':\",\n \"default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float,\",\n \"el) if not (isinstance(el, (list, tuple)) and len(el) == 2\",\n \"of (name, key) pairs. \\\"\\\"\\\" save_args = self.get_save_args() save_values =\",\n \"list of name=hash pairs\\\" ) if not isinstance(load_args, dict): self.raise_error(\",\n \"if scale_method not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\"\",\n \"def validate_size_str(size_str): \\\"\\\"\\\" Validate size string in WxH format. Can\",\n \"viewport. Raises ``ValueError`` if anything goes wrong. :param size_str: string\",\n \"max_timeout): \\\"\\\"\\\" Initialize options from a Twisted Request. \\\"\\\"\\\" #\",\n \"of argument names from load_args which can't be loaded \\\"\\\"\\\"\",\n \"defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\",\",\n \"else: assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\",\",\n \"msg, required_type=type.__name__) if range is not None and not (range[0]\",\n \"browser_engines_enabled is not None: if engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\",\",\n \"JSON array with argument names\\\", ) # JSON array if\",\n \"== 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to render full webpage\\\")\",\n \"is missing: %s' % name, type='argument_required') else: return default def\",\n \"body = self.get(\\\"body\\\", None, to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if\",\n \"return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0,\",\n \"def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str): content_types\",\n \"self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def\",\n \"for f in filter_names.split(',') if f] if pool is None\",\n \"get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\", False) if result == 1\",\n \"not in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" % scale_method,\",\n \"to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object): \\\"\\\"\\\" Options\",\n \"not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid\",\n \"return filter_names def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if allowed_domains\",\n \"list \\\" \\\"of name=hash pairs\\\" ) return load_args def get_viewport(self,\",\n \"self.raise_error( argument='headers', description=\\\"'headers' must be either a JSON array of\",\n \"render a response. \\\"\\\"\\\" _REQUIRED = object() def __init__(self, data,\",\n \"return load_args def get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if\",\n \"if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters:\",\n \"default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url, bytes): url\",\n \"defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\", default=None) def get_js_source(self): return self.get(\\\"js_source\\\",\",\n \"'') filter_names = [f for f in filter_names.split(',') if f]\",\n \"\\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA\",\n \"get_expired_args(self, cache): \\\"\\\"\\\" Return a list of argument names from\",\n \"return self.get(\\\"js_source\\\", default=None) def get_width(self): return self.get(\\\"width\\\", None, type=int, range=(1,\",\n \"name=hash pairs\\\" ) return load_args def get_viewport(self, wait=None): viewport =\",\n \"self.raise_error(\\\"viewport\\\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names =\",\n \"get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self):\",\n \"params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\",\",\n \"import BadOption class RenderOptions(object): \\\"\\\"\\\" Options that control how to\",\n \"\\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method)) return method def get_body(self):\",\n \"* h < max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d) is out\",\n \"string to validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH max_heigth = defaults.VIEWPORT_MAX_HEIGTH\",\n \"tuple)) and len(el) == 2 and string_only): self.raise_error( argument='headers', description=\\\"'headers'\",\n \"== 'GET' and body: self.raise_error(\\\"body\\\", \\\"GET request should not have\",\n \"%s\\\" % size_str) else: if not ((0 < w <=\",\n \"array of \\\" \\\"(name, value) pairs or a JSON object\\\"\",\n \"defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not in allowed_scale_methods:\",\n \"POST requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0)\",\n \"value) pairs or a JSON object\\\" ) return headers def\",\n \"for el in headers: string_only = all(isinstance(e, str) for e\",\n \"get_http2(self): engine = self.get_engine() if self.get_engine() == \\\"webkit\\\": default =\",\n \"get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\", default=None)\",\n \"to render full webpage\\\") return result def get_lua_source(self): return self.get(\\\"lua_source\\\")\",\n \"allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY,\",\n \"keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for name, key\",\n \"HTTP method {}\\\".format(method)) return method def get_body(self): body = self.get(\\\"body\\\",\",\n \"headers if not isinstance(headers, (list, tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers'\",\n \"get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f\",\n \"is None: return headers if not isinstance(headers, (list, tuple, dict)):\",\n \"headers = self.get(\\\"headers\\\", default=None, type=None) if headers is None: return\",\n \"return self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return\",\n \"pairs. \\\"\\\"\\\" save_args = self.get_save_args() save_values = [self.data.get(name) for name\",\n \"# JSON array if not all(isinstance(a, str) for a in\",\n \"pool is None and adblock_rules is None: # skip validation\",\n \"'description': \\\"Can't decode JSON\\\", 'message': str(e), }) # 3. js_source\",\n \"not exist') return profile_dir def get_headers(self): headers = self.get(\\\"headers\\\", default=None,\",\n \"if isinstance(load_args, str): try: load_args = dict( kv.split(\\\"=\\\", 1) for\",\n \"[\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method)) return method def\",\n \"range=(1, defaults.MAX_HEIGTH)) def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods =\",\n \"wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers':\",\n \"comma-separated string save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error(\",\n \"ValueError as e: self.raise_error(\\\"viewport\\\", str(e)) return viewport def get_filters(self, pool=None,\",\n \"which can't be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache):\",\n \"str): content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return\",\n \"content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types =\",\n \"default=None) def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def\",\n \"self.get(name, default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url\",\n \"= list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\",\",\n \"return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self):\",\n \"not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript profile does not exist') return profile_dir\",\n \"content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data,\",\n \"self.data.get(name) if value is not None: if type is not\",\n \"render full webpage\\\") return result def get_lua_source(self): return self.get(\\\"lua_source\\\") def\",\n \"\\\" \\\"of name=hash pairs\\\" ) return load_args def get_viewport(self, wait=None):\",\n \"return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile = self.get(\\\"js\\\", default=None) if\",\n \"key in (load_args or {}).items(): self.data[name] = cache[key] def get(self,\",\n \"argument, description, type='bad_argument', **kwargs): params = { 'type': type, 'argument':\",\n \"on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as\",\n \"self.get_quality()} params.update(self.get_image_params()) return params def get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML),\",\n \"= cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args =\",\n \"save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache):\",\n \"default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value is not\",\n \"(range[0] <= value <= range[1]): self.raise_error(name, 'Argument is out of\",\n \"and not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument is\",\n \"range[1]): self.raise_error(name, 'Argument is out of the allowed range', min=range[0],\",\n \"def get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not in\",\n \"{ 'type': type, 'argument': argument, 'description': description } params.update(kwargs) raise\",\n \"Return a list of argument names from load_args which can't\",\n \"for e in el) if not (isinstance(el, (list, tuple)) and\",\n \"if load_args is None: return {} if isinstance(load_args, str): try:\",\n \"unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter names:\",\n \"self.get_engine() == \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine ==\",\n \"engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine not in {\\\"webkit\\\",\",\n \"self.raise_error(\\\"body\\\", \\\"GET request should not have a body\\\") return body\",\n \"viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '') filter_names\",\n \"if f] if pool is None and adblock_rules is None:\",\n \"\\\"\\\"\\\" Validate size string in WxH format. Can be used\",\n \"{}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED, type=str, range=None):\",\n \"'Javascript profile does not exist') if not os.path.isdir(profile_dir): self.raise_error('js', 'Javascript\",\n \"max_heigth) and (w * h < max_area)): raise ValueError(\\\"Viewport (%dx%d,\",\n \"self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs):\",\n \"strings\\\", ) return save_args def get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None,\",\n \"def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self):\",\n \"for kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args'\",\n \"Options that control how to render a response. \\\"\\\"\\\" _REQUIRED\",\n \"default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types\",\n \"-*- coding: utf-8 -*- import os import json from splash\",\n \"None: return [] if isinstance(save_args, str): # comma-separated string save_args\",\n \"data if b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content))\",\n \"= self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(',')))\",\n \"content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST\",\n \"\\\"GET\\\").upper() if method == 'GET' and body: self.raise_error(\\\"body\\\", \\\"GET request\",\n \"in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return\",\n \"non-zero 'wait' to render full webpage\\\") else: try: validate_size_str(viewport) except\",\n \"= self.get('filters', '') filter_names = [f for f in filter_names.split(',')\",\n \"def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine =\",\n \"{ 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport':\",\n \"be either a comma-separated \\\" \\\"string or a JSON array\",\n \"type(value) except ValueError: msg = \\\"Argument %r has a wrong\",\n \"self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return\",\n \"viewport format: %s\\\" % size_str) else: if not ((0 <\",\n \"< h <= max_heigth) and (w * h < max_area)):\",\n \"allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str):\",\n \"id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\" Return a\",\n \"'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def\",\n \"request, max_timeout): \\\"\\\"\\\" Initialize options from a Twisted Request. \\\"\\\"\\\"\",\n \"self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return\",\n \"get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport == 'full':\",\n \"== 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def get_common_params(self,\",\n \"as e: raise BadOption({ 'type': 'invalid_json', 'description': \\\"Can't decode JSON\\\",\",\n \"%s\\\" % (filter_names,) ) if adblock_rules is not None: unknown_filters\",\n \"JSON\\\", 'message': str(e), }) # 3. js_source from application/javascript POST\",\n \"not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be either a\",\n \"html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT),\",\n \"``ValueError`` if anything goes wrong. :param size_str: string to validate\",\n \"'Required argument is missing: %s' % name, type='argument_required') else: return\",\n \"self.get(\\\"headers\\\", default=None, type=None) if headers is None: return headers if\",\n \"type=None) if load_args is None: return {} if isinstance(load_args, str):\",\n \"(%dx%d, area=%d)\\\" % (w, h, w * h, max_width, max_heigth,\",\n \"JSON array of \\\" \\\"(name, value) pairs or a JSON\",\n \"key) pairs. \\\"\\\"\\\" save_args = self.get_save_args() save_values = [self.data.get(name) for\",\n \"'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod def\",\n \"load_args = self.get(\\\"load_args\\\", default=None, type=None) if load_args is None: return\",\n \"\\\"semicolon-separated list of name=hash pairs\\\" ) if not isinstance(load_args, dict):\",\n \"value = type(value) except ValueError: msg = \\\"Argument %r has\",\n \"self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be either a comma-separated \\\" \\\"string\",\n \"'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), # 'lua':\",\n \"pairs or a JSON object\\\" ) if isinstance(headers, (list, tuple)):\",\n \"if content_type: request.content.seek(0) # 2. application/json POST data if b'application/json'\",\n \"list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for name,\",\n \"False) if result == 1 and wait == 0: self.raise_error(\\\"render_all\\\",\",\n \"string value is not a \\\" \\\"semicolon-separated list of name=hash\",\n \"content_types def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str):\",\n \"tuple)): for el in headers: string_only = all(isinstance(e, str) for\",\n \"type, 'argument': argument, 'description': description } params.update(kwargs) raise BadOption(params) @classmethod\",\n \"js_source from application/javascript POST requests if b'application/javascript' in content_type: data['js_source']\",\n \"= dict( kv.split(\\\"=\\\", 1) for kv in load_args.split(';') ) except\",\n \"self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2': self.get_http2(), #\",\n \"msg = \\\"Argument %r has a wrong type\\\" % (name,)\",\n \"Request. \\\"\\\"\\\" # 1. GET / POST data data =\",\n \"<reponame>tashidexiaoL/splashnew # -*- coding: utf-8 -*- import os import json\",\n \"def get_proxy(self): return self.get(\\\"proxy\\\", default=None) def get_js_source(self): return self.get(\\\"js_source\\\", default=None)\",\n \"validate_size_str(size_str): \\\"\\\"\\\" Validate size string in WxH format. Can be\",\n \"self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return { 'width':\",\n \"{}\\\".format(engine)) if browser_engines_enabled is not None: if engine not in\",\n \"fails print(e) self.raise_error('js', 'Javascript profile does not exist') if not\",\n \"return scale_method def get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100))\",\n \"except ValueError as e: # security check fails print(e) self.raise_error('js',\",\n \"filter_names.split(',') if f] if pool is None and adblock_rules is\",\n \"not None and not (range[0] <= value <= range[1]): self.raise_error(name,\",\n \"= min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout)) def\",\n \"cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args and put all\",\n \"3. js_source from application/javascript POST requests if b'application/javascript' in content_type:\",\n \"None, to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method == 'GET'\",\n \"get_js_source(self): return self.get(\\\"js_source\\\", default=None) def get_width(self): return self.get(\\\"width\\\", None, type=int,\",\n \"check fails print(e) self.raise_error('js', 'Javascript profile does not exist') if\",\n \"if allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types\",\n \"content_types = self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None,\",\n \"self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method, )\",\n \"'wait' to render full webpage\\\") else: try: validate_size_str(viewport) except ValueError\",\n \"self.get_wait() return { 'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout':\",\n \"render engine {}\\\".format(engine)) return engine def get_http2(self): engine = self.get_engine()\",\n \"def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1))\",\n \"def _get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None) if\",\n \"return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default =\",\n \"result == 1 and wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero\",\n \"isinstance(save_args, str): # comma-separated string save_args = save_args.split(',') if not\",\n \"elif default is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s'\",\n \"= self.get(\\\"save_args\\\", default=None, type=None) if save_args is None: return []\",\n \"[] if isinstance(save_args, str): # comma-separated string save_args = save_args.split(',')\",\n \"and (w * h < max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d)\",\n \"cache[key] def get(self, name, default=_REQUIRED, type=str, range=None): value = self.data.get(name)\",\n \"Twisted Request. \\\"\\\"\\\" # 1. GET / POST data data\",\n \"be either a JSON array of \\\" \\\"(name, value) pairs\",\n \"wrong type\\\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range is\",\n \"self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be a list of strings\\\", )\",\n \"def get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def\",\n \"self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\", default=None) def get_js_source(self): return\",\n \"a body\\\") return body def get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\",\",\n \"data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout)\",\n \"get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def get_proxy(self): return self.get(\\\"proxy\\\", default=None) def\",\n \"content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self, browser_engines_enabled=None): engine\",\n \"should be a list of strings\\\", ) return save_args def\",\n \"(load_args or {}).items(): self.data[name] = cache[key] def get(self, name, default=_REQUIRED,\",\n \"keys = cache.add_many(save_values) return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args\",\n \"self.get(\\\"js\\\", default=None) if not js_profile: return js_profile if js_profiles_path is\",\n \"not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error(\",\n \"get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types, str): content_types =\",\n \"engine {}\\\".format(engine)) return engine def get_http2(self): engine = self.get_engine() if\",\n \"else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\\\"viewport\\\", str(e)) return\",\n \"f in filter_names.split(',') if f] if pool is None and\",\n \"self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method == 'GET' and body: self.raise_error(\\\"body\\\", \\\"GET\",\n \"Process save_args and put all values to cache. Return a\",\n \"ValueError(\\\"Viewport (%dx%d, area=%d) is out of range (%dx%d, area=%d)\\\" %\",\n \"value) pairs or a JSON object\\\" ) if isinstance(headers, (list,\",\n \"a list of argument names from load_args which can't be\",\n \"2 and string_only): self.raise_error( argument='headers', description=\\\"'headers' must be either a\",\n \"kv.split(\\\"=\\\", 1) for kv in load_args.split(';') ) except ValueError: self.raise_error(\",\n \"try: value = type(value) except ValueError: msg = \\\"Argument %r\",\n \"JSON array if not all(isinstance(a, str) for a in save_args):\",\n \"'Argument is out of the allowed range', min=range[0], max=range[1], value=value)\",\n \"\\\"\\\"\\\" _REQUIRED = object() def __init__(self, data, max_timeout): self.data =\",\n \"names from load_args which can't be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items())\",\n \"BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize options from\",\n \"self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout()))\",\n \"def get_engine(self, browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine\",\n \"e in el) if not (isinstance(el, (list, tuple)) and len(el)\",\n \"dict( kv.split(\\\"=\\\", 1) for kv in load_args.split(';') ) except ValueError:\",\n \"} params.update(kwargs) raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \\\"\\\"\\\"\",\n \"defaults.SHOW_HISTORY), har=self._get_bool(\\\"har\\\", defaults.SHOW_HAR), ) def validate_size_str(size_str): \\\"\\\"\\\" Validate size string\",\n \"return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG),\",\n \"= self.get_load_args() for name, key in (load_args or {}).items(): self.data[name]\",\n \"js_profile) except ValueError as e: # security check fails print(e)\",\n \"be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\" Process\",\n \"h <= max_heigth) and (w * h < max_area)): raise\",\n \"self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' % name, type='argument_required')\",\n \"str): content_types = list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types\",\n \"server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e:\",\n \"if method == 'GET' and body: self.raise_error(\\\"body\\\", \\\"GET request should\",\n \"filter_names if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if\",\n \"BadOption class RenderOptions(object): \\\"\\\"\\\" Options that control how to render\",\n \"for name in save_args] keys = cache.add_many(save_values) return list(zip(save_args, keys))\",\n \"load_args def get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport\",\n \"full webpage\\\") else: try: validate_size_str(viewport) except ValueError as e: self.raise_error(\\\"viewport\\\",\",\n \"return profile_dir def get_headers(self): headers = self.get(\\\"headers\\\", default=None, type=None) if\",\n \"name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def _get_url(self,\",\n \"profile_dir = path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security\",\n \"data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if\",\n \"if not (isinstance(el, (list, tuple)) and len(el) == 2 and\",\n \"default=None, type=None) if save_args is None: return [] if isinstance(save_args,\",\n \"data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type': 'invalid_json', 'description':\",\n \"isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self):\",\n \"url def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\\\"url\\\") def\",\n \"in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine)) return engine def\",\n \"value = self.data.get(name) if value is not None: if type\",\n \"((0 < w <= max_width) and (0 < h <=\",\n \"return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters', '')\",\n \"if engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine))\",\n \"data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()}\",\n \"request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self, cache):\",\n \"and wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to render\",\n \"# 'lua': self.get_lua(), } def get_image_params(self): return { 'width': self.get_width(),\",\n \"{\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine)) if browser_engines_enabled is\",\n \"self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT,\",\n \"default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default =\",\n \"to validate both viewport and window size strings. Does not\",\n \"default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path): wait\",\n \"self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (filter_names,) ) if\",\n \"allowed_scale_methods = ['raster', 'vector'] if scale_method not in allowed_scale_methods: self.raise_error(\",\n \"default=[]) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return content_types\",\n \"isinstance(headers, (list, tuple)): for el in headers: string_only = all(isinstance(e,\",\n \"content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def get_engine(self,\",\n \"isinstance(url, bytes): url = url.decode('utf8') return url def get_uid(self): return\",\n \"cache. Return a list of (name, key) pairs. \\\"\\\"\\\" save_args\",\n \") if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if\",\n \"is not None: if engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled\",\n \"self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method = self.get(\\\"http_method\\\",\",\n \"ValueError: msg = \\\"Argument %r has a wrong type\\\" %\",\n \"defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x'))\",\n \"w <= max_width) and (0 < h <= max_heigth) and\",\n \"path_join_secure from splash.errors import BadOption class RenderOptions(object): \\\"\\\"\\\" Options that\",\n \"defaults.RESOURCE_TIMEOUT, type=float, range=(0, 1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def\",\n \"def get_body(self): body = self.get(\\\"body\\\", None, to_bytes) method = self.get(\\\"http_method\\\",\",\n \"(filter_names,) ) if adblock_rules is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names)\",\n \"ValueError: raise ValueError(\\\"Invalid viewport format: %s\\\" % size_str) else: if\",\n \"profile does not exist') return profile_dir def get_headers(self): headers =\",\n \"allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is not None: return\",\n \"self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value is not a \\\" \\\"semicolon-separated\",\n \"names: %s\\\" % (filter_names,) ) if adblock_rules is not None:\",\n \"from load_args which can't be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def\",\n \"def get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self):\",\n \"in allowed_scale_methods: self.raise_error( argument='scale_method', description=\\\"Invalid 'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods,\",\n \"data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\"\",\n \"not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be either a\",\n \"filter_names def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is\",\n \"self.get_html5_media(), 'http2': self.get_http2(), # 'lua': self.get_lua(), } def get_image_params(self): return\",\n \"wrong. :param size_str: string to validate \\\"\\\"\\\" max_width = defaults.VIEWPORT_MAX_WIDTH\",\n \"does not exist') return profile_dir def get_headers(self): headers = self.get(\\\"headers\\\",\",\n \"if save_args is None: return [] if isinstance(save_args, str): #\",\n \"b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError\",\n \"= list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED)\",\n \"= pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \\\"filters\\\", \\\"Invalid filter\",\n \"js_profile = self.get(\\\"js\\\", default=None) if not js_profile: return js_profile if\",\n \"return [] if isinstance(save_args, str): # comma-separated string save_args =\",\n \"# skip validation return filter_names if not filter_names: return filter_names\",\n \"= self.get_save_args() save_values = [self.data.get(name) for name in save_args] keys\",\n \"raise BadOption(params) @classmethod def fromrequest(cls, request, max_timeout): \\\"\\\"\\\" Initialize options\",\n \"= [self.data.get(name) for name in save_args] keys = cache.add_many(save_values) return\",\n \"self.raise_error(name, msg, required_type=type.__name__) if range is not None and not\",\n \"'url': self.get_url(), 'baseurl': self.get_baseurl(), 'wait': wait, 'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait),\",\n \"return cls(data, max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\" Return a list\",\n \"not (range[0] <= value <= range[1]): self.raise_error(name, 'Argument is out\",\n \"not ((0 < w <= max_width) and (0 < h\",\n \"not None: try: value = type(value) except ValueError: msg =\",\n \"is out of range (%dx%d, area=%d)\\\" % (w, h, w\",\n \"as e: # security check fails print(e) self.raise_error('js', 'Javascript profile\",\n \"'wait' to render full webpage\\\") return result def get_lua_source(self): return\",\n \"get_quality(self): return self.get(\\\"quality\\\", defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method\",\n \"_get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None) if isinstance(url,\",\n \"{ 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self):\",\n \"\\\" \\\"semicolon-separated list of name=hash pairs\\\" ) if not isinstance(load_args,\",\n \"get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if\",\n \"\\\"Invalid filter names: %s\\\" % (unknown_filters,) ) return filter_names def\",\n \"class RenderOptions(object): \\\"\\\"\\\" Options that control how to render a\",\n \"== 1 and wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait'\",\n \"return {} if isinstance(load_args, str): try: load_args = dict( kv.split(\\\"=\\\",\",\n \"= data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description,\",\n \"try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise\",\n \"full webpage\\\") return result def get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self,\",\n \"%s' % name, type='argument_required') else: return default def _get_bool(self, name,\",\n \"b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request)\",\n \"type=None) if isinstance(url, bytes): url = url.decode('utf8') return url def\",\n \"defaults.JPEG_QUALITY, type=int, range=(0, 100)) def get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\")\",\n \"self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method':\",\n \"else: if not ((0 < w <= max_width) and (0\",\n \"if request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type: request.content.seek(0)\",\n \"self.get(\\\"js_source\\\", default=None) def get_width(self): return self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH))\",\n \"JSON object\\\" ) if isinstance(headers, (list, tuple)): for el in\",\n \"allowed_domains is not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types =\",\n \"headers def get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None, type=None) if save_args\",\n \"/ POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for key, values\",\n \"def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is not\",\n \"engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine)) return\",\n \"= id(request) return cls(data, max_timeout) def get_expired_args(self, cache): \\\"\\\"\\\" Return\",\n \"str): # comma-separated string save_args = save_args.split(',') if not isinstance(save_args,\",\n \"return filter_names if not filter_names: return filter_names if pool is\",\n \"\\\"Argument %r has a wrong type\\\" % (name,) self.raise_error(name, msg,\",\n \"type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\", defaults.RESOURCE_TIMEOUT, type=float, range=(0,\",\n \"self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to render full webpage\\\") return result\",\n \"def get_headers(self): headers = self.get(\\\"headers\\\", default=None, type=None) if headers is\",\n \"should not have a body\\\") return body def get_render_all(self, wait=None):\",\n \"security check fails print(e) self.raise_error('js', 'Javascript profile does not exist')\",\n \"window size strings. Does not special-case ``'full'`` viewport. Raises ``ValueError``\",\n \"method.upper() not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method))\",\n \"< max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d) is out of range\",\n \"1) for kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\\\"load_args\\\",\",\n \"default, type=int, range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url =\",\n \"max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d) is out of range (%dx%d,\",\n \"default is self._REQUIRED: self.raise_error(name, 'Required argument is missing: %s' %\",\n \"is None: # skip validation return filter_names if not filter_names:\",\n \"names\\\", ) # JSON array if not all(isinstance(a, str) for\",\n \"def get_image_params(self): return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method()\",\n \"None: if type is not None: try: value = type(value)\",\n \"# -*- coding: utf-8 -*- import os import json from\",\n \"POST data if b'application/json' in content_type: try: content = request.content.read().decode('utf-8')\",\n \"range=(0, 1e6)) def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return\",\n \"application/json POST data if b'application/json' in content_type: try: content =\",\n \"pairs\\\" ) return load_args def get_viewport(self, wait=None): viewport = self.get(\\\"viewport\\\",\",\n \"viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport == 'full': if wait\",\n \"anything goes wrong. :param size_str: string to validate \\\"\\\"\\\" max_width\",\n \"missing: %s' % name, type='argument_required') else: return default def _get_bool(self,\",\n \"max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params =\",\n \"is None: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (filter_names,)\",\n \"pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \\\"filters\\\", \\\"Invalid filter names:\",\n \"not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine)) return engine\",\n \"get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(), 'baseurl':\",\n \"return { 'width': self.get_width(), 'height': self.get_height(), 'scale_method': self.get_scale_method() } def\",\n \"url = url.decode('utf8') return url def get_uid(self): return self.get('uid') def\",\n \"1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name, default, type=None)\",\n \"self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (unknown_filters,) ) return\",\n \"of name=hash pairs\\\" ) if not isinstance(load_args, dict): self.raise_error( argument=\\\"load_args\\\",\",\n \"name, default=_REQUIRED, type=str, range=None): value = self.data.get(name) if value is\",\n \"'height': self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def\",\n \"self.get(\\\"load_args\\\", default=None, type=None) if load_args is None: return {} if\",\n \"# security check fails print(e) self.raise_error('js', 'Javascript profile does not\",\n \"get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def\",\n \"None: if engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render engine\",\n \"{} if isinstance(load_args, str): try: load_args = dict( kv.split(\\\"=\\\", 1)\",\n \"e: raise BadOption({ 'type': 'invalid_json', 'description': \\\"Can't decode JSON\\\", 'message':\",\n \"js_profiles_path): js_profile = self.get(\\\"js\\\", default=None) if not js_profile: return js_profile\",\n \"save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be a list of strings\\\",\",\n \"'full': if wait == 0: self.raise_error(\\\"viewport\\\", \\\"Pass non-zero 'wait' to\",\n \"str(e), }) # 3. js_source from application/javascript POST requests if\",\n \"not enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile) except\",\n \"self.get(name, default, type=None) if isinstance(url, bytes): url = url.decode('utf8') return\",\n \"== \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium'\",\n \"control how to render a response. \\\"\\\"\\\" _REQUIRED = object()\",\n \"return self.get('uid') def get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\",\",\n \"== 2 and string_only): self.raise_error( argument='headers', description=\\\"'headers' must be either\",\n \"(unknown_filters,) ) return filter_names def get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None)\",\n \"min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self):\",\n \"(w * h < max_area)): raise ValueError(\\\"Viewport (%dx%d, area=%d) is\",\n \"= request.getHeader(b'content-type') if content_type: request.content.seek(0) # 2. application/json POST data\",\n \"default, type=None) if isinstance(url, bytes): url = url.decode('utf8') return url\",\n \"splash.utils import to_bytes, path_join_secure from splash.errors import BadOption class RenderOptions(object):\",\n \"is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None:\",\n \"'type': 'invalid_json', 'description': \\\"Can't decode JSON\\\", 'message': str(e), }) #\",\n \"loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args\",\n \"self.get_height(), 'scale_method': self.get_scale_method() } def get_png_params(self): return self.get_image_params() def get_jpeg_params(self):\",\n \"of range (%dx%d, area=%d)\\\" % (w, h, w * h,\",\n \"WxH format. Can be used to validate both viewport and\",\n \"if b'application/json' in content_type: try: content = request.content.read().decode('utf-8') data.update(json.loads(content)) except\",\n \"self.get_load_args() for name, key in (load_args or {}).items(): self.data[name] =\",\n \"engine {}\\\".format(engine)) if browser_engines_enabled is not None: if engine not\",\n \"request.content.read().decode('utf-8') request.content.seek(0) data['uid'] = id(request) return cls(data, max_timeout) def get_expired_args(self,\",\n \"try: load_args = dict( kv.split(\\\"=\\\", 1) for kv in load_args.split(';')\",\n \"= self.get(\\\"js\\\", default=None) if not js_profile: return js_profile if js_profiles_path\",\n \"{key.decode('utf-8'): values[0].decode('utf-8') for key, values in request.args.items()} if request.method ==\",\n \"= max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument', **kwargs): params\",\n \"decode JSON\\\", 'message': str(e), }) # 3. js_source from application/javascript\",\n \"list of argument names from load_args which can't be loaded\",\n \"method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method == 'GET' and body:\",\n \"(name, key) pairs. \\\"\\\"\\\" save_args = self.get_save_args() save_values = [self.data.get(name)\",\n \"# comma-separated string save_args = save_args.split(',') if not isinstance(save_args, list):\",\n \"default=None) if not js_profile: return js_profile if js_profiles_path is None:\",\n \"exist') return profile_dir def get_headers(self): headers = self.get(\\\"headers\\\", default=None, type=None)\",\n \"get_headers(self): headers = self.get(\\\"headers\\\", default=None, type=None) if headers is None:\",\n \"get_allowed_domains(self): allowed_domains = self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is not None:\",\n \"self.get(\\\"body\\\", None, to_bytes) method = self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method ==\",\n \"a semicolon-separated list \\\" \\\"of name=hash pairs\\\" ) return load_args\",\n \"if range is not None and not (range[0] <= value\",\n \"to cache. Return a list of (name, key) pairs. \\\"\\\"\\\"\",\n \"defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE), history=self._get_bool(\\\"history\\\",\",\n \"engine def get_http2(self): engine = self.get_engine() if self.get_engine() == \\\"webkit\\\":\",\n \"filter_names = self.get('filters', '') filter_names = [f for f in\",\n \"object() def __init__(self, data, max_timeout): self.data = data self.max_timeout =\",\n \"def get_expired_args(self, cache): \\\"\\\"\\\" Return a list of argument names\",\n \"self.raise_error(\\\"engine\\\", \\\"Disabled render engine {}\\\".format(engine)) return engine def get_http2(self): engine\",\n \"return method def get_body(self): body = self.get(\\\"body\\\", None, to_bytes) method\",\n \"= self.get(\\\"http_method\\\", \\\"GET\\\").upper() if method == 'GET' and body: self.raise_error(\\\"body\\\",\",\n \"\\\"(name, value) pairs or a JSON object\\\" ) if isinstance(headers,\",\n \"self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(), 'http2':\",\n \"is None: return {} if isinstance(load_args, str): try: load_args =\",\n \"list(filter(None, content_types.split(','))) return content_types def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[])\",\n \"Raises ``ValueError`` if anything goes wrong. :param size_str: string to\",\n \"get_include_params(self): return dict( html=self._get_bool(\\\"html\\\", defaults.DO_HTML), iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\",\",\n \"**kwargs): params = { 'type': type, 'argument': argument, 'description': description\",\n \"adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules is None: self.raise_error( \\\"filters\\\", \\\"Invalid\",\n \"= path_join_secure(js_profiles_path, js_profile) except ValueError as e: # security check\",\n \"'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(),\",\n \"= self.get(\\\"allowed_domains\\\", default=None) if allowed_domains is not None: return allowed_domains.split(',')\",\n \"defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return\",\n \"= self._get_bool(\\\"render_all\\\", False) if result == 1 and wait ==\",\n \"except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value is not a\",\n \"save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args and put all values to\",\n \"if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" % (unknown_filters,)\",\n \"(list, tuple, dict)): self.raise_error( argument='headers', description=\\\"'headers' must be either a\",\n \"try: validate_size_str(viewport) except ValueError as e: self.raise_error(\\\"viewport\\\", str(e)) return viewport\",\n \"'resource_timeout': self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(),\",\n \"kv in load_args.split(';') ) except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string\",\n \"result def get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile =\",\n \"__init__(self, data, max_timeout): self.data = data self.max_timeout = max_timeout @classmethod\",\n \"get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not in [\\\"POST\\\",\",\n \"be either a JSON object with \\\" \\\"argument hashes or\",\n \"adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\", \\\"Invalid filter names: %s\\\" %\",\n \"request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type') if content_type:\",\n \"semicolon-separated list \\\" \\\"of name=hash pairs\\\" ) return load_args def\",\n \"return body def get_render_all(self, wait=None): result = self._get_bool(\\\"render_all\\\", False) if\",\n \"argument=\\\"load_args\\\", description=\\\"'load_args' string value is not a \\\" \\\"semicolon-separated list\",\n \"str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names = self.get('filters',\",\n \"defaults.VIEWPORT_MAX_AREA try: w, h = map(int, size_str.split('x')) except ValueError: raise\",\n \"skip validation return filter_names if not filter_names: return filter_names if\",\n \"def get_common_params(self, js_profiles_path): wait = self.get_wait() return { 'url': self.get_url(),\",\n \"data self.max_timeout = max_timeout @classmethod def raise_error(cls, argument, description, type='bad_argument',\",\n \"in filter_names.split(',') if f] if pool is None and adblock_rules\",\n \"type=float, range=(0, self.get_timeout())) def get_timeout(self): default = min(self.max_timeout, defaults.TIMEOUT) return\",\n \"return self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout)) def get_resource_timeout(self): return self.get(\\\"resource_timeout\\\",\",\n \"= defaults.VIEWPORT_MAX_HEIGTH max_area = defaults.VIEWPORT_MAX_AREA try: w, h = map(int,\",\n \"requests if b'application/javascript' in content_type: data['js_source'] = request.content.read().decode('utf-8') request.content.seek(0) data['uid']\",\n \"type='bad_argument', **kwargs): params = { 'type': type, 'argument': argument, 'description':\",\n \"from application/javascript POST requests if b'application/javascript' in content_type: data['js_source'] =\",\n \"get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None, type=None) if save_args is None:\",\n \"type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\", None, type=int, range=(1,\",\n \"load_args which can't be loaded \\\"\\\"\\\" return cache.get_missing(self.get_load_args().items()) def save_args_to_cache(self,\",\n \"to render full webpage\\\") else: try: validate_size_str(viewport) except ValueError as\",\n \"% (filter_names,) ) if adblock_rules is not None: unknown_filters =\",\n \"len(el) == 2 and string_only): self.raise_error( argument='headers', description=\\\"'headers' must be\",\n \"\\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert engine == 'chromium' default\",\n \"= all(isinstance(e, str) for e in el) if not (isinstance(el,\",\n \"method {}\\\".format(method)) return method def get_body(self): body = self.get(\\\"body\\\", None,\",\n \"must be either a JSON array of \\\" \\\"(name, value)\",\n \"None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\", None, type=int,\",\n \"object\\\" ) if isinstance(headers, (list, tuple)): for el in headers:\",\n \"a JSON object\\\" ) return headers def get_save_args(self): save_args =\",\n \"def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES)\",\n \"not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine)) if\",\n \"and window size strings. Does not special-case ``'full'`` viewport. Raises\",\n \"get_uid(self): return self.get('uid') def get_url(self): return self._get_url(\\\"url\\\") def get_baseurl(self): return\",\n \"'Javascript profile does not exist') return profile_dir def get_headers(self): headers\",\n \"= self.get(\\\"headers\\\", default=None, type=None) if headers is None: return headers\",\n \"type=int, range=(0, 100)) def get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\") if\",\n \"in request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type') if\",\n \"str) for e in el) if not (isinstance(el, (list, tuple))\",\n \"array with argument names\\\", ) # JSON array if not\",\n \"return self.get_image_params() def get_jpeg_params(self): params = {'quality': self.get_quality()} params.update(self.get_image_params()) return\",\n \"self.raise_error('js', 'Javascript profile does not exist') return profile_dir def get_headers(self):\",\n \"def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME,\",\n \"self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not in [\\\"POST\\\", \\\"GET\\\"]: self.raise_error(\\\"http_method\\\", \\\"Unsupported\",\n \"get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None, type=None) if load_args is None:\",\n \"list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\", defaults.HTML5_MEDIA_ENABLED) def\",\n \"browser_engines_enabled=None): engine = self.get(\\\"engine\\\", default=\\\"webkit\\\", type=str) if engine not in\",\n \"if js_profiles_path is None: self.raise_error('js', 'Javascript profiles are not enabled\",\n \"description=\\\"Invalid 'scale_method': %s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method\",\n \"self.get(\\\"save_args\\\", default=None, type=None) if save_args is None: return [] if\",\n \"\\\"GET request should not have a body\\\") return body def\",\n \"all(isinstance(e, str) for e in el) if not (isinstance(el, (list,\",\n \"BadOption({ 'type': 'invalid_json', 'description': \\\"Can't decode JSON\\\", 'message': str(e), })\",\n \"get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\", defaults.AUTOLOAD_IMAGES) def\",\n \"filter_names = [f for f in filter_names.split(',') if f] if\",\n \"= self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector'] if scale_method not\",\n \"not filter_names: return filter_names if pool is not None: adblock_rules\",\n \"format. Can be used to validate both viewport and window\",\n \"< w <= max_width) and (0 < h <= max_heigth)\",\n \"size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid viewport format: %s\\\" % size_str)\",\n \"be a list of strings\\\", ) return save_args def get_load_args(self):\",\n \"values in request.args.items()} if request.method == b'POST': content_type = request.getHeader(b'content-type')\",\n \"put all values to cache. Return a list of (name,\",\n \"self.raise_error('js', 'Javascript profiles are not enabled on server') try: profile_dir\",\n \"save_args is None: return [] if isinstance(save_args, str): # comma-separated\",\n \"def get_scale_method(self): scale_method = self.get(\\\"scale_method\\\", defaults.IMAGE_SCALE_METHOD) allowed_scale_methods = ['raster', 'vector']\",\n \"name, type='argument_required') else: return default def _get_bool(self, name, default=_REQUIRED): return\",\n \"self.get_save_args() save_values = [self.data.get(name) for name in save_args] keys =\",\n \"\\\"Disabled render engine {}\\\".format(engine)) return engine def get_http2(self): engine =\",\n \"is not None: unknown_filters = adblock_rules.get_unknown_filters(filter_names) if unknown_filters: self.raise_error( \\\"filters\\\",\",\n \"self.get(\\\"allowed_content_types\\\", default=['*']) if isinstance(content_types, str): content_types = list(filter(None, content_types.split(','))) return\",\n \"not None: return allowed_domains.split(',') def get_allowed_content_types(self): content_types = self.get(\\\"allowed_content_types\\\", default=['*'])\",\n \"and string_only): self.raise_error( argument='headers', description=\\\"'headers' must be either a JSON\",\n \"size string in WxH format. Can be used to validate\",\n \"self.get_resource_timeout(), 'viewport': self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy':\",\n \"adblock_rules=None): filter_names = self.get('filters', '') filter_names = [f for f\",\n \"wait=None): viewport = self.get(\\\"viewport\\\", defaults.VIEWPORT_SIZE) if viewport == 'full': if\",\n \"default=\\\"webkit\\\", type=str) if engine not in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown\",\n \"'GET' and body: self.raise_error(\\\"body\\\", \\\"GET request should not have a\",\n \"Validate size string in WxH format. Can be used to\",\n \"range=(0, 100)) def get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper()\",\n \"get_wait(self): return self.get(\\\"wait\\\", defaults.WAIT_TIME, type=float, range=(0, self.get_timeout())) def get_timeout(self): default\",\n \"request.content.read().decode('utf-8') data.update(json.loads(content)) except ValueError as e: raise BadOption({ 'type': 'invalid_json',\",\n \"range is not None and not (range[0] <= value <=\",\n \"\\\"(name, value) pairs or a JSON object\\\" ) return headers\",\n \"is None: return [] if isinstance(save_args, str): # comma-separated string\",\n \"dict): self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' should be either a JSON object\",\n \"def get_width(self): return self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self):\",\n \"values to cache. Return a list of (name, key) pairs.\",\n \"None: return headers if not isinstance(headers, (list, tuple, dict)): self.raise_error(\",\n \"self.get_viewport(wait), 'render_all': self.get_render_all(wait), 'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile':\",\n \"ValueError(\\\"Invalid viewport format: %s\\\" % size_str) else: if not ((0\",\n \"100)) def get_http_method(self): method = self.get(\\\"http_method\\\", \\\"GET\\\") if method.upper() not\",\n \"1. GET / POST data data = {key.decode('utf-8'): values[0].decode('utf-8') for\",\n \"'images': self.get_images(), 'headers': self.get_headers(), 'proxy': self.get_proxy(), 'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(),\",\n \"<= range[1]): self.raise_error(name, 'Argument is out of the allowed range',\",\n \"out of range (%dx%d, area=%d)\\\" % (w, h, w *\",\n \"%s\\\" % scale_method, allowed=allowed_scale_methods, received=scale_method, ) return scale_method def get_quality(self):\",\n \"'js_profile': self.get_js_profile(js_profiles_path), 'js_source': self.get_js_source(), 'http_method': self.get_http_method(), 'body': self.get_body(), 'html5_media': self.get_html5_media(),\",\n \") # JSON array if not all(isinstance(a, str) for a\",\n \"defaults from splash.utils import to_bytes, path_join_secure from splash.errors import BadOption\",\n \"a JSON object with \\\" \\\"argument hashes or a semicolon-separated\",\n \"self._get_url(\\\"url\\\") def get_baseurl(self): return self._get_url(\\\"baseurl\\\", default=None) def get_wait(self): return self.get(\\\"wait\\\",\",\n \"defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return self._get_bool(\\\"images\\\",\",\n \"return headers def get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None, type=None) if\",\n \"else: return default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default,\",\n \"are not enabled on server') try: profile_dir = path_join_secure(js_profiles_path, js_profile)\",\n \"= [f for f in filter_names.split(',') if f] if pool\",\n \"return self._get_bool(\\\"http2\\\", default) def get_common_params(self, js_profiles_path): wait = self.get_wait() return\",\n \"def save_args_to_cache(self, cache): \\\"\\\"\\\" Process save_args and put all values\",\n \"is not None: try: value = type(value) except ValueError: msg\",\n \"filter names: %s\\\" % (unknown_filters,) ) return filter_names def get_allowed_domains(self):\",\n \"headers is None: return headers if not isinstance(headers, (list, tuple,\",\n \"is not None and not (range[0] <= value <= range[1]):\",\n \"self.get(\\\"proxy\\\", default=None) def get_js_source(self): return self.get(\\\"js_source\\\", default=None) def get_width(self): return\",\n \"wait == 0: self.raise_error(\\\"render_all\\\", \\\"Pass non-zero 'wait' to render full\",\n \"a response. \\\"\\\"\\\" _REQUIRED = object() def __init__(self, data, max_timeout):\",\n \"str) for a in save_args): self.raise_error( argument=\\\"save_args\\\", description=\\\"'save_args' should be\",\n \"content_types = list(filter(None, content_types.split(','))) return content_types def get_html5_media(self): return self._get_bool(\\\"html5_media\\\",\",\n \"self.raise_error(\\\"http_method\\\", \\\"Unsupported HTTP method {}\\\".format(method)) return method def get_body(self): body\",\n \"in {\\\"webkit\\\", \\\"chromium\\\"}: self.raise_error(\\\"engine\\\", \\\"Unknown render engine {}\\\".format(engine)) if browser_engines_enabled\",\n \"return list(zip(save_args, keys)) def load_cached_args(self, cache): load_args = self.get_load_args() for\",\n \"_get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0, 1)) def\",\n \"not None: if engine not in browser_engines_enabled: self.raise_error(\\\"engine\\\", \\\"Disabled render\",\n \"def get_save_args(self): save_args = self.get(\\\"save_args\\\", default=None, type=None) if save_args is\",\n \"for key, values in request.args.items()} if request.method == b'POST': content_type\",\n \"= object() def __init__(self, data, max_timeout): self.data = data self.max_timeout\",\n \"\\\"argument hashes or a semicolon-separated list \\\" \\\"of name=hash pairs\\\"\",\n \"def get_response_body(self): return self._get_bool(\\\"response_body\\\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\\\"request_body\\\", defaults.REQUEST_BODY_ENABLED)\",\n \"default def _get_bool(self, name, default=_REQUIRED): return self.get(name, default, type=int, range=(0,\",\n \"print(e) self.raise_error('js', 'Javascript profile does not exist') if not os.path.isdir(profile_dir):\",\n \"a \\\" \\\"semicolon-separated list of name=hash pairs\\\" ) if not\",\n \"viewport and window size strings. Does not special-case ``'full'`` viewport.\",\n \"if not ((0 < w <= max_width) and (0 <\",\n \"load_args = dict( kv.split(\\\"=\\\", 1) for kv in load_args.split(';') )\",\n \"not (isinstance(el, (list, tuple)) and len(el) == 2 and string_only):\",\n \"body: self.raise_error(\\\"body\\\", \\\"GET request should not have a body\\\") return\",\n \"if pool is not None: adblock_rules = pool.network_manager_factory.adblock_rules if adblock_rules\",\n \"iframes=self._get_bool(\\\"iframes\\\", defaults.DO_IFRAMES), png=self._get_bool(\\\"png\\\", defaults.DO_PNG), jpeg=self._get_bool(\\\"jpeg\\\", defaults.DO_JPEG), script=self._get_bool(\\\"script\\\", defaults.SHOW_SCRIPT), console=self._get_bool(\\\"console\\\", defaults.SHOW_CONSOLE),\",\n \"% (name,) self.raise_error(name, msg, required_type=type.__name__) if range is not None\",\n \"default=None) def get_width(self): return self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH)) def\",\n \"= map(int, size_str.split('x')) except ValueError: raise ValueError(\\\"Invalid viewport format: %s\\\"\",\n \"string save_args = save_args.split(',') if not isinstance(save_args, list): self.raise_error( argument=\\\"save_args\\\",\",\n \"in load_args.split(';') ) except ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value\",\n \"return content_types def get_forbidden_content_types(self): content_types = self.get(\\\"forbidden_content_types\\\", default=[]) if isinstance(content_types,\",\n \"self._get_bool(\\\"render_all\\\", False) if result == 1 and wait == 0:\",\n \"%r has a wrong type\\\" % (name,) self.raise_error(name, msg, required_type=type.__name__)\",\n \"with \\\" \\\"argument hashes or a semicolon-separated list \\\" \\\"of\",\n \"assert engine == 'chromium' default = defaults.CHROMIUM_HTTP2_ENABLED return self._get_bool(\\\"http2\\\", default)\",\n \"def get_load_args(self): load_args = self.get(\\\"load_args\\\", default=None, type=None) if load_args is\",\n \"e: self.raise_error(\\\"viewport\\\", str(e)) return viewport def get_filters(self, pool=None, adblock_rules=None): filter_names\",\n \"range=(0, 1)) def _get_url(self, name, default=_REQUIRED): url = self.get(name, default,\",\n \"self.get_engine() if self.get_engine() == \\\"webkit\\\": default = defaults.WEBKIT_HTTP2_ENABLED else: assert\",\n \"return self.get(\\\"width\\\", None, type=int, range=(1, defaults.MAX_WIDTH)) def get_height(self): return self.get(\\\"height\\\",\",\n \"max_width) and (0 < h <= max_heigth) and (w *\",\n \"type=str, range=None): value = self.data.get(name) if value is not None:\",\n \"default = min(self.max_timeout, defaults.TIMEOUT) return self.get(\\\"timeout\\\", default, type=float, range=(0, self.max_timeout))\",\n \"splash import defaults from splash.utils import to_bytes, path_join_secure from splash.errors\",\n \"return result def get_lua_source(self): return self.get(\\\"lua_source\\\") def get_js_profile(self, js_profiles_path): js_profile\",\n \"a wrong type\\\" % (name,) self.raise_error(name, msg, required_type=type.__name__) if range\",\n \"ValueError: self.raise_error( argument=\\\"load_args\\\", description=\\\"'load_args' string value is not a \\\"\"\n]","isConversational":false}}},{"rowIdx":179,"cells":{"ngram":{"kind":"list like","value":["r = s/3 print(r) # code.... ''' def average(): a=1","s/3 print(r) # code.... ''' def average(): a=1 b=2 c=3","a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input","#parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def","average(a, b, c): s = a+b+c r = s/3 return","code.... ''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r)","= 2 c = 3 s = a+b+c r =","def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() '''","c): s = a+b+c r = s/3 return r print(average(10,","c = 3 s = a+b+c r = s/3 print(r)","= a+b+c r = s/3 return r print(average(10, 20, 30))","average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b, c):","r=s/3 print(r) average(10,20,30) ''' def average(a, b, c): s =","def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b,","average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' '''","b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter","b, c): s = a+b+c r = s/3 return r","#input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) '''","r=s/3 print(r) average() ''' ''' #input #parameter #argument def average(a,b,c):","c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter #argument","3 s = a+b+c r = s/3 print(r) # code....","code.... a = 1 b = 2 c = 3","''' def average(a, b, c): s = a+b+c r =","<filename>syntax/func.py # code.... a = 1 b = 2 c","= s/3 print(r) # code.... ''' def average(): a=1 b=2","b = 2 c = 3 s = a+b+c r","#argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a,","average() ''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3","1 b = 2 c = 3 s = a+b+c","s = a+b+c r = s/3 print(r) # code.... '''","''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r)","# code.... a = 1 b = 2 c =","= a+b+c r = s/3 print(r) # code.... ''' def","s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter #argument def","# code.... ''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3","= 1 b = 2 c = 3 s =","print(r) # code.... ''' def average(): a=1 b=2 c=3 s=a+b+c","print(r) average(10,20,30) ''' def average(a, b, c): s = a+b+c","s = a+b+c r = s/3 return r print(average(10, 20,","average(10,20,30) ''' def average(a, b, c): s = a+b+c r","2 c = 3 s = a+b+c r = s/3","a = 1 b = 2 c = 3 s","= 3 s = a+b+c r = s/3 print(r) #","''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average()","def average(a, b, c): s = a+b+c r = s/3","a+b+c r = s/3 print(r) # code.... ''' def average():","print(r) average() ''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c","s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b, c): s","''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30)"],"string":"[\n \"r = s/3 print(r) # code.... ''' def average(): a=1\",\n \"s/3 print(r) # code.... ''' def average(): a=1 b=2 c=3\",\n \"a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input\",\n \"#parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def\",\n \"average(a, b, c): s = a+b+c r = s/3 return\",\n \"code.... ''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r)\",\n \"= 2 c = 3 s = a+b+c r =\",\n \"def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() '''\",\n \"c): s = a+b+c r = s/3 return r print(average(10,\",\n \"c = 3 s = a+b+c r = s/3 print(r)\",\n \"= a+b+c r = s/3 return r print(average(10, 20, 30))\",\n \"average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b, c):\",\n \"r=s/3 print(r) average(10,20,30) ''' def average(a, b, c): s =\",\n \"def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b,\",\n \"average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' '''\",\n \"b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter\",\n \"b, c): s = a+b+c r = s/3 return r\",\n \"#input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) '''\",\n \"r=s/3 print(r) average() ''' ''' #input #parameter #argument def average(a,b,c):\",\n \"c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter #argument\",\n \"3 s = a+b+c r = s/3 print(r) # code....\",\n \"code.... a = 1 b = 2 c = 3\",\n \"''' def average(a, b, c): s = a+b+c r =\",\n \"<filename>syntax/func.py # code.... a = 1 b = 2 c\",\n \"= s/3 print(r) # code.... ''' def average(): a=1 b=2\",\n \"b = 2 c = 3 s = a+b+c r\",\n \"#argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a,\",\n \"average() ''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3\",\n \"1 b = 2 c = 3 s = a+b+c\",\n \"s = a+b+c r = s/3 print(r) # code.... '''\",\n \"''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r)\",\n \"# code.... a = 1 b = 2 c =\",\n \"= a+b+c r = s/3 print(r) # code.... ''' def\",\n \"s=a+b+c r=s/3 print(r) average() ''' ''' #input #parameter #argument def\",\n \"# code.... ''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3\",\n \"= 1 b = 2 c = 3 s =\",\n \"print(r) # code.... ''' def average(): a=1 b=2 c=3 s=a+b+c\",\n \"print(r) average(10,20,30) ''' def average(a, b, c): s = a+b+c\",\n \"s = a+b+c r = s/3 return r print(average(10, 20,\",\n \"average(10,20,30) ''' def average(a, b, c): s = a+b+c r\",\n \"2 c = 3 s = a+b+c r = s/3\",\n \"a = 1 b = 2 c = 3 s\",\n \"= 3 s = a+b+c r = s/3 print(r) #\",\n \"''' def average(): a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average()\",\n \"def average(a, b, c): s = a+b+c r = s/3\",\n \"a+b+c r = s/3 print(r) # code.... ''' def average():\",\n \"print(r) average() ''' ''' #input #parameter #argument def average(a,b,c): s=a+b+c\",\n \"s=a+b+c r=s/3 print(r) average(10,20,30) ''' def average(a, b, c): s\",\n \"''' #input #parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30)\"\n]","isConversational":false}}},{"rowIdx":180,"cells":{"ngram":{"kind":"list like","value":["python # runs after the job (and after the default","start, end and strand columns.\") error_map[name] = error_msg data_param_names.add( name","param in page_param_map.items(): if isinstance( param, DataToolParameter ): # for","not None: dbkeys.add( param_values[name].dbkey ) data_params += 1 # check","int( param.metadata.strandCol ) except: error_msg = (\"The attributes of this","import Set as set def validate_input( trans, error_map, param_values, page_param_map","chrom, start, end and strand columns.\") error_map[name] = error_msg data_param_names.add(","strand columns.\") error_map[name] = error_msg data_param_names.add( name ) if len(","set. \" \"Click the pencil icon in the history item","the pencil icon in the history item to set the","set() data_params = 0 for name, param in page_param_map.items(): if","data_param_names: error_map[name] = \"All datasets must belong to same genomic","error_map[name] = \"A dataset of the appropriate type is required\"","param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey ) data_params +=","job (and after the default post-filter) from galaxy.tools.parameters import DataToolParameter","if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey ) data_params","# for each dataset parameter if param_values.get(name, None) is not","param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol )","runs after the job (and after the default post-filter) from","set() data_param_names = set() data_params = 0 for name, param","in data_param_names: error_map[name] = \"A dataset of the appropriate type","error_map[name] = \"All datasets must belong to same genomic build,","\"this dataset is linked to build '%s'\" % param_values[name].dbkey if","#!/usr/bin/env python # runs after the job (and after the","if param.metadata.strandCol is not None: int( param.metadata.strandCol ) except: error_msg","post-filter) from galaxy.tools.parameters import DataToolParameter # Older py compatibility try:","param, DataToolParameter ): # for each dataset parameter if param_values.get(name,","the history item to set the chrom, start, end and","in data_param_names: error_map[name] = \"All datasets must belong to same","properly set. \" \"Click the pencil icon in the history","dataset is linked to build '%s'\" % param_values[name].dbkey if data_params","int( param.metadata.chromCol ) if param.metadata.strandCol is not None: int( param.metadata.strandCol","default post-filter) from galaxy.tools.parameters import DataToolParameter # Older py compatibility",") data_params += 1 # check meta data try: param","and strand columns.\") error_map[name] = error_msg data_param_names.add( name ) if","data_params = 0 for name, param in page_param_map.items(): if isinstance(","None) is not None: dbkeys.add( param_values[name].dbkey ) data_params += 1","1 # check meta data try: param = param_values[name] int(","\"Click the pencil icon in the history item to set","param.metadata.strandCol ) except: error_msg = (\"The attributes of this dataset","linked to build '%s'\" % param_values[name].dbkey if data_params != len(data_param_names):","(\"The attributes of this dataset are not properly set. \"","error_map[name] = error_msg data_param_names.add( name ) if len( dbkeys )","except: from sets import Set as set def validate_input( trans,","None: dbkeys.add( param_values[name].dbkey ) data_params += 1 # check meta",") > 1: for name in data_param_names: error_map[name] = \"All","page_param_map.items(): if isinstance( param, DataToolParameter ): # for each dataset","+= 1 # check meta data try: param = param_values[name]","len(data_param_names): for name in data_param_names: error_map[name] = \"A dataset of","): # for each dataset parameter if param_values.get(name, None) is","int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is not","for each dataset parameter if param_values.get(name, None) is not None:","this dataset are not properly set. \" \"Click the pencil","data_params != len(data_param_names): for name in data_param_names: error_map[name] = \"A","\\ \"this dataset is linked to build '%s'\" % param_values[name].dbkey","is not None: dbkeys.add( param_values[name].dbkey ) data_params += 1 #","py compatibility try: set() except: from sets import Set as","# Older py compatibility try: set() except: from sets import","galaxy.tools.parameters import DataToolParameter # Older py compatibility try: set() except:","name ) if len( dbkeys ) > 1: for name","param_values, page_param_map ): dbkeys = set() data_param_names = set() data_params","is not None: int( param.metadata.strandCol ) except: error_msg = (\"The","to build '%s'\" % param_values[name].dbkey if data_params != len(data_param_names): for","the chrom, start, end and strand columns.\") error_map[name] = error_msg","if isinstance( param, DataToolParameter ): # for each dataset parameter","# runs after the job (and after the default post-filter)","dbkeys ) > 1: for name in data_param_names: error_map[name] =","data_param_names: error_map[name] = \"A dataset of the appropriate type is","name in data_param_names: error_map[name] = \"A dataset of the appropriate","icon in the history item to set the chrom, start,","each dataset parameter if param_values.get(name, None) is not None: dbkeys.add(","param.metadata.chromCol ) if param.metadata.strandCol is not None: int( param.metadata.strandCol )","if len( dbkeys ) > 1: for name in data_param_names:","the job (and after the default post-filter) from galaxy.tools.parameters import","= set() data_params = 0 for name, param in page_param_map.items():","0 for name, param in page_param_map.items(): if isinstance( param, DataToolParameter","page_param_map ): dbkeys = set() data_param_names = set() data_params =","% param_values[name].dbkey if data_params != len(data_param_names): for name in data_param_names:","'%s'\" % param_values[name].dbkey if data_params != len(data_param_names): for name in","from sets import Set as set def validate_input( trans, error_map,","except: error_msg = (\"The attributes of this dataset are not","1: for name in data_param_names: error_map[name] = \"All datasets must","build '%s'\" % param_values[name].dbkey if data_params != len(data_param_names): for name","validate_input( trans, error_map, param_values, page_param_map ): dbkeys = set() data_param_names","columns.\") error_map[name] = error_msg data_param_names.add( name ) if len( dbkeys","\" \\ \"this dataset is linked to build '%s'\" %","param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int(","= (\"The attributes of this dataset are not properly set.","not None: int( param.metadata.strandCol ) except: error_msg = (\"The attributes","Set as set def validate_input( trans, error_map, param_values, page_param_map ):","same genomic build, \" \\ \"this dataset is linked to","DataToolParameter ): # for each dataset parameter if param_values.get(name, None)","parameter if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey )","# check meta data try: param = param_values[name] int( param.metadata.startCol",") except: error_msg = (\"The attributes of this dataset are","data try: param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol","for name in data_param_names: error_map[name] = \"A dataset of the","attributes of this dataset are not properly set. \" \"Click","set the chrom, start, end and strand columns.\") error_map[name] =","if data_params != len(data_param_names): for name in data_param_names: error_map[name] =","param_values[name].dbkey if data_params != len(data_param_names): for name in data_param_names: error_map[name]","the default post-filter) from galaxy.tools.parameters import DataToolParameter # Older py","name, param in page_param_map.items(): if isinstance( param, DataToolParameter ): #","\"All datasets must belong to same genomic build, \" \\","compatibility try: set() except: from sets import Set as set","int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol ) if","history item to set the chrom, start, end and strand","after the default post-filter) from galaxy.tools.parameters import DataToolParameter # Older","dataset are not properly set. \" \"Click the pencil icon","data_param_names = set() data_params = 0 for name, param in","param_values[name].dbkey ) data_params += 1 # check meta data try:",") if len( dbkeys ) > 1: for name in","pencil icon in the history item to set the chrom,","end and strand columns.\") error_map[name] = error_msg data_param_names.add( name )","None: int( param.metadata.strandCol ) except: error_msg = (\"The attributes of","check meta data try: param = param_values[name] int( param.metadata.startCol )","= error_msg data_param_names.add( name ) if len( dbkeys ) >","data_param_names.add( name ) if len( dbkeys ) > 1: for","item to set the chrom, start, end and strand columns.\")","= 0 for name, param in page_param_map.items(): if isinstance( param,","not properly set. \" \"Click the pencil icon in the","belong to same genomic build, \" \\ \"this dataset is","sets import Set as set def validate_input( trans, error_map, param_values,","to set the chrom, start, end and strand columns.\") error_map[name]","trans, error_map, param_values, page_param_map ): dbkeys = set() data_param_names =","= \"All datasets must belong to same genomic build, \"","is linked to build '%s'\" % param_values[name].dbkey if data_params !=","after the job (and after the default post-filter) from galaxy.tools.parameters","try: param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol )","param.metadata.strandCol is not None: int( param.metadata.strandCol ) except: error_msg =","of this dataset are not properly set. \" \"Click the","): dbkeys = set() data_param_names = set() data_params = 0","param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is not None:","error_msg = (\"The attributes of this dataset are not properly","param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol",") int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is","error_map, param_values, page_param_map ): dbkeys = set() data_param_names = set()","dbkeys.add( param_values[name].dbkey ) data_params += 1 # check meta data","!= len(data_param_names): for name in data_param_names: error_map[name] = \"A dataset","DataToolParameter # Older py compatibility try: set() except: from sets","from galaxy.tools.parameters import DataToolParameter # Older py compatibility try: set()","import DataToolParameter # Older py compatibility try: set() except: from","try: set() except: from sets import Set as set def","= param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol","name in data_param_names: error_map[name] = \"All datasets must belong to","def validate_input( trans, error_map, param_values, page_param_map ): dbkeys = set()","are not properly set. \" \"Click the pencil icon in","genomic build, \" \\ \"this dataset is linked to build","isinstance( param, DataToolParameter ): # for each dataset parameter if","meta data try: param = param_values[name] int( param.metadata.startCol ) int(",") int( param.metadata.chromCol ) if param.metadata.strandCol is not None: int(","(and after the default post-filter) from galaxy.tools.parameters import DataToolParameter #","for name, param in page_param_map.items(): if isinstance( param, DataToolParameter ):","for name in data_param_names: error_map[name] = \"All datasets must belong","data_params += 1 # check meta data try: param =","to same genomic build, \" \\ \"this dataset is linked","> 1: for name in data_param_names: error_map[name] = \"All datasets","error_msg data_param_names.add( name ) if len( dbkeys ) > 1:","= set() data_param_names = set() data_params = 0 for name,","dataset parameter if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey","\" \"Click the pencil icon in the history item to","Older py compatibility try: set() except: from sets import Set","must belong to same genomic build, \" \\ \"this dataset","as set def validate_input( trans, error_map, param_values, page_param_map ): dbkeys","set() except: from sets import Set as set def validate_input(","in the history item to set the chrom, start, end","len( dbkeys ) > 1: for name in data_param_names: error_map[name]","in page_param_map.items(): if isinstance( param, DataToolParameter ): # for each",") if param.metadata.strandCol is not None: int( param.metadata.strandCol ) except:","build, \" \\ \"this dataset is linked to build '%s'\"","set def validate_input( trans, error_map, param_values, page_param_map ): dbkeys =","dbkeys = set() data_param_names = set() data_params = 0 for","datasets must belong to same genomic build, \" \\ \"this"],"string":"[\n \"python # runs after the job (and after the default\",\n \"start, end and strand columns.\\\") error_map[name] = error_msg data_param_names.add( name\",\n \"param in page_param_map.items(): if isinstance( param, DataToolParameter ): # for\",\n \"not None: dbkeys.add( param_values[name].dbkey ) data_params += 1 # check\",\n \"int( param.metadata.strandCol ) except: error_msg = (\\\"The attributes of this\",\n \"import Set as set def validate_input( trans, error_map, param_values, page_param_map\",\n \"chrom, start, end and strand columns.\\\") error_map[name] = error_msg data_param_names.add(\",\n \"strand columns.\\\") error_map[name] = error_msg data_param_names.add( name ) if len(\",\n \"set. \\\" \\\"Click the pencil icon in the history item\",\n \"the pencil icon in the history item to set the\",\n \"set() data_params = 0 for name, param in page_param_map.items(): if\",\n \"data_param_names: error_map[name] = \\\"All datasets must belong to same genomic\",\n \"error_map[name] = \\\"A dataset of the appropriate type is required\\\"\",\n \"param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey ) data_params +=\",\n \"job (and after the default post-filter) from galaxy.tools.parameters import DataToolParameter\",\n \"if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey ) data_params\",\n \"# for each dataset parameter if param_values.get(name, None) is not\",\n \"param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol )\",\n \"runs after the job (and after the default post-filter) from\",\n \"set() data_param_names = set() data_params = 0 for name, param\",\n \"in data_param_names: error_map[name] = \\\"A dataset of the appropriate type\",\n \"error_map[name] = \\\"All datasets must belong to same genomic build,\",\n \"\\\"this dataset is linked to build '%s'\\\" % param_values[name].dbkey if\",\n \"#!/usr/bin/env python # runs after the job (and after the\",\n \"if param.metadata.strandCol is not None: int( param.metadata.strandCol ) except: error_msg\",\n \"post-filter) from galaxy.tools.parameters import DataToolParameter # Older py compatibility try:\",\n \"param, DataToolParameter ): # for each dataset parameter if param_values.get(name,\",\n \"the history item to set the chrom, start, end and\",\n \"in data_param_names: error_map[name] = \\\"All datasets must belong to same\",\n \"properly set. \\\" \\\"Click the pencil icon in the history\",\n \"dataset is linked to build '%s'\\\" % param_values[name].dbkey if data_params\",\n \"int( param.metadata.chromCol ) if param.metadata.strandCol is not None: int( param.metadata.strandCol\",\n \"default post-filter) from galaxy.tools.parameters import DataToolParameter # Older py compatibility\",\n \") data_params += 1 # check meta data try: param\",\n \"and strand columns.\\\") error_map[name] = error_msg data_param_names.add( name ) if\",\n \"data_params = 0 for name, param in page_param_map.items(): if isinstance(\",\n \"None) is not None: dbkeys.add( param_values[name].dbkey ) data_params += 1\",\n \"1 # check meta data try: param = param_values[name] int(\",\n \"\\\"Click the pencil icon in the history item to set\",\n \"param.metadata.strandCol ) except: error_msg = (\\\"The attributes of this dataset\",\n \"linked to build '%s'\\\" % param_values[name].dbkey if data_params != len(data_param_names):\",\n \"(\\\"The attributes of this dataset are not properly set. \\\"\",\n \"error_map[name] = error_msg data_param_names.add( name ) if len( dbkeys )\",\n \"except: from sets import Set as set def validate_input( trans,\",\n \"None: dbkeys.add( param_values[name].dbkey ) data_params += 1 # check meta\",\n \") > 1: for name in data_param_names: error_map[name] = \\\"All\",\n \"page_param_map.items(): if isinstance( param, DataToolParameter ): # for each dataset\",\n \"+= 1 # check meta data try: param = param_values[name]\",\n \"len(data_param_names): for name in data_param_names: error_map[name] = \\\"A dataset of\",\n \"): # for each dataset parameter if param_values.get(name, None) is\",\n \"int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is not\",\n \"for each dataset parameter if param_values.get(name, None) is not None:\",\n \"this dataset are not properly set. \\\" \\\"Click the pencil\",\n \"data_params != len(data_param_names): for name in data_param_names: error_map[name] = \\\"A\",\n \"\\\\ \\\"this dataset is linked to build '%s'\\\" % param_values[name].dbkey\",\n \"is not None: dbkeys.add( param_values[name].dbkey ) data_params += 1 #\",\n \"py compatibility try: set() except: from sets import Set as\",\n \"# Older py compatibility try: set() except: from sets import\",\n \"galaxy.tools.parameters import DataToolParameter # Older py compatibility try: set() except:\",\n \"name ) if len( dbkeys ) > 1: for name\",\n \"param_values, page_param_map ): dbkeys = set() data_param_names = set() data_params\",\n \"is not None: int( param.metadata.strandCol ) except: error_msg = (\\\"The\",\n \"to build '%s'\\\" % param_values[name].dbkey if data_params != len(data_param_names): for\",\n \"the chrom, start, end and strand columns.\\\") error_map[name] = error_msg\",\n \"if isinstance( param, DataToolParameter ): # for each dataset parameter\",\n \"# runs after the job (and after the default post-filter)\",\n \"dbkeys ) > 1: for name in data_param_names: error_map[name] =\",\n \"data_param_names: error_map[name] = \\\"A dataset of the appropriate type is\",\n \"name in data_param_names: error_map[name] = \\\"A dataset of the appropriate\",\n \"icon in the history item to set the chrom, start,\",\n \"each dataset parameter if param_values.get(name, None) is not None: dbkeys.add(\",\n \"param.metadata.chromCol ) if param.metadata.strandCol is not None: int( param.metadata.strandCol )\",\n \"if len( dbkeys ) > 1: for name in data_param_names:\",\n \"the job (and after the default post-filter) from galaxy.tools.parameters import\",\n \"= set() data_params = 0 for name, param in page_param_map.items():\",\n \"0 for name, param in page_param_map.items(): if isinstance( param, DataToolParameter\",\n \"page_param_map ): dbkeys = set() data_param_names = set() data_params =\",\n \"% param_values[name].dbkey if data_params != len(data_param_names): for name in data_param_names:\",\n \"'%s'\\\" % param_values[name].dbkey if data_params != len(data_param_names): for name in\",\n \"from sets import Set as set def validate_input( trans, error_map,\",\n \"except: error_msg = (\\\"The attributes of this dataset are not\",\n \"1: for name in data_param_names: error_map[name] = \\\"All datasets must\",\n \"build '%s'\\\" % param_values[name].dbkey if data_params != len(data_param_names): for name\",\n \"validate_input( trans, error_map, param_values, page_param_map ): dbkeys = set() data_param_names\",\n \"columns.\\\") error_map[name] = error_msg data_param_names.add( name ) if len( dbkeys\",\n \"\\\" \\\\ \\\"this dataset is linked to build '%s'\\\" %\",\n \"param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int(\",\n \"= (\\\"The attributes of this dataset are not properly set.\",\n \"not None: int( param.metadata.strandCol ) except: error_msg = (\\\"The attributes\",\n \"Set as set def validate_input( trans, error_map, param_values, page_param_map ):\",\n \"same genomic build, \\\" \\\\ \\\"this dataset is linked to\",\n \"DataToolParameter ): # for each dataset parameter if param_values.get(name, None)\",\n \"parameter if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey )\",\n \"# check meta data try: param = param_values[name] int( param.metadata.startCol\",\n \") except: error_msg = (\\\"The attributes of this dataset are\",\n \"data try: param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol\",\n \"for name in data_param_names: error_map[name] = \\\"A dataset of the\",\n \"attributes of this dataset are not properly set. \\\" \\\"Click\",\n \"set the chrom, start, end and strand columns.\\\") error_map[name] =\",\n \"if data_params != len(data_param_names): for name in data_param_names: error_map[name] =\",\n \"param_values[name].dbkey if data_params != len(data_param_names): for name in data_param_names: error_map[name]\",\n \"the default post-filter) from galaxy.tools.parameters import DataToolParameter # Older py\",\n \"name, param in page_param_map.items(): if isinstance( param, DataToolParameter ): #\",\n \"\\\"All datasets must belong to same genomic build, \\\" \\\\\",\n \"compatibility try: set() except: from sets import Set as set\",\n \"int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol ) if\",\n \"history item to set the chrom, start, end and strand\",\n \"after the default post-filter) from galaxy.tools.parameters import DataToolParameter # Older\",\n \"dataset are not properly set. \\\" \\\"Click the pencil icon\",\n \"data_param_names = set() data_params = 0 for name, param in\",\n \"param_values[name].dbkey ) data_params += 1 # check meta data try:\",\n \") if len( dbkeys ) > 1: for name in\",\n \"pencil icon in the history item to set the chrom,\",\n \"end and strand columns.\\\") error_map[name] = error_msg data_param_names.add( name )\",\n \"None: int( param.metadata.strandCol ) except: error_msg = (\\\"The attributes of\",\n \"check meta data try: param = param_values[name] int( param.metadata.startCol )\",\n \"= error_msg data_param_names.add( name ) if len( dbkeys ) >\",\n \"data_param_names.add( name ) if len( dbkeys ) > 1: for\",\n \"item to set the chrom, start, end and strand columns.\\\")\",\n \"= 0 for name, param in page_param_map.items(): if isinstance( param,\",\n \"not properly set. \\\" \\\"Click the pencil icon in the\",\n \"belong to same genomic build, \\\" \\\\ \\\"this dataset is\",\n \"sets import Set as set def validate_input( trans, error_map, param_values,\",\n \"to set the chrom, start, end and strand columns.\\\") error_map[name]\",\n \"trans, error_map, param_values, page_param_map ): dbkeys = set() data_param_names =\",\n \"= \\\"All datasets must belong to same genomic build, \\\"\",\n \"is linked to build '%s'\\\" % param_values[name].dbkey if data_params !=\",\n \"after the job (and after the default post-filter) from galaxy.tools.parameters\",\n \"try: param = param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol )\",\n \"param.metadata.strandCol is not None: int( param.metadata.strandCol ) except: error_msg =\",\n \"of this dataset are not properly set. \\\" \\\"Click the\",\n \"): dbkeys = set() data_param_names = set() data_params = 0\",\n \"param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is not None:\",\n \"error_msg = (\\\"The attributes of this dataset are not properly\",\n \"param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol\",\n \") int( param.metadata.endCol ) int( param.metadata.chromCol ) if param.metadata.strandCol is\",\n \"error_map, param_values, page_param_map ): dbkeys = set() data_param_names = set()\",\n \"dbkeys.add( param_values[name].dbkey ) data_params += 1 # check meta data\",\n \"!= len(data_param_names): for name in data_param_names: error_map[name] = \\\"A dataset\",\n \"DataToolParameter # Older py compatibility try: set() except: from sets\",\n \"from galaxy.tools.parameters import DataToolParameter # Older py compatibility try: set()\",\n \"import DataToolParameter # Older py compatibility try: set() except: from\",\n \"try: set() except: from sets import Set as set def\",\n \"= param_values[name] int( param.metadata.startCol ) int( param.metadata.endCol ) int( param.metadata.chromCol\",\n \"name in data_param_names: error_map[name] = \\\"All datasets must belong to\",\n \"def validate_input( trans, error_map, param_values, page_param_map ): dbkeys = set()\",\n \"are not properly set. \\\" \\\"Click the pencil icon in\",\n \"genomic build, \\\" \\\\ \\\"this dataset is linked to build\",\n \"isinstance( param, DataToolParameter ): # for each dataset parameter if\",\n \"meta data try: param = param_values[name] int( param.metadata.startCol ) int(\",\n \") int( param.metadata.chromCol ) if param.metadata.strandCol is not None: int(\",\n \"(and after the default post-filter) from galaxy.tools.parameters import DataToolParameter #\",\n \"for name, param in page_param_map.items(): if isinstance( param, DataToolParameter ):\",\n \"for name in data_param_names: error_map[name] = \\\"All datasets must belong\",\n \"data_params += 1 # check meta data try: param =\",\n \"to same genomic build, \\\" \\\\ \\\"this dataset is linked\",\n \"> 1: for name in data_param_names: error_map[name] = \\\"All datasets\",\n \"error_msg data_param_names.add( name ) if len( dbkeys ) > 1:\",\n \"= set() data_param_names = set() data_params = 0 for name,\",\n \"dataset parameter if param_values.get(name, None) is not None: dbkeys.add( param_values[name].dbkey\",\n \"\\\" \\\"Click the pencil icon in the history item to\",\n \"Older py compatibility try: set() except: from sets import Set\",\n \"must belong to same genomic build, \\\" \\\\ \\\"this dataset\",\n \"as set def validate_input( trans, error_map, param_values, page_param_map ): dbkeys\",\n \"set() except: from sets import Set as set def validate_input(\",\n \"in the history item to set the chrom, start, end\",\n \"len( dbkeys ) > 1: for name in data_param_names: error_map[name]\",\n \"in page_param_map.items(): if isinstance( param, DataToolParameter ): # for each\",\n \") if param.metadata.strandCol is not None: int( param.metadata.strandCol ) except:\",\n \"build, \\\" \\\\ \\\"this dataset is linked to build '%s'\\\"\",\n \"set def validate_input( trans, error_map, param_values, page_param_map ): dbkeys =\",\n \"dbkeys = set() data_param_names = set() data_params = 0 for\",\n \"datasets must belong to same genomic build, \\\" \\\\ \\\"this\"\n]","isConversational":false}}},{"rowIdx":181,"cells":{"ngram":{"kind":"list like","value":["self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0,","send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\")","0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid,","False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\"","2014-2015 The Bitcoin Core developers # Distributed under the MIT","0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid =","in expected.items(): if item[key] != value: raise AssertionError(\"%s : expected","self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\")","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")},","test_framework.util import * def check_array_result(object_array, to_match, expected): \"\"\" Pass in",") check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")},","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"} ) multisig = self.nodes[1].createmultisig(1,","class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to 1:","== 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" :","100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"} ) if","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")},","Distributed under the MIT software license, see the accompanying #","test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match,","for key,value in to_match.items(): if item[key] != value: all_match =","run_test(self): # Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(),",") check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"} ) multisig =","\"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from node1: twice to self, twice","{\"txid\":txid, \"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" :","dictionary with key/value pairs to match against, and another dictionary","# Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1)","= self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) ==","True for key,value in to_match.items(): if item[key] != value: all_match","{\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\"","0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid},","self, twice to node2: send_to = { self.nodes[0].getnewaddress() : 0.11,",": \"toself\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False,","self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a","if not all_match: continue for key,value in expected.items(): if item[key]","send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(),","to node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() :","self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100,","\"watchonly\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\",","assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True),","or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework import","%s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to","developers # Distributed under the MIT software license, see the","num_matched = num_matched+1 if num_matched == 0: raise AssertionError(\"No objects","{\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from","\"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"}","[self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1)","pairs. \"\"\" num_matched = 0 for item in object_array: all_match","check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block, confirmations should change:","!= value: all_match = False if not all_match: continue for","self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid}","{\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from node1: twice","from node1: twice to self, twice to node2: send_to =","#!/usr/bin/env python2 # Copyright (c) 2014-2015 The Bitcoin Core developers","{\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()])","{\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(),","0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) #","to_match.items(): if item[key] != value: all_match = False if not","http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework","# mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(),","to self, twice to node2: send_to = { self.nodes[0].getnewaddress() :","matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0","# Distributed under the MIT software license, see the accompanying","!= value: raise AssertionError(\"%s : expected %s=%s\"%(str(item), str(key), str(value))) num_matched","\"account\" : \"toself\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\",","pairs to match against, and another dictionary with expected key/value","JSON objects, a dictionary with key/value pairs to match against,","a dictionary with key/value pairs to match against, and another","item[key] != value: all_match = False if not all_match: continue","block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(),","{\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from node1: twice to self,","self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self:","sendmany from node1: twice to self, twice to node2: send_to","node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22,","} txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} )","value: all_match = False if not all_match: continue for key,value","send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} )","check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid,","listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import *","and another dictionary with expected key/value pairs. \"\"\" num_matched =","not all_match: continue for key,value in expected.items(): if item[key] !=","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid,","to_match, expected): \"\"\" Pass in array of JSON objects, a","against, and another dictionary with expected key/value pairs. \"\"\" num_matched","should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1})","Core developers # Distributed under the MIT software license, see","{\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"} )","expected key/value pairs. \"\"\" num_matched = 0 for item in","the listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import","python2 # Copyright (c) 2014-2015 The Bitcoin Core developers #","file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from",") check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" :",": 0.44 } txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")},","check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")},","= num_matched+1 if num_matched == 0: raise AssertionError(\"No objects matched","# send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")})","self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(),","confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid},","raise AssertionError(\"%s : expected %s=%s\"%(str(item), str(key), str(value))) num_matched = num_matched+1","txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid,","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"}","num_matched = 0 for item in object_array: all_match = True","{\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"} ) if __name__ == '__main__':","def run_test(self): # Simple send, 0 to 1: txid =",": 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") :","send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(),","= { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") :","{\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid}","accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions","BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match, expected): \"\"\"","import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match, expected):","{\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block, confirmations should","True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0,","{\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all()","check_array_result(object_array, to_match, expected): \"\"\" Pass in array of JSON objects,","MIT software license, see the accompanying # file COPYING or","(c) 2014-2015 The Bitcoin Core developers # Distributed under the",": 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid = self.nodes[1].sendmany(\"\", send_to)","{\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(),",": \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"} )","key/value pairs. \"\"\" num_matched = 0 for item in object_array:","{\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} )","num_matched == 0: raise AssertionError(\"No objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework):","{ self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33,","expected): \"\"\" Pass in array of JSON objects, a dictionary","== 0: raise AssertionError(\"No objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def","twice to self, twice to node2: send_to = { self.nodes[0].getnewaddress()","0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44","object_array: all_match = True for key,value in to_match.items(): if item[key]","to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0})","check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"} )","ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to 1: txid","if item[key] != value: raise AssertionError(\"%s : expected %s=%s\"%(str(item), str(key),","of JSON objects, a dictionary with key/value pairs to match","txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid},","0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine","True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"} ) if __name__ ==",") check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(),","{\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\"","# Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework from","under the MIT software license, see the accompanying # file","def check_array_result(object_array, to_match, expected): \"\"\" Pass in array of JSON","value: raise AssertionError(\"%s : expected %s=%s\"%(str(item), str(key), str(value))) num_matched =","item in object_array: all_match = True for key,value in to_match.items():","objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send,","str(key), str(value))) num_matched = num_matched+1 if num_matched == 0: raise","expected.items(): if item[key] != value: raise AssertionError(\"%s : expected %s=%s\"%(str(item),","change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) #","the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the","self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) #","mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid},","\"\"\" num_matched = 0 for item in object_array: all_match =","check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid =","Copyright (c) 2014-2015 The Bitcoin Core developers # Distributed under","0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"} ) if __name__","\"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"}","\"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(),","software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php.","for item in object_array: all_match = True for key,value in","# Copyright (c) 2014-2015 The Bitcoin Core developers # Distributed","False if not all_match: continue for key,value in expected.items(): if","key,value in expected.items(): if item[key] != value: raise AssertionError(\"%s :","# file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API","0.44 } txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid}","# sendmany from node1: twice to self, twice to node2:","continue for key,value in expected.items(): if item[key] != value: raise",") check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\" : \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")},",") check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(),","with key/value pairs to match against, and another dictionary with","all_match: continue for key,value in expected.items(): if item[key] != value:","COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework","dictionary with expected key/value pairs. \"\"\" num_matched = 0 for","{\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block, confirmations should change: self.nodes[0].generate(1)","if num_matched == 0: raise AssertionError(\"No objects matched %s\"%(str(to_match))) class","{\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"},","node1: twice to self, twice to node2: send_to = {","= 0 for item in object_array: all_match = True for","txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(),","= self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"],","{\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block, confirmations","{\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid, \"account\"","0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid = self.nodes[1].sendmany(\"\", send_to) self.sync_all()","another dictionary with expected key/value pairs. \"\"\" num_matched = 0","for key,value in expected.items(): if item[key] != value: raise AssertionError(\"%s","match against, and another dictionary with expected key/value pairs. \"\"\"","1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(),","\"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" : \"toself\"} ) multisig","in to_match.items(): if item[key] != value: all_match = False if","check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany","{\"txid\":txid, \"account\" : \"\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.44\")}, {\"txid\":txid, \"account\" :",") multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid","0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")}, {\"txid\":txid, \"account\" : \"watchonly\"}","import * def check_array_result(object_array, to_match, expected): \"\"\" Pass in array","0 for item in object_array: all_match = True for key,value",": \"from1\"} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.44\")}, {\"txid\":txid, \"account\" : \"\"} )","* def check_array_result(object_array, to_match, expected): \"\"\" Pass in array of","key,value in to_match.items(): if item[key] != value: all_match = False","objects, a dictionary with key/value pairs to match against, and","if item[key] != value: all_match = False if not all_match:",": expected %s=%s\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched","{\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2)","multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid =","0: raise AssertionError(\"No objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self):","Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all()","self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")})","= self.nodes[1].sendmany(\"\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")},","to match against, and another dictionary with expected key/value pairs.","Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util","self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1)","{\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")}, {\"txid\":txid,","100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0, True), {\"category\":\"receive\",\"amount\":Decimal(\"0.1\")},","\"\"\" Pass in array of JSON objects, a dictionary with","in object_array: all_match = True for key,value in to_match.items(): if","key/value pairs to match against, and another dictionary with expected","{\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid,","API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def","\"toself\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True)",": 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid","check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(),","= False if not all_match: continue for key,value in expected.items():","raise AssertionError(\"No objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): #","array of JSON objects, a dictionary with key/value pairs to","{\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid}","check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from node1: twice to","check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0}) # mine a block,",") check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(),","{\"txid\":txid, \"account\" : \"toself\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\"redeemScript\"],","{\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} )","= self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"},","item[key] != value: raise AssertionError(\"%s : expected %s=%s\"%(str(item), str(key), str(value)))","= True for key,value in to_match.items(): if item[key] != value:","from test_framework.util import * def check_array_result(object_array, to_match, expected): \"\"\" Pass","in array of JSON objects, a dictionary with key/value pairs","num_matched+1 if num_matched == 0: raise AssertionError(\"No objects matched %s\"%(str(to_match)))","self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":1}) # send-to-self: txid","twice to node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress()","0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\",","{\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid} )","self.nodes[0].importaddress(multisig[\"redeemScript\"], \"watchonly\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all()","The Bitcoin Core developers # Distributed under the MIT software","Bitcoin Core developers # Distributed under the MIT software license,","self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(),","a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":1})","{\"category\":\"send\",\"amount\":Decimal(\"-0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.11\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.22\")}, {\"txid\":txid}","{\"txid\":txid, \"account\" : \"watchonly\"} ) if __name__ == '__main__': ListTransactionsTest().main()","see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise","all_match = False if not all_match: continue for key,value in","the MIT software license, see the accompanying # file COPYING","AssertionError(\"%s : expected %s=%s\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if","self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 } txid = self.nodes[1].sendmany(\"\",","with expected key/value pairs. \"\"\" num_matched = 0 for item","check_array_result(self.nodes[1].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.22\")}, {\"txid\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\"category\":\"send\",\"amount\":Decimal(\"-0.33\")}, {\"txid\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\"category\":\"receive\",\"amount\":Decimal(\"0.33\")},","self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0)","= self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid}, {\"category\":\"send\",\"account\":\"\",\"amount\":Decimal(\"-0.1\"),\"confirmations\":0}) check_array_result(self.nodes[1].listtransactions(), {\"txid\":txid}, {\"category\":\"receive\",\"account\":\"\",\"amount\":Decimal(\"0.1\"),\"confirmations\":0})","license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. #","Pass in array of JSON objects, a dictionary with key/value","str(value))) num_matched = num_matched+1 if num_matched == 0: raise AssertionError(\"No","AssertionError(\"No objects matched %s\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple","{\"amount\":Decimal(\"0.2\")}) # sendmany from node1: twice to self, twice to","\"category\":\"send\"}, {\"amount\":Decimal(\"-0.2\")}) check_array_result(self.nodes[0].listtransactions(), {\"txid\":txid, \"category\":\"receive\"}, {\"amount\":Decimal(\"0.2\")}) # sendmany from node1:","False, True) txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100,","%s=%s\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0:","expected %s=%s\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched ==","all_match = True for key,value in to_match.items(): if item[key] !=","from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array,","self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\") : 0.33, self.nodes[1].getaccountaddress(\"toself\") : 0.44 }","txid = self.nodes[1].sendtoaddress(multisig[\"address\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False))"],"string":"[\n \"self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0,\",\n \"send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\")\",\n \"0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid,\",\n \"False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\"\",\n \"2014-2015 The Bitcoin Core developers # Distributed under the MIT\",\n \"0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid =\",\n \"in expected.items(): if item[key] != value: raise AssertionError(\\\"%s : expected\",\n \"self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\")\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")},\",\n \"test_framework.util import * def check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass in\",\n \") check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")},\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1,\",\n \"class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to 1:\",\n \"== 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" :\",\n \"100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} ) if\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")},\",\n \"Distributed under the MIT software license, see the accompanying #\",\n \"test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match,\",\n \"for key,value in to_match.items(): if item[key] != value: all_match =\",\n \"run_test(self): # Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(),\",\n \") check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} ) multisig =\",\n \"\\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1: twice to self, twice\",\n \"{\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" :\",\n \"dictionary with key/value pairs to match against, and another dictionary\",\n \"# Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1)\",\n \"= self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) ==\",\n \"True for key,value in to_match.items(): if item[key] != value: all_match\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\"\",\n \"0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid},\",\n \"self, twice to node2: send_to = { self.nodes[0].getnewaddress() : 0.11,\",\n \": \\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False,\",\n \"self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a\",\n \"if not all_match: continue for key,value in expected.items(): if item[key]\",\n \"send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(),\",\n \"to node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() :\",\n \"self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100,\",\n \"\\\"watchonly\\\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\",\",\n \"assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True),\",\n \"or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework import\",\n \"%s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to\",\n \"developers # Distributed under the MIT software license, see the\",\n \"num_matched = num_matched+1 if num_matched == 0: raise AssertionError(\\\"No objects\",\n \"{\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from\",\n \"\\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"}\",\n \"[self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1)\",\n \"pairs. \\\"\\\"\\\" num_matched = 0 for item in object_array: all_match\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block, confirmations should change:\",\n \"!= value: all_match = False if not all_match: continue for\",\n \"self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid}\",\n \"{\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1: twice\",\n \"from node1: twice to self, twice to node2: send_to =\",\n \"#!/usr/bin/env python2 # Copyright (c) 2014-2015 The Bitcoin Core developers\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()])\",\n \"{\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(),\",\n \"0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) #\",\n \"to_match.items(): if item[key] != value: all_match = False if not\",\n \"http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework\",\n \"# mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(),\",\n \"to self, twice to node2: send_to = { self.nodes[0].getnewaddress() :\",\n \"matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0\",\n \"# Distributed under the MIT software license, see the accompanying\",\n \"!= value: raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched\",\n \"\\\"account\\\" : \\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\",\",\n \"pairs to match against, and another dictionary with expected key/value\",\n \"JSON objects, a dictionary with key/value pairs to match against,\",\n \"a dictionary with key/value pairs to match against, and another\",\n \"item[key] != value: all_match = False if not all_match: continue\",\n \"block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(),\",\n \"{\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1: twice to self,\",\n \"self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self:\",\n \"sendmany from node1: twice to self, twice to node2: send_to\",\n \"node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22,\",\n \"} txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} )\",\n \"value: all_match = False if not all_match: continue for key,value\",\n \"send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} )\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid,\",\n \"listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import *\",\n \"and another dictionary with expected key/value pairs. \\\"\\\"\\\" num_matched =\",\n \"not all_match: continue for key,value in expected.items(): if item[key] !=\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid,\",\n \"to_match, expected): \\\"\\\"\\\" Pass in array of JSON objects, a\",\n \"against, and another dictionary with expected key/value pairs. \\\"\\\"\\\" num_matched\",\n \"should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1})\",\n \"Core developers # Distributed under the MIT software license, see\",\n \"{\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} )\",\n \"expected key/value pairs. \\\"\\\"\\\" num_matched = 0 for item in\",\n \"the listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import\",\n \"python2 # Copyright (c) 2014-2015 The Bitcoin Core developers #\",\n \"file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from\",\n \") check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" :\",\n \": 0.44 } txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")},\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")},\",\n \"= num_matched+1 if num_matched == 0: raise AssertionError(\\\"No objects matched\",\n \"# send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")})\",\n \"self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(),\",\n \"confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid},\",\n \"raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched = num_matched+1\",\n \"txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid,\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"}\",\n \"num_matched = 0 for item in object_array: all_match = True\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} ) if __name__ == '__main__':\",\n \"def run_test(self): # Simple send, 0 to 1: txid =\",\n \": 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") :\",\n \"send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(),\",\n \"= { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") :\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid}\",\n \"accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions\",\n \"BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match, expected): \\\"\\\"\\\"\",\n \"import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array, to_match, expected):\",\n \"{\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block, confirmations should\",\n \"True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0,\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all()\",\n \"check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass in array of JSON objects,\",\n \"MIT software license, see the accompanying # file COPYING or\",\n \"(c) 2014-2015 The Bitcoin Core developers # Distributed under the\",\n \": 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid = self.nodes[1].sendmany(\\\"\\\", send_to)\",\n \"{\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(),\",\n \": \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} )\",\n \"key/value pairs. \\\"\\\"\\\" num_matched = 0 for item in object_array:\",\n \"{\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} )\",\n \"num_matched == 0: raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework):\",\n \"{ self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33,\",\n \"expected): \\\"\\\"\\\" Pass in array of JSON objects, a dictionary\",\n \"== 0: raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def\",\n \"twice to self, twice to node2: send_to = { self.nodes[0].getnewaddress()\",\n \"0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44\",\n \"object_array: all_match = True for key,value in to_match.items(): if item[key]\",\n \"to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0})\",\n \"check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} )\",\n \"ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send, 0 to 1: txid\",\n \"if item[key] != value: raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key),\",\n \"of JSON objects, a dictionary with key/value pairs to match\",\n \"txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid},\",\n \"0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine\",\n \"True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} ) if __name__ ==\",\n \") check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(),\",\n \"{\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\"\",\n \"# Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework from\",\n \"under the MIT software license, see the accompanying # file\",\n \"def check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass in array of JSON\",\n \"value: raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched =\",\n \"item in object_array: all_match = True for key,value in to_match.items():\",\n \"objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple send,\",\n \"str(key), str(value))) num_matched = num_matched+1 if num_matched == 0: raise\",\n \"expected.items(): if item[key] != value: raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item),\",\n \"change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) #\",\n \"the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the\",\n \"self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) #\",\n \"mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid},\",\n \"\\\"\\\"\\\" num_matched = 0 for item in object_array: all_match =\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid =\",\n \"Copyright (c) 2014-2015 The Bitcoin Core developers # Distributed under\",\n \"0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} ) if __name__\",\n \"\\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"}\",\n \"\\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(),\",\n \"software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php.\",\n \"for item in object_array: all_match = True for key,value in\",\n \"# Copyright (c) 2014-2015 The Bitcoin Core developers # Distributed\",\n \"False if not all_match: continue for key,value in expected.items(): if\",\n \"key,value in expected.items(): if item[key] != value: raise AssertionError(\\\"%s :\",\n \"# file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API\",\n \"0.44 } txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid}\",\n \"# sendmany from node1: twice to self, twice to node2:\",\n \"continue for key,value in expected.items(): if item[key] != value: raise\",\n \") check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")},\",\n \") check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(),\",\n \"with key/value pairs to match against, and another dictionary with\",\n \"all_match: continue for key,value in expected.items(): if item[key] != value:\",\n \"COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework\",\n \"dictionary with expected key/value pairs. \\\"\\\"\\\" num_matched = 0 for\",\n \"{\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block, confirmations should change: self.nodes[0].generate(1)\",\n \"if num_matched == 0: raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"},\",\n \"node1: twice to self, twice to node2: send_to = {\",\n \"= 0 for item in object_array: all_match = True for\",\n \"txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(),\",\n \"= self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"],\",\n \"{\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block, confirmations\",\n \"{\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid, \\\"account\\\"\",\n \"0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid = self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all()\",\n \"another dictionary with expected key/value pairs. \\\"\\\"\\\" num_matched = 0\",\n \"for key,value in expected.items(): if item[key] != value: raise AssertionError(\\\"%s\",\n \"match against, and another dictionary with expected key/value pairs. \\\"\\\"\\\"\",\n \"1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(),\",\n \"\\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} ) multisig\",\n \"in to_match.items(): if item[key] != value: all_match = False if\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany\",\n \"{\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" :\",\n \") multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid\",\n \"0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"}\",\n \"import * def check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass in array\",\n \"0 for item in object_array: all_match = True for key,value\",\n \": \\\"from1\\\"} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.44\\\")}, {\\\"txid\\\":txid, \\\"account\\\" : \\\"\\\"} )\",\n \"* def check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass in array of\",\n \"key,value in to_match.items(): if item[key] != value: all_match = False\",\n \"objects, a dictionary with key/value pairs to match against, and\",\n \"if item[key] != value: all_match = False if not all_match:\",\n \": expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched\",\n \"{\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2)\",\n \"multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid =\",\n \"0: raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self):\",\n \"Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all()\",\n \"self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")})\",\n \"= self.nodes[1].sendmany(\\\"\\\", send_to) self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")},\",\n \"to match against, and another dictionary with expected key/value pairs.\",\n \"Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util\",\n \"self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1)\",\n \"{\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")}, {\\\"txid\\\":txid,\",\n \"100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, True), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.1\\\")},\",\n \"\\\"\\\"\\\" Pass in array of JSON objects, a dictionary with\",\n \"in object_array: all_match = True for key,value in to_match.items(): if\",\n \"key/value pairs to match against, and another dictionary with expected\",\n \"{\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid,\",\n \"API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def\",\n \"\\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True)\",\n \": 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(),\",\n \"= False if not all_match: continue for key,value in expected.items():\",\n \"raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): #\",\n \"array of JSON objects, a dictionary with key/value pairs to\",\n \"{\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid}\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1: twice to\",\n \"check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0}) # mine a block,\",\n \") check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(),\",\n \"{\\\"txid\\\":txid, \\\"account\\\" : \\\"toself\\\"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"],\",\n \"{\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} )\",\n \"= self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"},\",\n \"item[key] != value: raise AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key), str(value)))\",\n \"= True for key,value in to_match.items(): if item[key] != value:\",\n \"from test_framework.util import * def check_array_result(object_array, to_match, expected): \\\"\\\"\\\" Pass\",\n \"in array of JSON objects, a dictionary with key/value pairs\",\n \"num_matched+1 if num_matched == 0: raise AssertionError(\\\"No objects matched %s\\\"%(str(to_match)))\",\n \"self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":1}) # send-to-self: txid\",\n \"twice to node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress()\",\n \"0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\\\"watchonly\\\",\",\n \"{\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid} )\",\n \"self.nodes[0].importaddress(multisig[\\\"redeemScript\\\"], \\\"watchonly\\\", False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all()\",\n \"The Bitcoin Core developers # Distributed under the MIT software\",\n \"Bitcoin Core developers # Distributed under the MIT software license,\",\n \"self.sync_all() check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(),\",\n \"a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":1})\",\n \"{\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.11\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.22\\\")}, {\\\"txid\\\":txid}\",\n \"{\\\"txid\\\":txid, \\\"account\\\" : \\\"watchonly\\\"} ) if __name__ == '__main__': ListTransactionsTest().main()\",\n \"see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise\",\n \"all_match = False if not all_match: continue for key,value in\",\n \"the MIT software license, see the accompanying # file COPYING\",\n \"AssertionError(\\\"%s : expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if\",\n \"self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 } txid = self.nodes[1].sendmany(\\\"\\\",\",\n \"with expected key/value pairs. \\\"\\\"\\\" num_matched = 0 for item\",\n \"check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.22\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[1].listtransactions(), {\\\"category\\\":\\\"send\\\",\\\"amount\\\":Decimal(\\\"-0.33\\\")}, {\\\"txid\\\":txid} ) check_array_result(self.nodes[0].listtransactions(), {\\\"category\\\":\\\"receive\\\",\\\"amount\\\":Decimal(\\\"0.33\\\")},\",\n \"self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False)) == 0)\",\n \"= self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"send\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"-0.1\\\"),\\\"confirmations\\\":0}) check_array_result(self.nodes[1].listtransactions(), {\\\"txid\\\":txid}, {\\\"category\\\":\\\"receive\\\",\\\"account\\\":\\\"\\\",\\\"amount\\\":Decimal(\\\"0.1\\\"),\\\"confirmations\\\":0})\",\n \"license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. #\",\n \"Pass in array of JSON objects, a dictionary with key/value\",\n \"str(value))) num_matched = num_matched+1 if num_matched == 0: raise AssertionError(\\\"No\",\n \"AssertionError(\\\"No objects matched %s\\\"%(str(to_match))) class ListTransactionsTest(BitcoinTestFramework): def run_test(self): # Simple\",\n \"{\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1: twice to self, twice to\",\n \"\\\"category\\\":\\\"send\\\"}, {\\\"amount\\\":Decimal(\\\"-0.2\\\")}) check_array_result(self.nodes[0].listtransactions(), {\\\"txid\\\":txid, \\\"category\\\":\\\"receive\\\"}, {\\\"amount\\\":Decimal(\\\"0.2\\\")}) # sendmany from node1:\",\n \"False, True) txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100,\",\n \"%s=%s\\\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0:\",\n \"expected %s=%s\\\"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched ==\",\n \"all_match = True for key,value in to_match.items(): if item[key] !=\",\n \"from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * def check_array_result(object_array,\",\n \"self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\\\"from1\\\") : 0.33, self.nodes[1].getaccountaddress(\\\"toself\\\") : 0.44 }\",\n \"txid = self.nodes[1].sendtoaddress(multisig[\\\"address\\\"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions(\\\"watchonly\\\", 100, 0, False))\"\n]","isConversational":false}}},{"rowIdx":182,"cells":{"ngram":{"kind":"list like","value":["Invalid entry lines.append(line) continue if comps[1] == name or comps[0]","comps[1] == name and comps[0] == device: continue else: if","automaster(config) if name not in contents: return True # The","comps = re.sub(r\"\\s+\", \" \", line).split() nod = __salt__['cmd.run_stdout']('ls -l","{1}\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device,","name, fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+')",") ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove","{0}'.format( config ) ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'):","ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os']","Blank line lines.append(line) continue comps = line.split() if len(comps) !=","out def swaps(): ''' Return a dict containing information on","str(exc))) return True def set_fstab( name, device, fstype, opts='defaults', dump=0,","comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") opts = comps[1].split(',') ret[name] =","ret def swapoff(name): ''' Deactivate a named swap mount CLI","newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype, opts, dump,","from salt.utils import which as _which from salt.exceptions import CommandNotFoundError,","ret def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems","-t {0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args, device,","raise CommandExecutionError( 'File not writable {0}'.format( config ) ) return","mount point {0} needs to be ' 'updated'.format(name) ) newline","device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid':","# -*- coding: utf-8 -*- ''' Salt module to manage","len(comps) != 6: # Invalid entry lines.append(line) continue comps =","'/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File not readable","= __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True #","versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts =","device: change = True comps[0] = device if comps[1] !=","'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List","if fstype == 'smbfs': force_mount = True if isinstance(opts, string_types):","'priority': comps[5]} return ret def swapon(name, priority=None): ''' Activate a","continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump':","= ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype, opts, dump, pass_num","name in mnts: # The mount point is mounted, attempt","code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret = {}","device = comps[2].split(':') device_name = comps[8] device_uuid = None if","the mount but first we should unmount umount(name, device, user=user)","None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid","opts = opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user)","command passed is a fuse mountable application. CLI Example: ..","opts.split(',') mnts = active() if name in mnts: # The","comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if","comps[2] == device_fmt: # check to see if there are","comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active mounts on","{'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for","args += ' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD',","!= device: change = True comps[0] = device if comps[1]","= ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line)","'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return","to manage unix mounts and the fstab file ''' from","not None: lopts = ','.join(opts) args = '-o {0}'.format(lopts) if","The line was changed, commit it! ofile.writelines(lines) except (IOError, OSError):","fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config,","elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret)","expect defaults when mounting without other options if 'defaults' in","out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true if the","fstype='', opts='defaults', user=None): ''' Attempt to remount a device, if","is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if the path","change the mount to match the data passed, or add","__grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd =","here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The","salt libs import salt.utils from salt._compat import string_types from salt.utils","lines.append(line) continue if not line.strip(): # Blank line lines.append(line) continue","opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount is","args = '' if opts is not None: lopts =","{5}\\n'.format( device, name, fstype, opts, dump, pass_num ) ) lines.append(newline)","mounted, mount is called CLI Example: .. code-block:: bash salt","''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \"","if len(comps) != 3: # Invalid entry lines.append(line) continue if","remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount","entry is already here return 'present' else: if not salt.utils.test_mode(test=test,","cmd = 'mount -u {0} {1} {2} '.format(args, device, name)","cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out =","= comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]}","not in mnts: return \"{0} does not have anything mounted\".format(name)","__future__ import absolute_import # Import python libs import os import","= \"Couldn't read from {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) try:","systems ''' # Disable on Windows, a specific file module","present = False if not os.path.isfile(config): raise CommandExecutionError('Bad config file","with salt.utils.fopen(config, 'r') as ifile: for line in ifile: if","if the path is mounted CLI Example: .. code-block:: bash","from salt._compat import string_types from salt.utils import which as _which","ret = {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as","List the active mounts. CLI Example: .. code-block:: bash salt","device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this","active mounts on Linux systems ''' _list = _list_mounts() filename","else: lines.append(line) except (IOError, OSError) as exc: msg = 'Couldn\\'t","'Darwin'] or force_mount: cmd = 'mount {0} {1} {2} '.format(args,","salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as exc:","'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List","point from the fstab CLI Example: .. code-block:: bash salt","device: if name_chk == name and device_fmt[1] == device: continue","there are any present = True if comps[0] != device:","'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg","device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]]","device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower()","__salt__['cmd.run']('automount -cv') return True def set_automaster( name, device, fstype, opts='',","= line.split() if device: if comps[1] == name and comps[0]","device: # check to see if there are changes #","umount(name, device=None, user=None): ''' Attempt to unmount a device by","= re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]] = {'device': comps[0], 'fstype':","str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError,","return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents of the","Example: .. code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded::","_list = _list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK):","'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active mounts","def fstab(config='/etc/fstab'): ''' List the contents of the fstab CLI","isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name) and mkmnt:","out['stderr'] return True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None):","continue else: if name_chk == name: continue lines.append(line) except (IOError,","on Windows, a specific file module exists: if salt.utils.is_windows(): return","ret def _active_mounts_darwin(ret): ''' List active mounts on Mac OS","on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d","line.strip(): # Blank line lines.append(line) continue comps = line.split() if","' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in","Invalid entry lines.append(line) continue comps = line.split() if device: if","not present. CLI Example: .. code-block:: bash salt '*' mount.set_fstab","type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile:","comps[0] = name if comps[1] != type_opts: change = True",".. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 '''","def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point from","not os.path.isfile(config): raise CommandExecutionError('Bad config file \"{0}\"'.format(config)) try: with salt.utils.fopen(config,","\"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name)","a swap disk CLI Example: .. code-block:: bash salt '*'","'*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts","line continue comps = line.split() if len(comps) != 6: #","mnts: return \"{0} does not have anything mounted\".format(name) if not","if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line","True return None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide","in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount","''' # Disable on Windows, a specific file module exists:","comps = line.split() if len(comps) != 6: # Invalid entry","attempt to remount it with the given data if 'remount'","POSIX-like systems ''' # Disable on Windows, a specific file","''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\", \"","'.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] = {'device':","'opts': opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults',","CLI Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo '''","True def is_fuse_exec(cmd): ''' Returns true if the command passed","CLI Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1","= device_fmt if change: log.debug( 'auto_master entry for mount point","cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if","Import salt libs import salt.utils from salt._compat import string_types from","\"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) # Update","'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List","'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for","module to manage unix mounts and the fstab file '''","'*' mount.is_mounted /mnt/share ''' active_ = active() if name in","= \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype, opts)","unmount a device by specifying the directory it is mounted","''' List active mounts on FreeBSD systems ''' for line","does not have anything mounted\".format(name) if not device: cmd =","exist if not cmd_path: return False elif not _which('ldd'): raise","# Import python libs import os import re import logging","/dev/sdz1 ext4 ''' # Fix the opts type if it","is a fuse mountable application. CLI Example: .. code-block:: bash","= line.split() if len(comps) != 6: # Invalid entry continue","== 'smbfs': force_mount = True if isinstance(opts, string_types): opts =","return ret def active(extended=False): ''' List the active mounts. CLI","-cv') return True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt',","utf-8 -*- ''' Salt module to manage unix mounts and","raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines)","is mounted CLI Example: .. code-block:: bash salt '*' mount.is_mounted","# Invalid entry continue prefix = \"/..\" name = comps[0].replace(prefix,","on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps","return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount","in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts = None","__salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps(): ''' Return","if not change: if present: # The right entry is","{1}\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile:","Blank line continue comps = line.split() if len(comps) != 3:","if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ =","return True def is_fuse_exec(cmd): ''' Returns true if the command","comps = line.split() if comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]] =","called CLI Example: .. code-block:: bash salt '*' mount.remount /mnt/foo","if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name))","import re import logging # Import salt libs import salt.utils","user=None): ''' Mount a device CLI Example: .. code-block:: bash","in fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] =","to remount it with the given data if 'remount' not","as ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg =","if comps[0] != device: change = True comps[0] = device","active mounts on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount","set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify","the directory it is mounted on CLI Example: .. code-block::","entry lines.append(line) continue if comps[0] == name or comps[2] ==","options if 'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']:","the fstab file ''' from __future__ import absolute_import # Import","= 'noowners' if fstype == 'smbfs': force_mount = True if","{'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3],","-t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or","\"-fstype={0},{1}\".format(fstype, opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \"\")","device, mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None): '''","ret def active(extended=False): ''' List the active mounts. CLI Example:","# Set up logger log = logging.getLogger(__name__) # Define the","name in on_: ret['stats'] = on_[name] ret['new'] = False return","Remove the mount point from the auto_master CLI Example: ..","'smbfs': force_mount = True if isinstance(opts, string_types): opts = opts.split(',')","ret cmd = 'swapon {0}'.format(name) if priority: cmd += '","List active mounts on Solaris systems ''' for line in","name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError)","line in ifile: if line.startswith('#'): # Commented continue if not","mountable application. CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec","salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if line.startswith('Filename'): continue","line.startswith('#'): # Commented lines.append(line) continue if not line.strip(): # Blank","on_[name] ret['new'] = True return ret return ret def swapoff(name):","__grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults': opts =","to unmount a device by specifying the directory it is","comps[2], 'used': comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl","bash salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount =","ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret","mount.swapoff /root/swapfile ''' on_ = swaps() if name in on_:","if len(comps) != 3: # Invalid entry lines.append(line) continue comps","code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' #","in mnts: # The mount point is mounted, attempt to","comps[2] = device_fmt if change: log.debug( 'auto_master entry for mount","salt.utils.fopen(config) as ifile: for line in ifile: if line.startswith('#'): #","line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2],","{0}'.format(name) else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user)","for line in ifile: comps = line.split() ret[comps[1]] = {'device':","as exc: msg = \"Couldn't write to {0}: {1}\" raise","when mounting without other options if 'defaults' in opts and","def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False,","_active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems ''' for","if not line.strip(): # Blank line continue comps = line.split()","not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse'","= {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def","__salt__['cmd.run'](cmd) on_ = swaps() if name in on_: ret['stats'] =","not line.strip(): # Blank line continue comps = line.split() if","change = True comps[5] = str(pass_num) if change: log.debug( 'fstab","passed is a fuse mountable application. CLI Example: .. code-block::","and __grains__['os'] in ['MacOS', 'Darwin']: opts = None if isinstance(opts,","from __future__ import absolute_import # Import python libs import os","= re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5],","name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return","**kwargs): # The entry is new, add it to the","''' Deactivate a named swap mount CLI Example: .. code-block::","__grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in","comps[0], 'fstype': comps[5], 'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]),","= None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid =","opts = 'noowners' if fstype == 'smbfs': force_mount = True","# Blank line continue comps = line.split() if len(comps) !=","for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \",","line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return","config='/etc/fstab'): ''' Remove the mount point from the fstab CLI","Darwin doesn't expect defaults when mounting without other options if","module exists: if salt.utils.is_windows(): return False return True def _list_mounts():","mount point from the auto_master CLI Example: .. code-block:: bash","''' Attempt to unmount a device by specifying the directory","Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4","return ret cmd = 'swapon {0}'.format(name) if priority: cmd +=","(IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format( config )","fstab(config) if name not in contents: return True # The","= name if comps[1] != type_opts: change = True comps[1]","write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount","/dev/xvdc1 ''' mnts = active() if name not in mnts:","True comps[2] = device_fmt if change: log.debug( 'auto_master entry for","The mount point is mounted, attempt to remount it with","{0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'): ''' List","to remount a device, if the device is not already","swaps() if name in on_: ret['stats'] = on_[name] ret['new'] =","= {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def","OSError): msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return","line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split()","raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try:","up logger log = logging.getLogger(__name__) # Define the module's virtual","present: # The right entry is already here return 'present'","config file \"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for","return True def _list_mounts(): ret = {} if __grains__['os'] in","ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret","Salt module to manage unix mounts and the fstab file","change: log.debug( 'fstab entry for mount point {0} needs to","comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active","True if isinstance(opts, string_types): opts = opts.split(',') mnts = active()","elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try:","comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret","ldd on a command that doesn't exist if not cmd_path:","if not device: cmd = 'umount {0}'.format(name) else: cmd =","that this mount is represented in the fstab, change the","'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7],","opts: change = True comps[3] = opts if comps[4] !=","the mount if it is not present. CLI Example: ..","bash salt '*' mount.active ''' ret = {} if __grains__['os']","path is mounted CLI Example: .. code-block:: bash salt '*'","change = False present = False if not os.path.isfile(config): raise","ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format(","device, name) else: cmd = 'mount -u {0} {1} {2}","if name in mnts: # The mount point is mounted,","msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info =","line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list =","/mnt/foo ''' contents = automaster(config) if name not in contents:","True comps[4] = str(dump) if comps[5] != str(pass_num): change =","comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List active","{0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv')","msg = 'Couldn\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc)))","fstype: change = True comps[2] = fstype if comps[3] !=","'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device',","= True comps[1] = type_opts if comps[2] != device_fmt: change",".. code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents =","== name: continue lines.append(line) except (IOError, OSError) as exc: msg","True comps[1] = type_opts if comps[2] != device_fmt: change =","'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active mounts","blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in","''' contents = fstab(config) if name not in contents: return","re.sub(r\"\\s+\", \" \", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod","{} if not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile:","opts = opts.split(',') mnts = active() if name in mnts:","needs to be ' 'updated'.format(name) ) newline = ( '{0}\\t{1}\\t{2}\\n'.format(","directory it is mounted on CLI Example: .. code-block:: bash","# Darwin doesn't expect defaults when mounting without other options","config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount is represented","device if comps[1] != name: change = True comps[1] =","by specifying the directory it is mounted on CLI Example:","__salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device)","= opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args","{0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) if __grains__['os']","name, fstype, opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line)","_list_mounts(): ret = {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts","if there are any present = True if comps[0] !=","= {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')}","{0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in","exists: if salt.utils.is_windows(): return False return True def _list_mounts(): ret","'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4],","if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \"\") try: with","{} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris':","_which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in","if change: log.debug( 'auto_master entry for mount point {0} needs","\"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device) type_opts =","ifile: if line.startswith('#'): # Commented continue if not line.strip(): #","'opts': parens[1:]} return ret def active(extended=False): ''' List the active","return ret def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD","return 'change' if not change: if present: # The right","ret['stats'] = on_[name] ret['new'] = False return ret cmd =","= logging.getLogger(__name__) # Define the module's virtual name __virtualname__ =","__grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_","mount if it is not present. CLI Example: .. code-block::","mount CLI Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile",") newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype, opts,","re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2],","'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The entry is","systems ''' _list = _list_mounts() filename = '/proc/self/mounts' if not","== device: continue else: if name_chk == name: continue lines.append(line)","not writable {0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'):","mounts on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'):","len(comps) != 6: # Invalid entry continue ret[comps[1]] = {'device':","(IOError, OSError): msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config))","# Invalid entry lines.append(line) continue if comps[1] == name or","comps = line.split() prefix = \"/..\" name_chk = comps[0].replace(prefix, \"\")","line.split() device = comps[2].split(':') device_name = comps[8] device_uuid = None","and fix them if there are any present = True","'Darwin']: if opts == 'defaults': opts = 'noowners' if fstype",".. code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path =","unix mounts and the fstab file ''' from __future__ import","is a list if isinstance(opts, list): opts = ','.join(opts) lines","# Invalid entry lines.append(line) continue comps = line.split() if device:","= True if comps[0] != name: change = True comps[0]","mount point from the fstab CLI Example: .. code-block:: bash","' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']","commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not","''' .. versionadded:: 2014.7.0 Provide information if the path is","{} if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else:","[] change = False present = False if not os.path.isfile(config):","{'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret","to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab(","not in contents: return True # The entry is present,","-*- coding: utf-8 -*- ''' Salt module to manage unix","point in running ldd on a command that doesn't exist","salt '*' mount.active ''' ret = {} if __grains__['os'] ==","bash salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if","the mount to match the data passed, or add the","= device if comps[1] != name: change = True comps[1]","of the fstab CLI Example: .. code-block:: bash salt '*'","automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name, device, fstype,","'defaults': opts = 'noowners' if fstype == 'smbfs': force_mount =","re.sub(r\"\\s+\", \" \", line).split() parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \")","versionadded:: 2014.7.0 Provide information if the path is mounted CLI","if the device is not already mounted, mount is called","== device_fmt: # check to see if there are changes","sshfs ''' cmd_path = _which(cmd) # No point in running","is present, get rid of it lines = [] try:","False present = False automaster_file = \"/etc/auto_master\" if not os.path.isfile(config):","args += ' -t {0}'.format(fstype) cmd = 'mount {0} {1}","__virtual__(): ''' Only load on POSIX-like systems ''' # Disable","mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device CLI Example:","not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount","= 'Couldn\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if","raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for","return 'libfuse' in out def swaps(): ''' Return a dict","active mounts on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount","mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type","ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg = \"Couldn't","the fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) )","in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0}","if force_mount: # We need to force the mount but","ret def _active_mounts(ret): ''' List active mounts on Linux systems","bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix","comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None): ''' Activate","lines.append(line) except (IOError, OSError) as exc: msg = 'Couldn\\'t read","opts = None if isinstance(opts, string_types): opts = opts.split(',') if","''' Attempt to remount a device, if the device is","fstype, opts, user=user) def umount(name, device=None, user=None): ''' Attempt to","Set up logger log = logging.getLogger(__name__) # Define the module's","rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point from the","# We need to force the mount but first we","re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = comps[0] return ret def","{}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0],","dump, pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError, OSError)","from the fstab CLI Example: .. code-block:: bash salt '*'","opts='defaults', user=None): ''' Attempt to remount a device, if the","lines.append(line) continue if comps[1] == name or comps[0] == device:","-v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() nod = __salt__['cmd.run_stdout']('ls","Disable on Windows, a specific file module exists: if salt.utils.is_windows():","comps[1] = name if comps[2] != fstype: change = True","change = True comps[1] = name if comps[2] != fstype:","= on_[name] ret['new'] = True return ret return ret def","else: if name_chk == name: continue lines.append(line) except (IOError, OSError)","= active() if name in active_: return True else: return","if comps[1] == name: continue lines.append(line) except (IOError, OSError) as","on a command that doesn't exist if not cmd_path: return","comps[5], 'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]}","[] change = False present = False automaster_file = \"/etc/auto_master\"","= True comps[1] = name if comps[2] != fstype: change","{'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]}","''' Remove the mount point from the auto_master CLI Example:","_active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended:","= type_opts if comps[2] != device_fmt: change = True comps[2]",".. code-block:: bash salt '*' mount.fstab ''' ret = {}","'*' mount.swapon /root/swapfile ''' ret = {} on_ = swaps()","\"\") try: with salt.utils.fopen(config, 'r') as ifile: for line in","salt.utils.fopen(filename) as ifile: for line in ifile: comps = line.split()","salt '*' mount.fstab ''' ret = {} if not os.path.isfile(config):","List active mounts on FreeBSD systems ''' for line in","device_fmt = \"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype, opts) if fstype","''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \" \",","to force the mount but first we should unmount umount(name,","= 'swapon {0}'.format(name) if priority: cmd += ' -p {0}'.format(priority)","{1}\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return","comps[2].split(\":\") if device: if name_chk == name and device_fmt[1] ==","on_[name] ret['new'] = False return ret cmd = 'swapon {0}'.format(name)","is already here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs):","= '' if opts is not None: lopts = ','.join(opts)","'/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File not readable","{0} needs to be ' 'updated'.format(name) ) newline = (","mount but first we should unmount umount(name, device, user=user) lopts","a command that doesn't exist if not cmd_path: return False","fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts, dump, pass_num)","device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device CLI","not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt =","\" \", line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret):","comps[0] == device: # check to see if there are","the end of the fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format( name,","the device is not already mounted, mount is called CLI","name = \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype,","umount(name, device, user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts)","device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify","/mnt/foo ''' contents = fstab(config) if name not in contents:",".. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts","Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active()","command that doesn't exist if not cmd_path: return False elif","file ''' from __future__ import absolute_import # Import python libs","continue if comps[0] == name or comps[2] == device_fmt: #","{0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile:","doesn't expect defaults when mounting without other options if 'defaults'","continue if not line.strip(): # Blank line continue comps =","opts and __grains__['os'] in ['MacOS', 'Darwin']: opts = None if","for line in ifile: if line.startswith('#'): # Commented continue if","and comps[0] == device: continue else: if comps[1] == name:","_active_mounts_solaris(ret): ''' List active mounts on Solaris systems ''' for","test=False, **kwargs): ''' Verify that this mount is represented in","commit it! ofile.writelines(lines) except (IOError, OSError): msg = 'File not","opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args =","device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name, device,","ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1],","# No point in running ldd on a command that","data passed, or add the mount if it is not","on CLI Example: .. code-block:: bash salt '*' mount.umount /mnt/foo","CLI Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share '''","= 'mount -u {0} {1} {2} '.format(args, device, name) out","str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List active","\"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def","Mount a device CLI Example: .. code-block:: bash salt '*'","/mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect defaults when","line was changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg","in opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount')","in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except","-d {0}'.format(name)) on_ = swaps() if name in on_: return","for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type","on POSIX-like systems ''' # Disable on Windows, a specific","{'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor':","device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config, 'r') as ifile: for line","without other options if 'defaults' in opts and __grains__['os'] in","bash salt '*' mount.swaps ''' ret = {} if __grains__['os']","except (IOError, OSError): msg = 'File not writable {0}' raise","parens[0], 'opts': parens[1:]} return ret def active(extended=False): ''' List the","if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and","comps[2].split(\":\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0],","line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority':","CommandExecutionError(msg.format(config)) return 'change' if not change: if present: # The","+= ' -t {0}'.format(fstype) cmd = 'mount {0} {1} {2}","'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active mounts","Linux systems ''' _list = _list_mounts() filename = '/proc/self/mounts' if","if not os.path.isfile(config): raise CommandExecutionError('Bad config file \"{0}\"'.format(config)) try: with","changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg = 'File","comps[0] = device if comps[1] != name: change = True","named swap mount CLI Example: .. code-block:: bash salt '*'","= \"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype, opts) if fstype ==","'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def","in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() parens =","= _list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg","= 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']()","the data passed, or add the mount if it is","isn't already return mount(name, device, mkmnt, fstype, opts, user=user) def","''' List the contents of the fstab CLI Example: ..","def is_fuse_exec(cmd): ''' Returns true if the command passed is","def _active_mounts_darwin(ret): ''' List active mounts on Mac OS systems","ret = {} on_ = swaps() if name in on_:","runas=user) if out['retcode']: return out['stderr'] return True def remount(name, device,","Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents","comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def","opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this","if name_chk == name and device_fmt[1] == device: continue else:","and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts is","!= 6: # Invalid entry lines.append(line) continue if comps[1] ==","not in opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']:","'swapon {0}'.format(name) if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd)","fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type':","''' List active mounts on Linux systems ''' _list =","mount.active ''' ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret)","'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if","len(comps) != 6: # Invalid entry lines.append(line) continue if comps[1]","salt._compat import string_types from salt.utils import which as _which from","_list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret):","comps[4] != str(dump): change = True comps[4] = str(dump) if","'mount {0} {1} {2} '.format(args, device, name) else: cmd =","comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = {'device': comps[0],","if the command passed is a fuse mountable application. CLI","OSError) as exc: msg = \"Couldn't read from {0}: {1}\"","in the auto_salt, change the mount to match the data","= \"file\" comps = line.split() if comps[0].startswith('/dev/'): swap_type = \"partition\"","Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts':","= False if not os.path.isfile(config): raise CommandExecutionError('Bad config file \"{0}\"'.format(config))","priority=None): ''' Activate a swap disk CLI Example: .. code-block::","out['retcode']: return out['stderr'] return True def remount(name, device, mkmnt=False, fstype='',","__salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() parens = re.findall(r'\\((.*?)\\)',","# Commented continue if not line.strip(): # Blank line continue","'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile:","== name or comps[0] == device: # check to see","= {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def","CLI Example: .. code-block:: bash salt '*' mount.fstab ''' ret","_which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger","device CLI Example: .. code-block:: bash salt '*' mount.mount /mnt/foo","'*' mount.swapoff /root/swapfile ''' on_ = swaps() if name in","''' Only load on POSIX-like systems ''' # Disable on","Import python libs import os import re import logging #","comps = line.split() device = comps[2].split(':') device_name = comps[8] device_uuid","name in on_: return False return True return None def","not line.strip(): # Blank line lines.append(line) continue comps = line.split()","writable {0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'): '''","device, config='/etc/auto_salt'): ''' Remove the mount point from the auto_master","if not os.access(filename, os.R_OK): msg = 'File not readable {0}'","'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with","return True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False,","it is a list if isinstance(opts, list): opts = ','.join(opts)","user=None): ''' Attempt to remount a device, if the device","'w+') as ofile: # The line was changed, commit it!","is not present. CLI Example: .. code-block:: bash salt '*'","fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount","as ofile: # The line was changed, commit it! ofile.writelines(lines)","if name in on_: return False return True return None","prefix = \"/..\" name = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\")","a device CLI Example: .. code-block:: bash salt '*' mount.mount","_list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret):","re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\", \"),","{} on_ = swaps() if name in on_: ret['stats'] =","user=user) args = '' if opts is not None: lopts","Verify that this mount is represented in the auto_salt, change","mounts on Linux systems ''' _list = _list_mounts() filename =","newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try:","comps[4] = str(dump) if comps[5] != str(pass_num): change = True","if change: log.debug( 'fstab entry for mount point {0} needs","args = '-o {0}'.format(lopts) if fstype: args += ' -t","mounts on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'):","'auto_master entry for mount point {0} needs to be '","comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid}","raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device, fstype,","\" \", line).split() parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]]","# check to see if there are changes # and","''' Salt module to manage unix mounts and the fstab","return ret def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo'","= line.split() if len(comps) != 6: # Invalid entry lines.append(line)","Attempt to unmount a device by specifying the directory it","active mounts on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount","code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_ = swaps()","from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger log","!= 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_:","not cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out","bash salt '*' mount.swapon /root/swapfile ''' ret = {} on_","or add the mount if it is not present. CLI","= on_[name] ret['new'] = False return ret cmd = 'swapon","on_ = swaps() if name in on_: if __grains__['os'] !=","change = True comps[2] = fstype if comps[3] != opts:","application. CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs","salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: # The","with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as","there are any present = True if comps[0] != name:","runas=user) if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): '''","opts if comps[4] != str(dump): change = True comps[4] =","ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]}","right entry is already here return 'present' else: if not","{0}'.format(cmd_path)) return 'libfuse' in out def swaps(): ''' Return a","nod = ' '.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)","file module exists: if salt.utils.is_windows(): return False return True def","isinstance(opts, list): opts = ','.join(opts) lines = [] change =","''' _list = _list_mounts() filename = '/proc/self/mounts' if not os.access(filename,","'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove","\" \", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts':","comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return","exc: msg = 'Couldn\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config,","['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l')","True ''' force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']:","'Total')): continue swap_type = \"file\" comps = line.split() if comps[0].startswith('/dev/'):","swap mount CLI Example: .. code-block:: bash salt '*' mount.swapoff","comps[2] != device_fmt: change = True comps[2] = device_fmt if","''' Remove the mount point from the fstab CLI Example:","else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\n'): comps","\", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')}","'mount' def __virtual__(): ''' Only load on POSIX-like systems '''","need to force the mount but first we should unmount","= comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") if device: if name_chk","''' ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif","of it lines = [] try: with salt.utils.fopen(config, 'r') as","/root/swapfile ''' on_ = swaps() if name in on_: if","present. CLI Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo","'r') as ifile: for line in ifile: if line.startswith('#'): #","= re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device': comps[0], 'fstype':","salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name","in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]]","except (IOError, OSError) as exc: msg = \"Couldn't write to","if not line.strip(): # Blank line lines.append(line) continue comps =","device_name = comps[8] device_uuid = None if device_name: device_uuid =","have anything mounted\".format(name) if not device: cmd = 'umount {0}'.format(name)","in ['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types): opts","list): opts = ','.join(opts) lines = [] change = False","'.format(args, device, name) else: cmd = 'mount -u {0} {1}","Deactivate a named swap mount CLI Example: .. code-block:: bash","line in ifile: comps = line.split() ret[comps[1]] = {'device': comps[0],","if opts == 'defaults': opts = 'noowners' if fstype ==","continue prefix = \"/..\" name = comps[0].replace(prefix, \"\") device_fmt =","bash salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin","parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device': comps[0],","len(comps) != 3: # Invalid entry lines.append(line) continue if comps[0]","device is not already mounted, mount is called CLI Example:","opts[0], 'opts': opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='',","out['stderr'] return True # Mount a filesystem that isn't already","not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for line","if isinstance(opts, string_types): opts = opts.split(',') mnts = active() if","{} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for","opts = ','.join(opts) lines = [] change = False present","CommandExecutionError('Bad config file \"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile:","and device_fmt[1] == device: continue else: if name_chk == name:","= [] try: with salt.utils.fopen(config, 'r') as ifile: for line","rid of it lines = [] try: with salt.utils.fopen(config, 'r')","code-block:: bash salt '*' mount.active ''' ret = {} if","salt.utils.test_mode(test=test, **kwargs): # The entry is new, add it to","with salt.utils.fopen(filename) as ifile: for line in ifile: comps =","swap_type = \"partition\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used':","\"{0} does not have anything mounted\".format(name) if not device: cmd","OSError) as exc: msg = 'Couldn\\'t read from {0}: {1}'","device_fmt: # check to see if there are changes #","out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True","CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device, fstype, opts='defaults',","6: # Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype':","__salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\n'): comps = re.sub(r\"\\s+\", \"","_active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS',","import absolute_import # Import python libs import os import re","opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError,","= [] change = False present = False automaster_file =","\"\") device_fmt = comps[2].split(\":\") opts = comps[1].split(',') ret[name] = {'device':","Example: .. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True","None if isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name)","and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0],","mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type","opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None):","device_fmt[1] == device: continue else: if name_chk == name: continue","== 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os']","change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as","opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if","fstab CLI Example: .. code-block:: bash salt '*' mount.fstab '''","dict containing information on active swap CLI Example: .. code-block::","{0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user)","return ret def _active_mounts_darwin(ret): ''' List active mounts on Mac","= True comps[0] = name if comps[1] != type_opts: change","\", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')}","List the contents of the fstab CLI Example: .. code-block::","entry is present, get rid of it lines = []","contents = fstab(config) if name not in contents: return True","contents = automaster(config) if name not in contents: return True","active() if name in mnts: # The mount point is","a fuse mountable application. CLI Example: .. code-block:: bash salt","= 'mount {0} {1} {2} '.format(args, device, name) else: cmd","= __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\n'): comps = re.sub(r\"\\s+\",","Blank line continue comps = line.split() if len(comps) != 6:","= True comps[4] = str(dump) if comps[5] != str(pass_num): change","try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError)","present, get rid of it lines = [] try: with","the mount point from the fstab CLI Example: .. code-block::","device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major':","newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline)","def active(extended=False): ''' List the active mounts. CLI Example: ..","return True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): '''","Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_","opts is not None: lopts = ','.join(opts) args = '-o","{0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return","== 'defaults': opts = 'noowners' if fstype == 'smbfs': force_mount","line.startswith('#'): # Commented continue if not line.strip(): # Blank line","code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' #","fp_: for line in fp_: if line.startswith('Filename'): continue comps =","to the end of the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device,","comps[0] == device: continue else: if comps[1] == name: continue","comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): '''","out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps():","the mount point from the auto_master CLI Example: .. code-block::","'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret def swapon(name,","the opts type if it is a list if isinstance(opts,","\" \", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts':","if device: if comps[1] == name and comps[0] == device:","False elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path))","'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name,","'smbfs': device_fmt = device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config, 'r') as","comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts':","if comps[1] != name: change = True comps[1] = name","'*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name not","== 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os']","point is mounted, attempt to remount it with the given","on Linux systems ''' _list = _list_mounts() filename = '/proc/self/mounts'","# The entry is present, get rid of it lines","code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd)","# The right entry is already here return 'present' else:","Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_","line.split() if len(comps) != 6: # Invalid entry lines.append(line) continue","(IOError, OSError) as exc: msg = \"Couldn't write to {0}:","device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: #","try: with salt.utils.fopen(config, 'r') as ifile: for line in ifile:","+= ' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS',","string_types from salt.utils import which as _which from salt.exceptions import","'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] ==","or force_mount: cmd = 'mount {0} {1} {2} '.format(args, device,","'updated'.format(name) ) newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) )","# The mount point is mounted, attempt to remount it","name or comps[2] == device_fmt: # check to see if","on Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps","def umount(name, device=None, user=None): ''' Attempt to unmount a device","code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt","( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except","We need to force the mount but first we should","return False return True def _list_mounts(): ret = {} if","ret['new'] = False return ret cmd = 'swapon {0}'.format(name) if","CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs '''","if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns","-v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = {'device':","ret = {} if not os.path.isfile(config): return ret with salt.utils.fopen(config)","ret['new'] = True return ret return ret def swapoff(name): '''","salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't","= __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in","= {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else:","= _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg","mount point is mounted, attempt to remount it with the","mounts.split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = comps[0]","'-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) cmd","if isinstance(opts, list): opts = ','.join(opts) lines = [] change","'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List","'.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return","except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): '''","entry continue prefix = \"/..\" name = comps[0].replace(prefix, \"\") device_fmt","= line.split() if len(comps) != 3: # Invalid entry lines.append(line)","' 'updated'.format(name) ) newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt)","comps[1] = type_opts if comps[2] != device_fmt: change = True","except (IOError, OSError) as exc: msg = \"Couldn't read from","'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return","__salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] =","def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if the","ret def swapon(name, priority=None): ''' Activate a swap disk CLI","auto_master CLI Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo","any present = True if comps[0] != name: change =","on_: return False return True return None def is_mounted(name): '''","else: if not salt.utils.test_mode(test=test, **kwargs): # The entry is new,","if comps[1] != type_opts: change = True comps[1] = type_opts","return ret def _active_mounts(ret): ''' List active mounts on Linux","ret def _active_mounts_solaris(ret): ''' List active mounts on Solaris systems","a dict containing information on active swap CLI Example: ..","-p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in on_:","str(dump) if comps[5] != str(pass_num): change = True comps[5] =","type_opts = \"-fstype={0},{1}\".format(fstype, opts) if fstype == 'smbfs': device_fmt =","already here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): #","pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount is","swapon(name, priority=None): ''' Activate a swap disk CLI Example: ..","cmd = 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out","None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): '''","return True # The entry is present, get rid of","are changes # and fix them if there are any","{2} '.format(args, device, name) else: cmd = 'mount -u {0}","ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts()","Windows, a specific file module exists: if salt.utils.is_windows(): return False",") ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc:","-kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \"file\" comps =","comps = line.split() if device: if comps[1] == name and","def automaster(config='/etc/auto_salt'): ''' List the contents of the fstab CLI","the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts, dump,","== name or comps[2] == device_fmt: # check to see","libs import os import re import logging # Import salt","'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid':","''' ret = {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps')","comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for line","in mnts: return \"{0} does not have anything mounted\".format(name) if","python libs import os import re import logging # Import","log = logging.getLogger(__name__) # Define the module's virtual name __virtualname__","fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as","fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline)","on active swap CLI Example: .. code-block:: bash salt '*'","'used': comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines():","from the auto_master CLI Example: .. code-block:: bash salt '*'","line in ifile: if line.startswith('#'): # Commented lines.append(line) continue if","writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change: if","= True comps[2] = device_fmt if change: log.debug( 'auto_master entry","mounted on CLI Example: .. code-block:: bash salt '*' mount.umount","= comps[8] device_uuid = None if device_name: device_uuid = blkid_info.get(device_name,","readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as","if it is a list if isinstance(opts, list): opts =","import salt.utils from salt._compat import string_types from salt.utils import which","== device: # check to see if there are changes","of the fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt)","else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name in","CommandExecutionError # Set up logger log = logging.getLogger(__name__) # Define",".. versionadded:: 2014.7.0 Provide information if the path is mounted","is new, add it to the end of the fstab","comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): '''","['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types): opts =","if line.startswith('#'): # Commented lines.append(line) continue if not line.strip(): #","on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps","'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List","name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that","{0} {1} {2} '.format(args, device, name) else: cmd = 'mount","= swaps() if name in on_: return False return True","line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return","type_opts: change = True comps[1] = type_opts if comps[2] !=","def _list_mounts(): ret = {} if __grains__['os'] in ['MacOS', 'Darwin']:","str(pass_num) if change: log.debug( 'fstab entry for mount point {0}","['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need to","is mounted, attempt to remount it with the given data","device_uuid} return ret def _active_mounts(ret): ''' List active mounts on","def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): '''","The right entry is already here return 'present' else: if","not already mounted, mount is called CLI Example: .. code-block::","\"file\" comps = line.split() if comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]]","if comps[5] != str(pass_num): change = True comps[5] = str(pass_num)","(IOError, OSError) as exc: msg = 'Couldn\\'t read from {0}:","''' contents = automaster(config) if name not in contents: return","# The entry is new, add it to the end","True comps[5] = str(pass_num) if change: log.debug( 'fstab entry for","name_chk == name and device_fmt[1] == device: continue else: if","return ret def swapoff(name): ''' Deactivate a named swap mount","salt.utils import which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError","= ' '.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]]","(IOError, OSError) as exc: msg = \"Couldn't read from {0}:","already mounted, mount is called CLI Example: .. code-block:: bash","was changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg =",") return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents of","( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype, opts, dump, pass_num )","= fstype if comps[3] != opts: change = True comps[3]","type_opts if comps[2] != device_fmt: change = True comps[2] =","out['retcode']: return out['stderr'] return True # Mount a filesystem that","if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret)","if opts is not None: lopts = ','.join(opts) args =","device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr']","# Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name,","is not already mounted, mount is called CLI Example: ..","True ''' # Darwin doesn't expect defaults when mounting without","for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\", \" \",","= \"/..\" name_chk = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") if","CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True def","!= str(pass_num): change = True comps[5] = str(pass_num) if change:","/mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type if","in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl","mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount a device,","remount it with the given data if 'remount' not in","mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device","lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc: msg =","priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps()","CLI Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo '''","auto_salt, change the mount to match the data passed, or","line.startswith(('Device', 'Total')): continue swap_type = \"file\" comps = line.split() if","!= str(dump): change = True comps[4] = str(dump) if comps[5]","re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts':","see if there are changes # and fix them if","def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if not","= \"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) #","swap disk CLI Example: .. code-block:: bash salt '*' mount.swapon","passed, or add the mount if it is not present.","{0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount:","type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as","No point in running ldd on a command that doesn't","if comps[2] != device_fmt: change = True comps[2] = device_fmt","present. CLI Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo","if comps[0] != name: change = True comps[0] = name","\"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) return True","raise CommandExecutionError('Bad config file \"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r') as","False return True def _list_mounts(): ret = {} if __grains__['os']","string_types): opts = opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name,","in contents: return True # The entry is present, get","comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','),","True if comps[0] != device: change = True comps[0] =","Only load on POSIX-like systems ''' # Disable on Windows,","represented in the auto_salt, change the mount to match the","mount.is_mounted /mnt/share ''' active_ = active() if name in active_:","opts == 'defaults': opts = 'noowners' if fstype == 'smbfs':","'used': comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None): '''","= line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype':","{0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as","add the mount if it is not present. CLI Example:","fstab(config='/etc/fstab'): ''' List the contents of the fstab CLI Example:","opts.append('remount') if force_mount: # We need to force the mount","len(comps) != 3: # Invalid entry continue prefix = \"/..\"","comps[1] == name or comps[0] == device: # check to","comps[5] = str(pass_num) if change: log.debug( 'fstab entry for mount","to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount","line.split() if len(comps) != 3: # Invalid entry continue prefix","Example: .. code-block:: bash salt '*' mount.active ''' ret =","Activate a swap disk CLI Example: .. code-block:: bash salt","= name if comps[2] != fstype: change = True comps[2]","= 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return","comps[1], 'used': comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None):","cmd_path = _which(cmd) # No point in running ldd on","__grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif","raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True","== 'smbfs': device_fmt = device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config, 'r')","{0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args, device, name)","def _active_mounts_solaris(ret): ''' List active mounts on Solaris systems '''","= {} on_ = swaps() if name in on_: ret['stats']","_active_mounts(ret): ''' List active mounts on Linux systems ''' _list","not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: #","mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name not in","Example: .. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True","str(dump): change = True comps[4] = str(dump) if comps[5] !=","if 'remount' not in opts and __grains__['os'] not in ['OpenBSD',","comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active","= True comps[3] = opts if comps[4] != str(dump): change","it lines = [] try: with salt.utils.fopen(config, 'r') as ifile:","= None if isinstance(opts, string_types): opts = opts.split(',') if not","Provide information if the path is mounted CLI Example: ..","return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The entry","_list = _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK):","as ifile: for line in ifile: if line.startswith('#'): # Commented","mount is represented in the fstab, change the mount to","msg = \"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc)))","them if there are any present = True if comps[0]","comps[8] device_uuid = None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID')","entry is new, add it to the end of the","bash salt '*' mount.fstab ''' ret = {} if not","salt '*' mount.swapoff /root/swapfile ''' on_ = swaps() if name","fstab file ''' from __future__ import absolute_import # Import python","name_chk = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") if device: if","+= ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name","list if isinstance(opts, list): opts = ','.join(opts) lines = []","device_fmt: change = True comps[2] = device_fmt if change: log.debug(","and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount:","!= name: change = True comps[1] = name if comps[2]","'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point","''' Verify that this mount is represented in the fstab,","mount.swapon /root/swapfile ''' ret = {} on_ = swaps() if","if comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]] = {'type': swap_type, 'size':","return ret return ret def swapoff(name): ''' Deactivate a named","'change' if not change: if present: # The right entry","'*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No point","salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the","fstype: args += ' -t {0}'.format(fstype) cmd = 'mount {0}","name: change = True comps[0] = name if comps[1] !=","= True comps[5] = str(pass_num) if change: log.debug( 'fstab entry","'mount {0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd,","'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd,","comps[2] != fstype: change = True comps[2] = fstype if","''' Activate a swap disk CLI Example: .. code-block:: bash","OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\",","code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' #","{0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change: if present:","CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def","device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor':","mount to match the data passed, or add the mount","def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point from","\" \", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod =","= {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")),","swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret def","is represented in the auto_salt, change the mount to match","try: with salt.utils.fopen(config, 'w+') as ofile: # The line was","name: change = True comps[1] = name if comps[2] !=","not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We","comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype':","device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device':","on_ = swaps() if name in on_: return False return","= True comps[0] = device if comps[1] != name: change","str(pass_num): change = True comps[5] = str(pass_num) if change: log.debug(","True # Mount a filesystem that isn't already return mount(name,","def swapoff(name): ''' Deactivate a named swap mount CLI Example:","\") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return","in on_: ret['stats'] = on_[name] ret['new'] = True return ret","Example: .. code-block:: bash salt '*' mount.swaps ''' ret =","in on_: return False return True return None def is_mounted(name):","entry lines.append(line) continue comps = line.split() prefix = \"/..\" name_chk","the auto_salt, change the mount to match the data passed,","ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\", \"), 'major':","opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile:","'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr']","str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config,","== device: continue else: if comps[1] == name: continue lines.append(line)","True comps[1] = name if comps[2] != fstype: change =","exc: msg = \"Couldn't read from {0}: {1}\" raise CommandExecutionError(msg.format(config,","specifying the directory it is mounted on CLI Example: ..","in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \"file\"","# Fix the opts type if it is a list","as exc: msg = \"Couldn't read from {0}: {1}\" raise","not change: if present: # The right entry is already","salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount","''' Return a dict containing information on active swap CLI","import string_types from salt.utils import which as _which from salt.exceptions","in out def swaps(): ''' Return a dict containing information","def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems '''","if not salt.utils.test_mode(test=test, **kwargs): # The entry is new, add","manage unix mounts and the fstab file ''' from __future__","if comps[1] == name and comps[0] == device: continue else:","'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False): ''' List","swapoff(name): ''' Deactivate a named swap mount CLI Example: ..","return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount","change = True comps[0] = device if comps[1] != name:","'*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo","'{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError,","'*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name not","change = True comps[0] = name if comps[1] != type_opts:","True def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab',","if it is not present. CLI Example: .. code-block:: bash","= False automaster_file = \"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file,","try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def","ret def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if","[] try: with salt.utils.fopen(config, 'r') as ifile: for line in","as ifile: for line in ifile: comps = line.split() device","__grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret)","for mount point {0} needs to be ' 'updated'.format(name) )","this mount is represented in the fstab, change the mount","from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not","2014.7.0 Provide information if the path is mounted CLI Example:","'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def","not have anything mounted\".format(name) if not device: cmd = 'umount","# Disable on Windows, a specific file module exists: if","{1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs):","'{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+')","if salt.utils.is_windows(): return False return True def _list_mounts(): ret =","opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts':","['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError:","the given data if 'remount' not in opts and __grains__['os']","line in fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]]","= active() if name not in mnts: return \"{0} does","are any present = True if comps[0] != name: change","= re.sub(r\"\\s+\", \" \", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0]))","= False if __grains__['os'] in ['MacOS', 'Darwin']: if opts ==","ifile: comps = line.split() device = comps[2].split(':') device_name = comps[8]","continue swap_type = \"file\" comps = line.split() if comps[0].startswith('/dev/'): swap_type","= ','.join(opts) args = '-o {0}'.format(lopts) if fstype: args +=","if len(comps) != 6: # Invalid entry lines.append(line) continue comps","mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts is not","# Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2],","load on POSIX-like systems ''' # Disable on Windows, a","if name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name))","''' Verify that this mount is represented in the auto_salt,","'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret):","else: cmd = 'mount -u {0} {1} {2} '.format(args, device,","comps[5] != str(pass_num): change = True comps[5] = str(pass_num) if","line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\",","represented in the fstab, change the mount to match the","'*' mount.fstab ''' ret = {} if not os.path.isfile(config): return","with the given data if 'remount' not in opts and","str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster(","if name in on_: ret['stats'] = on_[name] ret['new'] = False","is not None: lopts = ','.join(opts) args = '-o {0}'.format(lopts)","mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if name not","'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List active mounts","= \"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name =","mounting without other options if 'defaults' in opts and __grains__['os']","raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in ifile:","continue comps = line.split() if len(comps) != 3: # Invalid","name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else:","Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps =",".. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 '''","Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps =","'remount' not in opts and __grains__['os'] not in ['OpenBSD', 'MacOS',","lines.append(line) continue comps = line.split() if len(comps) != 3: #","# Mount a filesystem that isn't already return mount(name, device,","# Define the module's virtual name __virtualname__ = 'mount' def","the path is mounted CLI Example: .. code-block:: bash salt","salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No","= __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in ifile:","os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for line in","!= device_fmt: change = True comps[2] = device_fmt if change:","it with the given data if 'remount' not in opts","= comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") opts = comps[1].split(',') ret[name]","is_fuse_exec(cmd): ''' Returns true if the command passed is a","device) type_opts = \"-fstype={0},{1}\".format(fstype, opts) if fstype == 'smbfs': device_fmt","change = True comps[2] = device_fmt if change: log.debug( 'auto_master","\") parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0],","'Darwin']: opts = None if isinstance(opts, string_types): opts = opts.split(',')","device_uuid = None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid","be ' 'updated'.format(name) ) newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts,","user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype:","if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = ''","'Darwin']: opts.append('remount') if force_mount: # We need to force the","_list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg =","'updated'.format(name) ) newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype,","defaults when mounting without other options if 'defaults' in opts","__grains__['os'] in ['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types):","= re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = comps[0] return ret","automaster(config='/etc/auto_salt'): ''' List the contents of the fstab CLI Example:","= False present = False if not os.path.isfile(config): raise CommandExecutionError('Bad","return True # Mount a filesystem that isn't already return","on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps","device=None, user=None): ''' Attempt to unmount a device by specifying","'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List active mounts","False present = False if not os.path.isfile(config): raise CommandExecutionError('Bad config","lines.append(line) continue comps = line.split() if len(comps) != 6: #","if comps[3] != opts: change = True comps[3] = opts","mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name not in","'{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name, fstype, opts, dump, pass_num ) )","not present. CLI Example: .. code-block:: bash salt '*' mount.set_automaster","/mnt/foo /dev/sdz1 True ''' force_mount = False if __grains__['os'] in","''' # Darwin doesn't expect defaults when mounting without other","{2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']:","= line.split() device = comps[2].split(':') device_name = comps[8] device_uuid =","{'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret):","cmd = 'mount {0} {1} {2} '.format(args, device, name) else:","nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\" \")","'fstab entry for mount point {0} needs to be '","return True return None def is_mounted(name): ''' .. versionadded:: 2014.7.0","read from {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config,","','.join(opts) args = '-o {0}'.format(lopts) if fstype: args += '","end of the fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts,","!= 3: # Invalid entry continue prefix = \"/..\" name","if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts","type if it is a list if isinstance(opts, list): opts","'new' def automaster(config='/etc/auto_salt'): ''' List the contents of the fstab","line in mounts.split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]]","if name not in mnts: return \"{0} does not have","swap CLI Example: .. code-block:: bash salt '*' mount.swaps '''","changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File","= {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def","/dev/sdz1 True ''' force_mount = False if __grains__['os'] in ['MacOS',","ofile: # The line was changed, commit it! ofile.writelines(lines) except","on_ = swaps() if name in on_: ret['stats'] = on_[name]","Verify that this mount is represented in the fstab, change","opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \"\") try:","present = True if comps[0] != name: change = True","''' ret = {} if not os.path.isfile(config): return ret with","if fstype: args += ' -t {0}'.format(fstype) cmd = 'mount","Commented lines.append(line) continue if not line.strip(): # Blank line lines.append(line)","line lines.append(line) continue comps = line.split() if len(comps) != 6:","'{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try: with","= '-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype)","raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out","comps[0] != name: change = True comps[0] = name if","return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): '''","= {} if not os.path.isfile(config): return ret with salt.utils.fopen(config) as","if line.startswith('#'): # Commented continue if not line.strip(): # Blank","= automaster(config) if name not in contents: return True #","comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active mounts on","which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set","mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect defaults","cmd = 'swapon {0}'.format(name) if priority: cmd += ' -p","not os.access(filename, os.R_OK): msg = 'File not readable {0}' raise","'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need to force","True comps[0] = name if comps[1] != type_opts: change =","the fstab, change the mount to match the data passed,","if __grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults': opts","import logging # Import salt libs import salt.utils from salt._compat","device, user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts) if","Define the module's virtual name __virtualname__ = 'mount' def __virtual__():","end of the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype,","fstype if comps[3] != opts: change = True comps[3] =","return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true if","__salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device) type_opts","line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type': comps[1], 'size':","systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\",","bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) #","return ret def fstab(config='/etc/fstab'): ''' List the contents of the","mnts = active() if name not in mnts: return \"{0}","if 'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts","comps[0] == name or comps[2] == device_fmt: # check to","_active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the",") newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline)","name if comps[1] != type_opts: change = True comps[1] =","ret return ret def swapoff(name): ''' Deactivate a named swap","force_mount = True if isinstance(opts, string_types): opts = opts.split(',') mnts","{0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)', line,","mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False if __grains__['os']","active() if name in active_: return True else: return False","CLI Example: .. code-block:: bash salt '*' mount.umount /mnt/foo ..","= True comps[2] = fstype if comps[3] != opts: change","'noowners' if fstype == 'smbfs': force_mount = True if isinstance(opts,","__grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts =","CLI Example: .. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1","not writable {0}'.format( config ) ) return 'new' def rm_automaster(name,","comps = re.sub(r\"\\s+\", \" \", line).split() parens = re.findall(r'\\((.*?)\\)', line,","or comps[2] == device_fmt: # check to see if there","fuse mountable application. CLI Example: .. code-block:: bash salt '*'","user=None): ''' Attempt to unmount a device by specifying the","device_fmt = device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config, 'r') as ifile:","mounted CLI Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share","' '.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] =","containing information on active swap CLI Example: .. code-block:: bash","__salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\" \") parens =","CLI Example: .. code-block:: bash salt '*' mount.active ''' ret","in ifile: if line.startswith('#'): # Commented continue if not line.strip():","def swapon(name, priority=None): ''' Activate a swap disk CLI Example:","salt '*' mount.is_mounted /mnt/share ''' active_ = active() if name","device: cmd = 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device)","= {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass':","'File not writable {0}'.format( config ) ) return 'new' def","line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\"","fstab, change the mount to match the data passed, or","it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not writable","len(comps) != 3: # Invalid entry lines.append(line) continue comps =","List active mounts on OpenBSD systems ''' for line in","return out['stderr'] return True # Mount a filesystem that isn't","fstype='', opts='defaults', user=None): ''' Mount a device CLI Example: ..","the command passed is a fuse mountable application. CLI Example:","as exc: msg = 'Couldn\\'t read from {0}: {1}' raise","systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\",","'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name, device, mkmnt=False,","dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: #","' -t {0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args,","elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']:","in ifile: comps = line.split() device = comps[2].split(':') device_name =","blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid':","comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active","Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4","''' List active mounts on Solaris systems ''' for line","!= 6: # Invalid entry continue ret[comps[1]] = {'device': comps[0],","comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device,","= True if comps[0] != device: change = True comps[0]","= re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = {'device': comps[0], 'fstype':","line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type =","( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config,","fstype: args += ' -t {0}'.format(fstype) if __grains__['os'] not in","msg = \"Couldn't read from {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc)))","for line in ifile: comps = line.split() device = comps[2].split(':')","prefix = \"/..\" name_chk = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\")","is represented in the fstab, change the mount to match","ofile.writelines(lines) except (IOError, OSError): msg = 'File not writable {0}'","= str(pass_num) if change: log.debug( 'fstab entry for mount point","name: continue lines.append(line) except (IOError, OSError) as exc: msg =","ret = {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts =","6: # Invalid entry lines.append(line) continue if comps[1] == name","/dev/sdz1 True ''' # Darwin doesn't expect defaults when mounting","if name_chk == name: continue lines.append(line) except (IOError, OSError) as","file \"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for line","line.split() prefix = \"/..\" name_chk = comps[0].replace(prefix, \"\") device_fmt =","logging.getLogger(__name__) # Define the module's virtual name __virtualname__ = 'mount'","comps[1] != type_opts: change = True comps[1] = type_opts if","re import logging # Import salt libs import salt.utils from","salt '*' mount.swaps ''' ret = {} if __grains__['os'] !=","extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret","parens[0]} return ret def _active_mounts_darwin(ret): ''' List active mounts on","__salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() nod =","lines = [] change = False present = False if","with salt.utils.fopen(config, 'w+') as ofile: # The line was changed,",".. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True '''","import CommandNotFoundError, CommandExecutionError # Set up logger log = logging.getLogger(__name__)","name and device_fmt[1] == device: continue else: if name_chk ==","comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]] = {'device': comps[0],","fstab CLI Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo","os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info","active mounts. CLI Example: .. code-block:: bash salt '*' mount.active","parens[1:]} return ret def active(extended=False): ''' List the active mounts.","string_types): opts = opts.split(',') mnts = active() if name in","Invalid entry continue prefix = \"/..\" name = comps[0].replace(prefix, \"\")","'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps()","None: lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype:","device_fmt if change: log.debug( 'auto_master entry for mount point {0}","the fstab CLI Example: .. code-block:: bash salt '*' mount.rm_fstab","salt.utils.fopen(config, 'r') as ifile: for line in ifile: if line.startswith('#'):","entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','),","to match the data passed, or add the mount if","__salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\n'):","with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if line.startswith('Filename'):","continue lines.append(line) except (IOError, OSError) as exc: msg = \"Couldn't","# Invalid entry lines.append(line) continue comps = line.split() prefix =","= {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return","CLI Example: .. code-block:: bash salt '*' mount.swaps ''' ret","from {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+')","return \"{0} does not have anything mounted\".format(name) if not device:","'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret","!= type_opts: change = True comps[1] = type_opts if comps[2]","continue if not line.strip(): # Blank line lines.append(line) continue comps","'mount -u {0} {1} {2} '.format(args, device, name) out =","line.split() if len(comps) != 3: # Invalid entry lines.append(line) continue","mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\n'): comps =","_active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename,","elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return","= {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_:","__salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \"file\" comps","ifile: for line in ifile: if line.startswith('#'): # Commented continue","if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return","if name in on_: ret['stats'] = on_[name] ret['new'] = True","true if the command passed is a fuse mountable application.","= 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out =","return False return True return None def is_mounted(name): ''' ..","os.access(filename, os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename))","Invalid entry lines.append(line) continue if comps[0] == name or comps[2]","a named swap mount CLI Example: .. code-block:: bash salt","True # The entry is present, get rid of it","it is not present. CLI Example: .. code-block:: bash salt","re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0],","it to the end of the fstab newline = (","filesystem that isn't already return mount(name, device, mkmnt, fstype, opts,","_active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the contents of","continue else: if comps[1] == name: continue lines.append(line) except (IOError,","!= opts: change = True comps[3] = opts if comps[4]","return ret with salt.utils.fopen(config) as ifile: for line in ifile:","= \"/..\" name = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") opts","a device by specifying the directory it is mounted on","'*' mount.swaps ''' ret = {} if __grains__['os'] != 'OpenBSD':","not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for","name not in contents: return True # The entry is","__salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if","absolute_import # Import python libs import os import re import","ret def fstab(config='/etc/fstab'): ''' List the contents of the fstab","given data if 'remount' not in opts and __grains__['os'] not","comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return","{1} {2} '.format(args, device, name) else: cmd = 'mount -u","''' active_ = active() if name in active_: return True","contents: return True # The entry is present, get rid","active() if name not in mnts: return \"{0} does not","comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]] = {'type': swap_type, 'size': comps[1],","def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems '''","{'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name,","mnts = active() if name in mnts: # The mount","mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None): ''' Attempt","code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config)","ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount","**kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: # The line","device: continue else: if name_chk == name: continue lines.append(line) except","if out['retcode']: return out['stderr'] return True # Mount a filesystem","ifile: if line.startswith('#'): # Commented lines.append(line) continue if not line.strip():","True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt","= ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with","fix them if there are any present = True if","''' on_ = swaps() if name in on_: if __grains__['os']","= __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def","''' Returns true if the command passed is a fuse","name in on_: ret['stats'] = on_[name] ret['new'] = True return","any present = True if comps[0] != device: change =","device, if the device is not already mounted, mount is","Attempt to remount a device, if the device is not","_active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else:","name not in mnts: return \"{0} does not have anything","swaps() if name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff","as ifile: for line in ifile: comps = line.split() ret[comps[1]]","name and comps[0] == device: continue else: if comps[1] ==","comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts() filename =","3: # Invalid entry lines.append(line) continue comps = line.split() prefix","__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def remount(name,","OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps =","except (IOError, OSError) as exc: msg = 'Couldn\\'t read from","isinstance(opts, string_types): opts = opts.split(',') mnts = active() if name","needs to be ' 'updated'.format(name) ) newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4}","The entry is present, get rid of it lines =","salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False","the module's virtual name __virtualname__ = 'mount' def __virtual__(): '''","= opts.split(',') mnts = active() if name in mnts: #",".. code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_ =",".. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True '''","= 'mount' def __virtual__(): ''' Only load on POSIX-like systems","'*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if name","Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents","not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename)","True comps[3] = opts if comps[4] != str(dump): change =","'libfuse' in out def swaps(): ''' Return a dict containing","in ['MacOS', 'Darwin']: if opts == 'defaults': opts = 'noowners'","config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount is represented","fstype, opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line) except","comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')):","True comps[0] = device if comps[1] != name: change =","salt.utils.fopen(config, 'w+') as ofile: # The line was changed, commit","code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config)","line.split() if len(comps) != 6: # Invalid entry continue ret[comps[1]]","in the fstab, change the mount to match the data","check to see if there are changes # and fix","if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type': comps[1],","'*' mount.active ''' ret = {} if __grains__['os'] == 'FreeBSD':","in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need","{0}'.format(name)) on_ = swaps() if name in on_: return False","= device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1],","if len(comps) != 6: # Invalid entry continue ret[comps[1]] =","filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File","CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in ifile: comps","/root/swapfile ''' ret = {} on_ = swaps() if name","force_mount: # We need to force the mount but first","msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename)","as fp_: for line in fp_: if line.startswith('Filename'): continue comps","anything mounted\".format(name) if not device: cmd = 'umount {0}'.format(name) else:","change: if present: # The right entry is already here","name __virtualname__ = 'mount' def __virtual__(): ''' Only load on","line in ifile: comps = line.split() device = comps[2].split(':') device_name","__virtualname__ = 'mount' def __virtual__(): ''' Only load on POSIX-like","but first we should unmount umount(name, device, user=user) lopts =","salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if","\", line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list","contents of the fstab CLI Example: .. code-block:: bash salt","'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'):","= line.split() if comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]] = {'type':","Returns true if the command passed is a fuse mountable","'-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) if","there are changes # and fix them if there are","\", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = '","opts='defaults', user=None): ''' Mount a device CLI Example: .. code-block::","device_fmt = comps[2].split(\":\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1],","comps[2].split(':') device_name = comps[8] device_uuid = None if device_name: device_uuid","= __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\" \") parens","the auto_master CLI Example: .. code-block:: bash salt '*' mount.rm_automaster","Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret","# Import salt libs import salt.utils from salt._compat import string_types","comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None),","== 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else:","CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List","def swaps(): ''' Return a dict containing information on active","comps[5]} return ret def swapon(name, priority=None): ''' Activate a swap","code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_ = active()","= line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3],","{'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return","Fix the opts type if it is a list if","first we should unmount umount(name, device, user=user) lopts = ','.join(opts)","else: if comps[1] == name: continue lines.append(line) except (IOError, OSError)","ifile: for line in ifile: comps = line.split() device =","{0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name",") ) return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents","!= 6: # Invalid entry lines.append(line) continue comps = line.split()","ifile: for line in ifile: if line.startswith('#'): # Commented lines.append(line)","'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret)","OSError): raise CommandExecutionError( 'File not writable {0}'.format( config ) )","mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1","CLI Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1","!= 3: # Invalid entry lines.append(line) continue if comps[0] ==","it! ofile.writelines(lines) except (IOError, OSError): msg = 'File not writable","comps[1] != name: change = True comps[1] = name if","in on_: ret['stats'] = on_[name] ret['new'] = False return ret","information if the path is mounted CLI Example: .. code-block::","remount a device, if the device is not already mounted,","mount(name, device, mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None):","opts, user=user) def umount(name, device=None, user=None): ''' Attempt to unmount","pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The","fstype == 'smbfs': force_mount = True if isinstance(opts, string_types): opts","'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0} {1} {2}","ext4 ''' # Fix the opts type if it is",".. code-block:: bash salt '*' mount.swaps ''' ret = {}","return ret def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD","rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point from the","filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File","salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the","!= 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ =","entry lines.append(line) continue if comps[1] == name or comps[0] ==","cmd = 'mount {0} {1} {2} '.format(args, device, name) out","for line in ifile: if line.startswith('#'): # Commented lines.append(line) continue","Commented continue if not line.strip(): # Blank line continue comps","it to the end of the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4}","swap_type = \"file\" comps = line.split() if comps[0].startswith('/dev/'): swap_type =","''' # Fix the opts type if it is a","import which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError #","if line.startswith(('Device', 'Total')): continue swap_type = \"file\" comps = line.split()","= fstab(config) if name not in contents: return True #","are any present = True if comps[0] != device: change","= comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts() filename","readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line","ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret","'Couldn\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change:","unmount umount(name, device, user=user) lopts = ','.join(opts) args = '-o","device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device':","lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype: args","disk CLI Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile","entry lines.append(line) continue comps = line.split() if device: if comps[1]","= False return ret cmd = 'swapon {0}'.format(name) if priority:","''' mnts = active() if name not in mnts: return","name_chk == name: continue lines.append(line) except (IOError, OSError) as exc:","os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts","!= name: change = True comps[0] = name if comps[1]","systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \"","config='/etc/auto_salt'): ''' Remove the mount point from the auto_master CLI","line continue comps = line.split() if len(comps) != 3: #","ifile: for line in ifile: comps = line.split() ret[comps[1]] =","-l') for line in mounts.split('\\n'): comps = re.sub(r\"\\s+\", \" \",","False automaster_file = \"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config))","a filesystem that isn't already return mount(name, device, mkmnt, fstype,","mount is called CLI Example: .. code-block:: bash salt '*'","if not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for","virtual name __virtualname__ = 'mount' def __virtual__(): ''' Only load","if comps[0] == name or comps[2] == device_fmt: # check","\", line).split() parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]] =","False return ret cmd = 'swapon {0}'.format(name) if priority: cmd","log.debug( 'fstab entry for mount point {0} needs to be","None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): '''","else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if","name = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") opts = comps[1].split(',')","= opts if comps[4] != str(dump): change = True comps[4]","if fstype: args += ' -t {0}'.format(fstype) if __grains__['os'] not","\"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype, opts) if","line, re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts':","mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line","import os import re import logging # Import salt libs","if len(comps) != 6: # Invalid entry lines.append(line) continue if","line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() parens","return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd",") lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc: msg","\"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for line in","bash salt '*' mount.is_mounted /mnt/share ''' active_ = active() if","= line.split() if len(comps) != 3: # Invalid entry continue","{1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if","name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as","comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = comps[0] return","CLI Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile '''","we should unmount umount(name, device, user=user) lopts = ','.join(opts) args","!= 3: # Invalid entry lines.append(line) continue comps = line.split()","get rid of it lines = [] try: with salt.utils.fopen(config,","List active mounts on Linux systems ''' _list = _list_mounts()","= '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try:","change = True comps[4] = str(dump) if comps[5] != str(pass_num):",".. code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium","# The line was changed, commit it! ofile.writelines(lines) except (IOError,","comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the","'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if","data if 'remount' not in opts and __grains__['os'] not in","mount.swaps ''' ret = {} if __grains__['os'] != 'OpenBSD': with","__grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif","already return mount(name, device, mkmnt, fstype, opts, user=user) def umount(name,","this mount is represented in the auto_salt, change the mount","\"/..\" name = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") opts =","**kwargs): ''' Verify that this mount is represented in the","' 'updated'.format(name) ) newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device, name,","Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path","present = False automaster_file = \"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config)","and the fstab file ''' from __future__ import absolute_import #","os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt = \"{0}:{1}\".format(fstype,","ret def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems","CLI Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile '''","raise CommandExecutionError(msg.format(config)) return 'change' if not change: if present: #","''' List the active mounts. CLI Example: .. code-block:: bash","if there are changes # and fix them if there","None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if","code-block:: bash salt '*' mount.fstab ''' ret = {} if","line lines.append(line) continue comps = line.split() if len(comps) != 3:","if present: # The right entry is already here return","mount.fstab ''' ret = {} if not os.path.isfile(config): return ret","False return True return None def is_mounted(name): ''' .. versionadded::","OSError) as exc: msg = \"Couldn't write to {0}: {1}\"","device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc:","config ) ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): '''","'*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts","ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret","device, name, fstype, opts, dump, pass_num ) ) lines.append(newline) else:","= blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] =","= {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root':","mounts on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\n'):","in mounts.split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] =","out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true","str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): '''","of the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name, fstype, opts,","continue comps = line.split() prefix = \"/..\" name_chk = comps[0].replace(prefix,","lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The line","as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up","-u {0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd,","\"Couldn't read from {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) try: with","other options if 'defaults' in opts and __grains__['os'] in ['MacOS',","Invalid entry lines.append(line) continue comps = line.split() prefix = \"/..\"","''' ret = {} on_ = swaps() if name in","__salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name in on_:","ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4],","return None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information","['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0} {1}","comps[0] != device: change = True comps[0] = device if","def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a","return ret def swapon(name, priority=None): ''' Activate a swap disk","= True return ret return ret def swapoff(name): ''' Deactivate","or comps[0] == device: # check to see if there","to be ' 'updated'.format(name) ) newline = ( '{0}\\t{1}\\t{2}\\n'.format( name,","line).split() parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device':","not device: cmd = 'umount {0}'.format(name) else: cmd = 'umount","__salt__['file.mkdir'](name=name, user=user) args = '' if opts is not None:","comps = line.split() if len(comps) != 3: # Invalid entry","comps[2] = fstype if comps[3] != opts: change = True","'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts':","'*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False if","cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']:","active_ = active() if name in active_: return True else:","point from the auto_master CLI Example: .. code-block:: bash salt","# Blank line lines.append(line) continue comps = line.split() if len(comps)","return out['stderr'] return True def remount(name, device, mkmnt=False, fstype='', opts='defaults',","List active mounts on Mac OS systems ''' for line","for line in __salt__['cmd.run_stdout']('mount').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split()","= re.sub(r\"\\s+\", \" \", line).split() parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL)[0].split(\",","\"{0}:{1}\".format(fstype, device) type_opts = \"-fstype={0},{1}\".format(fstype, opts) if fstype == 'smbfs':","in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() nod","{0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) cmd =","\"/..\" name_chk = comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") if device:","CLI Example: .. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1","'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for line in","bash salt '*' mount.swapoff /root/swapfile ''' on_ = swaps() if",".. code-block:: bash salt '*' mount.active ''' ret = {}","continue comps = line.split() if len(comps) != 6: # Invalid","module's virtual name __virtualname__ = 'mount' def __virtual__(): ''' Only","= '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File not",".. code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents =","continue if comps[1] == name or comps[0] == device: #","is mounted on CLI Example: .. code-block:: bash salt '*'","return True def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0,","True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs):","that this mount is represented in the auto_salt, change the","True return ret return ret def swapoff(name): ''' Deactivate a","lines.append(line) except (IOError, OSError) as exc: msg = \"Couldn't read","if name not in contents: return True # The entry","that doesn't exist if not cmd_path: return False elif not","__grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret)","change = True comps[3] = opts if comps[4] != str(dump):","change = False present = False automaster_file = \"/etc/auto_master\" if","changes # and fix them if there are any present",".. code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_ =","= [] change = False present = False if not","comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): '''","mounted\".format(name) if not device: cmd = 'umount {0}'.format(name) else: cmd","{'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False):","line was changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise","name) else: cmd = 'mount -u {0} {1} {2} '.format(args,","# Commented lines.append(line) continue if not line.strip(): # Blank line","\"\") device_fmt = comps[2].split(\":\") if device: if name_chk == name","-l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\" \") parens = re.findall(r'\\((.*?)\\)',","= 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if","= __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps(): '''","device, config='/etc/fstab'): ''' Remove the mount point from the fstab","bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix","continue comps = line.split() if device: if comps[1] == name","/mnt/foo /dev/xvdc1 ''' mnts = active() if name not in","if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name = \"/..{0}\".format(name) device_fmt","== name and comps[0] == device: continue else: if comps[1]","comps[1] == name: continue lines.append(line) except (IOError, OSError) as exc:","= ','.join(opts) lines = [] change = False present =","ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts':","{0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test,","_active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'):","= {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount')","coding: utf-8 -*- ''' Salt module to manage unix mounts","re.DOTALL)[0].split(\", \") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]}","__grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: #","in ifile: if line.startswith('#'): # Commented lines.append(line) continue if not","force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']: if opts","salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger log =","False if not os.path.isfile(config): raise CommandExecutionError('Bad config file \"{0}\"'.format(config)) try:","device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount a","running ldd on a command that doesn't exist if not","libs import salt.utils from salt._compat import string_types from salt.utils import","if len(comps) != 3: # Invalid entry continue prefix =","a specific file module exists: if salt.utils.is_windows(): return False return","for line in mounts.split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split()","comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False): '''","CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except","-p').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]] = {'device':","in running ldd on a command that doesn't exist if","else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret)","for line in fp_: if line.startswith('Filename'): continue comps = line.split()","it is mounted on CLI Example: .. code-block:: bash salt","True def _list_mounts(): ret = {} if __grains__['os'] in ['MacOS',","the end of the fstab newline = '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(device, name,","log.debug( 'auto_master entry for mount point {0} needs to be",") lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The","'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not","in ifile: comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device':","= \"partition\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2],","dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount","'*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect","['MacOS', 'Darwin']: if opts == 'defaults': opts = 'noowners' if","parens = re.findall(r'\\((.*?)\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype':","-*- ''' Salt module to manage unix mounts and the","# Invalid entry lines.append(line) continue if comps[0] == name or","return mount(name, device, mkmnt, fstype, opts, user=user) def umount(name, device=None,","if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd","os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with","newline = ( '{0}\\t{1}\\t{2}\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else:","bash salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if","'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','),","a list if isinstance(opts, list): opts = ','.join(opts) lines =","= 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as","device_fmt = comps[2].split(\":\") if device: if name_chk == name and","def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to","= '/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File not","comps = line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used':","3: # Invalid entry continue prefix = \"/..\" name =","{5}\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config,","mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No point in","a device, if the device is not already mounted, mount","should unmount umount(name, device, user=user) lopts = ','.join(opts) args =","3: # Invalid entry lines.append(line) continue if comps[0] == name","device: continue else: if comps[1] == name: continue lines.append(line) except","to see if there are changes # and fix them","logging # Import salt libs import salt.utils from salt._compat import","= 'mount {0} {1} {2} '.format(args, device, name) out =","pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as","= False present = False automaster_file = \"/etc/auto_master\" if not","os.path.isfile(config): raise CommandExecutionError('Bad config file \"{0}\"'.format(config)) try: with salt.utils.fopen(config, 'r')","os import re import logging # Import salt libs import","'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts =","lines = [] try: with salt.utils.fopen(config, 'r') as ifile: for",".. code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret =","= {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] ==","else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue","new, add it to the end of the fstab newline","','.join(opts) lines = [] change = False present = False","line.strip(): # Blank line continue comps = line.split() if len(comps)","to be ' 'updated'.format(name) ) newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format(","if device: if name_chk == name and device_fmt[1] == device:","information on active swap CLI Example: .. code-block:: bash salt","swaps(): ''' Return a dict containing information on active swap","''' from __future__ import absolute_import # Import python libs import","{0}'.format(name) if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_","'' if opts is not None: lopts = ','.join(opts) args","Remove the mount point from the fstab CLI Example: ..","except (IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format( config","config ) ) return 'new' def automaster(config='/etc/auto_salt'): ''' List the","_active_mounts_darwin(ret): ''' List active mounts on Mac OS systems '''","if change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+')","ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point","FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps =","if comps[2] != fstype: change = True comps[2] = fstype","ifile: comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1],","comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None),","_active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD':","= swaps() if name in on_: if __grains__['os'] != 'OpenBSD':","/mnt/share ''' active_ = active() if name in active_: return","return ret def _active_mounts_solaris(ret): ''' List active mounts on Solaris","True if comps[0] != name: change = True comps[0] =","to the end of the fstab newline = ( '{0}\\t{1}\\t{2}\\n'.format(","mounts. CLI Example: .. code-block:: bash salt '*' mount.active '''","swaps() if name in on_: return False return True return","''' List active mounts on OpenBSD systems ''' for line","change = True comps[1] = type_opts if comps[2] != device_fmt:","parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret","mount is represented in the auto_salt, change the mount to","if comps[1] == name or comps[0] == device: # check","= True if isinstance(opts, string_types): opts = opts.split(',') mnts =","/mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 '''","not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if","'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in","Return a dict containing information on active swap CLI Example:","if not cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd')","== name and device_fmt[1] == device: continue else: if name_chk","= comps[2].split(\":\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype':","Mount a filesystem that isn't already return mount(name, device, mkmnt,","bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*'","in __salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]]","The entry is new, add it to the end of","not salt.utils.test_mode(test=test, **kwargs): # The entry is new, add it","change: log.debug( 'auto_master entry for mount point {0} needs to","= active() if name in mnts: # The mount point","on_: ret['stats'] = on_[name] ret['new'] = True return ret return","{0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in on_: ret['stats']","automaster_file = \"/etc/auto_master\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \"/-\\t\\t\\t{0}\".format(config)) name","code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount","__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd):","user=user) def umount(name, device=None, user=None): ''' Attempt to unmount a","def _active_mounts(ret): ''' List active mounts on Linux systems '''","msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change'","ret with salt.utils.fopen(config) as ifile: for line in ifile: if","line.split() if comps[0].startswith('/dev/'): swap_type = \"partition\" ret[comps[0]] = {'type': swap_type,","continue comps = line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2],","salt.utils from salt._compat import string_types from salt.utils import which as","6: # Invalid entry lines.append(line) continue comps = line.split() if","__salt__['cmd.run_stdout']('mount -p').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split() ret[comps[1]] =","fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that","lines.append(line) continue comps = line.split() prefix = \"/..\" name_chk =","= str(dump) if comps[5] != str(pass_num): change = True comps[5]","name or comps[0] == device: # check to see if","_which(cmd) # No point in running ldd on a command","runas=user) if out['retcode']: return out['stderr'] return True # Mount a","'fstype': comps[5], 'opts': parens[1].split(\", \"), 'major': str(nod[4].strip(\",\")), 'minor': str(nod[5]), 'device_uuid':","device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret","_list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg =","False if __grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults':","= line.split() prefix = \"/..\" name_chk = comps[0].replace(prefix, \"\") device_fmt","name if comps[2] != fstype: change = True comps[2] =","lines.append(line) continue if comps[0] == name or comps[2] == device_fmt:","CommandNotFoundError, CommandExecutionError # Set up logger log = logging.getLogger(__name__) #","salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name","CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try: with","if comps[4] != str(dump): change = True comps[4] = str(dump)",") return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the","= _which(cmd) # No point in running ldd on a","the fstab CLI Example: .. code-block:: bash salt '*' mount.fstab","present = True if comps[0] != device: change = True","True comps[2] = fstype if comps[3] != opts: change =","set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs):","force_mount: cmd = 'mount {0} {1} {2} '.format(args, device, name)","ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]}","comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active mounts on","entry for mount point {0} needs to be ' 'updated'.format(name)","CommandExecutionError( 'File not writable {0}'.format( config ) ) return 'new'","''' cmd_path = _which(cmd) # No point in running ldd","= \"-fstype={0},{1}\".format(fstype, opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype,","be ' 'updated'.format(name) ) newline = ( '{0}\\t\\t{1}\\t{2}\\t{3}\\t{4} {5}\\n'.format( device,","doesn't exist if not cmd_path: return False elif not _which('ldd'):","mounted, attempt to remount it with the given data if","!= fstype: change = True comps[2] = fstype if comps[3]","= \"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) return","read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if","Example: .. code-block:: bash salt '*' mount.fstab ''' ret =","''' force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']: if","line in __salt__['cmd.run_stdout']('mount -v').split('\\n'): comps = re.sub(r\"\\s+\", \" \", line).split()","point {0} needs to be ' 'updated'.format(name) ) newline =","mnts: # The mount point is mounted, attempt to remount","active swap CLI Example: .. code-block:: bash salt '*' mount.swaps","comps[3] != opts: change = True comps[3] = opts if","'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name,","''' Mount a device CLI Example: .. code-block:: bash salt","specific file module exists: if salt.utils.is_windows(): return False return True","ofile.writelines(lines) except (IOError, OSError) as exc: msg = \"Couldn't write","write to {0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) return True def","force the mount but first we should unmount umount(name, device,","not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change:","comps[0].replace(prefix, \"\") device_fmt = comps[2].split(\":\") if device: if name_chk ==","def __virtual__(): ''' Only load on POSIX-like systems ''' #","if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile:","{'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret):","else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the contents","= swaps() if name in on_: ret['stats'] = on_[name] ret['new']","exc: msg = \"Couldn't write to {0}: {1}\" raise CommandExecutionError(msg.format(config,","device by specifying the directory it is mounted on CLI","_active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems ''' for","ret['stats'] = on_[name] ret['new'] = True return ret return ret","on_: ret['stats'] = on_[name] ret['new'] = False return ret cmd","that isn't already return mount(name, device, mkmnt, fstype, opts, user=user)","comps[3] = opts if comps[4] != str(dump): change = True","= comps[2].split(\":\") if device: if name_chk == name and device_fmt[1]","if out['retcode']: return out['stderr'] return True def remount(name, device, mkmnt=False,","opts type if it is a list if isinstance(opts, list):","Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name, device,","{0}: {1}\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name,","add it to the end of the fstab newline =","__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True # Mount","active mounts on Mac OS systems ''' for line in","line.split() if device: if comps[1] == name and comps[0] ==","if isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name) and","mounts and the fstab file ''' from __future__ import absolute_import","\"partition\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority':","re.sub(r\"\\s+\", \" \", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4],","lines = [] change = False present = False automaster_file","was changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError(","writable {0}'.format( config ) ) return 'new' def rm_automaster(name, device,","# and fix them if there are any present =","salt '*' mount.swapon /root/swapfile ''' ret = {} on_ =","lines.append(line) continue comps = line.split() if device: if comps[1] ==","= device_fmt.replace(fstype, \"\") try: with salt.utils.fopen(config, 'r') as ifile: for","salt.utils.is_windows(): return False return True def _list_mounts(): ret = {}","the active mounts. CLI Example: .. code-block:: bash salt '*'","the contents of the fstab CLI Example: .. code-block:: bash","device: if comps[1] == name and comps[0] == device: continue","logger log = logging.getLogger(__name__) # Define the module's virtual name","''' List active mounts on Mac OS systems ''' for","mounts on Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\n'):","is called CLI Example: .. code-block:: bash salt '*' mount.remount","match the data passed, or add the mount if it","= comps[2].split(':') device_name = comps[8] device_uuid = None if device_name:","active(extended=False): ''' List the active mounts. CLI Example: .. code-block::","code-block:: bash salt '*' mount.swaps ''' ret = {} if","with salt.utils.fopen(config) as ifile: for line in ifile: if line.startswith('#'):","name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): '''","__salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in ifile: comps","CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line"],"string":"[\n \"Invalid entry lines.append(line) continue if comps[1] == name or comps[0]\",\n \"comps[1] == name and comps[0] == device: continue else: if\",\n \"automaster(config) if name not in contents: return True # The\",\n \"comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod = __salt__['cmd.run_stdout']('ls -l\",\n \"{1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device,\",\n \"name, fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+')\",\n \") ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove\",\n \"{0}'.format( config ) ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'):\",\n \"ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os']\",\n \"Blank line lines.append(line) continue comps = line.split() if len(comps) !=\",\n \"out def swaps(): ''' Return a dict containing information on\",\n \"str(exc))) return True def set_fstab( name, device, fstype, opts='defaults', dump=0,\",\n \"comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name] =\",\n \"ret def swapoff(name): ''' Deactivate a named swap mount CLI\",\n \"newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype, opts, dump,\",\n \"from salt.utils import which as _which from salt.exceptions import CommandNotFoundError,\",\n \"ret def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems\",\n \"-t {0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args, device,\",\n \"raise CommandExecutionError( 'File not writable {0}'.format( config ) ) return\",\n \"mount point {0} needs to be ' 'updated'.format(name) ) newline\",\n \"device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid':\",\n \"# -*- coding: utf-8 -*- ''' Salt module to manage\",\n \"len(comps) != 6: # Invalid entry lines.append(line) continue comps =\",\n \"'/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File not readable\",\n \"= __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True #\",\n \"versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts =\",\n \"device: change = True comps[0] = device if comps[1] !=\",\n \"'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List\",\n \"if fstype == 'smbfs': force_mount = True if isinstance(opts, string_types):\",\n \"'priority': comps[5]} return ret def swapon(name, priority=None): ''' Activate a\",\n \"continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump':\",\n \"= ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype, opts, dump, pass_num\",\n \"name in mnts: # The mount point is mounted, attempt\",\n \"code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret = {}\",\n \"device = comps[2].split(':') device_name = comps[8] device_uuid = None if\",\n \"the mount but first we should unmount umount(name, device, user=user)\",\n \"None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid\",\n \"opts = opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user)\",\n \"command passed is a fuse mountable application. CLI Example: ..\",\n \"opts.split(',') mnts = active() if name in mnts: # The\",\n \"comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if\",\n \"comps[2] == device_fmt: # check to see if there are\",\n \"comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active mounts on\",\n \"{'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for\",\n \"args += ' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD',\",\n \"!= device: change = True comps[0] = device if comps[1]\",\n \"= ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line)\",\n \"'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return\",\n \"to manage unix mounts and the fstab file ''' from\",\n \"not None: lopts = ','.join(opts) args = '-o {0}'.format(lopts) if\",\n \"The line was changed, commit it! ofile.writelines(lines) except (IOError, OSError):\",\n \"fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config,\",\n \"elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret)\",\n \"expect defaults when mounting without other options if 'defaults' in\",\n \"out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true if the\",\n \"fstype='', opts='defaults', user=None): ''' Attempt to remount a device, if\",\n \"is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if the path\",\n \"change the mount to match the data passed, or add\",\n \"__grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd =\",\n \"here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The\",\n \"salt libs import salt.utils from salt._compat import string_types from salt.utils\",\n \"lines.append(line) continue if not line.strip(): # Blank line lines.append(line) continue\",\n \"opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount is\",\n \"args = '' if opts is not None: lopts =\",\n \"{5}\\\\n'.format( device, name, fstype, opts, dump, pass_num ) ) lines.append(newline)\",\n \"mounted, mount is called CLI Example: .. code-block:: bash salt\",\n \"''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\"\",\n \"if len(comps) != 3: # Invalid entry lines.append(line) continue if\",\n \"remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount\",\n \"entry is already here return 'present' else: if not salt.utils.test_mode(test=test,\",\n \"cmd = 'mount -u {0} {1} {2} '.format(args, device, name)\",\n \"cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out =\",\n \"= comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]}\",\n \"not in mnts: return \\\"{0} does not have anything mounted\\\".format(name)\",\n \"__future__ import absolute_import # Import python libs import os import\",\n \"= \\\"Couldn't read from {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try:\",\n \"systems ''' # Disable on Windows, a specific file module\",\n \"present = False if not os.path.isfile(config): raise CommandExecutionError('Bad config file\",\n \"with salt.utils.fopen(config, 'r') as ifile: for line in ifile: if\",\n \"if the path is mounted CLI Example: .. code-block:: bash\",\n \"from salt._compat import string_types from salt.utils import which as _which\",\n \"ret = {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as\",\n \"List the active mounts. CLI Example: .. code-block:: bash salt\",\n \"device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this\",\n \"active mounts on Linux systems ''' _list = _list_mounts() filename\",\n \"else: lines.append(line) except (IOError, OSError) as exc: msg = 'Couldn\\\\'t\",\n \"'Darwin'] or force_mount: cmd = 'mount {0} {1} {2} '.format(args,\",\n \"salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as exc:\",\n \"'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List\",\n \"point from the fstab CLI Example: .. code-block:: bash salt\",\n \"device: if name_chk == name and device_fmt[1] == device: continue\",\n \"there are any present = True if comps[0] != device:\",\n \"'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg\",\n \"device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]]\",\n \"device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower()\",\n \"__salt__['cmd.run']('automount -cv') return True def set_automaster( name, device, fstype, opts='',\",\n \"= line.split() if device: if comps[1] == name and comps[0]\",\n \"device: # check to see if there are changes #\",\n \"umount(name, device=None, user=None): ''' Attempt to unmount a device by\",\n \"= re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]] = {'device': comps[0], 'fstype':\",\n \"str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError,\",\n \"return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents of the\",\n \"Example: .. code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded::\",\n \"_list = _list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK):\",\n \"'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active mounts\",\n \"def fstab(config='/etc/fstab'): ''' List the contents of the fstab CLI\",\n \"isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name) and mkmnt:\",\n \"out['stderr'] return True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None):\",\n \"continue else: if name_chk == name: continue lines.append(line) except (IOError,\",\n \"on Windows, a specific file module exists: if salt.utils.is_windows(): return\",\n \"ret def _active_mounts_darwin(ret): ''' List active mounts on Mac OS\",\n \"on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d\",\n \"line.strip(): # Blank line lines.append(line) continue comps = line.split() if\",\n \"' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in\",\n \"Invalid entry lines.append(line) continue comps = line.split() if device: if\",\n \"not present. CLI Example: .. code-block:: bash salt '*' mount.set_fstab\",\n \"type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile:\",\n \"comps[0] = name if comps[1] != type_opts: change = True\",\n \".. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 '''\",\n \"def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point from\",\n \"not os.path.isfile(config): raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config,\",\n \"\\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name)\",\n \"a swap disk CLI Example: .. code-block:: bash salt '*'\",\n \"'*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts\",\n \"line continue comps = line.split() if len(comps) != 6: #\",\n \"mnts: return \\\"{0} does not have anything mounted\\\".format(name) if not\",\n \"if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line\",\n \"True return None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide\",\n \"in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount\",\n \"''' # Disable on Windows, a specific file module exists:\",\n \"comps = line.split() if len(comps) != 6: # Invalid entry\",\n \"attempt to remount it with the given data if 'remount'\",\n \"POSIX-like systems ''' # Disable on Windows, a specific file\",\n \"''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\"\",\n \"'.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] = {'device':\",\n \"'opts': opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults',\",\n \"CLI Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo '''\",\n \"True def is_fuse_exec(cmd): ''' Returns true if the command passed\",\n \"CLI Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1\",\n \"= device_fmt if change: log.debug( 'auto_master entry for mount point\",\n \"cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if\",\n \"Import salt libs import salt.utils from salt._compat import string_types from\",\n \"\\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) # Update\",\n \"'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List\",\n \"'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for\",\n \"module to manage unix mounts and the fstab file '''\",\n \"'*' mount.is_mounted /mnt/share ''' active_ = active() if name in\",\n \"= \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts)\",\n \"unmount a device by specifying the directory it is mounted\",\n \"''' List active mounts on FreeBSD systems ''' for line\",\n \"does not have anything mounted\\\".format(name) if not device: cmd =\",\n \"exist if not cmd_path: return False elif not _which('ldd'): raise\",\n \"# Import python libs import os import re import logging\",\n \"/dev/sdz1 ext4 ''' # Fix the opts type if it\",\n \"is a fuse mountable application. CLI Example: .. code-block:: bash\",\n \"= line.split() if len(comps) != 6: # Invalid entry continue\",\n \"== 'smbfs': force_mount = True if isinstance(opts, string_types): opts =\",\n \"return ret def active(extended=False): ''' List the active mounts. CLI\",\n \"-cv') return True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt',\",\n \"utf-8 -*- ''' Salt module to manage unix mounts and\",\n \"raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines)\",\n \"is mounted CLI Example: .. code-block:: bash salt '*' mount.is_mounted\",\n \"# Invalid entry continue prefix = \\\"/..\\\" name = comps[0].replace(prefix,\",\n \"on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps\",\n \"return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount\",\n \"in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts = None\",\n \"__salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps(): ''' Return\",\n \"if not change: if present: # The right entry is\",\n \"{1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile:\",\n \"Blank line continue comps = line.split() if len(comps) != 3:\",\n \"if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ =\",\n \"return True def is_fuse_exec(cmd): ''' Returns true if the command\",\n \"comps = line.split() if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]] =\",\n \"called CLI Example: .. code-block:: bash salt '*' mount.remount /mnt/foo\",\n \"if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name))\",\n \"import re import logging # Import salt libs import salt.utils\",\n \"user=None): ''' Mount a device CLI Example: .. code-block:: bash\",\n \"in fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] =\",\n \"to remount it with the given data if 'remount' not\",\n \"as ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg =\",\n \"if comps[0] != device: change = True comps[0] = device\",\n \"active mounts on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount\",\n \"set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify\",\n \"the directory it is mounted on CLI Example: .. code-block::\",\n \"entry lines.append(line) continue if comps[0] == name or comps[2] ==\",\n \"options if 'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']:\",\n \"the fstab file ''' from __future__ import absolute_import # Import\",\n \"= 'noowners' if fstype == 'smbfs': force_mount = True if\",\n \"{'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3],\",\n \"-t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or\",\n \"\\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\")\",\n \"device, mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None): '''\",\n \"ret def active(extended=False): ''' List the active mounts. CLI Example:\",\n \"# Set up logger log = logging.getLogger(__name__) # Define the\",\n \"name in on_: ret['stats'] = on_[name] ret['new'] = False return\",\n \"Remove the mount point from the auto_master CLI Example: ..\",\n \"'smbfs': force_mount = True if isinstance(opts, string_types): opts = opts.split(',')\",\n \"ret cmd = 'swapon {0}'.format(name) if priority: cmd += '\",\n \"List active mounts on Solaris systems ''' for line in\",\n \"name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError)\",\n \"line in ifile: if line.startswith('#'): # Commented continue if not\",\n \"mountable application. CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec\",\n \"salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if line.startswith('Filename'): continue\",\n \"line.startswith('#'): # Commented lines.append(line) continue if not line.strip(): # Blank\",\n \"on_[name] ret['new'] = True return ret return ret def swapoff(name):\",\n \"__grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults': opts =\",\n \"to unmount a device by specifying the directory it is\",\n \"comps[2], 'used': comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl\",\n \"bash salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount =\",\n \"ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret\",\n \"mount.swapoff /root/swapfile ''' on_ = swaps() if name in on_:\",\n \"if len(comps) != 3: # Invalid entry lines.append(line) continue comps\",\n \"code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' #\",\n \"in mnts: # The mount point is mounted, attempt to\",\n \"comps[2] = device_fmt if change: log.debug( 'auto_master entry for mount\",\n \"salt.utils.fopen(config) as ifile: for line in ifile: if line.startswith('#'): #\",\n \"line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2],\",\n \"{0}'.format(name) else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user)\",\n \"for line in ifile: comps = line.split() ret[comps[1]] = {'device':\",\n \"as exc: msg = \\\"Couldn't write to {0}: {1}\\\" raise\",\n \"when mounting without other options if 'defaults' in opts and\",\n \"def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False,\",\n \"_active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems ''' for\",\n \"if not line.strip(): # Blank line continue comps = line.split()\",\n \"not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse'\",\n \"= {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def\",\n \"__salt__['cmd.run'](cmd) on_ = swaps() if name in on_: ret['stats'] =\",\n \"not line.strip(): # Blank line continue comps = line.split() if\",\n \"change = True comps[5] = str(pass_num) if change: log.debug( 'fstab\",\n \"passed is a fuse mountable application. CLI Example: .. code-block::\",\n \"and __grains__['os'] in ['MacOS', 'Darwin']: opts = None if isinstance(opts,\",\n \"from __future__ import absolute_import # Import python libs import os\",\n \"= re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5],\",\n \"name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return\",\n \"**kwargs): # The entry is new, add it to the\",\n \"''' Deactivate a named swap mount CLI Example: .. code-block::\",\n \"__grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in\",\n \"comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]),\",\n \"= None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid =\",\n \"opts = 'noowners' if fstype == 'smbfs': force_mount = True\",\n \"# Blank line continue comps = line.split() if len(comps) !=\",\n \"for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\",\",\n \"line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return\",\n \"config='/etc/fstab'): ''' Remove the mount point from the fstab CLI\",\n \"Darwin doesn't expect defaults when mounting without other options if\",\n \"module exists: if salt.utils.is_windows(): return False return True def _list_mounts():\",\n \"mount point from the auto_master CLI Example: .. code-block:: bash\",\n \"''' Attempt to unmount a device by specifying the directory\",\n \"Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4\",\n \"return ret cmd = 'swapon {0}'.format(name) if priority: cmd +=\",\n \"(IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format( config )\",\n \"fstab(config) if name not in contents: return True # The\",\n \"= name if comps[1] != type_opts: change = True comps[1]\",\n \"write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount\",\n \"/dev/xvdc1 ''' mnts = active() if name not in mnts:\",\n \"True comps[2] = device_fmt if change: log.debug( 'auto_master entry for\",\n \"The mount point is mounted, attempt to remount it with\",\n \"{0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'): ''' List\",\n \"to remount a device, if the device is not already\",\n \"swaps() if name in on_: ret['stats'] = on_[name] ret['new'] =\",\n \"= {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def\",\n \"OSError): msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return\",\n \"line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split()\",\n \"raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try:\",\n \"up logger log = logging.getLogger(__name__) # Define the module's virtual\",\n \"present: # The right entry is already here return 'present'\",\n \"config file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for\",\n \"return True def _list_mounts(): ret = {} if __grains__['os'] in\",\n \"ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret\",\n \"Salt module to manage unix mounts and the fstab file\",\n \"change: log.debug( 'fstab entry for mount point {0} needs to\",\n \"comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active\",\n \"True if isinstance(opts, string_types): opts = opts.split(',') mnts = active()\",\n \"elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try:\",\n \"comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret\",\n \"ldd on a command that doesn't exist if not cmd_path:\",\n \"if not device: cmd = 'umount {0}'.format(name) else: cmd =\",\n \"that this mount is represented in the fstab, change the\",\n \"'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7],\",\n \"opts: change = True comps[3] = opts if comps[4] !=\",\n \"the mount if it is not present. CLI Example: ..\",\n \"bash salt '*' mount.active ''' ret = {} if __grains__['os']\",\n \"path is mounted CLI Example: .. code-block:: bash salt '*'\",\n \"change = False present = False if not os.path.isfile(config): raise\",\n \"ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format(\",\n \"device, name) else: cmd = 'mount -u {0} {1} {2}\",\n \"if name in mnts: # The mount point is mounted,\",\n \"msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info =\",\n \"line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list =\",\n \"/mnt/foo ''' contents = automaster(config) if name not in contents:\",\n \"True comps[4] = str(dump) if comps[5] != str(pass_num): change =\",\n \"comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List active\",\n \"{0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv')\",\n \"msg = 'Couldn\\\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc)))\",\n \"fstype: change = True comps[2] = fstype if comps[3] !=\",\n \"'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device',\",\n \"= True comps[1] = type_opts if comps[2] != device_fmt: change\",\n \".. code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents =\",\n \"== name: continue lines.append(line) except (IOError, OSError) as exc: msg\",\n \"True comps[1] = type_opts if comps[2] != device_fmt: change =\",\n \"'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active mounts\",\n \"blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in\",\n \"''' contents = fstab(config) if name not in contents: return\",\n \"re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod\",\n \"{} if not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile:\",\n \"opts = opts.split(',') mnts = active() if name in mnts:\",\n \"needs to be ' 'updated'.format(name) ) newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format(\",\n \"directory it is mounted on CLI Example: .. code-block:: bash\",\n \"# Darwin doesn't expect defaults when mounting without other options\",\n \"config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount is represented\",\n \"device if comps[1] != name: change = True comps[1] =\",\n \"by specifying the directory it is mounted on CLI Example:\",\n \"__salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device)\",\n \"= opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args\",\n \"{0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) if __grains__['os']\",\n \"name, fstype, opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line)\",\n \"_list_mounts(): ret = {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts\",\n \"if there are any present = True if comps[0] !=\",\n \"= {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')}\",\n \"{0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in\",\n \"exists: if salt.utils.is_windows(): return False return True def _list_mounts(): ret\",\n \"'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4],\",\n \"if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\") try: with\",\n \"{} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris':\",\n \"_which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in\",\n \"if change: log.debug( 'auto_master entry for mount point {0} needs\",\n \"\\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts =\",\n \"ifile: if line.startswith('#'): # Commented continue if not line.strip(): #\",\n \"'opts': parens[1:]} return ret def active(extended=False): ''' List the active\",\n \"return ret def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD\",\n \"return 'change' if not change: if present: # The right\",\n \"ret['stats'] = on_[name] ret['new'] = False return ret cmd =\",\n \"= logging.getLogger(__name__) # Define the module's virtual name __virtualname__ =\",\n \"__grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_\",\n \"mount if it is not present. CLI Example: .. code-block::\",\n \"mount CLI Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile\",\n \") newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype, opts,\",\n \"re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2],\",\n \"'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The entry is\",\n \"systems ''' _list = _list_mounts() filename = '/proc/self/mounts' if not\",\n \"== device: continue else: if name_chk == name: continue lines.append(line)\",\n \"not writable {0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'):\",\n \"mounts on OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'):\",\n \"len(comps) != 6: # Invalid entry continue ret[comps[1]] = {'device':\",\n \"(IOError, OSError): msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config))\",\n \"# Invalid entry lines.append(line) continue if comps[1] == name or\",\n \"comps = line.split() prefix = \\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\")\",\n \"line.split() device = comps[2].split(':') device_name = comps[8] device_uuid = None\",\n \"and fix them if there are any present = True\",\n \"'Darwin']: if opts == 'defaults': opts = 'noowners' if fstype\",\n \".. code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path =\",\n \"unix mounts and the fstab file ''' from __future__ import\",\n \"is a list if isinstance(opts, list): opts = ','.join(opts) lines\",\n \"# Invalid entry lines.append(line) continue comps = line.split() if device:\",\n \"= True if comps[0] != name: change = True comps[0]\",\n \"mount point from the fstab CLI Example: .. code-block:: bash\",\n \"' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']\",\n \"commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not\",\n \"''' .. versionadded:: 2014.7.0 Provide information if the path is\",\n \"{} if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else:\",\n \"[] change = False present = False if not os.path.isfile(config):\",\n \"{'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret\",\n \"to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab(\",\n \"not in contents: return True # The entry is present,\",\n \"-*- coding: utf-8 -*- ''' Salt module to manage unix\",\n \"point in running ldd on a command that doesn't exist\",\n \"salt '*' mount.active ''' ret = {} if __grains__['os'] ==\",\n \"bash salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if\",\n \"the mount to match the data passed, or add the\",\n \"= device if comps[1] != name: change = True comps[1]\",\n \"of the fstab CLI Example: .. code-block:: bash salt '*'\",\n \"automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name, device, fstype,\",\n \"'defaults': opts = 'noowners' if fstype == 'smbfs': force_mount =\",\n \"re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\")\",\n \"versionadded:: 2014.7.0 Provide information if the path is mounted CLI\",\n \"if the device is not already mounted, mount is called\",\n \"== device_fmt: # check to see if there are changes\",\n \"sshfs ''' cmd_path = _which(cmd) # No point in running\",\n \"is present, get rid of it lines = [] try:\",\n \"False present = False automaster_file = \\\"/etc/auto_master\\\" if not os.path.isfile(config):\",\n \"args += ' -t {0}'.format(fstype) cmd = 'mount {0} {1}\",\n \"__virtual__(): ''' Only load on POSIX-like systems ''' # Disable\",\n \"mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device CLI Example:\",\n \"not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount\",\n \"= 'Couldn\\\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if\",\n \"raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for\",\n \"return 'libfuse' in out def swaps(): ''' Return a dict\",\n \"active mounts on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount\",\n \"mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type\",\n \"ofile: ofile.writelines(lines) except (IOError, OSError) as exc: msg = \\\"Couldn't\",\n \"the fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) )\",\n \"in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0}\",\n \"if force_mount: # We need to force the mount but\",\n \"ret def _active_mounts(ret): ''' List active mounts on Linux systems\",\n \"bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix\",\n \"comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None): ''' Activate\",\n \"lines.append(line) except (IOError, OSError) as exc: msg = 'Couldn\\\\'t read\",\n \"opts = None if isinstance(opts, string_types): opts = opts.split(',') if\",\n \"''' Attempt to remount a device, if the device is\",\n \"fstype, opts, user=user) def umount(name, device=None, user=None): ''' Attempt to\",\n \"Set up logger log = logging.getLogger(__name__) # Define the module's\",\n \"rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point from the\",\n \"# We need to force the mount but first we\",\n \"re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = comps[0] return ret def\",\n \"{}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0],\",\n \"dump, pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError, OSError)\",\n \"from the fstab CLI Example: .. code-block:: bash salt '*'\",\n \"opts='defaults', user=None): ''' Attempt to remount a device, if the\",\n \"lines.append(line) continue if comps[1] == name or comps[0] == device:\",\n \"-v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod = __salt__['cmd.run_stdout']('ls\",\n \"Disable on Windows, a specific file module exists: if salt.utils.is_windows():\",\n \"comps[1] = name if comps[2] != fstype: change = True\",\n \"change = True comps[1] = name if comps[2] != fstype:\",\n \"= on_[name] ret['new'] = True return ret return ret def\",\n \"else: if name_chk == name: continue lines.append(line) except (IOError, OSError)\",\n \"= active() if name in active_: return True else: return\",\n \"if comps[1] == name: continue lines.append(line) except (IOError, OSError) as\",\n \"on a command that doesn't exist if not cmd_path: return\",\n \"comps[5], 'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]}\",\n \"[] change = False present = False automaster_file = \\\"/etc/auto_master\\\"\",\n \"= True comps[1] = name if comps[2] != fstype: change\",\n \"{'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]}\",\n \"''' Remove the mount point from the auto_master CLI Example:\",\n \"_active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended:\",\n \"= type_opts if comps[2] != device_fmt: change = True comps[2]\",\n \".. code-block:: bash salt '*' mount.fstab ''' ret = {}\",\n \"'*' mount.swapon /root/swapfile ''' ret = {} on_ = swaps()\",\n \"\\\"\\\") try: with salt.utils.fopen(config, 'r') as ifile: for line in\",\n \"salt.utils.fopen(filename) as ifile: for line in ifile: comps = line.split()\",\n \"salt '*' mount.fstab ''' ret = {} if not os.path.isfile(config):\",\n \"List active mounts on FreeBSD systems ''' for line in\",\n \"device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype\",\n \"''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\",\",\n \"to force the mount but first we should unmount umount(name,\",\n \"= 'swapon {0}'.format(name) if priority: cmd += ' -p {0}'.format(priority)\",\n \"{1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return\",\n \"comps[2].split(\\\":\\\") if device: if name_chk == name and device_fmt[1] ==\",\n \"on_[name] ret['new'] = False return ret cmd = 'swapon {0}'.format(name)\",\n \"is already here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs):\",\n \"= '' if opts is not None: lopts = ','.join(opts)\",\n \"'/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File not readable\",\n \"{0} needs to be ' 'updated'.format(name) ) newline = (\",\n \"mount but first we should unmount umount(name, device, user=user) lopts\",\n \"a command that doesn't exist if not cmd_path: return False\",\n \"fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts, dump, pass_num)\",\n \"device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device CLI\",\n \"not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt =\",\n \"\\\" \\\", line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret):\",\n \"comps[0] == device: # check to see if there are\",\n \"the end of the fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name,\",\n \"the device is not already mounted, mount is called CLI\",\n \"name = \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype,\",\n \"umount(name, device, user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts)\",\n \"device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify\",\n \"/mnt/foo ''' contents = fstab(config) if name not in contents:\",\n \".. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts\",\n \"Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active()\",\n \"command that doesn't exist if not cmd_path: return False elif\",\n \"file ''' from __future__ import absolute_import # Import python libs\",\n \"continue if comps[0] == name or comps[2] == device_fmt: #\",\n \"{0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile:\",\n \"doesn't expect defaults when mounting without other options if 'defaults'\",\n \"continue if not line.strip(): # Blank line continue comps =\",\n \"opts and __grains__['os'] in ['MacOS', 'Darwin']: opts = None if\",\n \"for line in ifile: if line.startswith('#'): # Commented continue if\",\n \"and comps[0] == device: continue else: if comps[1] == name:\",\n \"_active_mounts_solaris(ret): ''' List active mounts on Solaris systems ''' for\",\n \"test=False, **kwargs): ''' Verify that this mount is represented in\",\n \"commit it! ofile.writelines(lines) except (IOError, OSError): msg = 'File not\",\n \"opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args =\",\n \"device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name, device,\",\n \"ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1],\",\n \"# No point in running ldd on a command that\",\n \"data passed, or add the mount if it is not\",\n \"on CLI Example: .. code-block:: bash salt '*' mount.umount /mnt/foo\",\n \"CLI Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share '''\",\n \"= 'mount -u {0} {1} {2} '.format(args, device, name) out\",\n \"str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List active\",\n \"\\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def\",\n \"Mount a device CLI Example: .. code-block:: bash salt '*'\",\n \"/mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect defaults when\",\n \"line was changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg\",\n \"in opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount')\",\n \"in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except\",\n \"-d {0}'.format(name)) on_ = swaps() if name in on_: return\",\n \"for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type\",\n \"on POSIX-like systems ''' # Disable on Windows, a specific\",\n \"{'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor':\",\n \"device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config, 'r') as ifile: for line\",\n \"without other options if 'defaults' in opts and __grains__['os'] in\",\n \"bash salt '*' mount.swaps ''' ret = {} if __grains__['os']\",\n \"except (IOError, OSError): msg = 'File not writable {0}' raise\",\n \"parens[0], 'opts': parens[1:]} return ret def active(extended=False): ''' List the\",\n \"if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and\",\n \"comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0],\",\n \"line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority':\",\n \"CommandExecutionError(msg.format(config)) return 'change' if not change: if present: # The\",\n \"+= ' -t {0}'.format(fstype) cmd = 'mount {0} {1} {2}\",\n \"'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active mounts\",\n \"Linux systems ''' _list = _list_mounts() filename = '/proc/self/mounts' if\",\n \"if not os.path.isfile(config): raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try: with\",\n \"changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg = 'File\",\n \"comps[0] = device if comps[1] != name: change = True\",\n \"named swap mount CLI Example: .. code-block:: bash salt '*'\",\n \"= \\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype ==\",\n \"'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def\",\n \"in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens =\",\n \"= _list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg\",\n \"= 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']()\",\n \"the data passed, or add the mount if it is\",\n \"isn't already return mount(name, device, mkmnt, fstype, opts, user=user) def\",\n \"''' List the contents of the fstab CLI Example: ..\",\n \"def is_fuse_exec(cmd): ''' Returns true if the command passed is\",\n \"def _active_mounts_darwin(ret): ''' List active mounts on Mac OS systems\",\n \"ret = {} on_ = swaps() if name in on_:\",\n \"runas=user) if out['retcode']: return out['stderr'] return True def remount(name, device,\",\n \"Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents\",\n \"comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def\",\n \"opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this\",\n \"if name_chk == name and device_fmt[1] == device: continue else:\",\n \"and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts is\",\n \"!= 6: # Invalid entry lines.append(line) continue if comps[1] ==\",\n \"not in opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']:\",\n \"'swapon {0}'.format(name) if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd)\",\n \"fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type':\",\n \"''' List active mounts on Linux systems ''' _list =\",\n \"mount.active ''' ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret)\",\n \"'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if\",\n \"len(comps) != 6: # Invalid entry lines.append(line) continue if comps[1]\",\n \"salt._compat import string_types from salt.utils import which as _which from\",\n \"_list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret):\",\n \"comps[4] != str(dump): change = True comps[4] = str(dump) if\",\n \"'mount {0} {1} {2} '.format(args, device, name) else: cmd =\",\n \"comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = {'device': comps[0],\",\n \"if the command passed is a fuse mountable application. CLI\",\n \"OSError) as exc: msg = \\\"Couldn't read from {0}: {1}\\\"\",\n \"in the auto_salt, change the mount to match the data\",\n \"= \\\"file\\\" comps = line.split() if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\"\",\n \"Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts':\",\n \"= False if not os.path.isfile(config): raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config))\",\n \"priority=None): ''' Activate a swap disk CLI Example: .. code-block::\",\n \"out['retcode']: return out['stderr'] return True def remount(name, device, mkmnt=False, fstype='',\",\n \"__salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)',\",\n \"# Commented continue if not line.strip(): # Blank line continue\",\n \"'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile:\",\n \"== name or comps[0] == device: # check to see\",\n \"= {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def\",\n \"CLI Example: .. code-block:: bash salt '*' mount.fstab ''' ret\",\n \"_which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger\",\n \"device CLI Example: .. code-block:: bash salt '*' mount.mount /mnt/foo\",\n \"'*' mount.swapoff /root/swapfile ''' on_ = swaps() if name in\",\n \"''' Only load on POSIX-like systems ''' # Disable on\",\n \"Import python libs import os import re import logging #\",\n \"comps = line.split() device = comps[2].split(':') device_name = comps[8] device_uuid\",\n \"name in on_: return False return True return None def\",\n \"not line.strip(): # Blank line lines.append(line) continue comps = line.split()\",\n \"writable {0}'.format( config ) ) return 'new' def automaster(config='/etc/auto_salt'): '''\",\n \"device, config='/etc/auto_salt'): ''' Remove the mount point from the auto_master\",\n \"if not os.access(filename, os.R_OK): msg = 'File not readable {0}'\",\n \"'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with\",\n \"return True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False,\",\n \"it is a list if isinstance(opts, list): opts = ','.join(opts)\",\n \"user=None): ''' Attempt to remount a device, if the device\",\n \"'w+') as ofile: # The line was changed, commit it!\",\n \"is not present. CLI Example: .. code-block:: bash salt '*'\",\n \"fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that this mount\",\n \"as ofile: # The line was changed, commit it! ofile.writelines(lines)\",\n \"if name in on_: return False return True return None\",\n \"prefix = \\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\")\",\n \"a device CLI Example: .. code-block:: bash salt '*' mount.mount\",\n \"_list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret):\",\n \"re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"),\",\n \"{} on_ = swaps() if name in on_: ret['stats'] =\",\n \"user=user) args = '' if opts is not None: lopts\",\n \"Verify that this mount is represented in the auto_salt, change\",\n \"mounts on Linux systems ''' _list = _list_mounts() filename =\",\n \"newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try:\",\n \"comps[4] = str(dump) if comps[5] != str(pass_num): change = True\",\n \"if change: log.debug( 'fstab entry for mount point {0} needs\",\n \"args = '-o {0}'.format(lopts) if fstype: args += ' -t\",\n \"mounts on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'):\",\n \"'auto_master entry for mount point {0} needs to be '\",\n \"comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid}\",\n \"raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device, fstype,\",\n \"\\\" \\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]]\",\n \"# check to see if there are changes # and\",\n \"''' Salt module to manage unix mounts and the fstab\",\n \"return ret def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo'\",\n \"= line.split() if len(comps) != 6: # Invalid entry lines.append(line)\",\n \"Attempt to unmount a device by specifying the directory it\",\n \"active mounts on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount\",\n \"code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_ = swaps()\",\n \"from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger log\",\n \"!= 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_:\",\n \"not cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out\",\n \"bash salt '*' mount.swapon /root/swapfile ''' ret = {} on_\",\n \"or add the mount if it is not present. CLI\",\n \"= on_[name] ret['new'] = False return ret cmd = 'swapon\",\n \"on_ = swaps() if name in on_: if __grains__['os'] !=\",\n \"change = True comps[2] = fstype if comps[3] != opts:\",\n \"application. CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs\",\n \"salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: # The\",\n \"with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError) as\",\n \"there are any present = True if comps[0] != name:\",\n \"runas=user) if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): '''\",\n \"opts if comps[4] != str(dump): change = True comps[4] =\",\n \"ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]}\",\n \"right entry is already here return 'present' else: if not\",\n \"{0}'.format(cmd_path)) return 'libfuse' in out def swaps(): ''' Return a\",\n \"nod = ' '.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)\",\n \"file module exists: if salt.utils.is_windows(): return False return True def\",\n \"isinstance(opts, list): opts = ','.join(opts) lines = [] change =\",\n \"''' _list = _list_mounts() filename = '/proc/self/mounts' if not os.access(filename,\",\n \"'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove\",\n \"\\\" \\\", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts':\",\n \"comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return\",\n \"exc: msg = 'Couldn\\\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config,\",\n \"['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l')\",\n \"True ''' force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']:\",\n \"'Total')): continue swap_type = \\\"file\\\" comps = line.split() if comps[0].startswith('/dev/'):\",\n \"swap mount CLI Example: .. code-block:: bash salt '*' mount.swapoff\",\n \"comps[2] != device_fmt: change = True comps[2] = device_fmt if\",\n \"''' Remove the mount point from the fstab CLI Example:\",\n \"else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\\\n'): comps\",\n \"\\\", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')}\",\n \"'mount' def __virtual__(): ''' Only load on POSIX-like systems '''\",\n \"need to force the mount but first we should unmount\",\n \"= comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") if device: if name_chk\",\n \"''' ret = {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif\",\n \"of it lines = [] try: with salt.utils.fopen(config, 'r') as\",\n \"/root/swapfile ''' on_ = swaps() if name in on_: if\",\n \"present. CLI Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo\",\n \"'r') as ifile: for line in ifile: if line.startswith('#'): #\",\n \"= re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device': comps[0], 'fstype':\",\n \"salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name\",\n \"in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]]\",\n \"except (IOError, OSError) as exc: msg = \\\"Couldn't write to\",\n \"if not line.strip(): # Blank line lines.append(line) continue comps =\",\n \"device_name = comps[8] device_uuid = None if device_name: device_uuid =\",\n \"have anything mounted\\\".format(name) if not device: cmd = 'umount {0}'.format(name)\",\n \"in ['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types): opts\",\n \"list): opts = ','.join(opts) lines = [] change = False\",\n \"'.format(args, device, name) else: cmd = 'mount -u {0} {1}\",\n \"Deactivate a named swap mount CLI Example: .. code-block:: bash\",\n \"line in ifile: comps = line.split() ret[comps[1]] = {'device': comps[0],\",\n \"if opts == 'defaults': opts = 'noowners' if fstype ==\",\n \"continue prefix = \\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\") device_fmt =\",\n \"bash salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin\",\n \"parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device': comps[0],\",\n \"len(comps) != 3: # Invalid entry lines.append(line) continue if comps[0]\",\n \"device is not already mounted, mount is called CLI Example:\",\n \"opts[0], 'opts': opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='',\",\n \"out['stderr'] return True # Mount a filesystem that isn't already\",\n \"not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for line\",\n \"if isinstance(opts, string_types): opts = opts.split(',') mnts = active() if\",\n \"{} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_: for\",\n \"opts = ','.join(opts) lines = [] change = False present\",\n \"CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile:\",\n \"and device_fmt[1] == device: continue else: if name_chk == name:\",\n \"= [] try: with salt.utils.fopen(config, 'r') as ifile: for line\",\n \"rid of it lines = [] try: with salt.utils.fopen(config, 'r')\",\n \"code-block:: bash salt '*' mount.active ''' ret = {} if\",\n \"salt.utils.test_mode(test=test, **kwargs): # The entry is new, add it to\",\n \"with salt.utils.fopen(filename) as ifile: for line in ifile: comps =\",\n \"swap_type = \\\"partition\\\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used':\",\n \"\\\"{0} does not have anything mounted\\\".format(name) if not device: cmd\",\n \"OSError) as exc: msg = 'Couldn\\\\'t read from {0}: {1}'\",\n \"device_fmt: # check to see if there are changes #\",\n \"out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True\",\n \"CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name, device, fstype, opts='defaults',\",\n \"6: # Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype':\",\n \"__salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\"\",\n \"_active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS',\",\n \"import absolute_import # Import python libs import os import re\",\n \"opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError,\",\n \"= [] change = False present = False automaster_file =\",\n \"\\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name] = {'device':\",\n \"Example: .. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True\",\n \"None if isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name)\",\n \"and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0],\",\n \"mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type\",\n \"opts[1:]} return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None):\",\n \"device_fmt[1] == device: continue else: if name_chk == name: continue\",\n \"== 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os']\",\n \"change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as\",\n \"opts and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if\",\n \"fstab CLI Example: .. code-block:: bash salt '*' mount.fstab '''\",\n \"dict containing information on active swap CLI Example: .. code-block::\",\n \"{0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user)\",\n \"return ret def _active_mounts_darwin(ret): ''' List active mounts on Mac\",\n \"= True comps[0] = name if comps[1] != type_opts: change\",\n \"\\\", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')}\",\n \"List the contents of the fstab CLI Example: .. code-block::\",\n \"entry is present, get rid of it lines = []\",\n \"contents = fstab(config) if name not in contents: return True\",\n \"contents = automaster(config) if name not in contents: return True\",\n \"active() if name in mnts: # The mount point is\",\n \"a fuse mountable application. CLI Example: .. code-block:: bash salt\",\n \"= 'mount {0} {1} {2} '.format(args, device, name) else: cmd\",\n \"= __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\",\",\n \"Blank line continue comps = line.split() if len(comps) != 6:\",\n \"= True comps[4] = str(dump) if comps[5] != str(pass_num): change\",\n \"try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except (IOError, OSError)\",\n \"present, get rid of it lines = [] try: with\",\n \"the mount point from the fstab CLI Example: .. code-block::\",\n \"device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major':\",\n \"newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline)\",\n \"def active(extended=False): ''' List the active mounts. CLI Example: ..\",\n \"return True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): '''\",\n \"Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_\",\n \"opts is not None: lopts = ','.join(opts) args = '-o\",\n \"{0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return\",\n \"== 'defaults': opts = 'noowners' if fstype == 'smbfs': force_mount\",\n \"line.startswith('#'): # Commented continue if not line.strip(): # Blank line\",\n \"code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' #\",\n \"fp_: for line in fp_: if line.startswith('Filename'): continue comps =\",\n \"to the end of the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device,\",\n \"comps[0] == device: continue else: if comps[1] == name: continue\",\n \"comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret): '''\",\n \"out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps():\",\n \"the mount point from the auto_master CLI Example: .. code-block::\",\n \"'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret def swapon(name,\",\n \"the opts type if it is a list if isinstance(opts,\",\n \"\\\" \\\", line).split() ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts':\",\n \"if device: if comps[1] == name and comps[0] == device:\",\n \"False elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path))\",\n \"'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name,\",\n \"'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config, 'r') as\",\n \"comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts':\",\n \"if comps[1] != name: change = True comps[1] = name\",\n \"'*' mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name not\",\n \"== 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os']\",\n \"point is mounted, attempt to remount it with the given\",\n \"on Linux systems ''' _list = _list_mounts() filename = '/proc/self/mounts'\",\n \"# The entry is present, get rid of it lines\",\n \"code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd)\",\n \"# The right entry is already here return 'present' else:\",\n \"Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_\",\n \"line.split() if len(comps) != 6: # Invalid entry lines.append(line) continue\",\n \"(IOError, OSError) as exc: msg = \\\"Couldn't write to {0}:\",\n \"device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: #\",\n \"try: with salt.utils.fopen(config, 'r') as ifile: for line in ifile:\",\n \"+= ' -t {0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS',\",\n \"string_types from salt.utils import which as _which from salt.exceptions import\",\n \"'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] ==\",\n \"or force_mount: cmd = 'mount {0} {1} {2} '.format(args, device,\",\n \"'updated'.format(name) ) newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) )\",\n \"# The mount point is mounted, attempt to remount it\",\n \"name or comps[2] == device_fmt: # check to see if\",\n \"on Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps\",\n \"def umount(name, device=None, user=None): ''' Attempt to unmount a device\",\n \"code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt\",\n \"( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except\",\n \"We need to force the mount but first we should\",\n \"return False return True def _list_mounts(): ret = {} if\",\n \"ret['new'] = False return ret cmd = 'swapon {0}'.format(name) if\",\n \"CLI Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs '''\",\n \"if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns\",\n \"-v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = {'device':\",\n \"ret = {} if not os.path.isfile(config): return ret with salt.utils.fopen(config)\",\n \"ret['new'] = True return ret return ret def swapoff(name): '''\",\n \"salt '*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't\",\n \"= __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in\",\n \"= {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else:\",\n \"= _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg\",\n \"mount point is mounted, attempt to remount it with the\",\n \"mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = comps[0]\",\n \"'-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) cmd\",\n \"if isinstance(opts, list): opts = ','.join(opts) lines = [] change\",\n \"'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List\",\n \"'.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return\",\n \"except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): '''\",\n \"entry continue prefix = \\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\") device_fmt\",\n \"= line.split() if len(comps) != 3: # Invalid entry lines.append(line)\",\n \"' 'updated'.format(name) ) newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt)\",\n \"comps[1] = type_opts if comps[2] != device_fmt: change = True\",\n \"except (IOError, OSError) as exc: msg = \\\"Couldn't read from\",\n \"'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return\",\n \"__salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] =\",\n \"def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if the\",\n \"ret def swapon(name, priority=None): ''' Activate a swap disk CLI\",\n \"auto_master CLI Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo\",\n \"any present = True if comps[0] != name: change =\",\n \"on_: return False return True return None def is_mounted(name): '''\",\n \"else: if not salt.utils.test_mode(test=test, **kwargs): # The entry is new,\",\n \"if comps[1] != type_opts: change = True comps[1] = type_opts\",\n \"return ret def _active_mounts(ret): ''' List active mounts on Linux\",\n \"ret def _active_mounts_solaris(ret): ''' List active mounts on Solaris systems\",\n \"a dict containing information on active swap CLI Example: ..\",\n \"-p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in on_:\",\n \"str(dump) if comps[5] != str(pass_num): change = True comps[5] =\",\n \"type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype == 'smbfs': device_fmt =\",\n \"already here return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): #\",\n \"pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount is\",\n \"swapon(name, priority=None): ''' Activate a swap disk CLI Example: ..\",\n \"cmd = 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out\",\n \"None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret def _active_mounts(ret): '''\",\n \"return True # The entry is present, get rid of\",\n \"are changes # and fix them if there are any\",\n \"{2} '.format(args, device, name) else: cmd = 'mount -u {0}\",\n \"ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts()\",\n \"Windows, a specific file module exists: if salt.utils.is_windows(): return False\",\n \") ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc:\",\n \"-kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \\\"file\\\" comps =\",\n \"comps = line.split() if device: if comps[1] == name and\",\n \"def automaster(config='/etc/auto_salt'): ''' List the contents of the fstab CLI\",\n \"the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts, dump,\",\n \"== name or comps[2] == device_fmt: # check to see\",\n \"libs import os import re import logging # Import salt\",\n \"'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid':\",\n \"''' ret = {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps')\",\n \"comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for line\",\n \"in mnts: return \\\"{0} does not have anything mounted\\\".format(name) if\",\n \"python libs import os import re import logging # Import\",\n \"log = logging.getLogger(__name__) # Define the module's virtual name __virtualname__\",\n \"fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as\",\n \"fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline)\",\n \"on active swap CLI Example: .. code-block:: bash salt '*'\",\n \"'used': comps[3], 'priority': comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines():\",\n \"from the auto_master CLI Example: .. code-block:: bash salt '*'\",\n \"line in ifile: if line.startswith('#'): # Commented lines.append(line) continue if\",\n \"writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change: if\",\n \"= True comps[2] = device_fmt if change: log.debug( 'auto_master entry\",\n \"mounted on CLI Example: .. code-block:: bash salt '*' mount.umount\",\n \"= comps[8] device_uuid = None if device_name: device_uuid = blkid_info.get(device_name,\",\n \"readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as\",\n \"if it is a list if isinstance(opts, list): opts =\",\n \"import salt.utils from salt._compat import string_types from salt.utils import which\",\n \"== device: # check to see if there are changes\",\n \"of the fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt)\",\n \"else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name in\",\n \"CommandExecutionError # Set up logger log = logging.getLogger(__name__) # Define\",\n \".. versionadded:: 2014.7.0 Provide information if the path is mounted\",\n \"is new, add it to the end of the fstab\",\n \"comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): '''\",\n \"['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types): opts =\",\n \"if line.startswith('#'): # Commented lines.append(line) continue if not line.strip(): #\",\n \"on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps\",\n \"'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List\",\n \"name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): ''' Verify that\",\n \"{0} {1} {2} '.format(args, device, name) else: cmd = 'mount\",\n \"= swaps() if name in on_: return False return True\",\n \"line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return\",\n \"type_opts: change = True comps[1] = type_opts if comps[2] !=\",\n \"def _list_mounts(): ret = {} if __grains__['os'] in ['MacOS', 'Darwin']:\",\n \"str(pass_num) if change: log.debug( 'fstab entry for mount point {0}\",\n \"['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need to\",\n \"is mounted, attempt to remount it with the given data\",\n \"device_uuid} return ret def _active_mounts(ret): ''' List active mounts on\",\n \"def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs): '''\",\n \"The right entry is already here return 'present' else: if\",\n \"not already mounted, mount is called CLI Example: .. code-block::\",\n \"\\\"file\\\" comps = line.split() if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]]\",\n \"if comps[5] != str(pass_num): change = True comps[5] = str(pass_num)\",\n \"(IOError, OSError) as exc: msg = 'Couldn\\\\'t read from {0}:\",\n \"''' contents = automaster(config) if name not in contents: return\",\n \"# The entry is new, add it to the end\",\n \"True comps[5] = str(pass_num) if change: log.debug( 'fstab entry for\",\n \"name_chk == name and device_fmt[1] == device: continue else: if\",\n \"return ret def swapoff(name): ''' Deactivate a named swap mount\",\n \"salt.utils import which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError\",\n \"= ' '.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]]\",\n \"(IOError, OSError) as exc: msg = \\\"Couldn't read from {0}:\",\n \"already mounted, mount is called CLI Example: .. code-block:: bash\",\n \"was changed, commit it! ofile.writelines(lines) except (IOError, OSError): msg =\",\n \") return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents of\",\n \"( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype, opts, dump, pass_num )\",\n \"= fstype if comps[3] != opts: change = True comps[3]\",\n \"type_opts if comps[2] != device_fmt: change = True comps[2] =\",\n \"out['retcode']: return out['stderr'] return True # Mount a filesystem that\",\n \"if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret)\",\n \"if opts is not None: lopts = ','.join(opts) args =\",\n \"device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr']\",\n \"# Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name,\",\n \"is not already mounted, mount is called CLI Example: ..\",\n \"True ''' # Darwin doesn't expect defaults when mounting without\",\n \"for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\",\",\n \"= \\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") if\",\n \"CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True def\",\n \"!= str(pass_num): change = True comps[5] = str(pass_num) if change:\",\n \"/mnt/foo /dev/sdz1 ext4 ''' # Fix the opts type if\",\n \"in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl\",\n \"mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount a device,\",\n \"remount it with the given data if 'remount' not in\",\n \"mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a device\",\n \"lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc: msg =\",\n \"priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps()\",\n \"CLI Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo '''\",\n \"auto_salt, change the mount to match the data passed, or\",\n \"line.startswith(('Device', 'Total')): continue swap_type = \\\"file\\\" comps = line.split() if\",\n \"!= str(dump): change = True comps[4] = str(dump) if comps[5]\",\n \"re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts':\",\n \"see if there are changes # and fix them if\",\n \"def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if not\",\n \"= \\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) #\",\n \"swap disk CLI Example: .. code-block:: bash salt '*' mount.swapon\",\n \"passed, or add the mount if it is not present.\",\n \"{0}'.format(fstype) if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount:\",\n \"type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as\",\n \"No point in running ldd on a command that doesn't\",\n \"if comps[2] != device_fmt: change = True comps[2] = device_fmt\",\n \"present. CLI Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo\",\n \"if comps[0] != name: change = True comps[0] = name\",\n \"\\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return True\",\n \"raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r') as\",\n \"False return True def _list_mounts(): ret = {} if __grains__['os']\",\n \"string_types): opts = opts.split(',') if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name,\",\n \"in contents: return True # The entry is present, get\",\n \"comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','),\",\n \"True if comps[0] != device: change = True comps[0] =\",\n \"Only load on POSIX-like systems ''' # Disable on Windows,\",\n \"represented in the auto_salt, change the mount to match the\",\n \"mount.is_mounted /mnt/share ''' active_ = active() if name in active_:\",\n \"opts == 'defaults': opts = 'noowners' if fstype == 'smbfs':\",\n \"'used': comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None): '''\",\n \"= line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype':\",\n \"{0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as\",\n \"add the mount if it is not present. CLI Example:\",\n \"fstab(config='/etc/fstab'): ''' List the contents of the fstab CLI Example:\",\n \"opts.append('remount') if force_mount: # We need to force the mount\",\n \"len(comps) != 3: # Invalid entry continue prefix = \\\"/..\\\"\",\n \"comps[1] == name or comps[0] == device: # check to\",\n \"comps[5] = str(pass_num) if change: log.debug( 'fstab entry for mount\",\n \"to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount\",\n \"line.split() if len(comps) != 3: # Invalid entry continue prefix\",\n \"Example: .. code-block:: bash salt '*' mount.active ''' ret =\",\n \"Activate a swap disk CLI Example: .. code-block:: bash salt\",\n \"= name if comps[2] != fstype: change = True comps[2]\",\n \"= 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return\",\n \"comps[1], 'used': comps[2], 'priority': comps[5]} return ret def swapon(name, priority=None):\",\n \"cmd_path = _which(cmd) # No point in running ldd on\",\n \"__grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif\",\n \"raise CommandExecutionError(msg.format(config, str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True\",\n \"== 'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config, 'r')\",\n \"{0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args, device, name)\",\n \"def _active_mounts_solaris(ret): ''' List active mounts on Solaris systems '''\",\n \"= {} on_ = swaps() if name in on_: ret['stats']\",\n \"_active_mounts(ret): ''' List active mounts on Linux systems ''' _list\",\n \"not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: #\",\n \"mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name not in\",\n \"Example: .. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True\",\n \"str(dump): change = True comps[4] = str(dump) if comps[5] !=\",\n \"if 'remount' not in opts and __grains__['os'] not in ['OpenBSD',\",\n \"comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): ''' List active\",\n \"= True comps[3] = opts if comps[4] != str(dump): change\",\n \"it lines = [] try: with salt.utils.fopen(config, 'r') as ifile:\",\n \"= None if isinstance(opts, string_types): opts = opts.split(',') if not\",\n \"Provide information if the path is mounted CLI Example: ..\",\n \"return 'present' else: if not salt.utils.test_mode(test=test, **kwargs): # The entry\",\n \"_list = _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK):\",\n \"as ifile: for line in ifile: if line.startswith('#'): # Commented\",\n \"mount is represented in the fstab, change the mount to\",\n \"msg = \\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc)))\",\n \"them if there are any present = True if comps[0]\",\n \"comps[8] device_uuid = None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID')\",\n \"entry is new, add it to the end of the\",\n \"bash salt '*' mount.fstab ''' ret = {} if not\",\n \"salt '*' mount.swapoff /root/swapfile ''' on_ = swaps() if name\",\n \"fstab file ''' from __future__ import absolute_import # Import python\",\n \"name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") if device: if\",\n \"+= ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name\",\n \"list if isinstance(opts, list): opts = ','.join(opts) lines = []\",\n \"device_fmt: change = True comps[2] = device_fmt if change: log.debug(\",\n \"and __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount:\",\n \"!= name: change = True comps[1] = name if comps[2]\",\n \"'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point\",\n \"''' Verify that this mount is represented in the fstab,\",\n \"mount.swapon /root/swapfile ''' ret = {} on_ = swaps() if\",\n \"if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]] = {'type': swap_type, 'size':\",\n \"return ret return ret def swapoff(name): ''' Deactivate a named\",\n \"'change' if not change: if present: # The right entry\",\n \"'*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No point\",\n \"salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 ''' # Fix the\",\n \"fstype: args += ' -t {0}'.format(fstype) cmd = 'mount {0}\",\n \"name: change = True comps[0] = name if comps[1] !=\",\n \"= True comps[5] = str(pass_num) if change: log.debug( 'fstab entry\",\n \"'mount {0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd,\",\n \"'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd,\",\n \"comps[2] != fstype: change = True comps[2] = fstype if\",\n \"''' Activate a swap disk CLI Example: .. code-block:: bash\",\n \"OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\",\",\n \"code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' #\",\n \"{0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change: if present:\",\n \"CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def\",\n \"device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor':\",\n \"mount to match the data passed, or add the mount\",\n \"def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the mount point from\",\n \"\\\" \\\", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod =\",\n \"= {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")),\",\n \"swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return ret def\",\n \"is represented in the auto_salt, change the mount to match\",\n \"try: with salt.utils.fopen(config, 'w+') as ofile: # The line was\",\n \"name: change = True comps[1] = name if comps[2] !=\",\n \"not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We\",\n \"comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype':\",\n \"device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device':\",\n \"on_ = swaps() if name in on_: return False return\",\n \"= True comps[0] = device if comps[1] != name: change\",\n \"str(pass_num): change = True comps[5] = str(pass_num) if change: log.debug(\",\n \"True # Mount a filesystem that isn't already return mount(name,\",\n \"def swapoff(name): ''' Deactivate a named swap mount CLI Example:\",\n \"\\\") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return\",\n \"in on_: ret['stats'] = on_[name] ret['new'] = True return ret\",\n \"Example: .. code-block:: bash salt '*' mount.swaps ''' ret =\",\n \"in on_: return False return True return None def is_mounted(name):\",\n \"entry lines.append(line) continue comps = line.split() prefix = \\\"/..\\\" name_chk\",\n \"the auto_salt, change the mount to match the data passed,\",\n \"ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"), 'major':\",\n \"opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile:\",\n \"'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr']\",\n \"str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config,\",\n \"== device: continue else: if comps[1] == name: continue lines.append(line)\",\n \"True comps[1] = name if comps[2] != fstype: change =\",\n \"exc: msg = \\\"Couldn't read from {0}: {1}\\\" raise CommandExecutionError(msg.format(config,\",\n \"specifying the directory it is mounted on CLI Example: ..\",\n \"in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \\\"file\\\"\",\n \"# Fix the opts type if it is a list\",\n \"as exc: msg = \\\"Couldn't read from {0}: {1}\\\" raise\",\n \"not change: if present: # The right entry is already\",\n \"salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount\",\n \"''' Return a dict containing information on active swap CLI\",\n \"import string_types from salt.utils import which as _which from salt.exceptions\",\n \"in out def swaps(): ''' Return a dict containing information\",\n \"def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems '''\",\n \"if not salt.utils.test_mode(test=test, **kwargs): # The entry is new, add\",\n \"manage unix mounts and the fstab file ''' from __future__\",\n \"if comps[1] == name and comps[0] == device: continue else:\",\n \"'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False): ''' List\",\n \"swapoff(name): ''' Deactivate a named swap mount CLI Example: ..\",\n \"return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount\",\n \"change = True comps[0] = device if comps[1] != name:\",\n \"'*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo\",\n \"'{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError,\",\n \"'*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name not\",\n \"change = True comps[0] = name if comps[1] != type_opts:\",\n \"True def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab',\",\n \"if it is not present. CLI Example: .. code-block:: bash\",\n \"= False automaster_file = \\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file,\",\n \"try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def\",\n \"ret def _active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if\",\n \"[] try: with salt.utils.fopen(config, 'r') as ifile: for line in\",\n \"as ifile: for line in ifile: comps = line.split() device\",\n \"__grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret)\",\n \"for mount point {0} needs to be ' 'updated'.format(name) )\",\n \"this mount is represented in the fstab, change the mount\",\n \"from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not\",\n \"2014.7.0 Provide information if the path is mounted CLI Example:\",\n \"'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def\",\n \"not have anything mounted\\\".format(name) if not device: cmd = 'umount\",\n \"# Disable on Windows, a specific file module exists: if\",\n \"{1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs):\",\n \"'{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+')\",\n \"if salt.utils.is_windows(): return False return True def _list_mounts(): ret =\",\n \"opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts':\",\n \"['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError:\",\n \"the given data if 'remount' not in opts and __grains__['os']\",\n \"line in fp_: if line.startswith('Filename'): continue comps = line.split() ret[comps[0]]\",\n \"= active() if name not in mnts: return \\\"{0} does\",\n \"are any present = True if comps[0] != name: change\",\n \"= re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0]))\",\n \"= False if __grains__['os'] in ['MacOS', 'Darwin']: if opts ==\",\n \"ifile: comps = line.split() device = comps[2].split(':') device_name = comps[8]\",\n \"continue swap_type = \\\"file\\\" comps = line.split() if comps[0].startswith('/dev/'): swap_type\",\n \"= ','.join(opts) args = '-o {0}'.format(lopts) if fstype: args +=\",\n \"if len(comps) != 6: # Invalid entry lines.append(line) continue comps\",\n \"mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts is not\",\n \"# Invalid entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2],\",\n \"load on POSIX-like systems ''' # Disable on Windows, a\",\n \"if name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name))\",\n \"''' Verify that this mount is represented in the auto_salt,\",\n \"'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret):\",\n \"else: cmd = 'mount -u {0} {1} {2} '.format(args, device,\",\n \"comps[5] != str(pass_num): change = True comps[5] = str(pass_num) if\",\n \"line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype': comps[5], 'opts': parens[1].split(\\\",\",\n \"represented in the fstab, change the mount to match the\",\n \"'*' mount.fstab ''' ret = {} if not os.path.isfile(config): return\",\n \"with the given data if 'remount' not in opts and\",\n \"str(exc))) # Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster(\",\n \"if name in on_: ret['stats'] = on_[name] ret['new'] = False\",\n \"is not None: lopts = ','.join(opts) args = '-o {0}'.format(lopts)\",\n \"mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if name not\",\n \"'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): ''' List active mounts\",\n \"= \\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name =\",\n \"mounting without other options if 'defaults' in opts and __grains__['os']\",\n \"raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in ifile:\",\n \"continue comps = line.split() if len(comps) != 3: # Invalid\",\n \"name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else:\",\n \"Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps =\",\n \".. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4 '''\",\n \"Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps =\",\n \"'remount' not in opts and __grains__['os'] not in ['OpenBSD', 'MacOS',\",\n \"lines.append(line) continue comps = line.split() if len(comps) != 3: #\",\n \"# Mount a filesystem that isn't already return mount(name, device,\",\n \"# Define the module's virtual name __virtualname__ = 'mount' def\",\n \"the path is mounted CLI Example: .. code-block:: bash salt\",\n \"salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No\",\n \"= __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in ifile:\",\n \"os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for line in\",\n \"!= device_fmt: change = True comps[2] = device_fmt if change:\",\n \"it with the given data if 'remount' not in opts\",\n \"= comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name]\",\n \"is_fuse_exec(cmd): ''' Returns true if the command passed is a\",\n \"device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype == 'smbfs': device_fmt\",\n \"change = True comps[2] = device_fmt if change: log.debug( 'auto_master\",\n \"\\\") parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0],\",\n \"'Darwin']: opts = None if isinstance(opts, string_types): opts = opts.split(',')\",\n \"device_uuid = None if device_name: device_uuid = blkid_info.get(device_name, {}).get('UUID') device_uuid\",\n \"be ' 'updated'.format(name) ) newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts,\",\n \"user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype:\",\n \"if not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = ''\",\n \"'Darwin']: opts.append('remount') if force_mount: # We need to force the\",\n \"_list_mounts() filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg =\",\n \"'updated'.format(name) ) newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype,\",\n \"defaults when mounting without other options if 'defaults' in opts\",\n \"__grains__['os'] in ['MacOS', 'Darwin']: opts = None if isinstance(opts, string_types):\",\n \"= re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = comps[0] return ret\",\n \"automaster(config='/etc/auto_salt'): ''' List the contents of the fstab CLI Example:\",\n \"= False present = False if not os.path.isfile(config): raise CommandExecutionError('Bad\",\n \"return True # Mount a filesystem that isn't already return\",\n \"on FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps\",\n \"device=None, user=None): ''' Attempt to unmount a device by specifying\",\n \"'device_uuid': device_uuid} return ret def _active_mounts(ret): ''' List active mounts\",\n \"False present = False if not os.path.isfile(config): raise CommandExecutionError('Bad config\",\n \"lines.append(line) continue comps = line.split() if len(comps) != 6: #\",\n \"if comps[3] != opts: change = True comps[3] = opts\",\n \"mount.rm_automaster /mnt/foo ''' contents = automaster(config) if name not in\",\n \"'{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name, fstype, opts, dump, pass_num ) )\",\n \"not present. CLI Example: .. code-block:: bash salt '*' mount.set_automaster\",\n \"/mnt/foo /dev/sdz1 True ''' force_mount = False if __grains__['os'] in\",\n \"''' # Darwin doesn't expect defaults when mounting without other\",\n \"{2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']:\",\n \"= line.split() device = comps[2].split(':') device_name = comps[8] device_uuid =\",\n \"{'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret):\",\n \"cmd = 'mount {0} {1} {2} '.format(args, device, name) else:\",\n \"nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\" \\\")\",\n \"'fstab entry for mount point {0} needs to be '\",\n \"return True return None def is_mounted(name): ''' .. versionadded:: 2014.7.0\",\n \"read from {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config,\",\n \"','.join(opts) args = '-o {0}'.format(lopts) if fstype: args += '\",\n \"end of the fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts,\",\n \"!= 3: # Invalid entry continue prefix = \\\"/..\\\" name\",\n \"if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts\",\n \"type if it is a list if isinstance(opts, list): opts\",\n \"'new' def automaster(config='/etc/auto_salt'): ''' List the contents of the fstab\",\n \"line in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]]\",\n \"if name not in mnts: return \\\"{0} does not have\",\n \"swap CLI Example: .. code-block:: bash salt '*' mount.swaps '''\",\n \"changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File\",\n \"= {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def\",\n \"/dev/sdz1 True ''' force_mount = False if __grains__['os'] in ['MacOS',\",\n \"ofile: # The line was changed, commit it! ofile.writelines(lines) except\",\n \"on_ = swaps() if name in on_: ret['stats'] = on_[name]\",\n \"Verify that this mount is represented in the fstab, change\",\n \"opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype, \\\"\\\") try:\",\n \"present = True if comps[0] != name: change = True\",\n \"''' ret = {} if not os.path.isfile(config): return ret with\",\n \"if fstype: args += ' -t {0}'.format(fstype) cmd = 'mount\",\n \"Commented lines.append(line) continue if not line.strip(): # Blank line lines.append(line)\",\n \"line lines.append(line) continue comps = line.split() if len(comps) != 6:\",\n \"'{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try: with\",\n \"= '-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype)\",\n \"raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out\",\n \"comps[0] != name: change = True comps[0] = name if\",\n \"return ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): '''\",\n \"= {} if not os.path.isfile(config): return ret with salt.utils.fopen(config) as\",\n \"if line.startswith('#'): # Commented continue if not line.strip(): # Blank\",\n \"= automaster(config) if name not in contents: return True #\",\n \"comps[5].split('/')} return ret def _active_mounts_openbsd(ret): ''' List active mounts on\",\n \"which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set\",\n \"mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect defaults\",\n \"cmd = 'swapon {0}'.format(name) if priority: cmd += ' -p\",\n \"not os.access(filename, os.R_OK): msg = 'File not readable {0}' raise\",\n \"'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need to force\",\n \"True comps[0] = name if comps[1] != type_opts: change =\",\n \"the fstab, change the mount to match the data passed,\",\n \"if __grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults': opts\",\n \"import logging # Import salt libs import salt.utils from salt._compat\",\n \"device, user=user) lopts = ','.join(opts) args = '-o {0}'.format(lopts) if\",\n \"Define the module's virtual name __virtualname__ = 'mount' def __virtual__():\",\n \"end of the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype,\",\n \"fstype if comps[3] != opts: change = True comps[3] =\",\n \"return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true if\",\n \"__salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts\",\n \"line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type': comps[1], 'size':\",\n \"systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\",\",\n \"bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) #\",\n \"return ret def fstab(config='/etc/fstab'): ''' List the contents of the\",\n \"mnts = active() if name not in mnts: return \\\"{0}\",\n \"if 'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts\",\n \"comps[0] == name or comps[2] == device_fmt: # check to\",\n \"_active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the\",\n \") newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline)\",\n \"name if comps[1] != type_opts: change = True comps[1] =\",\n \"ret return ret def swapoff(name): ''' Deactivate a named swap\",\n \"force_mount = True if isinstance(opts, string_types): opts = opts.split(',') mnts\",\n \"{0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)', line,\",\n \"mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False if __grains__['os']\",\n \"active() if name in active_: return True else: return False\",\n \"CLI Example: .. code-block:: bash salt '*' mount.umount /mnt/foo ..\",\n \"= True comps[2] = fstype if comps[3] != opts: change\",\n \"'noowners' if fstype == 'smbfs': force_mount = True if isinstance(opts,\",\n \"__grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount') else: mounts =\",\n \"CLI Example: .. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1\",\n \"not writable {0}'.format( config ) ) return 'new' def rm_automaster(name,\",\n \"comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line,\",\n \"or comps[2] == device_fmt: # check to see if there\",\n \"fuse mountable application. CLI Example: .. code-block:: bash salt '*'\",\n \"user=None): ''' Attempt to unmount a device by specifying the\",\n \"device_fmt = device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config, 'r') as ifile:\",\n \"mounted CLI Example: .. code-block:: bash salt '*' mount.is_mounted /mnt/share\",\n \"' '.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] =\",\n \"containing information on active swap CLI Example: .. code-block:: bash\",\n \"__salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\" \\\") parens =\",\n \"CLI Example: .. code-block:: bash salt '*' mount.active ''' ret\",\n \"in ifile: if line.startswith('#'): # Commented continue if not line.strip():\",\n \"def swapon(name, priority=None): ''' Activate a swap disk CLI Example:\",\n \"salt '*' mount.is_mounted /mnt/share ''' active_ = active() if name\",\n \"device: cmd = 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device)\",\n \"= {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass':\",\n \"'File not writable {0}'.format( config ) ) return 'new' def\",\n \"line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\"\",\n \"fstab, change the mount to match the data passed, or\",\n \"it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError( 'File not writable\",\n \"len(comps) != 3: # Invalid entry lines.append(line) continue comps =\",\n \"List active mounts on OpenBSD systems ''' for line in\",\n \"return out['stderr'] return True # Mount a filesystem that isn't\",\n \"fstype='', opts='defaults', user=None): ''' Mount a device CLI Example: ..\",\n \"the command passed is a fuse mountable application. CLI Example:\",\n \"as exc: msg = 'Couldn\\\\'t read from {0}: {1}' raise\",\n \"systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\",\",\n \"'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name, device, mkmnt=False,\",\n \"dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: #\",\n \"' -t {0}'.format(fstype) cmd = 'mount {0} {1} {2} '.format(args,\",\n \"elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']:\",\n \"in ifile: comps = line.split() device = comps[2].split(':') device_name =\",\n \"blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid':\",\n \"comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active\",\n \"Example: .. code-block:: bash salt '*' mount.set_automaster /mnt/foo /dev/sdz1 ext4\",\n \"''' List active mounts on Solaris systems ''' for line\",\n \"!= 6: # Invalid entry continue ret[comps[1]] = {'device': comps[0],\",\n \"comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device,\",\n \"= True if comps[0] != device: change = True comps[0]\",\n \"= re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = {'device': comps[0], 'fstype':\",\n \"line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type =\",\n \"( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config,\",\n \"fstype: args += ' -t {0}'.format(fstype) if __grains__['os'] not in\",\n \"msg = \\\"Couldn't read from {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc)))\",\n \"for line in ifile: comps = line.split() device = comps[2].split(':')\",\n \"prefix = \\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\")\",\n \"is represented in the fstab, change the mount to match\",\n \"ofile.writelines(lines) except (IOError, OSError): msg = 'File not writable {0}'\",\n \"= str(pass_num) if change: log.debug( 'fstab entry for mount point\",\n \"name: continue lines.append(line) except (IOError, OSError) as exc: msg =\",\n \"ret = {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts =\",\n \"6: # Invalid entry lines.append(line) continue if comps[1] == name\",\n \"/dev/sdz1 True ''' # Darwin doesn't expect defaults when mounting\",\n \"if name_chk == name: continue lines.append(line) except (IOError, OSError) as\",\n \"file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for line\",\n \"line.split() prefix = \\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt =\",\n \"logging.getLogger(__name__) # Define the module's virtual name __virtualname__ = 'mount'\",\n \"comps[1] != type_opts: change = True comps[1] = type_opts if\",\n \"re import logging # Import salt libs import salt.utils from\",\n \"salt '*' mount.swaps ''' ret = {} if __grains__['os'] !=\",\n \"extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret\",\n \"parens[0]} return ret def _active_mounts_darwin(ret): ''' List active mounts on\",\n \"__salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod =\",\n \"lines = [] change = False present = False if\",\n \"with salt.utils.fopen(config, 'w+') as ofile: # The line was changed,\",\n \".. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1 True '''\",\n \"import CommandNotFoundError, CommandExecutionError # Set up logger log = logging.getLogger(__name__)\",\n \"name and device_fmt[1] == device: continue else: if name_chk ==\",\n \"comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]] = {'device': comps[0],\",\n \"fstab CLI Example: .. code-block:: bash salt '*' mount.rm_fstab /mnt/foo\",\n \"os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info\",\n \"active mounts. CLI Example: .. code-block:: bash salt '*' mount.active\",\n \"parens[1:]} return ret def active(extended=False): ''' List the active mounts.\",\n \"string_types): opts = opts.split(',') mnts = active() if name in\",\n \"Invalid entry continue prefix = \\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\")\",\n \"'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps()\",\n \"None: lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype:\",\n \"device_fmt if change: log.debug( 'auto_master entry for mount point {0}\",\n \"the fstab CLI Example: .. code-block:: bash salt '*' mount.rm_fstab\",\n \"salt.utils.fopen(config, 'r') as ifile: for line in ifile: if line.startswith('#'):\",\n \"entry continue ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','),\",\n \"to match the data passed, or add the mount if\",\n \"__salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\\\n'):\",\n \"with salt.utils.fopen('/proc/swaps') as fp_: for line in fp_: if line.startswith('Filename'):\",\n \"continue lines.append(line) except (IOError, OSError) as exc: msg = \\\"Couldn't\",\n \"# Invalid entry lines.append(line) continue comps = line.split() prefix =\",\n \"= {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority': comps[5]} return\",\n \"CLI Example: .. code-block:: bash salt '*' mount.swaps ''' ret\",\n \"from {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+')\",\n \"return \\\"{0} does not have anything mounted\\\".format(name) if not device:\",\n \"'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return ret\",\n \"!= type_opts: change = True comps[1] = type_opts if comps[2]\",\n \"continue if not line.strip(): # Blank line lines.append(line) continue comps\",\n \"'mount -u {0} {1} {2} '.format(args, device, name) out =\",\n \"line.split() if len(comps) != 3: # Invalid entry lines.append(line) continue\",\n \"mounts = __salt__['cmd.run_stdout']('mount -l') for line in mounts.split('\\\\n'): comps =\",\n \"_active_mountinfo(ret): _list = _list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename,\",\n \"elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return\",\n \"= {} if __grains__['os'] != 'OpenBSD': with salt.utils.fopen('/proc/swaps') as fp_:\",\n \"__salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue swap_type = \\\"file\\\" comps\",\n \"ifile: for line in ifile: if line.startswith('#'): # Commented continue\",\n \"if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return\",\n \"if name in on_: ret['stats'] = on_[name] ret['new'] = True\",\n \"true if the command passed is a fuse mountable application.\",\n \"= 'umount {0}'.format(name) else: cmd = 'umount {0}'.format(device) out =\",\n \"return False return True return None def is_mounted(name): ''' ..\",\n \"os.access(filename, os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename))\",\n \"Invalid entry lines.append(line) continue if comps[0] == name or comps[2]\",\n \"a named swap mount CLI Example: .. code-block:: bash salt\",\n \"True # The entry is present, get rid of it\",\n \"it is not present. CLI Example: .. code-block:: bash salt\",\n \"re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0],\",\n \"it to the end of the fstab newline = (\",\n \"filesystem that isn't already return mount(name, device, mkmnt, fstype, opts,\",\n \"_active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the contents of\",\n \"continue else: if comps[1] == name: continue lines.append(line) except (IOError,\",\n \"!= opts: change = True comps[3] = opts if comps[4]\",\n \"return ret with salt.utils.fopen(config) as ifile: for line in ifile:\",\n \"= \\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts\",\n \"a device by specifying the directory it is mounted on\",\n \"'*' mount.swaps ''' ret = {} if __grains__['os'] != 'OpenBSD':\",\n \"not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for\",\n \"name not in contents: return True # The entry is\",\n \"__salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if\",\n \"absolute_import # Import python libs import os import re import\",\n \"ret def fstab(config='/etc/fstab'): ''' List the contents of the fstab\",\n \"given data if 'remount' not in opts and __grains__['os'] not\",\n \"comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4], 'pass': comps[5]} return\",\n \"{1} {2} '.format(args, device, name) else: cmd = 'mount -u\",\n \"''' active_ = active() if name in active_: return True\",\n \"contents: return True # The entry is present, get rid\",\n \"active() if name not in mnts: return \\\"{0} does not\",\n \"comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]] = {'type': swap_type, 'size': comps[1],\",\n \"def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems '''\",\n \"{'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret def mount(name,\",\n \"mnts = active() if name in mnts: # The mount\",\n \"mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None): ''' Attempt\",\n \"code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents = automaster(config)\",\n \"ret def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount\",\n \"**kwargs): try: with salt.utils.fopen(config, 'w+') as ofile: # The line\",\n \"device: continue else: if name_chk == name: continue lines.append(line) except\",\n \"if out['retcode']: return out['stderr'] return True # Mount a filesystem\",\n \"ifile: if line.startswith('#'): # Commented lines.append(line) continue if not line.strip():\",\n \"True def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt\",\n \"= ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) try: with\",\n \"fix them if there are any present = True if\",\n \"''' on_ = swaps() if name in on_: if __grains__['os']\",\n \"= __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def\",\n \"''' Returns true if the command passed is a fuse\",\n \"name in on_: ret['stats'] = on_[name] ret['new'] = True return\",\n \"any present = True if comps[0] != device: change =\",\n \"device, if the device is not already mounted, mount is\",\n \"Attempt to remount a device, if the device is not\",\n \"_active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else:\",\n \"name not in mnts: return \\\"{0} does not have anything\",\n \"swaps() if name in on_: if __grains__['os'] != 'OpenBSD': __salt__['cmd.run']('swapoff\",\n \"as ifile: for line in ifile: comps = line.split() ret[comps[1]]\",\n \"name and comps[0] == device: continue else: if comps[1] ==\",\n \"comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts() filename =\",\n \"3: # Invalid entry lines.append(line) continue comps = line.split() prefix\",\n \"__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def remount(name,\",\n \"OpenBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps =\",\n \"except (IOError, OSError) as exc: msg = 'Couldn\\\\'t read from\",\n \"isinstance(opts, string_types): opts = opts.split(',') mnts = active() if name\",\n \"needs to be ' 'updated'.format(name) ) newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4}\",\n \"The entry is present, get rid of it lines =\",\n \"salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False\",\n \"the module's virtual name __virtualname__ = 'mount' def __virtual__(): '''\",\n \"= opts.split(',') mnts = active() if name in mnts: #\",\n \".. code-block:: bash salt '*' mount.swapoff /root/swapfile ''' on_ =\",\n \".. code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True '''\",\n \"= 'mount' def __virtual__(): ''' Only load on POSIX-like systems\",\n \"'*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if name\",\n \"Example: .. code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents\",\n \"not readable {0}' raise CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename)\",\n \"True comps[3] = opts if comps[4] != str(dump): change =\",\n \"'libfuse' in out def swaps(): ''' Return a dict containing\",\n \"in ['MacOS', 'Darwin']: if opts == 'defaults': opts = 'noowners'\",\n \"config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount is represented\",\n \"fstype, opts, dump, pass_num ) ) lines.append(newline) else: lines.append(line) except\",\n \"comps[4]} else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')):\",\n \"True comps[0] = device if comps[1] != name: change =\",\n \"salt.utils.fopen(config, 'w+') as ofile: # The line was changed, commit\",\n \"code-block:: bash salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config)\",\n \"line.split() if len(comps) != 6: # Invalid entry continue ret[comps[1]]\",\n \"in the fstab, change the mount to match the data\",\n \"check to see if there are changes # and fix\",\n \"if line.startswith('Filename'): continue comps = line.split() ret[comps[0]] = {'type': comps[1],\",\n \"'*' mount.active ''' ret = {} if __grains__['os'] == 'FreeBSD':\",\n \"in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: # We need\",\n \"{0}'.format(name)) on_ = swaps() if name in on_: return False\",\n \"= device_uuid and device_uuid.lower() ret[comps[4]] = {'mountid': comps[0], 'parentid': comps[1],\",\n \"if len(comps) != 6: # Invalid entry continue ret[comps[1]] =\",\n \"filename = '/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File\",\n \"CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line in ifile: comps\",\n \"/root/swapfile ''' ret = {} on_ = swaps() if name\",\n \"force_mount: # We need to force the mount but first\",\n \"msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename)\",\n \"as fp_: for line in fp_: if line.startswith('Filename'): continue comps\",\n \"anything mounted\\\".format(name) if not device: cmd = 'umount {0}'.format(name) else:\",\n \"change: if present: # The right entry is already here\",\n \"name __virtualname__ = 'mount' def __virtual__(): ''' Only load on\",\n \"line in ifile: comps = line.split() device = comps[2].split(':') device_name\",\n \"__virtualname__ = 'mount' def __virtual__(): ''' Only load on POSIX-like\",\n \"but first we should unmount umount(name, device, user=user) lopts =\",\n \"salt '*' mount.umount /mnt/foo /dev/xvdc1 ''' mnts = active() if\",\n \"\\\", line).split() ret[comps[2]] = comps[0] return ret def _active_mountinfo(ret): _list\",\n \"contents of the fstab CLI Example: .. code-block:: bash salt\",\n \"'dump': comps[4], 'pass': comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'):\",\n \"= line.split() if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]] = {'type':\",\n \"Returns true if the command passed is a fuse mountable\",\n \"'-o {0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) if\",\n \"there are changes # and fix them if there are\",\n \"\\\", line).split() nod = __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = '\",\n \"opts='defaults', user=None): ''' Mount a device CLI Example: .. code-block::\",\n \"device_fmt = comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1],\",\n \"comps[2].split(':') device_name = comps[8] device_uuid = None if device_name: device_uuid\",\n \"= __salt__['cmd.run_stdout']('ls -l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\" \\\") parens\",\n \"the auto_master CLI Example: .. code-block:: bash salt '*' mount.rm_automaster\",\n \"Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret\",\n \"# Import salt libs import salt.utils from salt._compat import string_types\",\n \"comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None),\",\n \"== 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else:\",\n \"CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List\",\n \"def swaps(): ''' Return a dict containing information on active\",\n \"comps[5]} return ret def swapon(name, priority=None): ''' Activate a swap\",\n \"code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_ = active()\",\n \"= line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3],\",\n \"{'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts': comps[3].split(',')} return\",\n \"Fix the opts type if it is a list if\",\n \"first we should unmount umount(name, device, user=user) lopts = ','.join(opts)\",\n \"else: if comps[1] == name: continue lines.append(line) except (IOError, OSError)\",\n \"ifile: for line in ifile: comps = line.split() device =\",\n \"{0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name\",\n \") ) return 'new' def automaster(config='/etc/auto_salt'): ''' List the contents\",\n \"!= 6: # Invalid entry lines.append(line) continue comps = line.split()\",\n \"ifile: for line in ifile: if line.startswith('#'): # Commented lines.append(line)\",\n \"'Darwin']: _active_mounts_darwin(ret) else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret)\",\n \"OSError): raise CommandExecutionError( 'File not writable {0}'.format( config ) )\",\n \"mount.umount /mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1\",\n \"CLI Example: .. code-block:: bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1\",\n \"!= 3: # Invalid entry lines.append(line) continue if comps[0] ==\",\n \"it! ofile.writelines(lines) except (IOError, OSError): msg = 'File not writable\",\n \"comps[1] != name: change = True comps[1] = name if\",\n \"in on_: ret['stats'] = on_[name] ret['new'] = False return ret\",\n \"information if the path is mounted CLI Example: .. code-block::\",\n \"remount a device, if the device is not already mounted,\",\n \"mount(name, device, mkmnt, fstype, opts, user=user) def umount(name, device=None, user=None):\",\n \"opts, user=user) def umount(name, device=None, user=None): ''' Attempt to unmount\",\n \"pass_num) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The\",\n \"fstype == 'smbfs': force_mount = True if isinstance(opts, string_types): opts\",\n \"'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0} {1} {2}\",\n \"ext4 ''' # Fix the opts type if it is\",\n \".. code-block:: bash salt '*' mount.swaps ''' ret = {}\",\n \"return ret def _active_mounts_freebsd(ret): ''' List active mounts on FreeBSD\",\n \"rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point from the\",\n \"filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File\",\n \"salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the\",\n \"!= 'OpenBSD': __salt__['cmd.run']('swapoff {0}'.format(name)) else: __salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ =\",\n \"entry lines.append(line) continue if comps[1] == name or comps[0] ==\",\n \"cmd = 'mount {0} {1} {2} '.format(args, device, name) out\",\n \"for line in ifile: if line.startswith('#'): # Commented lines.append(line) continue\",\n \"Commented continue if not line.strip(): # Blank line continue comps\",\n \"it to the end of the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4}\",\n \"swap_type = \\\"file\\\" comps = line.split() if comps[0].startswith('/dev/'): swap_type =\",\n \"''' # Fix the opts type if it is a\",\n \"import which as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError #\",\n \"if line.startswith(('Device', 'Total')): continue swap_type = \\\"file\\\" comps = line.split()\",\n \"= fstab(config) if name not in contents: return True #\",\n \"are any present = True if comps[0] != device: change\",\n \"= comps[0] return ret def _active_mountinfo(ret): _list = _list_mounts() filename\",\n \"readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as ifile: for line\",\n \"ret[comps[2]] = {'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret\",\n \"'Couldn\\\\'t read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change:\",\n \"unmount umount(name, device, user=user) lopts = ','.join(opts) args = '-o\",\n \"device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device':\",\n \"lopts = ','.join(opts) args = '-o {0}'.format(lopts) if fstype: args\",\n \"disk CLI Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile\",\n \"entry lines.append(line) continue comps = line.split() if device: if comps[1]\",\n \"= False return ret cmd = 'swapon {0}'.format(name) if priority:\",\n \"''' mnts = active() if name not in mnts: return\",\n \"name_chk == name: continue lines.append(line) except (IOError, OSError) as exc:\",\n \"os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if opts\",\n \"!= name: change = True comps[0] = name if comps[1]\",\n \"systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\"\",\n \"config='/etc/auto_salt'): ''' Remove the mount point from the auto_master CLI\",\n \"line continue comps = line.split() if len(comps) != 3: #\",\n \"ifile: for line in ifile: comps = line.split() ret[comps[1]] =\",\n \"-l') for line in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\",\",\n \"False automaster_file = \\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config))\",\n \"a filesystem that isn't already return mount(name, device, mkmnt, fstype,\",\n \"mount is called CLI Example: .. code-block:: bash salt '*'\",\n \"if not os.path.isfile(config): return ret with salt.utils.fopen(config) as ifile: for\",\n \"virtual name __virtualname__ = 'mount' def __virtual__(): ''' Only load\",\n \"if comps[0] == name or comps[2] == device_fmt: # check\",\n \"\\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] =\",\n \"False return ret cmd = 'swapon {0}'.format(name) if priority: cmd\",\n \"log.debug( 'fstab entry for mount point {0} needs to be\",\n \"None), 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_freebsd(ret): '''\",\n \"else: cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if\",\n \"name = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts = comps[1].split(',')\",\n \"= opts if comps[4] != str(dump): change = True comps[4]\",\n \"if fstype: args += ' -t {0}'.format(fstype) if __grains__['os'] not\",\n \"\\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if\",\n \"line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts':\",\n \"mounts = __salt__['cmd.run_stdout']('mount') else: mounts = __salt__['cmd.run_stdout']('mount -l') for line\",\n \"import os import re import logging # Import salt libs\",\n \"if len(comps) != 6: # Invalid entry lines.append(line) continue if\",\n \"line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens\",\n \"return False elif not _which('ldd'): raise CommandNotFoundError('ldd') out = __salt__['cmd.run']('ldd\",\n \") lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc: msg\",\n \"\\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r') as ifile: for line in\",\n \"bash salt '*' mount.is_mounted /mnt/share ''' active_ = active() if\",\n \"= line.split() if len(comps) != 3: # Invalid entry continue\",\n \"{1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd, runas=user) if\",\n \"name, type_opts, device_fmt) ) lines.append(newline) try: with salt.utils.fopen(config, 'w+') as\",\n \"comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = comps[0] return\",\n \"CLI Example: .. code-block:: bash salt '*' mount.swapon /root/swapfile '''\",\n \"we should unmount umount(name, device, user=user) lopts = ','.join(opts) args\",\n \"!= 3: # Invalid entry lines.append(line) continue comps = line.split()\",\n \"get rid of it lines = [] try: with salt.utils.fopen(config,\",\n \"List active mounts on Linux systems ''' _list = _list_mounts()\",\n \"= '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try:\",\n \"change = True comps[4] = str(dump) if comps[5] != str(pass_num):\",\n \".. code-block:: bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium\",\n \"# The line was changed, commit it! ofile.writelines(lines) except (IOError,\",\n \"comps[5]} return ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the\",\n \"'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret) else: if\",\n \"data if 'remount' not in opts and __grains__['os'] not in\",\n \"mount.swaps ''' ret = {} if __grains__['os'] != 'OpenBSD': with\",\n \"__grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif\",\n \"already return mount(name, device, mkmnt, fstype, opts, user=user) def umount(name,\",\n \"this mount is represented in the auto_salt, change the mount\",\n \"\\\"/..\\\" name = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") opts =\",\n \"**kwargs): ''' Verify that this mount is represented in the\",\n \"' 'updated'.format(name) ) newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device, name,\",\n \"Example: .. code-block:: bash salt '*' mount.is_fuse_exec sshfs ''' cmd_path\",\n \"present = False automaster_file = \\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config)\",\n \"and the fstab file ''' from __future__ import absolute_import #\",\n \"os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt = \\\"{0}:{1}\\\".format(fstype,\",\n \"ret def _active_mounts_openbsd(ret): ''' List active mounts on OpenBSD systems\",\n \"CLI Example: .. code-block:: bash salt '*' mount.swapoff /root/swapfile '''\",\n \"raise CommandExecutionError(msg.format(config)) return 'change' if not change: if present: #\",\n \"''' List the active mounts. CLI Example: .. code-block:: bash\",\n \"if there are changes # and fix them if there\",\n \"None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information if\",\n \"code-block:: bash salt '*' mount.fstab ''' ret = {} if\",\n \"line lines.append(line) continue comps = line.split() if len(comps) != 3:\",\n \"if present: # The right entry is already here return\",\n \"mount.fstab ''' ret = {} if not os.path.isfile(config): return ret\",\n \"False return True return None def is_mounted(name): ''' .. versionadded::\",\n \"OSError) as exc: msg = \\\"Couldn't write to {0}: {1}\\\"\",\n \"device_fmt) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as exc:\",\n \"config ) ) return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): '''\",\n \"'*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix the opts\",\n \"ret[name] = {'device': device_fmt[1], 'fstype': opts[0], 'opts': opts[1:]} return ret\",\n \"device, name, fstype, opts, dump, pass_num ) ) lines.append(newline) else:\",\n \"= blkid_info.get(device_name, {}).get('UUID') device_uuid = device_uuid and device_uuid.lower() ret[comps[4]] =\",\n \"= {'mountid': comps[0], 'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root':\",\n \"mounts on Solaris systems ''' for line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'):\",\n \"in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] =\",\n \"out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd): ''' Returns true\",\n \"str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret def _active_mounts_darwin(ret): '''\",\n \"of the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name, fstype, opts,\",\n \"continue comps = line.split() prefix = \\\"/..\\\" name_chk = comps[0].replace(prefix,\",\n \"lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The line\",\n \"as _which from salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up\",\n \"-u {0} {1} {2} '.format(args, device, name) out = __salt__['cmd.run_all'](cmd,\",\n \"\\\"Couldn't read from {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) try: with\",\n \"other options if 'defaults' in opts and __grains__['os'] in ['MacOS',\",\n \"Invalid entry lines.append(line) continue comps = line.split() prefix = \\\"/..\\\"\",\n \"''' ret = {} on_ = swaps() if name in\",\n \"__salt__['cmd.run']('swapctl -d {0}'.format(name)) on_ = swaps() if name in on_:\",\n \"ret[comps[1]] = {'device': comps[0], 'fstype': comps[2], 'opts': comps[3].split(','), 'dump': comps[4],\",\n \"return None def is_mounted(name): ''' .. versionadded:: 2014.7.0 Provide information\",\n \"['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd = 'mount {0} {1}\",\n \"comps[0] != device: change = True comps[0] = device if\",\n \"def mount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Mount a\",\n \"return ret def swapon(name, priority=None): ''' Activate a swap disk\",\n \"= True return ret return ret def swapoff(name): ''' Deactivate\",\n \"or comps[0] == device: # check to see if there\",\n \"to be ' 'updated'.format(name) ) newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name,\",\n \"line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device':\",\n \"not device: cmd = 'umount {0}'.format(name) else: cmd = 'umount\",\n \"__salt__['file.mkdir'](name=name, user=user) args = '' if opts is not None:\",\n \"comps = line.split() if len(comps) != 3: # Invalid entry\",\n \"comps[2] = fstype if comps[3] != opts: change = True\",\n \"'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name, 'alt_device': _list.get(comps[4], None), 'superopts':\",\n \"'*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount = False if\",\n \"cmd = 'umount {0}'.format(device) out = __salt__['cmd.run_all'](cmd, runas=user) if out['retcode']:\",\n \"active_ = active() if name in active_: return True else:\",\n \"point from the auto_master CLI Example: .. code-block:: bash salt\",\n \"# Blank line lines.append(line) continue comps = line.split() if len(comps)\",\n \"return out['stderr'] return True def remount(name, device, mkmnt=False, fstype='', opts='defaults',\",\n \"List active mounts on Mac OS systems ''' for line\",\n \"for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split()\",\n \"= re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL)[0].split(\\\",\",\n \"\\\"{0}:{1}\\\".format(fstype, device) type_opts = \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype == 'smbfs':\",\n \"in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() nod\",\n \"{0}'.format(lopts) if fstype: args += ' -t {0}'.format(fstype) cmd =\",\n \"\\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") if device:\",\n \"CLI Example: .. code-block:: bash salt '*' mount.mount /mnt/foo /dev/sdz1\",\n \"'size': comps[2], 'used': comps[3], 'priority': comps[4]} else: for line in\",\n \"bash salt '*' mount.swapoff /root/swapfile ''' on_ = swaps() if\",\n \".. code-block:: bash salt '*' mount.active ''' ret = {}\",\n \"continue comps = line.split() if len(comps) != 6: # Invalid\",\n \"module's virtual name __virtualname__ = 'mount' def __virtual__(): ''' Only\",\n \"= '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg = 'File not\",\n \".. code-block:: bash salt '*' mount.rm_automaster /mnt/foo ''' contents =\",\n \"continue if comps[1] == name or comps[0] == device: #\",\n \"is mounted on CLI Example: .. code-block:: bash salt '*'\",\n \"return True def set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0,\",\n \"True def set_automaster( name, device, fstype, opts='', config='/etc/auto_salt', test=False, **kwargs):\",\n \"that this mount is represented in the auto_salt, change the\",\n \"True return ret return ret def swapoff(name): ''' Deactivate a\",\n \"lines.append(line) except (IOError, OSError) as exc: msg = \\\"Couldn't read\",\n \"if name not in contents: return True # The entry\",\n \"that doesn't exist if not cmd_path: return False elif not\",\n \"__grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in ['MacOS', 'Darwin']: _active_mounts_darwin(ret)\",\n \"change = True comps[3] = opts if comps[4] != str(dump):\",\n \"change = False present = False automaster_file = \\\"/etc/auto_master\\\" if\",\n \"changes # and fix them if there are any present\",\n \".. code-block:: bash salt '*' mount.is_mounted /mnt/share ''' active_ =\",\n \"= [] change = False present = False if not\",\n \"comps[0], 'fstype': comps[2], 'opts': comps[3].split(',')} return ret def _active_mounts_solaris(ret): '''\",\n \"mounted\\\".format(name) if not device: cmd = 'umount {0}'.format(name) else: cmd\",\n \"{'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False):\",\n \"line was changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise\",\n \"name) else: cmd = 'mount -u {0} {1} {2} '.format(args,\",\n \"# Commented lines.append(line) continue if not line.strip(): # Blank line\",\n \"\\\"\\\") device_fmt = comps[2].split(\\\":\\\") if device: if name_chk == name\",\n \"-l {0}'.format(comps[0])) nod = ' '.join(nod.split()).split(\\\" \\\") parens = re.findall(r'\\\\((.*?)\\\\)',\",\n \"= 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if\",\n \"= __salt__['cmd.run']('ldd {0}'.format(cmd_path)) return 'libfuse' in out def swaps(): '''\",\n \"device, config='/etc/fstab'): ''' Remove the mount point from the fstab\",\n \"bash salt '*' mount.set_fstab /mnt/foo /dev/sdz1 ext4 ''' # Fix\",\n \"continue comps = line.split() if device: if comps[1] == name\",\n \"/mnt/foo /dev/xvdc1 ''' mnts = active() if name not in\",\n \"if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name = \\\"/..{0}\\\".format(name) device_fmt\",\n \"== name and comps[0] == device: continue else: if comps[1]\",\n \"comps[1] == name: continue lines.append(line) except (IOError, OSError) as exc:\",\n \"= ','.join(opts) lines = [] change = False present =\",\n \"ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None), 'fstype': comps[2], 'opts':\",\n \"{0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test,\",\n \"_active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'):\",\n \"= {} if __grains__['os'] in ['MacOS', 'Darwin']: mounts = __salt__['cmd.run_stdout']('mount')\",\n \"coding: utf-8 -*- ''' Salt module to manage unix mounts\",\n \"re.DOTALL)[0].split(\\\", \\\") ret[comps[2]] = {'device': comps[0], 'fstype': parens[0], 'opts': parens[1:]}\",\n \"__grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin']: opts.append('remount') if force_mount: #\",\n \"in ifile: if line.startswith('#'): # Commented lines.append(line) continue if not\",\n \"force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']: if opts\",\n \"salt.exceptions import CommandNotFoundError, CommandExecutionError # Set up logger log =\",\n \"False if not os.path.isfile(config): raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try:\",\n \"device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to remount a\",\n \"running ldd on a command that doesn't exist if not\",\n \"libs import salt.utils from salt._compat import string_types from salt.utils import\",\n \"if len(comps) != 3: # Invalid entry continue prefix =\",\n \"a specific file module exists: if salt.utils.is_windows(): return False return\",\n \"for line in mounts.split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split()\",\n \"comps[0], 'fstype': parens[0], 'opts': parens[1:]} return ret def active(extended=False): '''\",\n \"CommandExecutionError(msg.format(config, str(exc))) try: with salt.utils.fopen(config, 'w+') as ofile: ofile.writelines(lines) except\",\n \"-p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]] = {'device':\",\n \"in running ldd on a command that doesn't exist if\",\n \"else: if extended: try: _active_mountinfo(ret) except CommandExecutionError: _active_mounts(ret) else: _active_mounts(ret)\",\n \"for line in fp_: if line.startswith('Filename'): continue comps = line.split()\",\n \"it is mounted on CLI Example: .. code-block:: bash salt\",\n \"True def _list_mounts(): ret = {} if __grains__['os'] in ['MacOS',\",\n \"the end of the fstab newline = '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(device, name,\",\n \"log.debug( 'auto_master entry for mount point {0} needs to be\",\n \") lines.append(newline) try: with salt.utils.fopen(config, 'w+') as ofile: # The\",\n \"'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not\",\n \"in ifile: comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device':\",\n \"= \\\"partition\\\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2],\",\n \"dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that this mount\",\n \"'*' mount.mount /mnt/foo /dev/sdz1 True ''' # Darwin doesn't expect\",\n \"['MacOS', 'Darwin']: if opts == 'defaults': opts = 'noowners' if\",\n \"parens = re.findall(r'\\\\((.*?)\\\\)', line, re.DOTALL) ret[comps[3]] = {'device': comps[0], 'fstype':\",\n \"-*- ''' Salt module to manage unix mounts and the\",\n \"# Invalid entry lines.append(line) continue if comps[0] == name or\",\n \"return mount(name, device, mkmnt, fstype, opts, user=user) def umount(name, device=None,\",\n \"if __grains__['os'] not in ['OpenBSD', 'MacOS', 'Darwin'] or force_mount: cmd\",\n \"os.R_OK): msg = 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with\",\n \"newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format( name, type_opts, device_fmt) ) lines.append(newline) else:\",\n \"bash salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if\",\n \"'parentid': comps[1], 'major': device[0], 'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','),\",\n \"a list if isinstance(opts, list): opts = ','.join(opts) lines =\",\n \"= 'File not readable {0}' raise CommandExecutionError(msg.format(filename)) with salt.utils.fopen(filename) as\",\n \"device_fmt = comps[2].split(\\\":\\\") if device: if name_chk == name and\",\n \"def remount(name, device, mkmnt=False, fstype='', opts='defaults', user=None): ''' Attempt to\",\n \"= '/proc/self/mounts' if not os.access(filename, os.R_OK): msg = 'File not\",\n \"comps = line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used':\",\n \"3: # Invalid entry continue prefix = \\\"/..\\\" name =\",\n \"{5}\\\\n'.format(device, name, fstype, opts, dump, pass_num) lines.append(newline) try: with salt.utils.fopen(config,\",\n \"mount.is_fuse_exec sshfs ''' cmd_path = _which(cmd) # No point in\",\n \"a device, if the device is not already mounted, mount\",\n \"should unmount umount(name, device, user=user) lopts = ','.join(opts) args =\",\n \"3: # Invalid entry lines.append(line) continue if comps[0] == name\",\n \"device: continue else: if comps[1] == name: continue lines.append(line) except\",\n \"to see if there are changes # and fix them\",\n \"logging # Import salt libs import salt.utils from salt._compat import\",\n \"= 'mount {0} {1} {2} '.format(args, device, name) out =\",\n \"pass_num ) ) lines.append(newline) else: lines.append(line) except (IOError, OSError) as\",\n \"= False present = False automaster_file = \\\"/etc/auto_master\\\" if not\",\n \"os.path.isfile(config): raise CommandExecutionError('Bad config file \\\"{0}\\\"'.format(config)) try: with salt.utils.fopen(config, 'r')\",\n \"os import re import logging # Import salt libs import\",\n \"'defaults' in opts and __grains__['os'] in ['MacOS', 'Darwin']: opts =\",\n \"lines = [] try: with salt.utils.fopen(config, 'r') as ifile: for\",\n \".. code-block:: bash salt '*' mount.swapon /root/swapfile ''' ret =\",\n \"= {} if __grains__['os'] == 'FreeBSD': _active_mounts_freebsd(ret) elif __grains__['os'] ==\",\n \"else: for line in __salt__['cmd.run_stdout']('swapctl -kl').splitlines(): if line.startswith(('Device', 'Total')): continue\",\n \"new, add it to the end of the fstab newline\",\n \"','.join(opts) lines = [] change = False present = False\",\n \"line.strip(): # Blank line continue comps = line.split() if len(comps)\",\n \"to be ' 'updated'.format(name) ) newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format(\",\n \"if device: if name_chk == name and device_fmt[1] == device:\",\n \"information on active swap CLI Example: .. code-block:: bash salt\",\n \"swaps(): ''' Return a dict containing information on active swap\",\n \"''' from __future__ import absolute_import # Import python libs import\",\n \"{0}'.format(name) if priority: cmd += ' -p {0}'.format(priority) __salt__['cmd.run'](cmd) on_\",\n \"'' if opts is not None: lopts = ','.join(opts) args\",\n \"Remove the mount point from the fstab CLI Example: ..\",\n \"except (IOError, OSError): raise CommandExecutionError( 'File not writable {0}'.format( config\",\n \"config ) ) return 'new' def automaster(config='/etc/auto_salt'): ''' List the\",\n \"_active_mounts_darwin(ret): ''' List active mounts on Mac OS systems '''\",\n \"if change: if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+')\",\n \"ret def rm_fstab(name, device, config='/etc/fstab'): ''' Remove the mount point\",\n \"FreeBSD systems ''' for line in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps =\",\n \"if comps[2] != fstype: change = True comps[2] = fstype\",\n \"ifile: comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1],\",\n \"comps = line.split() ret[comps[1]] = {'device': comps[0], 'alt_device': _list.get(comps[1], None),\",\n \"_active_mounts_freebsd(ret) elif __grains__['os'] == 'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD':\",\n \"= swaps() if name in on_: if __grains__['os'] != 'OpenBSD':\",\n \"/mnt/share ''' active_ = active() if name in active_: return\",\n \"return ret def _active_mounts_solaris(ret): ''' List active mounts on Solaris\",\n \"True if comps[0] != name: change = True comps[0] =\",\n \"to the end of the fstab newline = ( '{0}\\\\t{1}\\\\t{2}\\\\n'.format(\",\n \"mounts. CLI Example: .. code-block:: bash salt '*' mount.active '''\",\n \"swaps() if name in on_: return False return True return\",\n \"''' List active mounts on OpenBSD systems ''' for line\",\n \"change = True comps[1] = type_opts if comps[2] != device_fmt:\",\n \"parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid': parens[0]} return ret\",\n \"mount is represented in the auto_salt, change the mount to\",\n \"if comps[1] == name or comps[0] == device: # check\",\n \"= True if isinstance(opts, string_types): opts = opts.split(',') mnts =\",\n \"/mnt/foo .. versionadded:: Lithium salt '*' mount.umount /mnt/foo /dev/xvdc1 '''\",\n \"not os.path.exists(name) and mkmnt: __salt__['file.mkdir'](name=name, user=user) args = '' if\",\n \"'Solaris': _active_mounts_solaris(ret) elif __grains__['os'] == 'OpenBSD': _active_mounts_openbsd(ret) elif __grains__['os'] in\",\n \"Return a dict containing information on active swap CLI Example:\",\n \"if not cmd_path: return False elif not _which('ldd'): raise CommandNotFoundError('ldd')\",\n \"== name and device_fmt[1] == device: continue else: if name_chk\",\n \"= comps[2].split(\\\":\\\") opts = comps[1].split(',') ret[name] = {'device': device_fmt[1], 'fstype':\",\n \"Mount a filesystem that isn't already return mount(name, device, mkmnt,\",\n \"bash salt '*' mount.umount /mnt/foo .. versionadded:: Lithium salt '*'\",\n \"in __salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]]\",\n \"The entry is new, add it to the end of\",\n \"not salt.utils.test_mode(test=test, **kwargs): # The entry is new, add it\",\n \"change: log.debug( 'auto_master entry for mount point {0} needs to\",\n \"= active() if name in mnts: # The mount point\",\n \"on_: ret['stats'] = on_[name] ret['new'] = True return ret return\",\n \"{0}'.format(priority) __salt__['cmd.run'](cmd) on_ = swaps() if name in on_: ret['stats']\",\n \"automaster_file = \\\"/etc/auto_master\\\" if not os.path.isfile(config): __salt__['file.touch'](config) __salt__['file.append'](automaster_file, \\\"/-\\\\t\\\\t\\\\t{0}\\\".format(config)) name\",\n \"code-block:: bash salt '*' mount.remount /mnt/foo /dev/sdz1 True ''' force_mount\",\n \"__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True def is_fuse_exec(cmd):\",\n \"user=user) def umount(name, device=None, user=None): ''' Attempt to unmount a\",\n \"def _active_mounts(ret): ''' List active mounts on Linux systems '''\",\n \"msg = 'File not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change'\",\n \"ret with salt.utils.fopen(config) as ifile: for line in ifile: if\",\n \"line.split() if comps[0].startswith('/dev/'): swap_type = \\\"partition\\\" ret[comps[0]] = {'type': swap_type,\",\n \"continue comps = line.split() ret[comps[0]] = {'type': comps[1], 'size': comps[2],\",\n \"salt.utils from salt._compat import string_types from salt.utils import which as\",\n \"6: # Invalid entry lines.append(line) continue comps = line.split() if\",\n \"__salt__['cmd.run_stdout']('mount -p').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[1]] =\",\n \"fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): ''' Verify that\",\n \"lines.append(line) continue comps = line.split() prefix = \\\"/..\\\" name_chk =\",\n \"= str(dump) if comps[5] != str(pass_num): change = True comps[5]\",\n \"name or comps[0] == device: # check to see if\",\n \"_which(cmd) # No point in running ldd on a command\",\n \"runas=user) if out['retcode']: return out['stderr'] return True # Mount a\",\n \"'fstype': comps[5], 'opts': parens[1].split(\\\", \\\"), 'major': str(nod[4].strip(\\\",\\\")), 'minor': str(nod[5]), 'device_uuid':\",\n \"device_name, 'alt_device': _list.get(comps[4], None), 'superopts': comps[9].split(','), 'device_uuid': device_uuid} return ret\",\n \"_list_mounts() filename = '/proc/self/mountinfo' if not os.access(filename, os.R_OK): msg =\",\n \"False if __grains__['os'] in ['MacOS', 'Darwin']: if opts == 'defaults':\",\n \"= line.split() prefix = \\\"/..\\\" name_chk = comps[0].replace(prefix, \\\"\\\") device_fmt\",\n \"name if comps[2] != fstype: change = True comps[2] =\",\n \"lines.append(line) continue if comps[0] == name or comps[2] == device_fmt:\",\n \"CommandNotFoundError, CommandExecutionError # Set up logger log = logging.getLogger(__name__) #\",\n \"salt '*' mount.rm_fstab /mnt/foo ''' contents = fstab(config) if name\",\n \"CommandExecutionError(msg.format(config, str(exc))) if change: if not salt.utils.test_mode(test=test, **kwargs): try: with\",\n \"if comps[4] != str(dump): change = True comps[4] = str(dump)\",\n \") return 'new' def rm_automaster(name, device, config='/etc/auto_salt'): ''' Remove the\",\n \"= _which(cmd) # No point in running ldd on a\",\n \"the fstab CLI Example: .. code-block:: bash salt '*' mount.fstab\",\n \"present = True if comps[0] != device: change = True\",\n \"True comps[2] = fstype if comps[3] != opts: change =\",\n \"set_fstab( name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs):\",\n \"force_mount: cmd = 'mount {0} {1} {2} '.format(args, device, name)\",\n \"ret[comps[0]] = {'type': comps[1], 'size': comps[2], 'used': comps[3], 'priority': comps[4]}\",\n \"comps[3].split(',')} return ret def _active_mounts_solaris(ret): ''' List active mounts on\",\n \"entry for mount point {0} needs to be ' 'updated'.format(name)\",\n \"CommandExecutionError( 'File not writable {0}'.format( config ) ) return 'new'\",\n \"''' cmd_path = _which(cmd) # No point in running ldd\",\n \"= \\\"-fstype={0},{1}\\\".format(fstype, opts) if fstype == 'smbfs': device_fmt = device_fmt.replace(fstype,\",\n \"be ' 'updated'.format(name) ) newline = ( '{0}\\\\t\\\\t{1}\\\\t{2}\\\\t{3}\\\\t{4} {5}\\\\n'.format( device,\",\n \"doesn't exist if not cmd_path: return False elif not _which('ldd'):\",\n \"mounted, attempt to remount it with the given data if\",\n \"!= fstype: change = True comps[2] = fstype if comps[3]\",\n \"= \\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return\",\n \"read from {0}: {1}' raise CommandExecutionError(msg.format(config, str(exc))) if change: if\",\n \"Example: .. code-block:: bash salt '*' mount.fstab ''' ret =\",\n \"''' force_mount = False if __grains__['os'] in ['MacOS', 'Darwin']: if\",\n \"line in __salt__['cmd.run_stdout']('mount -v').split('\\\\n'): comps = re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split()\",\n \"point {0} needs to be ' 'updated'.format(name) ) newline =\",\n \"mnts: # The mount point is mounted, attempt to remount\",\n \"active swap CLI Example: .. code-block:: bash salt '*' mount.swaps\",\n \"comps[3] != opts: change = True comps[3] = opts if\",\n \"'minor': device[1], 'root': comps[3], 'opts': comps[5].split(','), 'fstype': comps[7], 'device': device_name,\",\n \"''' Mount a device CLI Example: .. code-block:: bash salt\",\n \"specific file module exists: if salt.utils.is_windows(): return False return True\",\n \"ofile.writelines(lines) except (IOError, OSError) as exc: msg = \\\"Couldn't write\",\n \"write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return True def\",\n \"force the mount but first we should unmount umount(name, device,\",\n \"not writable {0}' raise CommandExecutionError(msg.format(config)) return 'change' if not change:\",\n \"comps[0].replace(prefix, \\\"\\\") device_fmt = comps[2].split(\\\":\\\") if device: if name_chk ==\",\n \"def __virtual__(): ''' Only load on POSIX-like systems ''' #\",\n \"if not salt.utils.test_mode(test=test, **kwargs): try: with salt.utils.fopen(config, 'w+') as ofile:\",\n \"{'device': comps[0], 'fstype': comps[4], 'opts': comps[5].split('/')} return ret def _active_mounts_openbsd(ret):\",\n \"else: _active_mounts(ret) return ret def fstab(config='/etc/fstab'): ''' List the contents\",\n \"= swaps() if name in on_: ret['stats'] = on_[name] ret['new']\",\n \"exc: msg = \\\"Couldn't write to {0}: {1}\\\" raise CommandExecutionError(msg.format(config,\",\n \"device by specifying the directory it is mounted on CLI\",\n \"_active_mounts_freebsd(ret): ''' List active mounts on FreeBSD systems ''' for\",\n \"ret['stats'] = on_[name] ret['new'] = True return ret return ret\",\n \"on_: ret['stats'] = on_[name] ret['new'] = False return ret cmd\",\n \"that isn't already return mount(name, device, mkmnt, fstype, opts, user=user)\",\n \"comps[3] = opts if comps[4] != str(dump): change = True\",\n \"= comps[2].split(\\\":\\\") if device: if name_chk == name and device_fmt[1]\",\n \"if out['retcode']: return out['stderr'] return True def remount(name, device, mkmnt=False,\",\n \"opts type if it is a list if isinstance(opts, list):\",\n \"Update automount __salt__['cmd.run']('automount -cv') return True def set_automaster( name, device,\",\n \"{0}: {1}\\\" raise CommandExecutionError(msg.format(config, str(exc))) return True def set_fstab( name,\",\n \"add it to the end of the fstab newline =\",\n \"__salt__['cmd.run_all'](cmd, runas=user) if out['retcode']: return out['stderr'] return True # Mount\",\n \"active mounts on Mac OS systems ''' for line in\",\n \"line.split() if device: if comps[1] == name and comps[0] ==\",\n \"if isinstance(opts, string_types): opts = opts.split(',') if not os.path.exists(name) and\",\n \"mounts and the fstab file ''' from __future__ import absolute_import\",\n \"\\\"partition\\\" ret[comps[0]] = {'type': swap_type, 'size': comps[1], 'used': comps[2], 'priority':\",\n \"re.sub(r\\\"\\\\s+\\\", \\\" \\\", line).split() ret[comps[2]] = {'device': comps[0], 'fstype': comps[4],\",\n \"lines = [] change = False present = False automaster_file\",\n \"was changed, commit it! ofile.writelines(lines) except (IOError, OSError): raise CommandExecutionError(\",\n \"writable {0}'.format( config ) ) return 'new' def rm_automaster(name, device,\",\n \"# and fix them if there are any present =\",\n \"salt '*' mount.swapon /root/swapfile ''' ret = {} on_ =\",\n \"lines.append(line) continue comps = line.split() if device: if comps[1] ==\",\n \"= device_fmt.replace(fstype, \\\"\\\") try: with salt.utils.fopen(config, 'r') as ifile: for\",\n \"salt.utils.is_windows(): return False return True def _list_mounts(): ret = {}\",\n \"the active mounts. CLI Example: .. code-block:: bash salt '*'\",\n \"the contents of the fstab CLI Example: .. code-block:: bash\",\n \"device: if comps[1] == name and comps[0] == device: continue\",\n \"logger log = logging.getLogger(__name__) # Define the module's virtual name\",\n \"''' List active mounts on Mac OS systems ''' for\",\n \"mounts on Mac OS systems ''' for line in __salt__['cmd.run_stdout']('mount').split('\\\\n'):\",\n \"is called CLI Example: .. code-block:: bash salt '*' mount.remount\",\n \"match the data passed, or add the mount if it\",\n \"= comps[2].split(':') device_name = comps[8] device_uuid = None if device_name:\",\n \"active(extended=False): ''' List the active mounts. CLI Example: .. code-block::\",\n \"code-block:: bash salt '*' mount.swaps ''' ret = {} if\",\n \"with salt.utils.fopen(config) as ifile: for line in ifile: if line.startswith('#'):\",\n \"name, device, fstype, opts='defaults', dump=0, pass_num=0, config='/etc/fstab', test=False, **kwargs): '''\",\n \"__salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line in ifile: comps\",\n \"CommandExecutionError(msg.format(filename)) blkid_info = __salt__['disk.blkid']() with salt.utils.fopen(filename) as ifile: for line\"\n]","isConversational":false}}},{"rowIdx":183,"cells":{"ngram":{"kind":"list like","value":["obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form, change) class","class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass admin.site.register(Topic,","from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request):","request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return initial def","'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO","super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] =","super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name',","'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return","change): if not obj.created_by: obj.created_by = request.user return super().save_model(request, obj,","return initial def save_model(self, request, obj, form, change): if not","in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return initial","class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified',","TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin) admin.site.register(Photo, PhotoAdmin)","from django.contrib import admin from base.models import Topic, Photo class","[ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class","save_model(self, request, obj, form, change): if not obj.created_by: obj.created_by =","TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by',","] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass","Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request)","= super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by']","def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']:","form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level',","'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display","'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List","= request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display","request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display =","class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add'","def save_model(self, request, obj, form, change): if not obj.created_by: obj.created_by","return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [","not obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form, change)","import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial =","= [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ]","'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): #","request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] =","if not obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form,","obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic',","'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin):","obj, form, change): if not obj.created_by: obj.created_by = request.user return","base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial","initial def save_model(self, request, obj, form, change): if not obj.created_by:","= request.user return initial def save_model(self, request, obj, form, change):","list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created',","get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by']","obj.created_by = request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin):","import admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def","PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin)","= request.user initial['modified_by'] = request.user return initial def save_model(self, request,","admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self,","initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user","initial['modified_by'] = request.user return initial def save_model(self, request, obj, form,","form, change): if not obj.created_by: obj.created_by = request.user return super().save_model(request,","# TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin) admin.site.register(Photo,","change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by',","'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add","initial['created_by'] = request.user initial['modified_by'] = request.user return initial def save_model(self,","if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user","Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if","request.user initial['modified_by'] = request.user return initial def save_model(self, request, obj,","request, obj, form, change): if not obj.created_by: obj.created_by = request.user","EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in","request.user return initial def save_model(self, request, obj, form, change): if","django.contrib import admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin):","'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper"],"string":"[\n \"obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form, change) class\",\n \"class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass admin.site.register(Topic,\",\n \"from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request):\",\n \"request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return initial def\",\n \"'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO\",\n \"super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] =\",\n \"super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name',\",\n \"'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return\",\n \"change): if not obj.created_by: obj.created_by = request.user return super().save_model(request, obj,\",\n \"return initial def save_model(self, request, obj, form, change): if not\",\n \"in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user return initial\",\n \"class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified',\",\n \"TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin) admin.site.register(Photo, PhotoAdmin)\",\n \"from django.contrib import admin from base.models import Topic, Photo class\",\n \"[ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class\",\n \"save_model(self, request, obj, form, change): if not obj.created_by: obj.created_by =\",\n \"TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by',\",\n \"] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass\",\n \"Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request)\",\n \"= super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by']\",\n \"def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']:\",\n \"form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level',\",\n \"'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display\",\n \"'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List\",\n \"= request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display\",\n \"request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display =\",\n \"class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add'\",\n \"def save_model(self, request, obj, form, change): if not obj.created_by: obj.created_by\",\n \"return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [\",\n \"not obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form, change)\",\n \"import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial =\",\n \"= [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ]\",\n \"'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): #\",\n \"request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] =\",\n \"if not obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form,\",\n \"obj, form, change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic',\",\n \"'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin):\",\n \"obj, form, change): if not obj.created_by: obj.created_by = request.user return\",\n \"base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial\",\n \"initial def save_model(self, request, obj, form, change): if not obj.created_by:\",\n \"= request.user return initial def save_model(self, request, obj, form, change):\",\n \"list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by', 'modified', 'created_by', 'created',\",\n \"get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by']\",\n \"obj.created_by = request.user return super().save_model(request, obj, form, change) class TopicAdmin(EONBaseAdmin):\",\n \"import admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def\",\n \"PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin)\",\n \"= request.user initial['modified_by'] = request.user return initial def save_model(self, request,\",\n \"admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self,\",\n \"initial = super().get_changeform_initial_data(request) if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user\",\n \"initial['modified_by'] = request.user return initial def save_model(self, request, obj, form,\",\n \"form, change): if not obj.created_by: obj.created_by = request.user return super().save_model(request,\",\n \"# TODO Add Proper List Display pass admin.site.register(Topic, TopicAdmin) admin.site.register(Photo,\",\n \"change) class TopicAdmin(EONBaseAdmin): list_display = [ 'name', 'parent_topic', 'top_level', 'modified_by',\",\n \"'modified_by', 'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add\",\n \"initial['created_by'] = request.user initial['modified_by'] = request.user return initial def save_model(self,\",\n \"if 'add' in request.META['PATH_INFO']: initial['created_by'] = request.user initial['modified_by'] = request.user\",\n \"Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if\",\n \"request.user initial['modified_by'] = request.user return initial def save_model(self, request, obj,\",\n \"request, obj, form, change): if not obj.created_by: obj.created_by = request.user\",\n \"EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_data(self, request): initial = super().get_changeform_initial_data(request) if 'add' in\",\n \"request.user return initial def save_model(self, request, obj, form, change): if\",\n \"django.contrib import admin from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin):\",\n \"'modified', 'created_by', 'created', ] class PhotoAdmin(EONBaseAdmin): # TODO Add Proper\"\n]","isConversational":false}}},{"rowIdx":184,"cells":{"ngram":{"kind":"list like","value":["django.db import models from django.utils.translation import ugettext_lazy as _ class","########################################################################## from django.db import models from django.utils.translation import ugettext_lazy as","TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de # Current version","International GmbH, http://www.daasi.de # Current version of MET has been","https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights reserved. #","Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c)","http://www.yaco.es/ # MET v2 was developed for TERENA by <NAME>,","<NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db import models","performance improvements by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from","by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db import","# MET v2 Metadate Explorer Tool # # This Software","MET v2 was developed for TERENA by <NAME>, DAASI International","unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in","\"\"\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname =","models from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \"\"\"","Current version of MET has been revised for performance improvements","based on MET v1 developed for TERENA by Yaco Sistemas,","Tool # # This Software is Open Source. See License:","http://www.garr.it ########################################################################## from django.db import models from django.utils.translation import ugettext_lazy","# # This Software is based on MET v1 developed","################################################################# # MET v2 Metadate Explorer Tool # # This","is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012,","ugettext_lazy as _ class EntityType(models.Model): \"\"\" Model describing the type","\"\"\" Model describing the type of an entity. \"\"\" name","rights reserved. # # This Software is based on MET","improvements by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db","describing the type of an entity. \"\"\" name = models.CharField(blank=False,","All rights reserved. # # This Software is based on","models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True,","See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights","developed for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de #","is based on MET v1 developed for TERENA by Yaco","for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2 was","was developed for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de","been revised for performance improvements by <NAME>, # Consortium GARR,","<NAME>, DAASI International GmbH, http://www.daasi.de # Current version of MET","Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed for TERENA","as _ class EntityType(models.Model): \"\"\" Model describing the type of","= models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self):","# Consortium GARR, http://www.garr.it ########################################################################## from django.db import models from","of an entity. \"\"\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'),","has been revised for performance improvements by <NAME>, # Consortium","This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright","v2 Metadate Explorer Tool # # This Software is Open","from django.db import models from django.utils.translation import ugettext_lazy as _","verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'),","MET v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/ #","TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed","License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights reserved.","Consortium GARR, http://www.garr.it ########################################################################## from django.db import models from django.utils.translation","import ugettext_lazy as _ class EntityType(models.Model): \"\"\" Model describing the","2012, TERENA All rights reserved. # # This Software is","type of an entity. \"\"\" name = models.CharField(blank=False, max_length=20, unique=True,","TERENA All rights reserved. # # This Software is based","max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name","from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \"\"\" Model","an entity. \"\"\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True)","DAASI International GmbH, http://www.daasi.de # Current version of MET has","Metadate Explorer Tool # # This Software is Open Source.","This Software is based on MET v1 developed for TERENA","# # This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md","MET v2 Metadate Explorer Tool # # This Software is","(c) 2012, TERENA All rights reserved. # # This Software","by Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed for","entity. \"\"\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname","on MET v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/","Model describing the type of an entity. \"\"\" name =","= models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20,","db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True)","models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self): return","v2 was developed for TERENA by <NAME>, DAASI International GmbH,","# Copyright (c) 2012, TERENA All rights reserved. # #","# This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md #","Sistemas, http://www.yaco.es/ # MET v2 was developed for TERENA by","xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def","Software is based on MET v1 developed for TERENA by","GmbH, http://www.daasi.de # Current version of MET has been revised","Explorer Tool # # This Software is Open Source. See","version of MET has been revised for performance improvements by","by <NAME>, DAASI International GmbH, http://www.daasi.de # Current version of","EntityType(models.Model): \"\"\" Model describing the type of an entity. \"\"\"","import models from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model):","Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All","v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET","for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de # Current","reserved. # # This Software is based on MET v1","_ class EntityType(models.Model): \"\"\" Model describing the type of an","# MET v2 was developed for TERENA by <NAME>, DAASI","name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False,","class EntityType(models.Model): \"\"\" Model describing the type of an entity.","of MET has been revised for performance improvements by <NAME>,","the type of an entity. \"\"\" name = models.CharField(blank=False, max_length=20,","MET has been revised for performance improvements by <NAME>, #","# Current version of MET has been revised for performance","for performance improvements by <NAME>, # Consortium GARR, http://www.garr.it ##########################################################################","django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \"\"\" Model describing","http://www.daasi.de # Current version of MET has been revised for","<filename>met/metadataparser/models/entity_type.py<gh_stars>10-100 ################################################################# # MET v2 Metadate Explorer Tool # #","GARR, http://www.garr.it ########################################################################## from django.db import models from django.utils.translation import","developed for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2","# This Software is based on MET v1 developed for","revised for performance improvements by <NAME>, # Consortium GARR, http://www.garr.it","max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self): return self.name","Copyright (c) 2012, TERENA All rights reserved. # # This","Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA"],"string":"[\n \"django.db import models from django.utils.translation import ugettext_lazy as _ class\",\n \"########################################################################## from django.db import models from django.utils.translation import ugettext_lazy as\",\n \"TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de # Current version\",\n \"International GmbH, http://www.daasi.de # Current version of MET has been\",\n \"https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights reserved. #\",\n \"Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c)\",\n \"http://www.yaco.es/ # MET v2 was developed for TERENA by <NAME>,\",\n \"<NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db import models\",\n \"performance improvements by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from\",\n \"by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db import\",\n \"# MET v2 Metadate Explorer Tool # # This Software\",\n \"MET v2 was developed for TERENA by <NAME>, DAASI International\",\n \"unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in\",\n \"\\\"\\\"\\\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname =\",\n \"models from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \\\"\\\"\\\"\",\n \"Current version of MET has been revised for performance improvements\",\n \"based on MET v1 developed for TERENA by Yaco Sistemas,\",\n \"Tool # # This Software is Open Source. See License:\",\n \"http://www.garr.it ########################################################################## from django.db import models from django.utils.translation import ugettext_lazy\",\n \"# # This Software is based on MET v1 developed\",\n \"################################################################# # MET v2 Metadate Explorer Tool # # This\",\n \"is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012,\",\n \"ugettext_lazy as _ class EntityType(models.Model): \\\"\\\"\\\" Model describing the type\",\n \"\\\"\\\"\\\" Model describing the type of an entity. \\\"\\\"\\\" name\",\n \"rights reserved. # # This Software is based on MET\",\n \"improvements by <NAME>, # Consortium GARR, http://www.garr.it ########################################################################## from django.db\",\n \"describing the type of an entity. \\\"\\\"\\\" name = models.CharField(blank=False,\",\n \"All rights reserved. # # This Software is based on\",\n \"models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True,\",\n \"See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights\",\n \"developed for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de #\",\n \"is based on MET v1 developed for TERENA by Yaco\",\n \"for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2 was\",\n \"was developed for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de\",\n \"been revised for performance improvements by <NAME>, # Consortium GARR,\",\n \"<NAME>, DAASI International GmbH, http://www.daasi.de # Current version of MET\",\n \"Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed for TERENA\",\n \"as _ class EntityType(models.Model): \\\"\\\"\\\" Model describing the type of\",\n \"= models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self):\",\n \"# Consortium GARR, http://www.garr.it ########################################################################## from django.db import models from\",\n \"of an entity. \\\"\\\"\\\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'),\",\n \"has been revised for performance improvements by <NAME>, # Consortium\",\n \"This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright\",\n \"v2 Metadate Explorer Tool # # This Software is Open\",\n \"from django.db import models from django.utils.translation import ugettext_lazy as _\",\n \"verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'),\",\n \"MET v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/ #\",\n \"TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed\",\n \"License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All rights reserved.\",\n \"Consortium GARR, http://www.garr.it ########################################################################## from django.db import models from django.utils.translation\",\n \"import ugettext_lazy as _ class EntityType(models.Model): \\\"\\\"\\\" Model describing the\",\n \"2012, TERENA All rights reserved. # # This Software is\",\n \"type of an entity. \\\"\\\"\\\" name = models.CharField(blank=False, max_length=20, unique=True,\",\n \"TERENA All rights reserved. # # This Software is based\",\n \"max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name\",\n \"from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \\\"\\\"\\\" Model\",\n \"an entity. \\\"\\\"\\\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True)\",\n \"DAASI International GmbH, http://www.daasi.de # Current version of MET has\",\n \"Metadate Explorer Tool # # This Software is Open Source.\",\n \"This Software is based on MET v1 developed for TERENA\",\n \"# # This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md\",\n \"MET v2 Metadate Explorer Tool # # This Software is\",\n \"(c) 2012, TERENA All rights reserved. # # This Software\",\n \"by Yaco Sistemas, http://www.yaco.es/ # MET v2 was developed for\",\n \"entity. \\\"\\\"\\\" name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname\",\n \"on MET v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/\",\n \"Model describing the type of an entity. \\\"\\\"\\\" name =\",\n \"= models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False, max_length=20,\",\n \"db_index=True) xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True)\",\n \"models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self): return\",\n \"v2 was developed for TERENA by <NAME>, DAASI International GmbH,\",\n \"# Copyright (c) 2012, TERENA All rights reserved. # #\",\n \"# This Software is Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md #\",\n \"Sistemas, http://www.yaco.es/ # MET v2 was developed for TERENA by\",\n \"xmlname = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def\",\n \"Software is based on MET v1 developed for TERENA by\",\n \"GmbH, http://www.daasi.de # Current version of MET has been revised\",\n \"Explorer Tool # # This Software is Open Source. See\",\n \"version of MET has been revised for performance improvements by\",\n \"by <NAME>, DAASI International GmbH, http://www.daasi.de # Current version of\",\n \"EntityType(models.Model): \\\"\\\"\\\" Model describing the type of an entity. \\\"\\\"\\\"\",\n \"import models from django.utils.translation import ugettext_lazy as _ class EntityType(models.Model):\",\n \"Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA All\",\n \"v1 developed for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET\",\n \"for TERENA by <NAME>, DAASI International GmbH, http://www.daasi.de # Current\",\n \"reserved. # # This Software is based on MET v1\",\n \"_ class EntityType(models.Model): \\\"\\\"\\\" Model describing the type of an\",\n \"# MET v2 was developed for TERENA by <NAME>, DAASI\",\n \"name = models.CharField(blank=False, max_length=20, unique=True, verbose_name=_(u'Name'), db_index=True) xmlname = models.CharField(blank=False,\",\n \"class EntityType(models.Model): \\\"\\\"\\\" Model describing the type of an entity.\",\n \"of MET has been revised for performance improvements by <NAME>,\",\n \"the type of an entity. \\\"\\\"\\\" name = models.CharField(blank=False, max_length=20,\",\n \"MET has been revised for performance improvements by <NAME>, #\",\n \"# Current version of MET has been revised for performance\",\n \"for performance improvements by <NAME>, # Consortium GARR, http://www.garr.it ##########################################################################\",\n \"django.utils.translation import ugettext_lazy as _ class EntityType(models.Model): \\\"\\\"\\\" Model describing\",\n \"http://www.daasi.de # Current version of MET has been revised for\",\n \"<filename>met/metadataparser/models/entity_type.py<gh_stars>10-100 ################################################################# # MET v2 Metadate Explorer Tool # #\",\n \"GARR, http://www.garr.it ########################################################################## from django.db import models from django.utils.translation import\",\n \"developed for TERENA by Yaco Sistemas, http://www.yaco.es/ # MET v2\",\n \"# This Software is based on MET v1 developed for\",\n \"revised for performance improvements by <NAME>, # Consortium GARR, http://www.garr.it\",\n \"max_length=20, unique=True, verbose_name=_(u'Name in XML'), db_index=True) def __unicode__(self): return self.name\",\n \"Copyright (c) 2012, TERENA All rights reserved. # # This\",\n \"Open Source. See License: https://github.com/TERENA/met/blob/master/LICENSE.md # Copyright (c) 2012, TERENA\"\n]","isConversational":false}}},{"rowIdx":185,"cells":{"ngram":{"kind":"list like","value":["logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.')","except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\"","loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper","self.message_configs.get_func(msg) if not func: return def process(): # noinspection PyBroadException","\"\"\" return self.core.logout() @property def alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为","单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param","msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except:","dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self,","listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(),","process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return","是否更新 :return: 聊天对象合集 \"\"\" return Chats(self.friends(update) + self.groups(update) + self.mps(update),",":param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request(): return",":param verify_content: 验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True","list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \"\"\"","from pprint import pformat from threading import Thread import itchat","self.self.name) @handle_response() def logout(self): \"\"\" 登出当前账号 \"\"\" return self.core.logout() @property","ret @handle_response(Group) def groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊 :param update:","\"\"\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self,","return def process(): # noinspection PyBroadException try: ret = func(msg)",":param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市)","login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core() itchat.instanceList.append(self)","except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def","= self self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__,","logout(self): \"\"\" 登出当前账号 \"\"\" return self.core.logout() @property def alive(self): \"\"\"","Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self) if","单个或多个用户用户的详细信息 \"\"\" def chunks(): total = ensure_list(user_or_users) for i in","return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False):","isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return ret else:","for i in range(0, len(total), chunk_size): yield total[i:i + chunk_size]","raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg):","创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\"","chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return list(filter(lambda x: get_user_name(x) !=","user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\" def chunks():",") def create_group(self, users, topic=None): \"\"\" 创建一个新的群聊 :param users: 用户列表","= Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name =","= func(msg) if ret is not None: if isinstance(ret, (tuple,","user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size:","def create_group(self, users, topic=None): \"\"\" 创建一个新的群聊 :param users: 用户列表 :param","registered function, ' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc())","import Thread import itchat import logging from wxpy.chat import Chat","register def start(self, block=True): \"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False",":param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path),","chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret","self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot","chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self,","logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register( self, chats=None,","\"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def __init__( self, save_path=None, console_qr=False,","MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self","ensure_list logger = logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能","self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback,","self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs","msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in registered","self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return","add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content:","\"\"\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return Chats(self.friends(update)","chunks(): total = ensure_list(user_or_users) for i in range(0, len(total), chunk_size):","func return register def start(self, block=True): \"\"\" 开始监听和处理消息 :param block:","detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register(",":return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic","\"\"\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do() ret.source","autoUpdate=True ) def create_group(self, users, topic=None): \"\"\" 创建一个新的群聊 :param users:","contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def",":return: 聊天对象合集 \"\"\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret =","verify_content: 验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3,","pformat from threading import Thread import itchat import logging from",") ret = request() user_name = ret.get('ChatRoomName') if user_name: return","Web 微信的功能 \"\"\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None,","\"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async,","Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def __init__( self, save_path=None,","Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except","模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode","验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self,","self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if","yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if","!= self.self.user_name, chats_or_dicts)) def chats(self, update=False): \"\"\" 获取所有聊天对象 :param update:","def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def","| 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块","@handle_response(User) def user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或","Thread import itchat import logging from wxpy.chat import Chat from","@handle_response() def add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友 :param user: 用户对象或用户名","MessageConfig from wxpy.mp import MP from wxpy.response import ResponseError from","in range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def","MP from wxpy.response import ResponseError from wxpy.user import User from","qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback:","msg): \"\"\" 处理接收到的消息 \"\"\" if not self.alive: return func, run_async","登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login(","self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \"\"\" 获取所有公众号 :param update:","save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \"\"\" :param save_path:","def logout(self): \"\"\" 登出当前账号 \"\"\" return self.core.logout() @property def alive(self):","): \"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM","return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name:","self.self.robot = self self.save_path = save_path def __repr__(self): return '<{}:","@handle_response(MP) def mps(self, update=False): \"\"\" 获取所有公众号 :param update: 是否更新 :return:","ret = list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk)","accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content:","self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path","= save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def","Chat from wxpy.chats import Chats from wxpy.friend import Friend from","qr_callback=None, login_callback=None, logout_callback=None ): \"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。","\"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr:","if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret)))","return ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \"\"\"","Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def",") self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper'))","except: logger.warning( 'An error occurred in registered function, ' 'use","'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path:","get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \"\"\" 获取所有聊天对象 :param","alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path","if ret is not None: if isinstance(ret, (tuple, list)): self.core.send(","import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object):","self) def friends(self, update=False): \"\"\" 获取所有好友 :param update: 是否更新 :return:","self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt:","Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self, update=False): \"\"\"","chats def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return:","PyBroadException try: ret = func(msg) if ret is not None:","时将在新的线程中运行 \"\"\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive:","self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self","匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes) # add / create @handle_response()","ret is not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]),","(SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled:","self.core.alive @alive.setter def alive(self, value): self.core.alive = value def dump_login_status(self,","save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts):","import logging from wxpy.chat import Chat from wxpy.chats import Chats","province='广东' :return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes) # add /","update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update,","= logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def","单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\" def","name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes:","获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\"","except_self=except_self, run_async=run_async, enabled=enabled )) return func return register def start(self,","userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''):","run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types:","return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts): \"\"\"","def process(): # noinspection PyBroadException try: ret = func(msg) if",":param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return","verify_content: 验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True )","self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list() for chunk","Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手'","' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async:","else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param","qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback:","function, ' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if",":param block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def listen(): logger.info('{} Auto-reply","获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上","= self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot =","__init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \"\"\"","return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \"\"\" 登出当前账号 \"\"\"","\"\"\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集","= ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to","mps(self, update=False): \"\"\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \"\"\"","wxpy.chat import Chat from wxpy.chats import Chats from wxpy.friend import","pprint import pformat from threading import Thread import itchat import","run_async: Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None, except_self=True,","False \"\"\" return self.core.alive @alive.setter def alive(self, value): self.core.alive =","验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content,","chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param chats:",":param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core()","import MP from wxpy.response import ResponseError from wxpy.user import User","exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper =","MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP from wxpy.response","获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return Chats(self.friends(update) +","logger.info('Bye.') if block: listen() else: t = Thread(target=listen, daemon=True) t.start()","update: 是否更新 :return: 聊天对象合集 \"\"\" return Chats(self.friends(update) + self.groups(update) +","user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed","self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen()","x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \"\"\" 获取所有聊天对象","qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self)","topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request(): return self.core.create_chatroom(","# noinspection PyBroadException try: ret = func(msg) if ret is","self.save_path) # chats def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts:","group:\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\" if","import itchat import logging from wxpy.chat import Chat from wxpy.chats",":param update: 是否更新 :return: 聊天对象合集 \"\"\" return Chats(self.friends(update) + self.groups(update)","`Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else:",":return: 聊天对象合集 \"\"\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self)","# chats def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表","enabled=enabled )) return func return register def start(self, block=True): \"\"\"","msg = Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg)","wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP","chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk))","statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self)","users, topic=None): \"\"\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称","单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param","qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如:","logger.warning( 'An error occurred in registered function, ' 'use `Robot().start(debug=True)`","if run_async: Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None,","def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表","request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret =","ResponseError from wxpy.user import User from wxpy.utils.constants import SYSTEM from","msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度","self.core.logout() @property def alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False",":param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装","{}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \"\"\" 登出当前账号 \"\"\" return self.core.logout()","create @handle_response() def add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友 :param user:","\"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外)","def do(): return self.core.get_friends(update=update) ret = do() ret.source = self","autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友 :param","是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self,","contact_only=False): \"\"\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return:","wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list","tuple)): ret = list() for chunk in chunks(): chunk_ret =","status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \"\"\" 创建一个新的群聊","status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core","handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \"\"\"",":param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async:","ret += chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users)","**attributes) # add / create @handle_response() def add_friend(self, user, verify_content=''):","self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret = request() user_name","开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def listen(): logger.info('{}",":param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP)","class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def __init__( self,","topic=None): \"\"\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return:","在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid,","): \"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param","in registered function, ' 'use `Robot().start(debug=True)` to show detailed information')","in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret +=","# messages def _process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\" if not","occurred in registered function, ' 'use `Robot().start(debug=True)` to show detailed","def groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊 :param update: 是否更新 :param","enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages","except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload:","False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t","\"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True","wxpy.group import Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig","import ResponseError from wxpy.user import User from wxpy.utils.constants import SYSTEM","'' ) ret = request() user_name = ret.get('ChatRoomName') if user_name:","process_one_chunk(user_or_users) def search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称","user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user), status=2,","else: process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True","= self.message_configs.get_func(msg) if not func: return def process(): # noinspection","Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def __repr__(self): return","itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback )","处理接收到的消息 \"\"\" if not self.alive: return func, run_async = self.message_configs.get_func(msg)","False 时将在新的线程中运行 \"\"\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while","从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return list(filter(lambda x:","uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\"","wxpy.user import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import","if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt","received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if","\"\"\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员","hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs =","chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return","total = ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield","return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \"\"\" 获取所有公众号 :param",":return: 排除自身后的列表 \"\"\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts))","chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return register","if not self.alive: return func, run_async = self.message_configs.get_func(msg) if not","self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param","msg_types=None, except_self=True, run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象","群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \"\"\"","= False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else:",":param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig(","self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身","\"\"\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path,","def start(self, block=True): \"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行","用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content,","return self.core.alive @alive.setter def alive(self, value): self.core.alive = value def","+ chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list,","'文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path","在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份),","user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) #","self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user,","\"\"\" 登出当前账号 \"\"\" return self.core.logout() @property def alive(self): \"\"\" 当前的登陆状态","total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users,","SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger =","topic=topic or '' ) ret = request() user_name = ret.get('ChatRoomName')","else: raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) # messages def _process_message(self,","'An error occurred in registered function, ' 'use `Robot().start(debug=True)` to","self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self","= itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback,","排除自身后的列表 \"\"\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def","@handle_response() def logout(self): \"\"\" 登出当前账号 \"\"\" return self.core.logout() @property def","= do() ret.source = self return ret @handle_response(Group) def groups(self,","名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东'","def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg =","run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self,","userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \"\"\"","except_self=True, run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param","import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger","\"\"\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self,","wxpy.response import ResponseError from wxpy.user import User from wxpy.utils.constants import","ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索",":param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback:","groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊 :param update: 是否更新 :param contact_only:","func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return","return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list() for","聊天对象合集 \"\"\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def","information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register( self,","block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def listen(): logger.info('{} Auto-reply started.'.format(self))","request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise","add / create @handle_response() def add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友",":param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友","process(): # noinspection PyBroadException try: ret = func(msg) if ret","update: 是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend) def do(): return self.core.get_friends(update=update)","是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try:","login_callback=None, logout_callback=None ): \"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 |","self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name","@alive.setter def alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None):","search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param","province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes)","# add / create @handle_response() def add_friend(self, user, verify_content=''): \"\"\"","return self.core.get_friends(update=update) ret = do() ret.source = self return ret","__repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \"\"\" 登出当前账号","import pformat from threading import Thread import itchat import logging","self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False","user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param",":param update: 是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend) def do(): return","def add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友 :param user: 用户对象或用户名 :param","if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t =","register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \"\"\" 装饰器：用于注册消息配置","\"\"\" def chunks(): total = ensure_list(user_or_users) for i in range(0,","'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start()","ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield total[i:i +","self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response()","def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret =","if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return ret","= Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg)","block=True): \"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def","try: ret = func(msg) if ret is not None: if","picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages =","create_group(self, users, topic=None): \"\"\" 创建一个新的群聊 :param users: 用户列表 :param topic:","chats(self, update=False): \"\"\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \"\"\"","threading import Thread import itchat import logging from wxpy.chat import","ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes):","import Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from","process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ):",":return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes) # add / create","self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is not SYSTEM:","started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type","value): self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or","(可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return:",":param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats,","do() ret.source = self return ret @handle_response(Group) def groups(self, update=False,","\"\"\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update)","= Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self =","wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web","update=False): \"\"\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return","return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self,","logout_callback=None ): \"\"\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆","msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received,","to create group:\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \"\"\" 处理接收到的消息","SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive =","self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users:","start(self, block=True): \"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\"","@handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret","self self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name)","def chats(self, update=False): \"\"\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集","else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred","return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self, update=False):","= MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot =","\"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return list(filter(lambda","ret = request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name))","填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param","微信的功能 \"\"\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None,","update=False): \"\"\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend)","添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" return self.core.add_friend(","itchat import logging from wxpy.chat import Chat from wxpy.chats import","enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types,","| 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径","status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \"\"\"","console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \"\"\" :param save_path: |","\"\"\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or ''","for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list):","None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] )","获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend) def do():","@property def alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \"\"\"","\"\"\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" #",") except: logger.warning( 'An error occurred in registered function, '","def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \"\"\"","\"\"\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象","show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def","Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp","当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \"\"\" return self.core.alive @alive.setter def","list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name","Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User'])","\"\"\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg","from wxpy.friend import Friend from wxpy.group import Group from wxpy.message","return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param","ret.source = self return ret @handle_response(Group) def groups(self, update=False, contact_only=False):","def user_details(self, user_or_users, chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name","run_async=run_async, enabled=enabled )) return func return register def start(self, block=True):","(tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret),","\"\"\" 处理接收到的消息 \"\"\" if not self.alive: return func, run_async =",":param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\" def chunks(): total","attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\"","try: while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is","wxpy.mp import MP from wxpy.response import ResponseError from wxpy.user import","登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr,","\"\"\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend) def","用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend(","toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning(","func(msg) if ret is not None: if isinstance(ret, (tuple, list)):","User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name,","self.chats().search(name, **attributes) # add / create @handle_response() def add_friend(self, user,","chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret",":return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50):","\"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \"\"\" def listen():","\"\"\" if not self.alive: return func, run_async = self.message_configs.get_func(msg) if","itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback","not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1]","def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret","Messages, Message, MessageConfig from wxpy.mp import MP from wxpy.response import","分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\" def chunks(): total = ensure_list(user_or_users)","self.mps(update), self) def friends(self, update=False): \"\"\" 获取所有好友 :param update: 是否更新","= ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield total[i:i","qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core =","verify_content=''): \"\"\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\"","return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) # messages","chats_or_dicts)) def chats(self, update=False): \"\"\" 获取所有聊天对象 :param update: 是否更新 :return:","register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled ))","ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block:","# Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True )","from wxpy.user import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools","@handle_response(Group) def groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊 :param update: 是否更新","chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\" def chunks(): total =","from wxpy.chats import Chats from wxpy.friend import Friend from wxpy.group","self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path)","robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func","not self.alive: return func, run_async = self.message_configs.get_func(msg) if not func:","self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot","self.dump_login_status() logger.info('Bye.') if block: listen() else: t = Thread(target=listen, daemon=True)","@handle_response() def accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友 :param user: 用户对象或用户名","Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status,",":param update: 是否更新 :return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User) def","city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes) #","获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return:",":param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user),","from wxpy.response import ResponseError from wxpy.user import User from wxpy.utils.constants","异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func,","\"\"\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None","\"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50","def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ):","isinstance(user_or_users, (list, tuple)): ret = list() for chunk in chunks():","friends(self, update=False): \"\"\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \"\"\"","return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users,","func, run_async = self.message_configs.get_func(msg) if not func: return def process():","= value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) #","def friends(self, update=False): \"\"\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集","保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上","isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send(","range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk):","当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self,",":param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\"","user, verify_content=''): \"\"\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息","return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret = request()",":param verify_content: 验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user),","_process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\" if not self.alive: return func,","from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import","from wxpy.chat import Chat from wxpy.chats import Chats from wxpy.friend",":return: 单个或多个用户用户的详细信息 \"\"\" def chunks(): total = ensure_list(user_or_users) for i","logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def __init__(","self.core.get_friends(update=update) ret = do() ret.source = self return ret @handle_response(Group)","user, verify_content=''): \"\"\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息","import traceback from pprint import pformat from threading import Thread","contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \"\"\" 获取所有公众号 :param update: 是否更新","messages def _process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\" if not self.alive:",") else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error","to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process()","get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分",":param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response()","若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or","def chunks(): total = ensure_list(user_or_users) for i in range(0, len(total),","self.groups(update) + self.mps(update), self) def friends(self, update=False): \"\"\" 获取所有好友 :param",":param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集","update: 是否更新 :return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self,","is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...')","msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return register def","KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status()","user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \"\"\"","is not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name,","error occurred in registered function, ' 'use `Robot().start(debug=True)` to show","save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self):","50 :return: 单个或多个用户用户的详细信息 \"\"\" def chunks(): total = ensure_list(user_or_users) for","是否更新 :return: 聊天对象合集 \"\"\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret","def _process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\" if not self.alive: return","+ self.groups(update) + self.mps(update), self) def friends(self, update=False): \"\"\" 获取所有好友","len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return","= list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if","name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定",":return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False):","聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \"\"\"","def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \"\"\"","\"\"\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" return","logger = logging.getLogger('wxpy') class Robot(object): \"\"\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\"","装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param","\"\"\" return self.chats().search(name, **attributes) # add / create @handle_response() def","process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list()","@handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' )","等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name, **attributes) # add",":param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param","logout_callback: 登出时的回调，接收参数同上 \"\"\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path,","聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return list(filter(lambda x: get_user_name(x) != self.self.user_name,","list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret,","= request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else:","return self.core.logout() @property def alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为","func: return def process(): # noinspection PyBroadException try: ret =","logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self)","from wxpy.mp import MP from wxpy.response import ResponseError from wxpy.user","self.self.user_name, chats_or_dicts)) def chats(self, update=False): \"\"\" 获取所有聊天对象 :param update: 是否更新","value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats","self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def","def mps(self, update=False): \"\"\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集","True，否则为 False \"\"\" return self.core.alive @alive.setter def alive(self, value): self.core.alive","from wxpy.group import Group from wxpy.message import MessageConfigs, Messages, Message,","**attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是","def alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \"\"\" return","while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is not","是否更新 :return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users,","return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊 :param","ret = func(msg) if ret is not None: if isinstance(ret,","\"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别),","def accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友 :param user: 用户对象或用户名 :param","用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request():","or self.save_path) # chats def except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param","console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数:","'<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \"\"\" 登出当前账号 \"\"\" return",":param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self:","do(): return self.core.get_friends(update=update) ret = do() ret.source = self return","若为登陆状态，则为 True，否则为 False \"\"\" return self.core.alive @alive.setter def alive(self, value):","self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in","logging from wxpy.chat import Chat from wxpy.chats import Chats from","return func, run_async = self.message_configs.get_func(msg) if not func: return def","traceback from pprint import pformat from threading import Thread import","import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP from","update=False): \"\"\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return","if isinstance(user_or_users, (list, tuple)): ret = list() for chunk in","聊天对象合集 \"\"\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do()","update=False, contact_only=False): \"\"\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊","= '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path =","是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only)","chunk_size=50): \"\"\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为","用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param","登出当前账号 \"\"\" return self.core.logout() @property def alive(self): \"\"\" 当前的登陆状态 :return:","+ self.mps(update), self) def friends(self, update=False): \"\"\" 获取所有好友 :param update:","import Chat from wxpy.chats import Chats from wxpy.friend import Friend","not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive","Message, MessageConfig from wxpy.mp import MP from wxpy.response import ResponseError","Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True,","user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return","if not func: return def process(): # noinspection PyBroadException try:","= process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret)","wxpy.friend import Friend from wxpy.group import Group from wxpy.message import","sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\" return self.chats().search(name,","toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in registered function,",":param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\" return list(filter(lambda x: get_user_name(x)","self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name )","def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled","create group:\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \"\"\" 处理接收到的消息 \"\"\"","from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy')","else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self, name=None,","接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\" # Todo:","\"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \"\"\" return self.core.alive @alive.setter","from threading import Thread import itchat import logging from wxpy.chat","save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow","获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \"\"\" return self.core.get_mps(update=update) @handle_response(User)","def search(self, name=None, **attributes): \"\"\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等)",":param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息","属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \"\"\" return","排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \"\"\" def register(func):","mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An","self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \"\"\" :param","chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else:","not func: return def process(): # noinspection PyBroadException try: ret","i in range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response()","verify_content=''): \"\"\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \"\"\"","类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭","import Chats from wxpy.friend import Friend from wxpy.group import Group","/ create @handle_response() def add_friend(self, user, verify_content=''): \"\"\" 添加用户为好友 :param","@handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do() ret.source =","Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) # messages def","wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class","chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)):",":param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param","\"\"\" return self.core.alive @alive.setter def alive(self, value): self.core.alive = value","verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友","if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else:","= Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def __repr__(self):","list): ret += chunk_ret else: ret.append(chunk_ret) return ret else: return","验证说明信息 \"\"\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response()","(list, tuple)): ret = list() for chunk in chunks(): chunk_ret","+= chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def","self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None):","wxpy.chats import Chats from wxpy.friend import Friend from wxpy.group import","self return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \"\"\" 获取所有群聊","memberList=wrap_user_name(users), topic=topic or '' ) ret = request() user_name =","群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users),","self.alive: return func, run_async = self.message_configs.get_func(msg) if not func: return","enabled=True ): \"\"\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型","chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息","users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \"\"\" @handle_response() def","or '' ) ret = request() user_name = ret.get('ChatRoomName') if","from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name,","ret = do() ret.source = self return ret @handle_response(Group) def","\"\"\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def",")) return func return register def start(self, block=True): \"\"\" 开始监听和处理消息","\"\"\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \"\"\" 获取所有公众号","ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create","= self return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \"\"\"","self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t = Thread(target=listen,","return func return register def start(self, block=True): \"\"\" 开始监听和处理消息 :param","ResponseError('Failed to create group:\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \"\"\"",":return: 若为登陆状态，则为 True，否则为 False \"\"\" return self.core.alive @alive.setter def alive(self,",") @handle_response() def accept_friend(self, user, verify_content=''): \"\"\" 接受用户为好友 :param user:","import Friend from wxpy.group import Group from wxpy.message import MessageConfigs,","Chats from wxpy.friend import Friend from wxpy.group import Group from","noinspection PyBroadException try: ret = func(msg) if ret is not","return register def start(self, block=True): \"\"\" 开始监听和处理消息 :param block: 是否堵塞线程，为","def alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None): return","return self.chats().search(name, **attributes) # add / create @handle_response() def add_friend(self,","Friend from wxpy.group import Group from wxpy.message import MessageConfigs, Messages,","机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \"\"\" def __init__( self, save_path=None, console_qr=False, qr_path=None,","run_async = self.message_configs.get_func(msg) if not func: return def process(): #","alive(self): \"\"\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \"\"\" return self.core.alive","import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response,","verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \"\"\" 创建一个新的群聊 :param"],"string":"[\n \"logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.')\",\n \"except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\"\",\n \"loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper\",\n \"self.message_configs.get_func(msg) if not func: return def process(): # noinspection PyBroadException\",\n \"\\\"\\\"\\\" return self.core.logout() @property def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为\",\n \"单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param\",\n \"msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except:\",\n \"dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self,\",\n \"listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(),\",\n \"process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return\",\n \"是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update) + self.mps(update),\",\n \":param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request(): return\",\n \":param verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True\",\n \"list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\"\",\n \"from pprint import pformat from threading import Thread import itchat\",\n \"self.self.name) @handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout() @property\",\n \"ret @handle_response(Group) def groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update:\",\n \"\\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self,\",\n \"return def process(): # noinspection PyBroadException try: ret = func(msg)\",\n \":param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市)\",\n \"login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self)\",\n \"except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def\",\n \"= self self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__,\",\n \"logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout() @property def alive(self): \\\"\\\"\\\"\",\n \"Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self) if\",\n \"单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total = ensure_list(user_or_users) for i in\",\n \"return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False):\",\n \"isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return ret else:\",\n \"for i in range(0, len(total), chunk_size): yield total[i:i + chunk_size]\",\n \"raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg):\",\n \"创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\"\",\n \"chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x: get_user_name(x) !=\",\n \"user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks():\",\n \") def create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表\",\n \"= Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name =\",\n \"= func(msg) if ret is not None: if isinstance(ret, (tuple,\",\n \"user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size:\",\n \"def create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表 :param\",\n \"registered function, ' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc())\",\n \"import Thread import itchat import logging from wxpy.chat import Chat\",\n \"register def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False\",\n \":param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path),\",\n \"chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret\",\n \"self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot\",\n \"chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self,\",\n \"logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register( self, chats=None,\",\n \"\\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def __init__( self, save_path=None, console_qr=False,\",\n \"MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self\",\n \"ensure_list logger = logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能\",\n \"self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback,\",\n \"self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs\",\n \"msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in registered\",\n \"self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return\",\n \"add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content:\",\n \"\\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update)\",\n \"chunks(): total = ensure_list(user_or_users) for i in range(0, len(total), chunk_size):\",\n \"func return register def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block:\",\n \"detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register(\",\n \":return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic\",\n \"\\\"\\\"\\\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do() ret.source\",\n \"autoUpdate=True ) def create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users:\",\n \"contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def\",\n \":return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret =\",\n \"verify_content: 验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3,\",\n \"pformat from threading import Thread import itchat import logging from\",\n \") ret = request() user_name = ret.get('ChatRoomName') if user_name: return\",\n \"Web 微信的功能 \\\"\\\"\\\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None,\",\n \"\\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async,\",\n \"Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def __init__( self, save_path=None,\",\n \"Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except\",\n \"模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode\",\n \"验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self,\",\n \"self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if\",\n \"yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if\",\n \"!= self.self.user_name, chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update:\",\n \"def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def\",\n \"| 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块\",\n \"@handle_response(User) def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或\",\n \"Thread import itchat import logging from wxpy.chat import Chat from\",\n \"@handle_response() def add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名\",\n \"MessageConfig from wxpy.mp import MP from wxpy.response import ResponseError from\",\n \"in range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def\",\n \"MP from wxpy.response import ResponseError from wxpy.user import User from\",\n \"qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback:\",\n \"msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not self.alive: return func, run_async\",\n \"登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login(\",\n \"self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param update:\",\n \"save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param save_path:\",\n \"def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout() @property def alive(self):\",\n \"): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM\",\n \"return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name:\",\n \"self.self.robot = self self.save_path = save_path def __repr__(self): return '<{}:\",\n \"@handle_response(MP) def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param update: 是否更新 :return:\",\n \"ret = list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk)\",\n \"accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content:\",\n \"self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path\",\n \"= save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def\",\n \"Chat from wxpy.chats import Chats from wxpy.friend import Friend from\",\n \"qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。\",\n \"\\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr:\",\n \"if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret)))\",\n \"return ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \\\"\\\"\\\"\",\n \"Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def\",\n \") self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper'))\",\n \"except: logger.warning( 'An error occurred in registered function, ' 'use\",\n \"'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path:\",\n \"get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param\",\n \"alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path\",\n \"if ret is not None: if isinstance(ret, (tuple, list)): self.core.send(\",\n \"import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object):\",\n \"self) def friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param update: 是否更新 :return:\",\n \"self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt:\",\n \"Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self, update=False): \\\"\\\"\\\"\",\n \"chats def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return:\",\n \"PyBroadException try: ret = func(msg) if ret is not None:\",\n \"时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive:\",\n \"self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self\",\n \"匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes) # add / create @handle_response()\",\n \"ret is not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]),\",\n \"(SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled:\",\n \"self.core.alive @alive.setter def alive(self, value): self.core.alive = value def dump_login_status(self,\",\n \"save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts):\",\n \"import logging from wxpy.chat import Chat from wxpy.chats import Chats\",\n \"province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes) # add /\",\n \"update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update,\",\n \"= logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def\",\n \"单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def\",\n \"name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes:\",\n \"获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\"\",\n \"except_self=except_self, run_async=run_async, enabled=enabled )) return func return register def start(self,\",\n \"userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''):\",\n \"run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types:\",\n \"return self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts): \\\"\\\"\\\"\",\n \"def process(): # noinspection PyBroadException try: ret = func(msg) if\",\n \":param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return\",\n \"verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True )\",\n \"self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list() for chunk\",\n \"Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手'\",\n \"' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async:\",\n \"else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param\",\n \"qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback:\",\n \"function, ' 'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if\",\n \":param block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{} Auto-reply\",\n \"获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上\",\n \"= self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot =\",\n \"__init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\"\",\n \"return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集\",\n \"= ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to\",\n \"mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\"\",\n \"wxpy.chat import Chat from wxpy.chats import Chats from wxpy.friend import\",\n \"pprint import pformat from threading import Thread import itchat import\",\n \"run_async: Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None, except_self=True,\",\n \"False \\\"\\\"\\\" return self.core.alive @alive.setter def alive(self, value): self.core.alive =\",\n \"验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content,\",\n \"chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats:\",\n \":param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core()\",\n \"import MP from wxpy.response import ResponseError from wxpy.user import User\",\n \"exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper =\",\n \"MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP from wxpy.response\",\n \"获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) +\",\n \"logger.info('Bye.') if block: listen() else: t = Thread(target=listen, daemon=True) t.start()\",\n \"update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update) +\",\n \"user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed\",\n \"self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen()\",\n \"x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象\",\n \"qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages = Messages(robot=self)\",\n \"topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request(): return self.core.create_chatroom(\",\n \"# noinspection PyBroadException try: ret = func(msg) if ret is\",\n \"self.save_path) # chats def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts:\",\n \"group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if\",\n \"import itchat import logging from wxpy.chat import Chat from wxpy.chats\",\n \":param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update)\",\n \"`Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else:\",\n \":return: 聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self)\",\n \"# chats def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表\",\n \"enabled=enabled )) return func return register def start(self, block=True): \\\"\\\"\\\"\",\n \"msg = Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg)\",\n \"wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP\",\n \"chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk))\",\n \"statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self)\",\n \"users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称\",\n \"单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param\",\n \"qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如:\",\n \"logger.warning( 'An error occurred in registered function, ' 'use `Robot().start(debug=True)`\",\n \"if run_async: Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None,\",\n \"def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表\",\n \"request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret =\",\n \"ResponseError from wxpy.user import User from wxpy.utils.constants import SYSTEM from\",\n \"msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度\",\n \"self.core.logout() @property def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False\",\n \":param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装\",\n \"{}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout()\",\n \"create @handle_response() def add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user:\",\n \"\\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外)\",\n \"def do(): return self.core.get_friends(update=update) ret = do() ret.source = self\",\n \"autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param\",\n \"是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self,\",\n \"contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return:\",\n \"wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list\",\n \"tuple)): ret = list() for chunk in chunks(): chunk_ret =\",\n \"status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊\",\n \"status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core\",\n \"handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\"\",\n \":param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async:\",\n \"ret += chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users)\",\n \"**attributes) # add / create @handle_response() def add_friend(self, user, verify_content=''):\",\n \"self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret = request() user_name\",\n \"开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{}\",\n \":param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP)\",\n \"class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def __init__( self,\",\n \"topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return:\",\n \"在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid,\",\n \"): \\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param\",\n \"in registered function, ' 'use `Robot().start(debug=True)` to show detailed information')\",\n \"in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret +=\",\n \"# messages def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not\",\n \"occurred in registered function, ' 'use `Robot().start(debug=True)` to show detailed\",\n \"def groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update: 是否更新 :param\",\n \"enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages\",\n \"except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload:\",\n \"False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t\",\n \"\\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True\",\n \"wxpy.group import Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig\",\n \"import ResponseError from wxpy.user import User from wxpy.utils.constants import SYSTEM\",\n \"'' ) ret = request() user_name = ret.get('ChatRoomName') if user_name:\",\n \"process_one_chunk(user_or_users) def search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称\",\n \"user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2,\",\n \"else: process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True\",\n \"= self.message_configs.get_func(msg) if not func: return def process(): # noinspection\",\n \"Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def __repr__(self): return\",\n \"itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback )\",\n \"处理接收到的消息 \\\"\\\"\\\" if not self.alive: return func, run_async = self.message_configs.get_func(msg)\",\n \"False 时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while\",\n \"从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x:\",\n \"uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\"\",\n \"wxpy.user import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import\",\n \"if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt\",\n \"received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if\",\n \"\\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员\",\n \"hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs =\",\n \"chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return\",\n \"total = ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield\",\n \"return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param\",\n \":return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts))\",\n \"chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return register\",\n \"if not self.alive: return func, run_async = self.message_configs.get_func(msg) if not\",\n \"self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param\",\n \"msg_types=None, except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象\",\n \"群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \\\"\\\"\\\"\",\n \"= False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else:\",\n \":param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig(\",\n \"self.core.dump_login_status(save_path or self.save_path) # chats def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身\",\n \"\\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path,\",\n \"def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行\",\n \"用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content,\",\n \"return self.core.alive @alive.setter def alive(self, value): self.core.alive = value def\",\n \"+ chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list,\",\n \"'文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path\",\n \"在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份),\",\n \"user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) #\",\n \"self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user,\",\n \"\\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout() @property def alive(self): \\\"\\\"\\\" 当前的登陆状态\",\n \"total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users,\",\n \"SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger =\",\n \"topic=topic or '' ) ret = request() user_name = ret.get('ChatRoomName')\",\n \"else: raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self,\",\n \"'An error occurred in registered function, ' 'use `Robot().start(debug=True)` to\",\n \"self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self\",\n \"= itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback,\",\n \"排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def\",\n \"@handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return self.core.logout() @property def\",\n \"= do() ret.source = self return ret @handle_response(Group) def groups(self,\",\n \"名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东'\",\n \"def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg =\",\n \"run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self,\",\n \"userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \\\"\\\"\\\"\",\n \"except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param\",\n \"import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger\",\n \"\\\"\\\"\\\" return list(filter(lambda x: get_user_name(x) != self.self.user_name, chats_or_dicts)) def chats(self,\",\n \"wxpy.response import ResponseError from wxpy.user import User from wxpy.utils.constants import\",\n \"ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索\",\n \":param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback:\",\n \"groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update: 是否更新 :param contact_only:\",\n \"func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return\",\n \"return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list() for\",\n \"聊天对象合集 \\\"\\\"\\\" return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def\",\n \"information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def register( self,\",\n \"block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{} Auto-reply started.'.format(self))\",\n \"request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise\",\n \"add / create @handle_response() def add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友\",\n \":param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友\",\n \"process(): # noinspection PyBroadException try: ret = func(msg) if ret\",\n \"update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do(): return self.core.get_friends(update=update)\",\n \"是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try:\",\n \"login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 |\",\n \"self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name\",\n \"@alive.setter def alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None):\",\n \"search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param\",\n \"province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes)\",\n \"# add / create @handle_response() def add_friend(self, user, verify_content=''): \\\"\\\"\\\"\",\n \"return self.core.get_friends(update=update) ret = do() ret.source = self return ret\",\n \"__repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号\",\n \"import pformat from threading import Thread import itchat import logging\",\n \"self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False\",\n \"user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param\",\n \":param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do(): return\",\n \"def add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名 :param\",\n \"if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t =\",\n \"register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置\",\n \"\\\"\\\"\\\" def chunks(): total = ensure_list(user_or_users) for i in range(0,\",\n \"'use `Robot().start(debug=True)` to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start()\",\n \"ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield total[i:i +\",\n \"self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response()\",\n \"def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret =\",\n \"if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret) return ret\",\n \"= Message(self.core.msgList.get(), self) if msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg)\",\n \"block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def\",\n \"try: ret = func(msg) if ret is not None: if\",\n \"picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback ) self.message_configs = MessageConfigs(self) self.messages =\",\n \"create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表 :param topic:\",\n \"chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\"\",\n \"threading import Thread import itchat import logging from wxpy.chat import\",\n \"ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self, name=None, **attributes):\",\n \"import Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from\",\n \"process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ):\",\n \":return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes) # add / create\",\n \"self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is not SYSTEM:\",\n \"started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type\",\n \"value): self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or\",\n \"(可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return:\",\n \":param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats,\",\n \"do() ret.source = self return ret @handle_response(Group) def groups(self, update=False,\",\n \"\\\"\\\"\\\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update)\",\n \"= Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self =\",\n \"wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web\",\n \"update=False): \\\"\\\"\\\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return\",\n \"return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self,\",\n \"logout_callback=None ): \\\"\\\"\\\" :param save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆\",\n \"msg.type is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received,\",\n \"to create group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息\",\n \"SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive =\",\n \"self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users:\",\n \"start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\"\",\n \"@handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret\",\n \"self self.save_path = save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name)\",\n \"def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集\",\n \"else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred\",\n \"return Chats(self.friends(update) + self.groups(update) + self.mps(update), self) def friends(self, update=False):\",\n \"= MessageConfigs(self) self.messages = Messages(robot=self) self.file_helper = Chat(wrap_user_name('filehelper')) self.file_helper.robot =\",\n \"\\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda\",\n \"ret = request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name))\",\n \"填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param\",\n \"微信的功能 \\\"\\\"\\\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None,\",\n \"update=False): \\\"\\\"\\\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend)\",\n \"添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend(\",\n \"itchat import logging from wxpy.chat import Chat from wxpy.chats import\",\n \"enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types,\",\n \"| 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径\",\n \"status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \\\"\\\"\\\"\",\n \"console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param save_path: |\",\n \"\\\"\\\"\\\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or ''\",\n \"for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list):\",\n \"None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] )\",\n \"获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do():\",\n \"@property def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" #\",\n \") except: logger.warning( 'An error occurred in registered function, '\",\n \"def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" 创建一个新的群聊 :param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象\",\n \"show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process() def\",\n \"Group from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp\",\n \"当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return self.core.alive @alive.setter def\",\n \"list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name\",\n \"Chat(wrap_user_name('filehelper')) self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User'])\",\n \"\\\"\\\"\\\" def listen(): logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg\",\n \"from wxpy.friend import Friend from wxpy.group import Group from wxpy.message\",\n \"return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param\",\n \"ret.source = self return ret @handle_response(Group) def groups(self, update=False, contact_only=False):\",\n \"def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name\",\n \"run_async=run_async, enabled=enabled )) return func return register def start(self, block=True):\",\n \"(tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret),\",\n \"\\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not self.alive: return func, run_async =\",\n \":param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total\",\n \"attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\"\",\n \"try: while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is\",\n \"wxpy.mp import MP from wxpy.response import ResponseError from wxpy.user import\",\n \"登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr,\",\n \"\\\"\\\"\\\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def\",\n \"用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend(\",\n \"toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning(\",\n \"func(msg) if ret is not None: if isinstance(ret, (tuple, list)):\",\n \"User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name,\",\n \"self.chats().search(name, **attributes) # add / create @handle_response() def add_friend(self, user,\",\n \"chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret\",\n \":return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50):\",\n \"\\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为 False 时将在新的线程中运行 \\\"\\\"\\\" def listen():\",\n \"\\\"\\\"\\\" if not self.alive: return func, run_async = self.message_configs.get_func(msg) if\",\n \"itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path, enableCmdQR=console_qr, picDir=qr_path, qrCallback=qr_callback, loginCallback=login_callback, exitCallback=logout_callback\",\n \"not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1]\",\n \"def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret\",\n \"Messages, Message, MessageConfig from wxpy.mp import MP from wxpy.response import\",\n \"分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total = ensure_list(user_or_users)\",\n \"self.mps(update), self) def friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param update: 是否更新\",\n \"= ensure_list(user_or_users) for i in range(0, len(total), chunk_size): yield total[i:i\",\n \"qrcode :param login_callback: 登陆时的回调，接收参数同上 :param logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core =\",\n \"verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\"\",\n \"return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) # messages\",\n \"chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新 :return:\",\n \"register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled ))\",\n \"ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block:\",\n \"# Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True )\",\n \"from wxpy.user import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools\",\n \"@handle_response(Group) def groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update: 是否更新\",\n \"chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total =\",\n \"from wxpy.chats import Chats from wxpy.friend import Friend from wxpy.group\",\n \"self.core.alive = value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path)\",\n \"robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func\",\n \"not self.alive: return func, run_async = self.message_configs.get_func(msg) if not func:\",\n \"self.file_helper.robot = self self.file_helper.nick_name = '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot\",\n \"self.dump_login_status() logger.info('Bye.') if block: listen() else: t = Thread(target=listen, daemon=True)\",\n \"@handle_response() def accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名\",\n \"Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status,\",\n \":param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def\",\n \"city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes) #\",\n \"获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return:\",\n \":param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user),\",\n \"from wxpy.response import ResponseError from wxpy.user import User from wxpy.utils.constants\",\n \"异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func,\",\n \"\\\"\\\"\\\" def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None\",\n \"\\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50\",\n \"def __init__( self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ):\",\n \"isinstance(user_or_users, (list, tuple)): ret = list() for chunk in chunks():\",\n \"friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\"\",\n \"return self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users,\",\n \"func, run_async = self.message_configs.get_func(msg) if not func: return def process():\",\n \"= value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) #\",\n \"def friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param update: 是否更新 :return: 聊天对象合集\",\n \"保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上\",\n \"isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send(\",\n \"range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk):\",\n \"当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self,\",\n \":param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\"\",\n \"user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息\",\n \"return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' ) ret = request()\",\n \":param verify_content: 验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return self.core.add_friend( userName=get_user_name(user),\",\n \"_process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not self.alive: return func,\",\n \"from wxpy.message import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import\",\n \"from wxpy.chat import Chat from wxpy.chats import Chats from wxpy.friend\",\n \":return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total = ensure_list(user_or_users) for i\",\n \"logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def __init__(\",\n \"self.core.get_friends(update=update) ret = do() ret.source = self return ret @handle_response(Group)\",\n \"user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息\",\n \"import traceback from pprint import pformat from threading import Thread\",\n \"contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param update: 是否更新\",\n \"messages def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not self.alive:\",\n \") else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error\",\n \"to show detailed information') logger.debug(traceback.format_exc()) if run_async: Thread(target=process).start() else: process()\",\n \"get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分\",\n \":param users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response()\",\n \"若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or\",\n \"def chunks(): total = ensure_list(user_or_users) for i in range(0, len(total),\",\n \"self.groups(update) + self.mps(update), self) def friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param\",\n \":param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集\",\n \"update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self,\",\n \"is not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...')\",\n \"msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled )) return func return register def\",\n \"KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status()\",\n \"user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\"\",\n \"is not None: if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name,\",\n \"error occurred in registered function, ' 'use `Robot().start(debug=True)` to show\",\n \"save_path def __repr__(self): return '<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self):\",\n \"50 :return: 单个或多个用户用户的详细信息 \\\"\\\"\\\" def chunks(): total = ensure_list(user_or_users) for\",\n \"是否更新 :return: 聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret\",\n \"def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\" if not self.alive: return\",\n \"+ self.groups(update) + self.mps(update), self) def friends(self, update=False): \\\"\\\"\\\" 获取所有好友\",\n \"len(total), chunk_size): yield total[i:i + chunk_size] @handle_response() def process_one_chunk(_chunk): return\",\n \"= list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if\",\n \"name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定\",\n \":return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False):\",\n \"聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users, chunk_size=50): \\\"\\\"\\\"\",\n \"def register( self, chats=None, msg_types=None, except_self=True, run_async=True, enabled=True ): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" return\",\n \"logger = logging.getLogger('wxpy') class Robot(object): \\\"\\\"\\\" 机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\"\",\n \"装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param\",\n \"\\\"\\\"\\\" return self.chats().search(name, **attributes) # add / create @handle_response() def\",\n \"process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)): ret = list()\",\n \"@handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users), topic=topic or '' )\",\n \"等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name, **attributes) # add\",\n \":param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param\",\n \"logout_callback: 登出时的回调，接收参数同上 \\\"\\\"\\\" self.core = itchat.Core() itchat.instanceList.append(self) self.core.auto_login( hotReload=bool(save_path), statusStorageDir=save_path,\",\n \"聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x: get_user_name(x) != self.self.user_name,\",\n \"list() for chunk in chunks(): chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret,\",\n \"= request() user_name = ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else:\",\n \"return self.core.logout() @property def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为\",\n \"func: return def process(): # noinspection PyBroadException try: ret =\",\n \"logger.info('{} Auto-reply started.'.format(self)) try: while self.alive: msg = Message(self.core.msgList.get(), self)\",\n \"from wxpy.mp import MP from wxpy.response import ResponseError from wxpy.user\",\n \"self.self.user_name, chats_or_dicts)) def chats(self, update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新\",\n \"value def dump_login_status(self, save_path=None): return self.core.dump_login_status(save_path or self.save_path) # chats\",\n \"self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def\",\n \"def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号 :param update: 是否更新 :return: 聊天对象合集\",\n \"True，否则为 False \\\"\\\"\\\" return self.core.alive @alive.setter def alive(self, value): self.core.alive\",\n \"from wxpy.group import Group from wxpy.message import MessageConfigs, Messages, Message,\",\n \"**attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是\",\n \"def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return\",\n \"while self.alive: msg = Message(self.core.msgList.get(), self) if msg.type is not\",\n \"是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User) def user_details(self, user_or_users,\",\n \"return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param\",\n \"ret = func(msg) if ret is not None: if isinstance(ret,\",\n \"\\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等) :param attributes: 属性键值对，键可以是 sex(性别),\",\n \"def accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user: 用户对象或用户名 :param\",\n \"用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request():\",\n \"or self.save_path) # chats def except_self(self, chats_or_dicts): \\\"\\\"\\\" 从聊天对象合集或用户字典列表中排除自身 :param\",\n \"console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param qr_path: 保存二维码的路径 :param qr_callback: 获得二维码时的回调，接收参数:\",\n \"'<{}: {}>'.format(self.__class__.__name__, self.self.name) @handle_response() def logout(self): \\\"\\\"\\\" 登出当前账号 \\\"\\\"\\\" return\",\n \":param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self:\",\n \"do(): return self.core.get_friends(update=update) ret = do() ret.source = self return\",\n \"若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return self.core.alive @alive.setter def alive(self, value):\",\n \"self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in\",\n \"logging from wxpy.chat import Chat from wxpy.chats import Chats from\",\n \"return func, run_async = self.message_configs.get_func(msg) if not func: return def\",\n \"traceback from pprint import pformat from threading import Thread import\",\n \"import MessageConfigs, Messages, Message, MessageConfig from wxpy.mp import MP from\",\n \"update=False): \\\"\\\"\\\" 获取所有聊天对象 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return\",\n \"if isinstance(user_or_users, (list, tuple)): ret = list() for chunk in\",\n \"聊天对象合集 \\\"\\\"\\\" @handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do()\",\n \"update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊 :param update: 是否更新 :param contact_only: 是否限于保存为联系人的群聊\",\n \"= '文件传输助手' self.self = Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path =\",\n \"是否更新 :param contact_only: 是否限于保存为联系人的群聊 :return: 群聊合集 \\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only)\",\n \"chunk_size=50): \\\"\\\"\\\" 获取单个或批量获取多个用户的详细信息(地区、性别、签名等)，但不可用于群聊成员 :param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为\",\n \"用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow 模块 :param\",\n \"登出当前账号 \\\"\\\"\\\" return self.core.logout() @property def alive(self): \\\"\\\"\\\" 当前的登陆状态 :return:\",\n \"+ self.mps(update), self) def friends(self, update=False): \\\"\\\"\\\" 获取所有好友 :param update:\",\n \"import Chat from wxpy.chats import Chats from wxpy.friend import Friend\",\n \"not SYSTEM: self.messages.append(msg) self._process_message(msg) except KeyboardInterrupt: logger.info('KeyboardInterrupt received, ending...') self.alive\",\n \"Message, MessageConfig from wxpy.mp import MP from wxpy.response import ResponseError\",\n \"Thread(target=process).start() else: process() def register( self, chats=None, msg_types=None, except_self=True, run_async=True,\",\n \"user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" # Todo: 验证好友接口可用性，并在接受好友时直接返回新好友 return\",\n \"if not func: return def process(): # noinspection PyBroadException try:\",\n \"= process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else: ret.append(chunk_ret)\",\n \"wxpy.friend import Friend from wxpy.group import Group from wxpy.message import\",\n \"sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return self.chats().search(name,\",\n \"toUserName=msg.chat.user_name ) except: logger.warning( 'An error occurred in registered function,\",\n \":param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \\\"\\\"\\\" return list(filter(lambda x: get_user_name(x)\",\n \"self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name )\",\n \"def register(func): self.message_configs.append(MessageConfig( robot=self, func=func, chats=chats, msg_types=msg_types, except_self=except_self, run_async=run_async, enabled=enabled\",\n \"create group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \\\"\\\"\\\" 处理接收到的消息 \\\"\\\"\\\"\",\n \"from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy')\",\n \"else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def search(self, name=None,\",\n \"接受用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\" # Todo:\",\n \"\\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return self.core.alive @alive.setter\",\n \"from threading import Thread import itchat import logging from wxpy.chat\",\n \"save_path: | 用于保存或载入登陆状态的文件路径，例如: 'wxpy.pkl'，为空则不尝试载入。 | 填写本参数后，可在短时间内重新载入登陆状态，避免重复扫码，失效时会重新要求登陆 :param console_qr: 在终端中显示登陆二维码，需要安装 Pillow\",\n \"获取所有公众号 :param update: 是否更新 :return: 聊天对象合集 \\\"\\\"\\\" return self.core.get_mps(update=update) @handle_response(User)\",\n \"def search(self, name=None, **attributes): \\\"\\\"\\\" 在所有类型的聊天对象中进行搜索 :param name: 名称 (可以是昵称、备注等)\",\n \":param user_or_users: 单个或多个用户对象或 user_name :param chunk_size: 分配请求时的单批数量，目前为 50 :return: 单个或多个用户用户的详细信息\",\n \"属性键值对，键可以是 sex(性别), province(省份), city(城市) 等。例如可指定 province='广东' :return: 匹配的聊天对象合集 \\\"\\\"\\\" return\",\n \"排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭 \\\"\\\"\\\" def register(func):\",\n \"mediaId=ret[1] ) else: self.core.send( msg=str(ret), toUserName=msg.chat.user_name ) except: logger.warning( 'An\",\n \"self, save_path=None, console_qr=False, qr_path=None, qr_callback=None, login_callback=None, logout_callback=None ): \\\"\\\"\\\" :param\",\n \"chunk_ret = process_one_chunk(chunk) if isinstance(chunk_ret, list): ret += chunk_ret else:\",\n \"not func: return def process(): # noinspection PyBroadException try: ret\",\n \"i in range(0, len(total), chunk_size): yield total[i:i + chunk_size] @handle_response()\",\n \"verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param user: 用户对象或用户名 :param verify_content: 验证说明信息 \\\"\\\"\\\"\",\n \"类消息除外) :param except_self: 排除自己在手机上发送的消息 :param run_async: 异步执行配置的函数，可提高响应速度 :param enabled: 当前配置的默认开启状态，可事后动态开启或关闭\",\n \"import Chats from wxpy.friend import Friend from wxpy.group import Group\",\n \"/ create @handle_response() def add_friend(self, user, verify_content=''): \\\"\\\"\\\" 添加用户为好友 :param\",\n \"@handle_response(Friend) def do(): return self.core.get_friends(update=update) ret = do() ret.source =\",\n \"Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) # messages def\",\n \"wxpy.utils.tools import handle_response, get_user_name, wrap_user_name, ensure_list logger = logging.getLogger('wxpy') class\",\n \"chunk_size] @handle_response() def process_one_chunk(_chunk): return self.core.update_friend(userName=get_user_name(_chunk)) if isinstance(user_or_users, (list, tuple)):\",\n \":param qr_callback: 获得二维码时的回调，接收参数: uuid, status, qrcode :param login_callback: 登陆时的回调，接收参数同上 :param\",\n \"\\\"\\\"\\\" return self.core.alive @alive.setter def alive(self, value): self.core.alive = value\",\n \"verifyContent=verify_content, autoUpdate=True ) @handle_response() def accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友\",\n \"if isinstance(ret, (tuple, list)): self.core.send( msg=str(ret[0]), toUserName=msg.chat.user_name, mediaId=ret[1] ) else:\",\n \"= Chat(self.core.loginInfo['User']) self.self.robot = self self.save_path = save_path def __repr__(self):\",\n \"list): ret += chunk_ret else: ret.append(chunk_ret) return ret else: return\",\n \"验证说明信息 \\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response()\",\n \"(list, tuple)): ret = list() for chunk in chunks(): chunk_ret\",\n \"+= chunk_ret else: ret.append(chunk_ret) return ret else: return process_one_chunk(user_or_users) def\",\n \"self.core.add_friend( userName=get_user_name(user), status=3, verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None):\",\n \"wxpy.chats import Chats from wxpy.friend import Friend from wxpy.group import\",\n \"self return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \\\"\\\"\\\" 获取所有群聊\",\n \"memberList=wrap_user_name(users), topic=topic or '' ) ret = request() user_name =\",\n \"群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def request(): return self.core.create_chatroom( memberList=wrap_user_name(users),\",\n \"self.alive: return func, run_async = self.message_configs.get_func(msg) if not func: return\",\n \"enabled=True ): \\\"\\\"\\\" 装饰器：用于注册消息配置 :param chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型\",\n \"chats: 单个或列表形式的多个聊天对象或聊天类型，为空时匹配所有聊天对象 :param msg_types: 单个或列表形式的多个消息类型，为空时匹配所有消息类型 (SYSTEM 类消息除外) :param except_self: 排除自己在手机上发送的消息\",\n \"users: 用户列表 :param topic: 群名称 :return: 若建群成功，返回一个新的群聊对象 \\\"\\\"\\\" @handle_response() def\",\n \"or '' ) ret = request() user_name = ret.get('ChatRoomName') if\",\n \"from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response, get_user_name, wrap_user_name,\",\n \"ret = do() ret.source = self return ret @handle_response(Group) def\",\n \"\\\"\\\"\\\" return self.core.add_friend( userName=get_user_name(user), status=2, verifyContent=verify_content, autoUpdate=True ) @handle_response() def\",\n \")) return func return register def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息\",\n \"\\\"\\\"\\\" return self.core.get_chatrooms(update=update, contactOnly=contact_only) @handle_response(MP) def mps(self, update=False): \\\"\\\"\\\" 获取所有公众号\",\n \"ret.get('ChatRoomName') if user_name: return Group(self.core.update_chatroom(userName=user_name)) else: raise ResponseError('Failed to create\",\n \"= self return ret @handle_response(Group) def groups(self, update=False, contact_only=False): \\\"\\\"\\\"\",\n \"self.core.useHotReload: self.dump_login_status() logger.info('Bye.') if block: listen() else: t = Thread(target=listen,\",\n \"return func return register def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param\",\n \"ResponseError('Failed to create group:\\\\n{}'.format(pformat(ret))) # messages def _process_message(self, msg): \\\"\\\"\\\"\",\n \":return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return self.core.alive @alive.setter def alive(self,\",\n \") @handle_response() def accept_friend(self, user, verify_content=''): \\\"\\\"\\\" 接受用户为好友 :param user:\",\n \"import Friend from wxpy.group import Group from wxpy.message import MessageConfigs,\",\n \"Chats from wxpy.friend import Friend from wxpy.group import Group from\",\n \"noinspection PyBroadException try: ret = func(msg) if ret is not\",\n \"return register def start(self, block=True): \\\"\\\"\\\" 开始监听和处理消息 :param block: 是否堵塞线程，为\",\n \"def alive(self, value): self.core.alive = value def dump_login_status(self, save_path=None): return\",\n \"return self.chats().search(name, **attributes) # add / create @handle_response() def add_friend(self,\",\n \"Friend from wxpy.group import Group from wxpy.message import MessageConfigs, Messages,\",\n \"机器人对象，用于登陆和操作微信账号，涵盖大部分 Web 微信的功能 \\\"\\\"\\\" def __init__( self, save_path=None, console_qr=False, qr_path=None,\",\n \"run_async = self.message_configs.get_func(msg) if not func: return def process(): #\",\n \"alive(self): \\\"\\\"\\\" 当前的登陆状态 :return: 若为登陆状态，则为 True，否则为 False \\\"\\\"\\\" return self.core.alive\",\n \"import User from wxpy.utils.constants import SYSTEM from wxpy.utils.tools import handle_response,\",\n \"verifyContent=verify_content, autoUpdate=True ) def create_group(self, users, topic=None): \\\"\\\"\\\" 创建一个新的群聊 :param\"\n]","isConversational":false}}},{"rowIdx":186,"cells":{"ngram":{"kind":"list like","value":["up configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import os","detect # the default excepthook is in place and override","configuration file from .config import load_configuration env = load_configuration() from","import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's","# Set up configuration variables __all__ = ['custom_viewer', 'qglue', 'test']","= ['custom_viewer', 'qglue', 'test'] import os import sys from pkg_resources","Qt application without printing out any details about the error.","in place and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type,","the error. # Below we revert the exception hook to","= load_configuration() from .qglue import qglue from .main import load_plugins","printing out any details about the error. # Below we","DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from","load_settings load_settings() # In PyQt 5.5+, PyQt overrides the default","from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer #","from .config import load_configuration env = load_configuration() from .qglue import","that we # can't just do sys.excepthook = sys.__excepthook__ otherwise","= sys.__excepthook__ otherwise PyQt will detect # the default excepthook","from glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+, PyQt","os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args)","5.5+, PyQt overrides the default exception catching and fatally #","and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback)","the Qt application without printing out any details about the","PyQt overrides the default exception catching and fatally # crashes","exception hook to the original Python one. Note that we","import qglue from .main import load_plugins # noqa def test(no_optional_skip=False):","import load_plugins # noqa def test(no_optional_skip=False): from pytest import main","from glue.viewers.custom.helper import custom_viewer # Load user's configuration file from","# In PyQt 5.5+, PyQt overrides the default exception catching","= 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import","can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect","and fatally # crashes the Qt application without printing out","original Python one. Note that we # can't just do","we revert the exception hook to the original Python one.","._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load",".main import load_plugins # noqa def test(no_optional_skip=False): from pytest import","root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip')","import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__","from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args = [root,","the default excepthook is in place and override it. def","user's configuration file from .config import load_configuration env = load_configuration()","env = load_configuration() from .qglue import qglue from .main import","file from .config import load_configuration env = load_configuration() from .qglue","sys.__excepthook__ otherwise PyQt will detect # the default excepthook is","from .main import load_plugins # noqa def test(no_optional_skip=False): from pytest","import load_settings load_settings() # In PyQt 5.5+, PyQt overrides the","MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's configuration","the original Python one. Note that we # can't just","'qglue', 'test'] import os import sys from pkg_resources import get_distribution,","test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args =","= [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers","glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+, PyQt overrides","load_configuration() from .qglue import qglue from .main import load_plugins #","load_configuration env = load_configuration() from .qglue import qglue from .main","without printing out any details about the error. # Below","load_plugins # noqa def test(no_optional_skip=False): from pytest import main root","sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's configuration file","['custom_viewer', 'qglue', 'test'] import os import sys from pkg_resources import","out any details about the error. # Below we revert","the exception hook to the original Python one. Note that","import sys from pkg_resources import get_distribution, DistributionNotFound try: __version__ =","no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings() #","# noqa def test(no_optional_skip=False): from pytest import main root =","exception catching and fatally # crashes the Qt application without","def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook = handle_exception","to the original Python one. Note that we # can't","any details about the error. # Below we revert the","<reponame>HPLegion/glue<filename>glue/__init__.py # Set up configuration variables __all__ = ['custom_viewer', 'qglue',","main(args=args) from glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+,","pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound:","one. Note that we # can't just do sys.excepthook =","it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook =","def test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args","Set up configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import","import os import sys from pkg_resources import get_distribution, DistributionNotFound try:","try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from","# Below we revert the exception hook to the original","hook to the original Python one. Note that we #","pytest import main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x']","from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except","Load user's configuration file from .config import load_configuration env =","__version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend","# Load user's configuration file from .config import load_configuration env","do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect # the","default excepthook is in place and override it. def handle_exception(exc_type,","configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import os import","args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings() # In","load_settings() # In PyQt 5.5+, PyQt overrides the default exception","'test'] import os import sys from pkg_resources import get_distribution, DistributionNotFound","just do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect #","DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined'","[root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import","'-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings","= get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import","except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter())","the default exception catching and fatally # crashes the Qt","details about the error. # Below we revert the exception","noqa def test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__))","error. # Below we revert the exception hook to the","fatally # crashes the Qt application without printing out any","__all__ = ['custom_viewer', 'qglue', 'test'] import os import sys from","sys.excepthook = sys.__excepthook__ otherwise PyQt will detect # the default","get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ =","variables __all__ = ['custom_viewer', 'qglue', 'test'] import os import sys","PyQt will detect # the default excepthook is in place","PyQt 5.5+, PyQt overrides the default exception catching and fatally",".qglue import qglue from .main import load_plugins # noqa def","glue.viewers.custom.helper import custom_viewer # Load user's configuration file from .config","from .qglue import qglue from .main import load_plugins # noqa","custom_viewer # Load user's configuration file from .config import load_configuration","otherwise PyQt will detect # the default excepthook is in","overrides the default exception catching and fatally # crashes the","we # can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt","Below we revert the exception hook to the original Python","Python one. Note that we # can't just do sys.excepthook","excepthook is in place and override it. def handle_exception(exc_type, exc_value,","place and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value,","# crashes the Qt application without printing out any details","qglue from .main import load_plugins # noqa def test(no_optional_skip=False): from",".config import load_configuration env = load_configuration() from .qglue import qglue","= os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return","revert the exception hook to the original Python one. Note","import custom_viewer # Load user's configuration file from .config import","__version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper","os import sys from pkg_resources import get_distribution, DistributionNotFound try: __version__","default exception catching and fatally # crashes the Qt application","crashes the Qt application without printing out any details about","# the default excepthook is in place and override it.","application without printing out any details about the error. #","import main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if","'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer","if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings()","main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip:","about the error. # Below we revert the exception hook","catching and fatally # crashes the Qt application without printing","will detect # the default excepthook is in place and","override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook","Note that we # can't just do sys.excepthook = sys.__excepthook__","import load_configuration env = load_configuration() from .qglue import qglue from","sys from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version","get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter","# can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt will","is in place and override it. def handle_exception(exc_type, exc_value, exc_traceback):","args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from","In PyQt 5.5+, PyQt overrides the default exception catching and","return main(args=args) from glue._settings_helpers import load_settings load_settings() # In PyQt"],"string":"[\n \"up configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import os\",\n \"detect # the default excepthook is in place and override\",\n \"configuration file from .config import load_configuration env = load_configuration() from\",\n \"import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's\",\n \"# Set up configuration variables __all__ = ['custom_viewer', 'qglue', 'test']\",\n \"= ['custom_viewer', 'qglue', 'test'] import os import sys from pkg_resources\",\n \"Qt application without printing out any details about the error.\",\n \"in place and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type,\",\n \"the error. # Below we revert the exception hook to\",\n \"= load_configuration() from .qglue import qglue from .main import load_plugins\",\n \"printing out any details about the error. # Below we\",\n \"DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from\",\n \"load_settings load_settings() # In PyQt 5.5+, PyQt overrides the default\",\n \"from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer #\",\n \"from .config import load_configuration env = load_configuration() from .qglue import\",\n \"that we # can't just do sys.excepthook = sys.__excepthook__ otherwise\",\n \"= sys.__excepthook__ otherwise PyQt will detect # the default excepthook\",\n \"from glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+, PyQt\",\n \"os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args)\",\n \"5.5+, PyQt overrides the default exception catching and fatally #\",\n \"and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback)\",\n \"the Qt application without printing out any details about the\",\n \"PyQt overrides the default exception catching and fatally # crashes\",\n \"exception hook to the original Python one. Note that we\",\n \"import qglue from .main import load_plugins # noqa def test(no_optional_skip=False):\",\n \"import load_plugins # noqa def test(no_optional_skip=False): from pytest import main\",\n \"from glue.viewers.custom.helper import custom_viewer # Load user's configuration file from\",\n \"# In PyQt 5.5+, PyQt overrides the default exception catching\",\n \"= 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import\",\n \"can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect\",\n \"and fatally # crashes the Qt application without printing out\",\n \"original Python one. Note that we # can't just do\",\n \"we revert the exception hook to the original Python one.\",\n \"._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load\",\n \".main import load_plugins # noqa def test(no_optional_skip=False): from pytest import\",\n \"root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip')\",\n \"import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__\",\n \"from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args = [root,\",\n \"the default excepthook is in place and override it. def\",\n \"user's configuration file from .config import load_configuration env = load_configuration()\",\n \"env = load_configuration() from .qglue import qglue from .main import\",\n \"file from .config import load_configuration env = load_configuration() from .qglue\",\n \"sys.__excepthook__ otherwise PyQt will detect # the default excepthook is\",\n \"from .main import load_plugins # noqa def test(no_optional_skip=False): from pytest\",\n \"import load_settings load_settings() # In PyQt 5.5+, PyQt overrides the\",\n \"MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's configuration\",\n \"the original Python one. Note that we # can't just\",\n \"'qglue', 'test'] import os import sys from pkg_resources import get_distribution,\",\n \"test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args =\",\n \"= [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers\",\n \"glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+, PyQt overrides\",\n \"load_configuration() from .qglue import qglue from .main import load_plugins #\",\n \"load_configuration env = load_configuration() from .qglue import qglue from .main\",\n \"without printing out any details about the error. # Below\",\n \"load_plugins # noqa def test(no_optional_skip=False): from pytest import main root\",\n \"sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer # Load user's configuration file\",\n \"['custom_viewer', 'qglue', 'test'] import os import sys from pkg_resources import\",\n \"out any details about the error. # Below we revert\",\n \"the exception hook to the original Python one. Note that\",\n \"import sys from pkg_resources import get_distribution, DistributionNotFound try: __version__ =\",\n \"no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings() #\",\n \"# noqa def test(no_optional_skip=False): from pytest import main root =\",\n \"exception catching and fatally # crashes the Qt application without\",\n \"def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook = handle_exception\",\n \"to the original Python one. Note that we # can't\",\n \"any details about the error. # Below we revert the\",\n \"<reponame>HPLegion/glue<filename>glue/__init__.py # Set up configuration variables __all__ = ['custom_viewer', 'qglue',\",\n \"main(args=args) from glue._settings_helpers import load_settings load_settings() # In PyQt 5.5+,\",\n \"pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound:\",\n \"one. Note that we # can't just do sys.excepthook =\",\n \"it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook =\",\n \"def test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__)) args\",\n \"Set up configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import\",\n \"import os import sys from pkg_resources import get_distribution, DistributionNotFound try:\",\n \"try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from\",\n \"# Below we revert the exception hook to the original\",\n \"hook to the original Python one. Note that we #\",\n \"pytest import main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x']\",\n \"from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except\",\n \"Load user's configuration file from .config import load_configuration env =\",\n \"__version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend\",\n \"# Load user's configuration file from .config import load_configuration env\",\n \"do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect # the\",\n \"default excepthook is in place and override it. def handle_exception(exc_type,\",\n \"configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import os import\",\n \"args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings() # In\",\n \"load_settings() # In PyQt 5.5+, PyQt overrides the default exception\",\n \"'test'] import os import sys from pkg_resources import get_distribution, DistributionNotFound\",\n \"just do sys.excepthook = sys.__excepthook__ otherwise PyQt will detect #\",\n \"DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined'\",\n \"[root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import\",\n \"'-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings\",\n \"= get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import\",\n \"except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter())\",\n \"the default exception catching and fatally # crashes the Qt\",\n \"details about the error. # Below we revert the exception\",\n \"noqa def test(no_optional_skip=False): from pytest import main root = os.path.abspath(os.path.dirname(__file__))\",\n \"error. # Below we revert the exception hook to the\",\n \"fatally # crashes the Qt application without printing out any\",\n \"__all__ = ['custom_viewer', 'qglue', 'test'] import os import sys from\",\n \"sys.excepthook = sys.__excepthook__ otherwise PyQt will detect # the default\",\n \"get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version except DistributionNotFound: __version__ =\",\n \"variables __all__ = ['custom_viewer', 'qglue', 'test'] import os import sys\",\n \"PyQt will detect # the default excepthook is in place\",\n \"PyQt 5.5+, PyQt overrides the default exception catching and fatally\",\n \".qglue import qglue from .main import load_plugins # noqa def\",\n \"glue.viewers.custom.helper import custom_viewer # Load user's configuration file from .config\",\n \"from .qglue import qglue from .main import load_plugins # noqa\",\n \"custom_viewer # Load user's configuration file from .config import load_configuration\",\n \"otherwise PyQt will detect # the default excepthook is in\",\n \"overrides the default exception catching and fatally # crashes the\",\n \"we # can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt\",\n \"Below we revert the exception hook to the original Python\",\n \"Python one. Note that we # can't just do sys.excepthook\",\n \"excepthook is in place and override it. def handle_exception(exc_type, exc_value,\",\n \"place and override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value,\",\n \"# crashes the Qt application without printing out any details\",\n \"qglue from .main import load_plugins # noqa def test(no_optional_skip=False): from\",\n \".config import load_configuration env = load_configuration() from .qglue import qglue\",\n \"= os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return\",\n \"revert the exception hook to the original Python one. Note\",\n \"import custom_viewer # Load user's configuration file from .config import\",\n \"__version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper\",\n \"os import sys from pkg_resources import get_distribution, DistributionNotFound try: __version__\",\n \"default exception catching and fatally # crashes the Qt application\",\n \"crashes the Qt application without printing out any details about\",\n \"# the default excepthook is in place and override it.\",\n \"application without printing out any details about the error. #\",\n \"import main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if\",\n \"'undefined' from ._mpl_backend import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSetter()) from glue.viewers.custom.helper import custom_viewer\",\n \"if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from glue._settings_helpers import load_settings load_settings()\",\n \"main root = os.path.abspath(os.path.dirname(__file__)) args = [root, '-x'] if no_optional_skip:\",\n \"about the error. # Below we revert the exception hook\",\n \"catching and fatally # crashes the Qt application without printing\",\n \"will detect # the default excepthook is in place and\",\n \"override it. def handle_exception(exc_type, exc_value, exc_traceback): sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook\",\n \"Note that we # can't just do sys.excepthook = sys.__excepthook__\",\n \"import load_configuration env = load_configuration() from .qglue import qglue from\",\n \"sys from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution('glue-core').version\",\n \"get_distribution('glue-core').version except DistributionNotFound: __version__ = 'undefined' from ._mpl_backend import MatplotlibBackendSetter\",\n \"# can't just do sys.excepthook = sys.__excepthook__ otherwise PyQt will\",\n \"is in place and override it. def handle_exception(exc_type, exc_value, exc_traceback):\",\n \"args = [root, '-x'] if no_optional_skip: args.append('--no-optional-skip') return main(args=args) from\",\n \"In PyQt 5.5+, PyQt overrides the default exception catching and\",\n \"return main(args=args) from glue._settings_helpers import load_settings load_settings() # In PyQt\"\n]","isConversational":false}}},{"rowIdx":187,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":188,"cells":{"ngram":{"kind":"list like","value":["Use our new middleware instead of the old one classes","will eventually be backported into the main settings file, but","classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw) return classes","them to be able to run the site with the","'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow cookies","mw in classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s',","super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware',","Proxito Some of these settings will eventually be backported into","'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return","( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw","\"no-referrer-when-downgrade\" # Allow cookies from cross-site requests on subdomains for","CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\"","# Allow cookies from cross-site requests on subdomains for now.","# As 'Lax' breaks when the page is embedded in","in middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed to","classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in","DATABASES(self): # This keeps connections to the DB alive, #","= list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for","one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove =","have them to be able to run the site with","which reduces latency with connecting to postgres dbs = getattr(super(),","list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw","site with the old middleware for a staged rollout of","dbs @property def MIDDLEWARE(self): # noqa # Use our new","to be able to run the site with the old","noqa # Use our new middleware instead of the old","from cross-site requests on subdomains for now. # As 'Lax'","return dbs @property def MIDDLEWARE(self): # noqa # Use our","'Lax' breaks when the page is embedded in an iframe.","= getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] =","keeps connections to the DB alive, # which reduces latency","# This keeps connections to the DB alive, # which","This keeps connections to the DB alive, # which reduces","into the main settings file, but currently we have them","be backported into the main settings file, but currently we","SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow cookies from cross-site requests on","connections to the DB alive, # which reduces latency with","an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This","for a staged rollout of the proxito code. \"\"\" class","subdomains for now. # As 'Lax' breaks when the page","in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self):","the page is embedded in an iframe. SESSION_COOKIE_SAMESITE = None","on subdomains for now. # As 'Lax' breaks when the","to the DB alive, # which reduces latency with connecting","86400 return dbs @property def MIDDLEWARE(self): # noqa # Use","eventually be backported into the main settings file, but currently","we have them to be able to run the site","is embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property def","if mw in classes: classes.remove(mw) else: log.warning('Failed to remove middleware:","to run the site with the old middleware for a","the proxito code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN","a staged rollout of the proxito code. \"\"\" class CommunityProxitoSettingsMixin:","DB alive, # which reduces latency with connecting to postgres","of the old one classes = super().MIDDLEWARE classes = list(classes)","Some of these settings will eventually be backported into the","requests on subdomains for now. # As 'Lax' breaks when","for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property","# which reduces latency with connecting to postgres dbs =","code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True","Allow cookies from cross-site requests on subdomains for now. #","for Proxito Some of these settings will eventually be backported","ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" #","now. # As 'Lax' breaks when the page is embedded","mw in middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed","iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This keeps","currently we have them to be able to run the","old middleware for a staged rollout of the proxito code.","middleware for a staged rollout of the proxito code. \"\"\"","dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self): # noqa","staged rollout of the proxito code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF","the DB alive, # which reduces latency with connecting to","As 'Lax' breaks when the page is embedded in an","settings will eventually be backported into the main settings file,","our new middleware instead of the old one classes =","file, but currently we have them to be able to","alive, # which reduces latency with connecting to postgres dbs","proxito code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN =","= ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if","True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow cookies from cross-site requests","\"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY","settings file, but currently we have them to be able","connecting to postgres dbs = getattr(super(), 'DATABASES', {}) for db","classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', )","the old one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware')","for mw in middleware_to_remove: if mw in classes: classes.remove(mw) else:","= \"no-referrer-when-downgrade\" # Allow cookies from cross-site requests on subdomains","{}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs",") for mw in middleware_to_remove: if mw in classes: classes.remove(mw)","middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed to remove","but currently we have them to be able to run","= super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware',","getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400","these settings will eventually be backported into the main settings","db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def","breaks when the page is embedded in an iframe. SESSION_COOKIE_SAMESITE","in an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): #","main settings file, but currently we have them to be","of these settings will eventually be backported into the main","# noqa # Use our new middleware instead of the","USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow cookies from","cookies from cross-site requests on subdomains for now. # As","run the site with the old middleware for a staged","SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This keeps connections","= 86400 return dbs @property def MIDDLEWARE(self): # noqa #","dbs = getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE']","'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw in","when the page is embedded in an iframe. SESSION_COOKIE_SAMESITE =","instead of the old one classes = super().MIDDLEWARE classes =","class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY =","def DATABASES(self): # This keeps connections to the DB alive,","@property def DATABASES(self): # This keeps connections to the DB","backported into the main settings file, but currently we have","MIDDLEWARE(self): # noqa # Use our new middleware instead of","# Use our new middleware instead of the old one","embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self):","'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw in classes:","able to run the site with the old middleware for","for now. # As 'Lax' breaks when the page is","reduces latency with connecting to postgres dbs = getattr(super(), 'DATABASES',","None @property def DATABASES(self): # This keeps connections to the","be able to run the site with the old middleware","rollout of the proxito code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF =","to postgres dbs = getattr(super(), 'DATABASES', {}) for db in","classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = (","with the old middleware for a staged rollout of the","new middleware instead of the old one classes = super().MIDDLEWARE","classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw) return","= None @property def DATABASES(self): # This keeps connections to","with connecting to postgres dbs = getattr(super(), 'DATABASES', {}) for","middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove:","of the proxito code. \"\"\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls'","old one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove","= 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow","def MIDDLEWARE(self): # noqa # Use our new middleware instead","Base settings for Proxito Some of these settings will eventually","the old middleware for a staged rollout of the proxito","middleware instead of the old one classes = super().MIDDLEWARE classes","in classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw)","latency with connecting to postgres dbs = getattr(super(), 'DATABASES', {})","= True SECURE_REFERRER_POLICY = \"no-referrer-when-downgrade\" # Allow cookies from cross-site","page is embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property","the main settings file, but currently we have them to","dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self): #","cross-site requests on subdomains for now. # As 'Lax' breaks","\"\"\" Base settings for Proxito Some of these settings will","the site with the old middleware for a staged rollout","@property def MIDDLEWARE(self): # noqa # Use our new middleware","settings for Proxito Some of these settings will eventually be","postgres dbs = getattr(super(), 'DATABASES', {}) for db in dbs:"],"string":"[\n \"Use our new middleware instead of the old one classes\",\n \"will eventually be backported into the main settings file, but\",\n \"classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw) return classes\",\n \"them to be able to run the site with the\",\n \"'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow cookies\",\n \"mw in classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s',\",\n \"super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware',\",\n \"Proxito Some of these settings will eventually be backported into\",\n \"'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return\",\n \"( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw\",\n \"\\\"no-referrer-when-downgrade\\\" # Allow cookies from cross-site requests on subdomains for\",\n \"CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\"\",\n \"# Allow cookies from cross-site requests on subdomains for now.\",\n \"# As 'Lax' breaks when the page is embedded in\",\n \"in middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed to\",\n \"classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in\",\n \"DATABASES(self): # This keeps connections to the DB alive, #\",\n \"= list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for\",\n \"one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove =\",\n \"have them to be able to run the site with\",\n \"which reduces latency with connecting to postgres dbs = getattr(super(),\",\n \"list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw\",\n \"site with the old middleware for a staged rollout of\",\n \"dbs @property def MIDDLEWARE(self): # noqa # Use our new\",\n \"to be able to run the site with the old\",\n \"noqa # Use our new middleware instead of the old\",\n \"from cross-site requests on subdomains for now. # As 'Lax'\",\n \"return dbs @property def MIDDLEWARE(self): # noqa # Use our\",\n \"'Lax' breaks when the page is embedded in an iframe.\",\n \"= getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] =\",\n \"keeps connections to the DB alive, # which reduces latency\",\n \"# This keeps connections to the DB alive, # which\",\n \"This keeps connections to the DB alive, # which reduces\",\n \"into the main settings file, but currently we have them\",\n \"be backported into the main settings file, but currently we\",\n \"SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow cookies from cross-site requests on\",\n \"connections to the DB alive, # which reduces latency with\",\n \"an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This\",\n \"for a staged rollout of the proxito code. \\\"\\\"\\\" class\",\n \"subdomains for now. # As 'Lax' breaks when the page\",\n \"in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self):\",\n \"the page is embedded in an iframe. SESSION_COOKIE_SAMESITE = None\",\n \"on subdomains for now. # As 'Lax' breaks when the\",\n \"to the DB alive, # which reduces latency with connecting\",\n \"86400 return dbs @property def MIDDLEWARE(self): # noqa # Use\",\n \"eventually be backported into the main settings file, but currently\",\n \"we have them to be able to run the site\",\n \"is embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property def\",\n \"if mw in classes: classes.remove(mw) else: log.warning('Failed to remove middleware:\",\n \"to run the site with the old middleware for a\",\n \"the proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN\",\n \"a staged rollout of the proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin:\",\n \"DB alive, # which reduces latency with connecting to postgres\",\n \"of the old one classes = super().MIDDLEWARE classes = list(classes)\",\n \"Some of these settings will eventually be backported into the\",\n \"requests on subdomains for now. # As 'Lax' breaks when\",\n \"for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property\",\n \"# which reduces latency with connecting to postgres dbs =\",\n \"code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True\",\n \"Allow cookies from cross-site requests on subdomains for now. #\",\n \"for Proxito Some of these settings will eventually be backported\",\n \"ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" #\",\n \"now. # As 'Lax' breaks when the page is embedded\",\n \"mw in middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed\",\n \"iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This keeps\",\n \"currently we have them to be able to run the\",\n \"old middleware for a staged rollout of the proxito code.\",\n \"middleware for a staged rollout of the proxito code. \\\"\\\"\\\"\",\n \"dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self): # noqa\",\n \"staged rollout of the proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF\",\n \"the DB alive, # which reduces latency with connecting to\",\n \"As 'Lax' breaks when the page is embedded in an\",\n \"settings will eventually be backported into the main settings file,\",\n \"our new middleware instead of the old one classes =\",\n \"file, but currently we have them to be able to\",\n \"alive, # which reduces latency with connecting to postgres dbs\",\n \"proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN =\",\n \"= ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if\",\n \"True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow cookies from cross-site requests\",\n \"\\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY\",\n \"settings file, but currently we have them to be able\",\n \"connecting to postgres dbs = getattr(super(), 'DATABASES', {}) for db\",\n \"classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', )\",\n \"the old one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware')\",\n \"for mw in middleware_to_remove: if mw in classes: classes.remove(mw) else:\",\n \"= \\\"no-referrer-when-downgrade\\\" # Allow cookies from cross-site requests on subdomains\",\n \"{}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs\",\n \") for mw in middleware_to_remove: if mw in classes: classes.remove(mw)\",\n \"middleware_to_remove: if mw in classes: classes.remove(mw) else: log.warning('Failed to remove\",\n \"but currently we have them to be able to run\",\n \"= super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = ( 'csp.middleware.CSPMiddleware',\",\n \"getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400\",\n \"these settings will eventually be backported into the main settings\",\n \"db in dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def\",\n \"breaks when the page is embedded in an iframe. SESSION_COOKIE_SAMESITE\",\n \"in an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): #\",\n \"main settings file, but currently we have them to be\",\n \"of these settings will eventually be backported into the main\",\n \"# noqa # Use our new middleware instead of the\",\n \"USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow cookies from\",\n \"cookies from cross-site requests on subdomains for now. # As\",\n \"run the site with the old middleware for a staged\",\n \"SESSION_COOKIE_SAMESITE = None @property def DATABASES(self): # This keeps connections\",\n \"= 86400 return dbs @property def MIDDLEWARE(self): # noqa #\",\n \"dbs = getattr(super(), 'DATABASES', {}) for db in dbs: dbs[db]['CONN_MAX_AGE']\",\n \"'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw in\",\n \"when the page is embedded in an iframe. SESSION_COOKIE_SAMESITE =\",\n \"instead of the old one classes = super().MIDDLEWARE classes =\",\n \"class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY =\",\n \"def DATABASES(self): # This keeps connections to the DB alive,\",\n \"@property def DATABASES(self): # This keeps connections to the DB\",\n \"backported into the main settings file, but currently we have\",\n \"MIDDLEWARE(self): # noqa # Use our new middleware instead of\",\n \"# Use our new middleware instead of the old one\",\n \"embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property def DATABASES(self):\",\n \"'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove: if mw in classes:\",\n \"able to run the site with the old middleware for\",\n \"for now. # As 'Lax' breaks when the page is\",\n \"reduces latency with connecting to postgres dbs = getattr(super(), 'DATABASES',\",\n \"None @property def DATABASES(self): # This keeps connections to the\",\n \"be able to run the site with the old middleware\",\n \"rollout of the proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF =\",\n \"to postgres dbs = getattr(super(), 'DATABASES', {}) for db in\",\n \"classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove = (\",\n \"with the old middleware for a staged rollout of the\",\n \"new middleware instead of the old one classes = super().MIDDLEWARE\",\n \"classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw) return\",\n \"= None @property def DATABASES(self): # This keeps connections to\",\n \"with connecting to postgres dbs = getattr(super(), 'DATABASES', {}) for\",\n \"middleware_to_remove = ( 'csp.middleware.CSPMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) for mw in middleware_to_remove:\",\n \"of the proxito code. \\\"\\\"\\\" class CommunityProxitoSettingsMixin: ROOT_URLCONF = 'readthedocs.proxito.urls'\",\n \"old one classes = super().MIDDLEWARE classes = list(classes) classes.append('readthedocs.proxito.middleware.ProxitoMiddleware') middleware_to_remove\",\n \"= 'readthedocs.proxito.urls' USE_SUBDOMAIN = True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow\",\n \"def MIDDLEWARE(self): # noqa # Use our new middleware instead\",\n \"Base settings for Proxito Some of these settings will eventually\",\n \"the old middleware for a staged rollout of the proxito\",\n \"middleware instead of the old one classes = super().MIDDLEWARE classes\",\n \"in classes: classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw)\",\n \"latency with connecting to postgres dbs = getattr(super(), 'DATABASES', {})\",\n \"= True SECURE_REFERRER_POLICY = \\\"no-referrer-when-downgrade\\\" # Allow cookies from cross-site\",\n \"page is embedded in an iframe. SESSION_COOKIE_SAMESITE = None @property\",\n \"the main settings file, but currently we have them to\",\n \"dbs: dbs[db]['CONN_MAX_AGE'] = 86400 return dbs @property def MIDDLEWARE(self): #\",\n \"cross-site requests on subdomains for now. # As 'Lax' breaks\",\n \"\\\"\\\"\\\" Base settings for Proxito Some of these settings will\",\n \"the site with the old middleware for a staged rollout\",\n \"@property def MIDDLEWARE(self): # noqa # Use our new middleware\",\n \"settings for Proxito Some of these settings will eventually be\",\n \"postgres dbs = getattr(super(), 'DATABASES', {}) for db in dbs:\"\n]","isConversational":false}}},{"rowIdx":189,"cells":{"ngram":{"kind":"list like","value":["0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3)","by fits # to individual folds will be caught and","predict def score(self, X=None, Y=None): if self.foo_param > 1: score","def test_grid_search_groups(): # Check if ValueError (when groups is None)","train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)]","parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0),","parameter grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert","scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param':","sampler] assert len(samples) == 10 for sample in samples: assert","loss is 'hinge'. assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X)","must be set to a scorer key\", GridSearchCV(clf, {}, refit=refit,","\"\"\" Test implementation catches the errors when 'best_index_' returns an","= {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy","= search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X,","test is sane assert score_auc < 1.0 assert score_accuracy <","for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search","1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1,","!= 0 # Assert every key matches those in `cv_results`","km = KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric evaluation unsupervised scoring","that succeeded estimators have lower ranks assert ranks[0] <= 2","X + self.foo_param def inverse_transform(self, X): return X - self.foo_param","{'n_neighbors': [10]} # defaults to euclidean metric (minkowski p =","in ParameterGrid(params): assert values in samples # test sampling without","return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf,","simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0,","number :) assert grid_search.best_params_[\"n_clusters\"] == 3 # Single metric evaluation","\"Attribute %s not equal\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV):","2nd and 3rd parameter will not be evaluated as no","cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2)","10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the","X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score)","comparison of the two cv_results dicts np.testing.assert_equal({k: v for k,","Ensure that grid scores were set to zero as required","{ 'loss': ['log'], } X = np.arange(24).reshape(6, -1) y =","= \"GridSearchCV not identical with precomputed metric\" assert (preds_original ==","'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train, y_train,","scorer = make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01},","cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check","est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in","np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0,","assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a':","of their mean cv-scores param_grid = {'C': [1, 1.001, 0.001]}","**fit_params): return self # this should not raise any exceptions","for u in cv] # cv is empty now train_size","centers=[[0, 1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01,","would be raised on refit and not # caught by","clf.fit(X, y) assert not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X =","10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv):","estimators have lower ranks assert ranks[0] <= 2 and ranks[1]","\"\"\" est = BaseEstimator() attr_message = \"BaseSearchCV _pairwise property must","Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search =","'raise' raises the error X, y = make_classification(n_samples=20, n_features=10, random_state=0)","sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import","30 # Scipy 0.12's stats dists do not accept seed,","test_grid_search_cv_splits_consistency(): # Check if a one time iterable is accepted","[1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV(","train/test indices are generated for all # the cv splits,","@pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions':","else: probability = False clf = SVC(probability=probability, random_state=42) random_search =","a test from KDE def custom_scoring(estimator, X): return 42 if","X - self.foo_param predict_proba = predict predict_log_proba = predict decision_function","n_features = 4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier()","class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self,","random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0)","to break without an explicit deprecation cycle and API #","in [\"\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring,","5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\", refit=True) clf.fit(X,","n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params =","score and timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert","= [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X,","make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf","# cv is empty now train_size = 100 ridge =","test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None):","that the grid searcher has no classes_ attribute without a",") test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array(","all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] ==","clf assert clf.best_index_ != 0 # Assert every key matches","cv=3, verbose=3) # make sure it selects the smallest parameter","also be # same as long as the same train/test","scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2,","def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y):","= grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results","adds flexibility in identifying the \"best\" estimator. \"\"\" def refit_callable(cv_results):","y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on","= \"BaseSearchCV _pairwise property must match estimator\" for _pairwise_setting in","as cv and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_),","test_pickle(): # Test that a fit search can be pickled","= np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv","scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for","[[1, ], [2, ], [3, ], [4, ]] y =","for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits)","= make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize(","x: x.shape[1:] == (5, 3, 2) check_y = lambda x:","'precomputed'. Non-regression test for issue #13920. \"\"\" n_samples = 50","assert all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys)","get_n_splits(self, *args, **kw): return 1 # create bad cv cv","0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3,","be non-zero for trivial X and y assert np.all(search.cv_results_[key] >=","assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure the","= [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs","return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =","in sampler] if sp_version >= (0, 16): param_distributions = {\"C\":","k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the","x in sampler] == [x for x in sampler] def","an assertion error if # a parameter is missing or","clf.best_index_ != 0 # Assert every key matches those in","in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert","random_search): search.fit(X, y) cv_results = search.cv_results_ # Check tie breaking","parameters param_distributions = {\"C\": [0, 1, 2, 3, 4, 5,","this test does something else??? # noise-free simple 2d-data X,","scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) # check that","the keys # are present assert all((np.all(cv_results_multi[k] <= 1) for","is sane assert score_auc < 1.0 assert score_accuracy < 1.0","into two folds that are not iid # first one","\"b\": 2} to (\"a\", 1, \"b\", 2) points = set(tuple(chain(*(sorted(p.items()))))","# Test grid-search on classifier that has no score function.","if estimator.bandwidth == .1 else 0 X, _ = make_blobs(cluster_std=.1,","bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples","and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf","over a \"grid\" with only one point. clf = MockClassifier()","error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test","{'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV,","DataFrame types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10, 10)","mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std ==","succeeded estimators have lower ranks assert ranks[0] <= 2 and","correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param","'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than","unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0,","cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1],","False): random_searches = [] for scoring in (('accuracy', 'recall'), 'accuracy',","= est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test","passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\",","methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d,","[1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf = SVC(gamma='auto') cv =","{'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV,","to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param():","C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert","4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict(): # test","check_cv_results_array_types(search, param_keys, score_keys): # Check if the search `cv_results`'s array","n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y)","y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'):","'recall'), 'accuracy', 'recall'): # If True, for multi-metric pass refit='accuracy'","is bigger than total parameter space params = [{'first': [0,","should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim","key in score_keys) assert all(cv_results[key].dtype == np.float64 for key in","for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X,","error in this test. gs = GridSearchCV(clf, [{'parameter': [0, 1,","n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") def test_no_refit(): #","test_y_as_list(): # Pass y as list in GridSearchCV X =","the test is sane assert score_auc < 1.0 assert score_accuracy","test_gridsearch_no_predict(): # test grid-search with an estimator without predict. #","to a scorer key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec':","GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X,","+ self.foo_param def inverse_transform(self, X): return X - self.foo_param predict_proba","pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search","GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0","= [x for x in sampler] assert len(samples) == 10","classifier that has no score function. clf = LinearSVC(random_state=0) X,","param_keys + score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4,","on data with different length X_, y_ = make_classification(n_samples=200, n_features=100,","will be caught and warnings raised instead. If # refit","clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X,","# validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid,","else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return","samples = list(sampler) assert len(samples) == 8 for values in","= set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points == set((\"bar\",","from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection","degree=[1, 2])] grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score),","['score'] for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype ==","0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert","p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p,","mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower() and","search returns an error with a non-square precomputed # training","make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points = 6 params","parameters' % fn_name), getattr(grid_search, fn_name), X) # Test that an","clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180],","np.all(search.cv_results_[key] < 1) assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float) assert","x, \"foo\", y) for x, y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2)","assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) # Smoke test the score etc:","scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_","SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier,","assert hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3)","the errors when 'best_index_' returns an out of bound result.","but got (<class 'str'>).\" \" Single values need to be","refit param raises appropriate error messages for refit in [\"\",","as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType)","scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure","for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,","== 42 def test_param_sampler(): # test basic properties of param","a list\" \" with one element.\", GridSearchCV, clf, param_dict) param_dict","hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator","issue #13920. \"\"\" est = BaseEstimator() attr_message = \"BaseSearchCV _pairwise","X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C':","2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse():","grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not","clf, param_dict) param_dict = {\"C\": []} clf = SVC() assert_raise_message(","# Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit","LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import","# OneTimeSplitter is a non-re-entrant cv where split can be","from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing","(RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially","grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest","[3, ], [4, ]] y = [0, 1, 1, 0]","scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will","destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas","# loss is 'hinge'. assert not hasattr(clf, \"predict_proba\") clf.fit(X, y)","cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for","dummy clf assert clf.best_index_ != 0 # Assert every key","make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180],","in cv] # cv is empty now train_size = 100","that best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ #","their mean cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search","test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score'","y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv):","3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for","n_features=100, random_state=0) # compute the training kernel matrix corresponding to","== y_test) >= 0 # test error is raised when","return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return","n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,","this is what we look for. assert_raises(ValueError, gs.fit, X, y)","'loss': ['log'], } X = np.arange(24).reshape(6, -1) y = [0,","dists do not accept seed, hence we use param grid","attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert","= {\"C\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf,","GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled","search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s in","score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): #","\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples =","def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when loss","'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search","defaults to euclidean metric (minkowski p = 2) clf =","test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' # Ensures that","@pytest.mark.parametrize( \"input, error_type, error_message\", [(0, TypeError, r'Parameter .* is not","x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"],","for refit in [\"\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For","clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv","find the right number :) assert grid_search.best_params_[\"n_clusters\"] == 3 #","class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a def","{'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y)","cv.best_score_ >= 0 # compute the test kernel matrix K_test","that this raises an error with pytest.raises(ValueError, match='No fits were","groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs","not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1]","n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ], C=[1,","clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X,","'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits,","this leads to perfect classification on one fold and a","y) # Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits,","search delegate n_features_in to the # best estimator n_features =","Test that regressors do not have a classes_ attribute grid_search","grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that parameters that","LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180],","== C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid","y) assert hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1,","ParameterGrid({}) assert len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty)","LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3),","y) assert clf.best_index_ == 0 # Ensure `best_score_` is disabled","4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] == 3 # Now","X, y) def test_parameters_sampler_replacement(): # raise warning if n_iter is","[0, None]} cv = KFold() for est in estimators: grid_search","method to compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y)","= 4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid","removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0)","# Test basic properties of ParameterGrid. params1 = {\"foo\": [1,","score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when groups is","Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates)","'accuracy' if isinstance(scoring, tuple) else refit else: probability = False","behaviour of the grid search part gs = GridSearchCV(clf, [{'parameter':","has the least `mean_test_prec`. \"\"\" assert 'mean_test_prec' in cv_results return","degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys =","10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty)","Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y","def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare multi-metric cv_results with the","blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y","FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a ValueError on fit()\"\"\" FAILING_PARAMETER =","# proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid),","search.fit(X, y) cv_results = search.cv_results_ # Check tie breaking strategy","3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a ValueError","\\\\d+ ' 'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): #","y = np.array([0] * 5 + [1] * 5) Cs","y_train = y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C':","search_multi.refit == 'accuracy' else: assert not search_multi.refit return # search","gives the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y)","alone without refitting clf = MockClassifier() for scoring in [None,","be data dependent (e.g. `sample_weight`) which # is not the","param_grid = { 'loss': ['log'], } X = np.arange(24).reshape(6, -1)","[0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) #","n_iter is bigger than total parameter space params = [{'first':","X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0)","in param_grid param_grid = { 'loss': ['hinge'], } clf =","gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]","C == C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]),","weighted mean irrespective of # i.i.d. or not assert train_mean","scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys,","param_distributions = {\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0, 1)} sampler =","cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)):","return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},","assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype ==","assert hasattr(grid_search, \"cv_results_\") def test_no_refit(): # Test that GSCV can","This test make sure _pairwise is delegated to the base","isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_')","a warning is raised and score reset where appropriate. X,","import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises","= grid_search.fit(X, y).cv_results_ # Test scorer names result_keys = list(cv_results.keys())","search_rec.cv_results_.items()}) # Check if score and timing are reasonable, also","for the 2nd and subsequent cv.split calls. # This is","is not the case for the following parameters. But this","= search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X,","<= sample[\"C\"] <= 1 # test that repeated calls yield","cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains","list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param':","for scalar values in fit_params, for instance in LightGBM #","sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if the search","for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare","scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert","is raised when the precomputed kernel is not array-like #","test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0] * 5","= gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower ranks","'str'>).\" \" Single values need to be wrapped in a","without an explicit deprecation cycle and API # recommendations for","old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) #","Test predict_proba when disabled on estimator. X = np.arange(20).reshape(5, -1)","v for k, v in search_rec.cv_results_.items()}) # Check if score","3, 4, 5, 6, 7, 8, 9, 10]} sampler =","sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import","= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True,","in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ # Check","from pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass","import GeneratorType import re import numpy as np import scipy.sparse","assert [x for x in sampler] == [x for x","score = 1. else: score = 0. return score def","a check to make sure cv.split is not called once","from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing","in search_rec.cv_results_.items()}) # Check if score and timing are reasonable,","make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1,","is not called once per param # setting. np.testing.assert_equal({k: v","random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with","'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search =","svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C':","assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1],","np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype == object for","compute the training kernel matrix corresponding to the linear kernel","param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results","search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the same","the keys, other than time keys, among multi-metric and #","and score reset where appropriate. X, y = make_classification(n_samples=20, n_features=10,","% attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):","sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import","required\") def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None):","those without following # proper deprecation cycle. fit_params = {","1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for scoring","numpy array, but got (<class 'str'>).\" \" Single values need","# compute the training kernel matrix corresponding to the linear","leads to perfect classification on one fold and a score","make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for score in","('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X,","cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy':","train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are","not a dict \\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.* value is","sampling without replacement in a large grid params = {'a':","hence we use param grid params = dict(C=np.logspace(-4, 1, 3),","[2], [3], [4], [5]], [0, 0, 0, 0, 1] estimators","LinearSVC(random_state=0) X = [[1, ], [2, ], [3, ], [4,","np.arange(24).reshape(6, -1) y = [0, 0, 0, 1, 1, 1]","evaluated as no train/test indices # will be generated for","make_classification from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from","a user provided # validation set. non-regression test for: #","= [[1, ], [2, ], [3, ], [4, ]] y","= SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\"raise\" )","# test sampling without replacement in a large grid params","clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs =","random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X,","a parameter is missing or has length != len(X). err_msg","assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error","the equivalence of 'precomputed'. Non-regression test for issue #13920. \"\"\"","= CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2,","import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV","from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing","if 'time' not in k and k != 'rank_test_score') #",">= 0 # compute the test kernel matrix K_test =","\"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is","assert regressor_results[1] is not grid_search.best_estimator_ # check that we didn't","fits # to individual folds will be caught and warnings","== np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the","class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None):","2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search,","make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\":","10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def","1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is","model that has the least `mean_test_score`. \"\"\" # Fit a","isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param':","sample[\"kernel\"] in [\"rbf\", \"linear\"] assert 0 <= sample[\"C\"] <= 1","\"linear\"] assert 0 <= sample[\"C\"] <= 1 # test that","'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score',","1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for","refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y)","2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for","search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid,","v for k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k:","# noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter","(e.g. `sample_weight`) which # is not the case for the","r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type,","list or numpy array, but got (<class 'str'>).\" \" Single","1) for k in score_keys if 'time' not in k","% s][0] for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0]","scores are the same as above assert_allclose(test_cv_scores, [1, 1. /","if not k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items()","assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys,","gscv) for attr in dir(gscv): if (attr[0].islower() and attr[-1:] ==","array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare():","# doesn't go into infinite loops params_distribution = {'first': bernoulli(.5),","# Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X)","== [x for x in sampler] if sp_version >= (0,","sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import","sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) ==","* 5 + [1] * 5) for InputFeatureType, TargetType in","make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C':","attr), \\ \"Attribute %s not equal\" % attr def test__custom_fit_no_run_search():","X, y # create cv cv = KFold(n_splits=3).split(X) # pop","if not k.endswith('_time')}) # Check consistency of folds across the","2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in","SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should be data","' 'Were there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): #","other than time keys, among multi-metric and # single metric","best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0] * 5","score(self): raise AttributeError X = np.array([[-1, -1], [-2, -1], [1,","cause the expected ValueError [u for u in cv] #","test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()","3, 15) clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs","'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits,","sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert","def custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1 else","0 # Assert every key matches those in `cv_results` for","import sys from types import GeneratorType import re import numpy","5, 3, 2) y_3d = np.arange(10 * 7 * 11).reshape(10,","assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot","exception would be raised on refit and not # caught","-1) y = [0, 0, 1, 1, 1] clf =","sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree","['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit)","assert not search_multi.refit return # search cannot predict/score without refit","= make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search","not iid # first one contains data of all 4","scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred,","GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:])","\"Parameter grid for parameter (C) needs to\" \" be a","case: empty grid (useful to get default estimator settings) empty","Test that the grid searcher has no classes_ attribute before","1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ =","smaller ' 'than n_iter=%d. Running %d iterations. For ' 'exhaustive","param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X,","gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k","sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform","(grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to","test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf =","metric cv_results from single metric grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_),","a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O':","= ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) ==","GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans():","is missing or has length != len(X). err_msg = r\"Expected","GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True,","self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier","TODO: Clearly this test does something else??? # noise-free simple","y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid","cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot be","only after ' 'refitting on the best parameters' % fn_name),","# search cannot predict/score without refit X, y = make_blobs(n_samples=100,","selection alone without refitting clf = MockClassifier() for scoring in","k, v in search_rec.cv_results_.items()}) # Check if score and timing","classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y)","n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision',","# Ensure `best_score_` is disabled when using `refit=callable` assert not","score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc,","\"and will be removed in version 0.25\") params = {'C':","y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score'","2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test that","param_search): # check support for scalar values in fit_params, for","[0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 =","cv_results from single metric grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy',","clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X,","2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) # Smoke test the score","timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >=","1. else: score = 0. return score def get_params(self, deep=False):","error is raised when the precomputed kernel is not array-like","self.parameter = parameter def fit(self, X, y=None): if self.parameter ==","assert len(grid2) == 6 # loop to assert we can","search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi,","'than n_iter=%d. Running %d iterations. For ' 'exhaustive searches, use","GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X,","cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param':","precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) #","for key in score_keys) assert all(cv_results[key].dtype == np.float64 for key","a non-empty sequence.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": \"1,2,3\"}","range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0]","in this test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],","2 def test_grid_search_bad_param_grid(): param_dict = {\"C\": 1} clf = SVC(gamma='auto')","param_dict = {\"C\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter","cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search","across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2,","sampler] if sp_version >= (0, 16): param_distributions = {\"C\": uniform(0,","pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits","param settings (C=0.1) and the next two param # settings","\"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2 = ParameterGrid(params2) assert len(grid2) ==","implementation catches the errors when 'best_index_' returns an out of","def test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold): def","multiple times for i in range(2): # tuple + chain","super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth':","* 5 * 3 * 2).reshape(10, 5, 3, 2) y_3d","should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use","3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly',","GridSearchCV X_4d = np.arange(10 * 5 * 3 * 2).reshape(10,","y # create cv cv = KFold(n_splits=3).split(X) # pop all","param_search ) # NOTE: `fit_params` should be data dependent (e.g.","inverse_transform(self, X): return X - self.foo_param predict_proba = predict predict_log_proba","dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x):","are used n_splits = 3 clf = LinearSVC(random_state=0) X, y","BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot be fit twice\"\"\" def __init__(self,","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y)","with precomputed metric\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV,","assert np.mean(y_pred == y_pred2) >= .9 assert C == C2","__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results =","dataset into two folds that are not iid # first","assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y =","'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable)","test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as #","len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba","from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline","!= len(X). err_msg = r\"Expected fit parameter\\(s\\) \\['eggs'\\] not seen.\"","X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y","gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits,","y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred ==","0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring)","[1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled =",">= (0, 16): param_distributions = {\"C\": uniform(0, 1)} sampler =","cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2","y) # As the first two param settings (C=0.1) and","index of a model that has the least `mean_test_prec`. \"\"\"","match='best_index_ index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\"","assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i","from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection","'raise' # Ensures that a warning is raised and score","key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The","y) assert not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6,","methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1,","giving no scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score,","param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_')","cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys)","n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs =","= GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc'","= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 =","k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items() if not","not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV])","Test search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0)","parameters. But this abuse is common in # popular third-party","predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y","n_search_iter = 30 # Scipy 0.12's stats dists do not","import chain, product from functools import partial import pickle import","the input features are given in the # form of","right value for foo_param clf = MockClassifier() grid_search = None","a ValueError so this is what we look for. assert_raises(ValueError,","y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42","not grid_search.best_estimator_ # check that we didn't modify the parameter","param_keys, score_keys, n_cand): # Test the search.cv_results_ contains all the","KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from","assert len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError,","= { 'loss': ['log'], } X = np.arange(24).reshape(6, -1) y","not available when setting loss=['hinge'] # in param_grid param_grid =","assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from","2 assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass","ParameterGrid. params1 = {\"foo\": [1, 2, 3]} grid1 = ParameterGrid(params1)","in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The 'groups'","= make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\"C\": [1.0], \"kernel\": [\"rbf\"],","sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import","GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X,","{ 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train,","['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km,","= {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X,","be a list or numpy array, but got (<class 'str'>).\"","0 # this leads to perfect classification on one fold","= np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups =","third-party libraries and we should tolerate this behavior for #","not be None.\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs","for cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError,","def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results)","refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit)","from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither","assert clf.best_index_ != 0 # Assert every key matches those","is not a dict \\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.* value","X, y = [[1], [2], [3], [4], [5]], [0, 0,","import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit","random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert","grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()]","y[:180]), # cv.score(X_[:180], y[:180])) # test loss where greater is","np.mean(y_pred == y_test) >= 0 # test error is raised","were performed. ' 'Was the CV iterator empty\\\\? ' 'Were","grid_search (expected behavior), and this would cause an # error","'std_fit_time']: # NOTE The precision of time.time in windows is","assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples == test.size","with `refit=True` to get a list of keys in #","def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results,","raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global","#13920. \"\"\" est = BaseEstimator() attr_message = \"BaseSearchCV _pairwise property","in dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and attr","when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\"","else: assert not search_multi.refit return # search cannot predict/score without","False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits,","assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i]","fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]}","non-square precomputed # training kernel matrix K_train = np.zeros((10, 20))","Check if score and timing are reasonable assert all(cv_results['rank_test_score'] >=","[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator","capture errors on data with different length X_, y_ =","0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\": [0,","For ' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size))","cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor':","X, y = make_blobs(n_samples=50, random_state=42) # The two C values","we do not take a weighted mean irrespective of #","euclidean metric (minkowski p = 2) clf = KNeighborsClassifier() cv","y = [0, 0, 1, 1, 1] p = Pipeline([","matches the _pairwise property of its estimator. This test ensures","refit must be set to a scorer key\", GridSearchCV(clf, {},","assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores, we","[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X,","estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params =","return X + self.foo_param def inverse_transform(self, X): return X -","raise an error gs.fit(X, y) def test_classes__property(): # Test that","C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that","def fit(self, X, y=None, groups=None, **fit_params): return self # this","are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i]","5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher","assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over","score_keys): # Check if the search `cv_results`'s array are of","[1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std","would result in a tie of their mean cv-scores param_grid","Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) # check","np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss where greater","{'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") random_search =","GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message =","occurs in situation where behavior changed: # estimator requires score","lambda x: x.shape[1:] == (5, 3, 2) check_y = lambda","== 4 def test_gridsearch_no_predict(): # test grid-search with an estimator","scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc","\"\"\"Broken classifier that cannot be fit twice\"\"\" def __init__(self, parameter=None):","= {\"foo\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1,","random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single(","\"linear\"], \"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples","clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X,","the _pairwise property of its estimator. This test ensures the","' 'inconsistent results. Expected \\\\d+ ' 'splits, got \\\\d+'): ridge.fit(X[:train_size],","= 100 n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0)","results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid,","+ k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def","in windows is not high # enough for the fit/score","== 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \"refit_time_\") assert","return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for bug","will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y","2) y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)","now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2,","in results: if not k.endswith('_time'): # XXX: results['params'] is a","= GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_","and API # recommendations for implementing early stopping with a","= LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C': 0.1},","[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def","< 1.0 assert score_accuracy < 1.0 assert score_auc != score_accuracy","[0, 0, 0, 1, 1, 1] clf = LinearSVC(random_state=0) gs","0.001}): for train, test in cv.split(X, y): this_scores, this_params, n_test_samples","== 0 # Ensure `best_score_` is disabled when using `refit=callable`","mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y","fits # that are expected to fail. def get_cand_scores(i): return","param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3,","the smallest parameter in case of ties old_stdout = sys.stdout","[4, ]] y = [0, 1, 1, 0] gs =","k != 'rank_test_score') assert (all(cv_results[k] <= 1) for k in","GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0],","def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an error","cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys)","to test the parameter search algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param","super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params): return self","grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1))","self.parameter = parameter def fit(self, X, y): assert not hasattr(self,","X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred ==","y) assert hasattr(grid_search, \"cv_results_\") def test_X_as_list(): # Pass X as","= next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params,","clf, param_dict) param_dict = {\"C\": np.ones((3, 2))} clf = SVC()","calls yield identical parameters param_distributions = {\"C\": [0, 1, 2,","= sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1,","isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1)","y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key)","be raised on refit and not # caught by grid_search","not identical with precomputed metric\" assert (preds_original == preds_precomputed).all(), attr_message","for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i","for sample in samples: assert sample[\"kernel\"] in [\"rbf\", \"linear\"] assert","clf = SVC() assert_raise_message( ValueError, \"Parameter values for parameter (C)","'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): # Test that","Make sure the functions predict/transform etc raise meaningful # error","{'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt,","test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) ==","= False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter,","random_searches = [] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'):","Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X)","1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ ==","predict_log_proba = predict decision_function = predict def score(self, X=None, Y=None):","'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings","y) # Both ARI and FMS can find the right","2) check_y = lambda x: x.shape[1:] == (7, 11) clf","['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}]","refitting # Simulates re-fitting a broken estimator; this used to","# refit=False because we only want to check that errors","y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def","cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try:","['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9)","stopping with a user provided # validation set. non-regression test","params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train])","grid_search.fit(X, y).cv_results_ # Test scorer names result_keys = list(cv_results.keys()) expected_keys","GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) #","y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for","list, sampler) # degenerates to GridSearchCV if n_iter the same","return cv_results # Check if generators are supported as cv","ValueError [u for u in cv] # cv is empty","random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc,","[1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] )","estimators inherit from BaseEstimator, # to test hyperparameter search on","results. np.testing.assert_equal performs a # deep nested comparison of the","[.1, 1, 10] for score in ['f1', 'roc_auc']: grid_search =","pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores,","in sampler] == [x for x in sampler] if sp_version","fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results,","cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1,","\"best\" estimator. \"\"\" def refit_callable(cv_results): \"\"\" A dummy function tests","[1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X))","test_parameters_sampler_replacement(): # raise warning if n_iter is bigger than total","for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not","'_recall', k): v for k, v in search_rec.cv_results_.items()}) # Check","# this leads to perfect classification on one fold and","hasattr(clf, \"predict_proba\") clf.fit(X, y) assert not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false():","of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for","foo_param=0): self.foo_param = foo_param def fit(self, X, Y): assert len(X)","\"\"\" Test refit=callable in multiple metric evaluation setting \"\"\" def","empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3,","from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets","+ k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class","non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid =","key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X,","available as the # loss is 'hinge'. assert not hasattr(clf,","import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common","if ValueError (when groups is None) propagates to GridSearchCV #","abuse is common in # popular third-party libraries and we","' 'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make","'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring, the \" \"parameter refit","], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches","if not k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items()","[1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \"cv_results_\")","with on_error != 'raise' # Ensures that a warning is","settings (C=0.1) and the next two param # settings (C=0.2)","Should not raise an error gs.fit(X, y) def test_classes__property(): #","n_iter=8) samples = list(sampler) assert len(samples) == 8 for values","hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy","loss=['hinge'] # in param_grid param_grid = { 'loss': ['hinge'], }","where greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_)","param # setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items()","assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y","sample in samples: assert sample[\"kernel\"] in [\"rbf\", \"linear\"] assert 0","[10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test","Using regressor to make sure each score differs clf =","popular third-party libraries and we should tolerate this behavior for","import numpy as np import scipy.sparse as sp import pytest","def test_grid_search_no_score(): # Test grid-search on classifier that has no","= GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches)","i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled on estimator.","from single metric grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall'))","LinearSVC classifier that has no score method.\"\"\" @property def score(self):","GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ #","[0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 =","= 3 n_search_iter = 30 # Scipy 0.12's stats dists","import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV","grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] == 3 # Now without a","[1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not","refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y =","in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should not","from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics","= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit,","{'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 #","isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test","clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train,","tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return","that do not exactly respect the scikit-learn API contract but","grid searcher has no classes_ attribute before it's fit grid_search","= 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check","sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import","greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss","noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter #","data with different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)","= [[1], [2], [3], [4], [5]], [0, 0, 0, 0,","y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test,","n_test_samples == test.size # Should raise an error upon multimetric","import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X =","7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert","def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return","to the # best estimator n_features = 4 X, y","= LinearSVC(random_state=0) X = [[1, ], [2, ], [3, ],","# tuple + chain transforms {\"a\": 1, \"b\": 2} to","= 3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2)","old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout =","self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that has no score","# Check consistency of folds across the parameters gs =","rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid = {'C': [1]}","parameter will not be evaluated as no train/test indices #","check cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)]","callable(callable_param) # The tuple of arrays should be preserved as","'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2,","chain transforms {\"a\": 1, \"b\": 2} to (\"a\", 1, \"b\",","was done, then an exception would be raised on refit","k and k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search,","# fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated(): X,","or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test","Cs = [.1, 1, 10] for score in ['f1', 'roc_auc']:","time iterable is accepted as a cv parameter. n_samples =","use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y, svc, params, train,","**kw): return 1 # create bad cv cv = BrokenKFold(n_splits=3)","as sp import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing","* 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"],","grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples)","in GridSearchCV.fit # the 2nd and 3rd parameter will not","grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict =","searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def","has length 1; expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1),","[1] * 5) Cs = [.1, 1, 10] grid_search =","ARI and FMS can find the right number :) assert","train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y,","self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search )","SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs,","# Test scorer names result_keys = list(cv_results.keys()) expected_keys = ((\"mean_test_score\",","for both param setting for score_type in ('train', 'test'): per_param_scores","'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after '","the IID parameter TODO: Clearly this test does something else???","assert_raise_message(ValueError, \"The 'groups' parameter should not be None.\", gs.fit, X,","grid search and random search delegate n_features_in to the #","in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] #","tests `refit=callable` interface. Return the index of a model that","10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C',","est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' %","], [2, ], [3, ], [4, ]] y = [0,","1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) #","C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i]","individual folds will be caught and warnings raised instead. If","refit multi-metric search methods with single metric methods\"\"\" assert search_acc.refit","Check if score and timing are reasonable, also checks if","cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the first two","splits cv = [[mask, ~mask], [~mask, mask]] common_params = {'estimator':","5 X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':","y) def test_empty_cv_iterator_error(): # Use global X, y # create","assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\"C\": 1}","grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no scoring function","], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys","+ [1] * 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x,","for the fit/score times to be non-zero for trivial X","# create \"cv\" for splits cv = [[mask, ~mask], [~mask,","search can be pickled clf = MockClassifier() grid_search = GridSearchCV(clf,","sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting #","random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]},","return X - self.foo_param predict_proba = predict predict_log_proba = predict","all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i,","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X,","a scorer key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'}","def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of BaseSearchCV has the _pairwise","use param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0,","[0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters","ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli,","'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert","\"refit_time_\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results():","len(has_empty) == 4 assert list(has_empty) == [{'C': 1}, {'C': 10},","match='best_index_ returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1,","= {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for","for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score","tie of their mean cv-scores param_grid = {'C': [1, 1.001,","setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting","result in a tie of their mean cv-scores param_grid =","time keys, among multi-metric and # single metric grid search","assert_raise_message(ValueError, \"For multi-metric scoring, the \" \"parameter refit must be","== 3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4, 2], \"bar\": [\"ham\",","we look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): #","y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict","y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc","catches the errors when 'best_index_' returns an invalid result. \"\"\"","sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples)","samples] assert len(set(hashable_samples)) == 99 # doesn't go into infinite","scoring='roc_auc').fit(X, y) # Check warning only occurs in situation where","3 # Now without a score, and without y grid_search","K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed')","i in range(2): # tuple + chain transforms {\"a\": 1,","estimator requires score method to compete with scoring parameter score_no_scoring","list or numpy array, but got (<class 'int'>).\" \" Single","equal\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator,","y) def test_grid_search_pipeline_steps(): # check that parameters that are estimators","get default estimator settings) empty = ParameterGrid({}) assert len(empty) ==","A dummy function tests `refit=callable` interface. Return the index of","param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_')","{'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best","= LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2),","https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 )","np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should","GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\", refit=True) clf.fit(X, y) def test_refit_callable():","unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']:","SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0] * 5","make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\"fit_grid_point","a non-re-entrant cv where split can be called only #","= GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency():","else refit else: probability = False clf = SVC(probability=probability, random_state=42)","# GridSearchCV with on_error != 'raise' # Ensures that a","SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]},","sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8","= np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y","generated for all # the cv splits, for both param","the other # create \"cv\" for splits cv = [[mask,","key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in","error_score='raise') # FailingClassifier issues a ValueError so this is what","ClassifierMixin from sklearn.base import clone from sklearn.exceptions import NotFittedError from","# create cv cv = KFold(n_splits=3).split(X) # pop all of","cv=3) random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") def test_no_refit(): # Test","make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from","has no score method.\"\"\" @property def score(self): raise AttributeError X","assert scalar_param > 0 assert callable(callable_param) # The tuple of","`refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X","fit()\"\"\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter = parameter","score and timing are reasonable, also checks if the keys","test does something else??? # noise-free simple 2d-data X, y","# Test the IID parameter TODO: Clearly this test does","= ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1)","random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search,","[.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C':","# This is a check to make sure cv.split is","1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not","y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) # The","Test the IID parameter TODO: Clearly this test does something","random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") def test_no_refit(): # Test that","errors when 'best_index_' returns an out of bound result. \"\"\"","close enough to give similar models # which would result","empty grid (useful to get default estimator settings) empty =","clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf","lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])","!= score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check","from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing","1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty","= np.unique(Y) return self def predict(self, T): return T.shape[0] def","exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X,","RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search)","y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1,","not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as list","def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType)","T): return T.shape[0] def transform(self, X): return X + self.foo_param","np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if not k.endswith('_time')},","n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of BaseSearchCV has the","cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc),","and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) #","estimator is not fitted, `predict_proba` is not available as the","grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search","return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params","y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False","return self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search","sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import","sklearn.datasets import make_classification from sklearn.datasets import make_blobs from sklearn.datasets import","None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),","search.cv_results_['param_C'][0] == 1 # scores are the same as above","sequence.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": \"1,2,3\"} clf =","non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self,","version 0.23 \" \"and will be removed in version 0.25\")","test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split(","[0, 0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid,","2, 3]) # Smoke test the score etc: grid_search.score(X, y)","not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check that","sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import","2)[0][::2]] = 0 # this leads to perfect classification on","classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y =","1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the","clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv","results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else:","3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] == 3 #","# training kernel matrix K_train = np.zeros((10, 20)) y_train =","that there is a tie in the mean scores between","def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X, Y):","y = np.array([0] * 5 + [1] * 5) for","cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc(","all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in","param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y)","np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_","flexibility in identifying the \"best\" estimator. \"\"\" def refit_callable(cv_results): \"\"\"","def test_grid_search_bad_param_grid(): param_dict = {\"C\": 1} clf = SVC(gamma='auto') assert_raise_message(","{}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def","clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) # Test","# Check that failed estimator has the highest rank assert","sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import","= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"]","X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier):","sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from","parameters %d is smaller ' 'than n_iter=%d. Running %d iterations.","user-defined classifiers. class MockClassifier: \"\"\"Dummy classifier to test the parameter","[0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a","[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues","in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of folds","[1] * 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list),","== 8 for values in ParameterGrid(params): assert values in samples","= np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for","RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_","cv_results with the ensemble of multiple single metric cv_results from","score_auc = search_auc.score(X, y) # ensure the test is sane","rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features","y as list in GridSearchCV X = np.arange(100).reshape(10, 10) y","import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns","np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where split","estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params):","= make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},","assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_","matrix corresponding to the linear kernel K_train = np.dot(X_[:180], X_[:180].T)","gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a one time","search_acc, refit): \"\"\"Compare refit multi-metric search methods with single metric","set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points == set((\"bar\", x,","an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no scoring\",","0}, TypeError, \"Parameter.* value is not iterable .*\" r\"\\(key='foo', value=0\\)\")]","single metric grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi","euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from","def test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4], [5]],","1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an","no classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':","= {\"C\": [0, 1, 2, 3, 4, 5, 6, 7,","from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection","'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True)","test from KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth","`mean_test_score`. \"\"\" # Fit a dummy clf with `refit=True` to","params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that","param_dict) param_dict = {\"C\": \"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message( ValueError,","X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def","Return the index of a model that has the least","\"cv_results_\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy pandas","= np.array([0] * 5 + [1] * 5) for InputFeatureType,","of all 4 blobs, second only from two. mask =","# NOTE The precision of time.time in windows is not","sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of","not implemented.\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y)","y = make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer","param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert","one time iterable is accepted as a cv parameter. n_samples","'Was the CV iterator empty\\\\? ' 'Were there no candidates\\\\?'):","and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if","not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned to","has no classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0),","get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param =","X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1","== 1 # scores are the same as above assert_allclose(test_cv_scores,","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X,","not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit multi-metric search","estimator. X = np.arange(20).reshape(5, -1) y = [0, 0, 1,","appropriate error messages for refit in [\"\", 5, True, 'recall',","yield identical parameters param_distributions = {\"C\": [0, 1, 2, 3,","is not integer. \"\"\" return None X, y = make_classification(n_samples=100,","\"For multi-metric scoring, the \" \"parameter refit must be set","results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape ==","y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid)","y, svc, params, train, test, {'score': scorer}, verbose=True) # FIXME","= make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer =","model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y,","SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train,","def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The two","not raise an error gs.fit(X, y) def test_classes__property(): # Test","# 'min' rank should be assigned to the tied candidates","ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) == 8 for","and # single metric grid search results. np.testing.assert_equal performs a","X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3,","make sure cv.split is not called once per param #","cv object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2,","gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits),","has the least `mean_test_score`. \"\"\" # Fit a dummy clf","rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups","used for model selection alone without refitting clf = MockClassifier()","# error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform',","two param settings (C=0.1) and the next two param #","assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as","else: score = 0. return score def get_params(self, deep=False): return","cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4,","grid_search.best_params_ # check that we can call score and that","assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert","grid-search with an estimator without predict. # slight duplication of","for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs},","y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for","def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys())","cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score']","'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the IID","len(samples) == 10 for sample in samples: assert sample[\"kernel\"] in","Test that an invalid refit param raises appropriate error messages","has length != len(X). err_msg = r\"Expected fit parameter\\(s\\) \\['eggs'\\]","attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,","SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit,","scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc =","cv.get_n_splits returned ' 'inconsistent results. Expected \\\\d+ ' 'splits, got","https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a","y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a ValueError on fit()\"\"\"","# non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def","names result_keys = list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\"","], [4, ]] y = [0, 1, 1, 0] gs","two param # settings (C=0.2) are same, the test and","4 blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool)","for multi-metric pass refit='accuracy' if refit: probability = True refit","' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning,","n_splits = 3 n_search_iter = 30 params = [{'kernel': ['rbf'],","a one time iterable is accepted as a cv parameter.","cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(),","search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in search_rec.cv_results_.items()})","LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180])","[0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split","raise meaningful # error messages for fn_name in ('predict', 'predict_proba',","y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True,","pass model = SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params`","is not a dict or a list \\(0\\)'), ([{'foo': [0]},","verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test],","or a list \\(0\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .*","assertion error if # a parameter is missing or has","Test that grid search works with both dense and sparse","y) def test_grid_search_error(): # Test that grid search will capture","'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after ' 'refitting","s][0] for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std","check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search","= GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is not","refit_callable_invalid_type(cv_results): \"\"\" A dummy function tests when returned 'best_index_' is","3 n_search_iter = 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10),","def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)","= GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_","a score of 1/3 on # the other # create","this abuse is common in # popular third-party libraries and","not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X =","Test with grid search cv for est in estimators: grid_search","\\ \"Attribute %s not equal\" % attr def test__custom_fit_no_run_search(): class","3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \"best_estimator_\")","with an estimator without predict. # slight duplication of a","same, the test and train scores must also be #","0 assert callable(callable_param) # The tuple of arrays should be","nested comparison of the two cv_results dicts np.testing.assert_equal({k: v for","gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1])","sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import","y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best","without predict. # slight duplication of a test from KDE","'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys,","# For the train scores, we do not take a","but that we do # not want to break without","removed in version 0.25\") params = {'C': 0.1} train, test","assert hasattr(grid_search, \"cv_results_\") def test_X_as_list(): # Pass X as list","refitting clf = MockClassifier() for scoring in [None, ['accuracy', 'precision']]:","est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train])","grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): #","strategy - # Check that there is a tie in","not to break support for those without following # proper","15) clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs =","GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): #","sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import","{'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr),","2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1,","[1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\")","\\ hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search, \"best_params_\") # Make sure","in samples: assert sample[\"kernel\"] in [\"rbf\", \"linear\"] assert 0 <=","the same as grid_size sampler = ParameterSampler(params, n_iter=8) samples =","def refit_callable(cv_results): \"\"\" A dummy function tests `refit=callable` interface. Return","length != len(X). err_msg = r\"Expected fit parameter\\(s\\) \\['eggs'\\] not","est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): #","sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import","called once per param setting in GridSearchCV.fit # the 2nd","gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise')","with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km =","grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check that we","multi-metric cv_results with the ensemble of multiple single metric cv_results","scipy.stats import bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin","product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to","params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train,","cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 # test error","{'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_,","= make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because","rank should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1,","stats dists do not accept seed, hence we use param","param_grid=param_grid, cv=3) assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) assert not","score function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2)","values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert","loss where greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_,","# test grid-search with unsupervised estimator X, y = make_blobs(n_samples=50,","Test that grid search works when the input features are","param_keys) assert all(cv_results[key].dtype == object for key in param_keys) assert","series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x,","7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when","= GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class","cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names result_keys","{\"foo\": [4, 2], \"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2 = ParameterGrid(params2)","GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator):","assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when groups","param_dict = {\"C\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV,","self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return self class","params2 = {\"foo\": [4, 2], \"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2","param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] )","4 assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C':","def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer","+ ('params',))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys","= \"Fit parameter spam has length 1; expected\" with pytest.raises(AssertionError,","sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that","y) def test_classes__property(): # Test that classes_ property matches best_estimator_.classes_","is a check to make sure cv.split is not called","ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid search and","implemented.\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def","folds that are not iid # first one contains data","key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results #","grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test","per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in","in the param_grid. param_grid = { 'loss': ['log'], } X","# Both ARI and FMS can find the right number","y) def test_refit_callable(): \"\"\" Test refit=callable, which adds flexibility in","StringIO from itertools import chain, product from functools import partial","assert we can iterate over the grid multiple times for","= foo_param def fit(self, X, Y): assert len(X) == len(Y)","do # not want to break without an explicit deprecation","import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the","refit: probability = True refit = 'accuracy' if isinstance(scoring, tuple)","= LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X,","[0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_)","list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k],","return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])","HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid)","cv for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv)","n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0)","assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted","= search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the","# clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =","gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X,","multi-metric scoring, the \" \"parameter refit must be set to","len(gs.cv_results_['params']) # Ensure that grid scores were set to zero","{'C': Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X,","sure _pairwise is delegated to the base estimator. Non-regression test","not in k and k != 'rank_test_score') # Check cv_results","grid_size = 8 expected_warning = ('The total space of parameters","cv is empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(),","in the mean scores between # candidates 1 and 2","'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning,","{'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty) == [{'C':","generates an assertion error if # a parameter is missing","y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"]",") class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None,","3]}, cv=3, verbose=3) # make sure it selects the smallest","the two cv_results dicts np.testing.assert_equal({k: v for k, v in","not k.endswith('_time')}) # Check consistency of folds across the parameters","+ score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for key","the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys +","in param_keys + score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50,","from io import StringIO from itertools import chain, product from","grid, scoring='roc_auc').fit(X, y) # Check warning only occurs in situation","gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs =","-1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1,","is delegated to the base estimator. Non-regression test for issue","expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y,","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y)","assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_","not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def","per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search =","as list in GridSearchCV X_4d = np.arange(10 * 5 *","1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset","class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self,","assert len(has_empty) == 4 assert list(has_empty) == [{'C': 1}, {'C':","make sure it selects the smallest parameter in case of","test basic properties of param sampler param_distributions = {\"kernel\": [\"rbf\",","supported as cv and # that the splits are consistent","'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring,","refit=False because we want to test the behaviour of the","0], TypeError, r'Parameter .* is not a dict \\(0\\)'), ({'foo':","X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict',","# Compare the keys, other than time keys, among multi-metric","search and random search delegate n_features_in to the # best","in [\"rbf\", \"linear\"] assert 0 <= sample[\"C\"] <= 1 #",") cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,","\\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.* value is not iterable .*\"","SVC() assert_raise_message( ValueError, \"Parameter values for parameter (C) need to","refit was done, then an exception would be raised on","'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring, the \" \"parameter refit must","y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i])","import FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree import","= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y)","function tests when returned 'best_index_' is out of bounds. \"\"\"","clone from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from","test for issue #13920. \"\"\" est = BaseEstimator() attr_message =","no score method.\"\"\" @property def score(self): raise AttributeError X =","test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when loss is","X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter","{'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_","the CV iterator empty\\\\? ' 'Were there no candidates\\\\?'): ridge.fit(X[:train_size],","= rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid = {'C':","something else??? # noise-free simple 2d-data X, y = make_blobs(centers=[[0,","right value for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf,","sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import","\"gamma\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_,","wrapped in a list\" \" with one element.\", GridSearchCV, clf,","'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if generators","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X,","test error is raised when the precomputed kernel is not","the function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point():","elif score == \"roc_auc\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test],","{'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return self","preds_original = cv.predict(X) # precompute euclidean metric to validate _pairwise","= KFold() for est in estimators: grid_search = GridSearchCV(est, est_parameters,","that grid search will capture errors on data with different",">= 0 # test error is raised when the precomputed","gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def","GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X,","test grid-search with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0)","# Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys,","[2, ], [3, ], [4, ]] y = [0, 1,","be fit twice\"\"\" def __init__(self, parameter=None): self.parameter = parameter def","\"best_index_\") and \\ hasattr(grid_search, \"best_params_\") # Make sure the functions","done, then an exception would be raised on refit and","sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param >","{k: v for k, v in cv_results_acc_rec.items() if not k.endswith('_time')})","the grid search part gs = GridSearchCV(clf, [{'parameter': [0, 1,","gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree',","== getattr(mycv, attr), \\ \"Attribute %s not equal\" % attr","= [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv for","keys, other than time keys, among multi-metric and # single","# Check tie breaking strategy - # Check that there","grid_search_no_score.fit(X, y) # check that best params are equal assert","len(X) == len(Y) self.classes_ = np.unique(Y) return self def predict(self,","random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare multi-metric","\"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})]","test and train scores must also be # same as","y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3","[5, 10]}])) # Using regressor to make sure each score","def test_grid_search_error(): # Test that grid search will capture errors","(DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\": [0, None]} cv = KFold()","if isinstance(scoring, tuple) else refit else: probability = False clf","without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X,","[0, 0, 1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean',","[0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_","custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1 else 0","all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',)))","mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search","= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30","values in samples # test sampling without replacement in a","def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar values","= [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf,","function tests when returned 'best_index_' is not integer. \"\"\" return","that cannot be fit twice\"\"\" def __init__(self, parameter=None): self.parameter =","refit: assert search_multi.refit == 'accuracy' else: assert not search_multi.refit return","8 expected_warning = ('The total space of parameters %d is","grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X,","0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) #","def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X, y","super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): # this should","\"\"\"Test the search module\"\"\" from collections.abc import Iterable, Sized from","from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon,","RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0]","element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": np.ones((3, 2))} clf","np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): #","[0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler","matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred =","# Check if score and timing are reasonable assert all(cv_results['rank_test_score']","MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV(","for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True,","y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0","list(sampler) assert len(samples) == 99 hashable_samples = [\"a%db%dc%d\" % (p['a'],","random search delegate n_features_in to the # best estimator n_features","be a non-empty sequence.\", GridSearchCV, clf, param_dict) param_dict = {\"C\":","\"predict_proba\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X =","meaningful # error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba',","# Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert","scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps():","param_dict) param_dict = {\"C\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError,","loss is specified # as one of the parameters in","for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X))","Running %d iterations. For ' 'exhaustive searches, use GridSearchCV.' %","arrays should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert","careful not to break support for those without following #","to be a non-empty sequence.\", GridSearchCV, clf, param_dict) param_dict =","not k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items() if","if self.foo_param > 1: score = 1. else: score =","grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_X_as_list(): # Pass","performs a # deep nested comparison of the two cv_results","y) assert not hasattr(grid_search, 'classes_') # Test that the grid","refit if refit: assert search_multi.refit == 'accuracy' else: assert not","raise ValueError(\"Failing classifier failed as required\") def predict(self, X): return","for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32","not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV,","score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception","form of a precomputed kernel matrix X_, y_ = make_classification(n_samples=200,","param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check","# Test that a fit search can be pickled clf","gs.fit(X, y) # As the first two param settings (C=0.1)","GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit,","refit=False because we only want to check that errors caused","_pairwise property which matches the _pairwise property of its estimator.","that are estimators are cloned before fitting pipe = Pipeline([('regressor',","n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1,","libraries and we should tolerate this behavior for # now","for x in sampler] == [x for x in sampler]","cv=2, error_score=0, n_iter=2) for search in (gs, rs): search.fit(X, y)","test_trivial_cv_results_attr(): # Test search over a \"grid\" with only one","[0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if","= make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel matrix","True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array(","LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid,","GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute","hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test implementation catches the errors","cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf","breaking strategy - # Check that there is a tie","y = [[1], [2], [3], [4], [5]], [0, 0, 0,","test_grid_search_error(): # Test that grid search will capture errors on","assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba","y) preds_original = cv.predict(X) # precompute euclidean metric to validate","# Check that there is a tie in the mean","random_state=0) km = KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric evaluation unsupervised","Test basic properties of ParameterGrid. params1 = {\"foo\": [1, 2,","clf.fit(X, y) # Ensure that `best_index_ != 0` for this","'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test implementation catches the errors when","= GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]]) def","for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results","y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param","None]} cv = KFold() for est in estimators: grid_search =","cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean metric","fit twice\"\"\" def __init__(self, parameter=None): self.parameter = parameter def fit(self,","y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score():","search in (gs, rs): search.fit(X, y) for key in ['mean_fit_time',","grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array(","also check if groups is correctly passed to the cv","Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})","is out of bounds. \"\"\" return out_bound_value X, y =","# Pass X as list in GridSearchCV X = np.arange(100).reshape(10,","[3, 4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid,","fit/score times to be non-zero for trivial X and y","and we should tolerate this behavior for # now and","= LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\"fit_grid_point is deprecated","scikit-learn API contract but that we do # not want","# settings (C=0.2) are same, the test and train scores","for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42),","return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator,","* 11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:] ==","train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from","cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed)","= RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params']","def test_refit(): # Regression test for bug in refitting #","from KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth ==","search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not","test the parameter search algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param =","user provided # validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805","[1] * 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list),","break without an explicit deprecation cycle and API # recommendations","from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics","a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X,","C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), #","# that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter","RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from","for issue #13920. \"\"\" n_samples = 50 n_splits = 2","foo_param clf = MockClassifier() grid_search = None grid_search = GridSearchCV(clf,","y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid","cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in","functools import partial import pickle import sys from types import","import clone from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification","'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\","mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0","#13920. \"\"\" n_samples = 50 n_splits = 2 X, y","{'estimator': SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params)","4, 5, 6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions,","would cause an # error in this test. gs =","two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0","1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute the","clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\" Test refit=callable in multiple metric","import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose","be generated for the 2nd and subsequent cv.split calls. #","'best_score_') def test_gridsearch_nd(): # Pass X as list in GridSearchCV","in score_keys if k != 'rank_test_score') assert (all(cv_results[k] <= 1)","1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C","KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original =","0 # compute the test kernel matrix K_test = np.dot(X_[180:],","getattr(gscv, attr) == getattr(mycv, attr), \\ \"Attribute %s not equal\"","grid search part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],","Both ARI and FMS can find the right number :)","in k and k != 'rank_test_score') # Check cv_results structure","break with # sparse SVMs. X = np.arange(100).reshape(10, 10) y","= [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1,","be set to a scorer key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc':","groups=None, **fit_params): return self # this should not raise any","(C=0.2) are same, the test and train scores must also","11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search =","with the ensemble of multiple single metric cv_results from single","100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4)","= predict def score(self, X=None, Y=None): if self.foo_param > 1:","with grid search cv for est in estimators: grid_search =","clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert","%s not equal\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def","grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] ==","accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from","grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring grid_search.scoring =","before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(),","for model selection alone without refitting clf = MockClassifier() for","check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search =","== object for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray)","= GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score,","[.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y)","# Assert every key matches those in `cv_results` for key","cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv =","bounds. \"\"\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42)","RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\")","in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful","[1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\",","X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5,","needs to\" \" be a list or numpy array, but","(C) need to be a non-empty sequence.\", GridSearchCV, clf, param_dict)","two estimators inherit from BaseEstimator, # to test hyperparameter search","(minkowski p = 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf,","np.arange(20).reshape(5, -1) y = [0, 0, 1, 1, 1] clf","11) check_X = lambda x: x.shape[1:] == (5, 3, 2)","to validate _pairwise is working X_precomputed = euclidean_distances(X) clf =","= cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred,","grid (useful to get default estimator settings) empty = ParameterGrid({})","= { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3)","30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel':","== 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i])","in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and","Test that the grid searcher has no classes_ attribute without","not a dict or a list \\(0\\)'), ([{'foo': [0]}, 0],","y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search works when","except ImportError: pass X = np.arange(100).reshape(10, 10) y = np.array([0]","should be data dependent (e.g. `sample_weight`) which # is not","are present assert all((np.all(cv_results_multi[k] <= 1) for k in (","DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf,","algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X,","klass(input) def test_parameter_grid(): # Test basic properties of ParameterGrid. params1","OneTimeSplitter is a non-re-entrant cv where split can be called","with a non-square precomputed # training kernel matrix K_train =","GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert","on the best parameters' % fn_name), getattr(grid_search, fn_name), X) #","model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}),","can iterate over the grid multiple times for i in","_FitParamClassifier(), param_search ) # NOTE: `fit_params` should be data dependent","in version 0.23 \" \"and will be removed in version","test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:]","sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) # The tuple","len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i","= GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\", refit=True) clf.fit(X, y) def","least `mean_test_score`. \"\"\" # Fit a dummy clf with `refit=True`","search.cv_results_ # Check if score and timing are reasonable assert","classification on one fold and a score of 1/3 on","= gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in results:","the following parameters. But this abuse is common in #","(X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train, y_train, **fit_params)","= 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0),","expected ValueError [u for u in cv] # cv is","the index of a model that has the least `mean_test_prec`.","for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s,","= make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01}, {'C':","cv_results dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if","as # fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated():","`cv_results` for key in clf.cv_results_.keys(): assert key in cv_results return","['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf,","missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X,","values for parameter (C) need to be a non-empty sequence.\",","grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized search for","import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message","not accept seed, hence we use param grid params =","err_msg = \"Fit parameter spam has length 1; expected\" with","MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class","two folds that are not iid # first one contains","grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params']","in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for","cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and","y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc =","= None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X,","def test_empty_cv_iterator_error(): # Use global X, y # create cv","sklearn.metrics import f1_score from sklearn.metrics import recall_score from sklearn.metrics import","def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing","if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that","random_state=0) samples = [x for x in sampler] assert len(samples)","`best_score_` is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_')","42 if estimator.bandwidth == .1 else 0 X, _ =","GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test that","hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as list in","Compare the keys, other than time keys, among multi-metric and","refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert","for this dummy clf assert clf.best_index_ != 0 # Assert","not k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items() if","score_keys, n_cand): # Test the search.cv_results_ contains all the required","# Test with a randomized search for est in estimators:","NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X,","!= 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results,","deprecated in version 0.23 \" \"and will be removed in","< 1) assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float) assert search.refit_time_","that regressors do not have a classes_ attribute grid_search =","= CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf, {'foo_param':","> 0 assert callable(callable_param) # The tuple of arrays should","iterable is accepted as a cv parameter. n_samples = 100","= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3,","= SVC() assert_raise_message( ValueError, \"Parameter values for parameter (C) need","y, svc, params, train, test, scorer, verbose=False) def test_pickle(): #","FailingClassifier() # refit=False because we want to test the behaviour","p['b'], p['c']) for p in samples] assert len(set(hashable_samples)) == 99","has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty)","an explicit deprecation cycle and API # recommendations for implementing","in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare","sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from","to perfect classification on one fold and a score of","% (p['a'], p['b'], p['c']) for p in samples] assert len(set(hashable_samples))","{'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled =","(useful to get default estimator settings) empty = ParameterGrid({}) assert","# refit was done, then an exception would be raised","assert not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1)","grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1],","and \\ hasattr(grid_search, \"best_params_\") # Make sure the functions predict/transform","flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring","MockDataFrame from scipy.stats import bernoulli, expon, uniform from sklearn.base import","\"cv\" for splits cv = [[mask, ~mask], [~mask, mask]] common_params","= len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i]","gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan'))","non-zero for trivial X and y assert np.all(search.cv_results_[key] >= 0)","empty\\\\? ' 'Were there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv():","make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV,","X): return X - self.foo_param predict_proba = predict predict_log_proba =","FIXME remove test_fit_grid_point as the function will be removed on","need to be wrapped in a list\" \" with one","we can call score and that it gives the correct","random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def","test_refit(): # Regression test for bug in refitting # Simulates","1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def","contains data of all 4 blobs, second only from two.","to GridSearchCV # And also check if groups is correctly","import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import recall_score","assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key","def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not","SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble","will be removed in version 0.25\") params = {'C': 0.1}","# Fit a dummy clf with `refit=True` to get a","grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc","test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values in fit_params,","\"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y, svc, params, train, test, {'score':","range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0,","for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for","y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf","with one element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": []}","exactly respect the scikit-learn API contract but that we do","3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True, False):","y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV,","function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError,","from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm","1/3 on # the other # create \"cv\" for splits","{'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9","= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y)","hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search, \"best_params_\") # Make sure the","[] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'):","\"\"\"Classifier that raises a ValueError on fit()\"\"\" FAILING_PARAMETER = 2","`predict_proba` is not available as the # loss is 'hinge'.","np.nan y = [0, 0, 1, 1, 1] p =","X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 +","in LightGBM # that do not exactly respect the scikit-learn","{'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2","@property def score(self): raise AttributeError X = np.array([[-1, -1], [-2,","not k.endswith('_time'): # XXX: results['params'] is a list :| results[k]","assert grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict(): # test grid-search with","models # which would result in a tie of their","= cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel():","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert","search_multi, search_acc, search_rec): \"\"\"Compare multi-metric cv_results with the ensemble of","cv.predict(X) # precompute euclidean metric to validate _pairwise is working","test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X, y =","remove test_fit_grid_point as the function will be removed on 0.25","this raises an error with pytest.raises(ValueError, match='No fits were performed.","result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\" A dummy function tests when","cannot predict/score without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42)","test_grid_search_precomputed_kernel(): # Test that grid search works when the input","# And also check if groups is correctly passed to","n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 # Scipy","]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator):","using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass","cv.split is not called once per param # setting. np.testing.assert_equal({k:","== 6 # loop to assert we can iterate over","metric (minkowski p = 2) clf = KNeighborsClassifier() cv =","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y)","# The two C values are close enough to give","match='No fits were performed. ' 'Was the CV iterator empty\\\\?","grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on","y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score'","grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0],","that grid search returns an error with a non-square precomputed","a \"grid\" with only one point. clf = MockClassifier() grid_search","search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$',","5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], )","5) for InputFeatureType, TargetType in types: # X dataframe, y","scipy.sparse as sp import pytest from sklearn.utils.fixes import sp_version from","test that correct scores are used n_splits = 3 clf","if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C':","est_parameters = {\"random_state\": [0, None]} cv = KFold() for est","= evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results","== (5, 3, 2) check_y = lambda x: x.shape[1:] ==","param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give","= RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search):","{'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C =","without refitting clf = MockClassifier() for scoring in [None, ['accuracy',","[{'parameter': [0, 1]}], scoring=\"precision\", refit=True) clf.fit(X, y) def test_refit_callable(): \"\"\"","high # enough for the fit/score times to be non-zero","import Iterable, Sized from io import StringIO from itertools import","= SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test","ranks[1] <= 2 # Check that failed estimator has the","param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict():","correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def","in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal(","0.1}, {'C': 0.01}, {'C': 0.001}): for train, test in cv.split(X,","param in param_keys) assert all(cv_results[key].dtype == object for key in","* 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"],","that are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' %","param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i]","sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]})","1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_ !=","test, scorer, verbose=False) def test_pickle(): # Test that a fit","cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names result_keys =","we want to test the behaviour of the grid search","from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing","'classes_') def test_trivial_cv_results_attr(): # Test search over a \"grid\" with","scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) #","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2","worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss,","all(cv_results[key].dtype == object for key in param_keys) assert not any(isinstance(cv_results[key],","n_iter=99, random_state=42) samples = list(sampler) assert len(samples) == 99 hashable_samples","isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,",").fit, X, y) def test_grid_search_error(): # Test that grid search","y) # check that best params are equal assert grid_search_no_score.best_params_","error with a non-square precomputed # training kernel matrix K_train","GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ #","= SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42)","* 2).reshape(10, 5, 3, 2) y_3d = np.arange(10 * 7","refit): \"\"\"Compare refit multi-metric search methods with single metric methods\"\"\"","('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y)","= {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params,","to individual folds will be caught and warnings raised instead.","grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search,","assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in","one element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": np.ones((3, 2))}","When the estimator is not fitted, `predict_proba` is not available","Test implementation catches the errors when 'best_index_' returns an invalid","param_grid = { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid,","StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C)","cause an # error in this test. gs = GridSearchCV(clf,","has the _pairwise property which matches the _pairwise property of","zero as required for those fits # that are expected","early stopping with a user provided # validation set. non-regression","randomized search for est in estimators: random_search = RandomizedSearchCV(est, est_parameters,","'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\ \"Attribute %s","**kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params): return","attr) == getattr(mycv, attr), \\ \"Attribute %s not equal\" %","[1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert","clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]},","= GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \"predict_proba\") def","= ('The total space of parameters %d is smaller '","LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2","params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler =","import make_classification from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification","in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check","pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\d+","train, test in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point(","pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic properties of","import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import make_blobs","{ 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert","check that we didn't modify the parameter grid that was","y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def","class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError,","search `cv_results`'s array are of correct types cv_results = search.cv_results_","@ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold()","itertools import chain, product from functools import partial import pickle","test_no_refit(): # Test that GSCV can be used for model","set_params(self, **params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \"\"\"An","is not available as the # loss is 'hinge'. assert","search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure the test","cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be","the next two param # settings (C=0.2) are same, the","basic properties of param sampler param_distributions = {\"kernel\": [\"rbf\", \"linear\"],","gs2.fit(X, y) # Give generator as a cv parameter assert","that we can call score and that it gives the","test make sure _pairwise is delegated to the base estimator.","without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method","sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import","from sklearn.metrics import f1_score from sklearn.metrics import recall_score from sklearn.metrics","one point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]},","* 5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter':","test_grid_search_groups(): # Check if ValueError (when groups is None) propagates","an error gs.fit(X, y) def test_classes__property(): # Test that classes_","= est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled():","cycle and API # recommendations for implementing early stopping with","est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X,","from sklearn.datasets import make_classification from sklearn.datasets import make_blobs from sklearn.datasets","assert callable(callable_param) # The tuple of arrays should be preserved","create \"cv\" for splits cv = [[mask, ~mask], [~mask, mask]]","# Check if ValueError (when groups is None) propagates to","0.25\") params = {'C': 0.1} train, test = next(StratifiedKFold().split(X, y))","or has length != len(X). err_msg = r\"Expected fit parameter\\(s\\)","expected_score) assert params == this_params assert n_test_samples == test.size #","1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(),","y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y)","# sparse SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0]","fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from","pass X = np.arange(100).reshape(10, 10) y = np.array([0] * 5","SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan","(C) needs to\" \" be a list or numpy array,","not iterable .*\" r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass, input, error_type,","assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min'","attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a':","random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring =","'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only","assert not hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\") and \\","err_msg = r\"Expected fit parameter\\(s\\) \\['eggs'\\] not seen.\" with pytest.raises(AssertionError,","'classes_') # Test that the grid searcher has no classes_","test_grid_search_pipeline_steps(): # check that parameters that are estimators are cloned","lower ranks assert ranks[0] <= 2 and ranks[1] <= 2","index of a model that has the least `mean_test_score`. \"\"\"","sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import","search_rec): \"\"\"Compare multi-metric cv_results with the ensemble of multiple single","range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not","above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) #","== 99 # doesn't go into infinite loops params_distribution =","def test_no_refit(): # Test that GSCV can be used for","T.shape[0] def transform(self, X): return X + self.foo_param def inverse_transform(self,","assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search,","# best estimator n_features = 4 X, y = make_classification(n_features=n_features)","X = np.arange(20).reshape(5, -1) y = [0, 0, 1, 1,","all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys) def","y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs:","assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for","SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0,","np.array([0] * 5 + [1] * 5) Cs = [.1,","is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def","results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking '","('refit=False. %s is available only after ' 'refitting on the","raise AttributeError X = np.array([[-1, -1], [-2, -1], [1, 1],","dummy function tests when returned 'best_index_' is out of bounds.","GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method():","LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C':","as the # loss is 'hinge'. assert not hasattr(clf, \"predict_proba\")","cv_results # Check if generators are supported as cv and","[0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X)","C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 #","score_keys, n_candidates) # Check masking cv_results = search.cv_results_ n_candidates =","has no classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0),","n_splits = 3 n_search_iter = 30 # Scipy 0.12's stats","len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4, 2], \"bar\":","cv=cv, n_jobs=4) # assert that this raises an error with","# check that best params are equal assert grid_search_no_score.best_params_ ==","= 50 n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0)","propagates to GridSearchCV # And also check if groups is","attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert","and timing are reasonable, also checks if the keys #","refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),","twice\"\"\" def __init__(self, parameter=None): self.parameter = parameter def fit(self, X,","X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def","x in sampler] if sp_version >= (0, 16): param_distributions =","CV iterator empty\\\\? ' 'Were there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size])","# Test that grid search will capture errors on data","mask[np.where(y == 2)[0][::2]] = 0 # this leads to perfect","{'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test","clone(svc), params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train],","(C=0.1) and the next two param # settings (C=0.2) are","= HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt,","== 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) # Smoke test the","float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that","[0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C","_pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where split can","np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv =","= search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 #","types import GeneratorType import re import numpy as np import","is empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha':","def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' # Ensures","assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200,","{re.sub('_score$', '_accuracy', k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$',","attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False)","search algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self,","interface. Return the index of a model that has the","test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold() svc =","error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i)))","fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor to","Check tie breaking strategy - # Check that there is","5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]))","tests when returned 'best_index_' is not integer. \"\"\" return None","0, 0, 1, 1, 1] clf = LinearSVC(random_state=0) gs =","has no score function. clf = LinearSVC(random_state=0) X, y =","is available only after ' 'refitting on the best parameters'","cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,","consistency of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':","y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not available","StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from","X[2, :] = np.nan y = [0, 0, 1, 1,","k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV):","<= 1) for k in score_keys if 'time' not in","random_state=0) param_dict = {\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf","= search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results,","np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X = lambda","y) cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys,","result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A dummy function tests when","return_train_score=True) gs.fit(X, y) # As the first two param settings","10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy')","Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1,","'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_","# caught by grid_search (expected behavior), and this would cause","scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError so this","== (n_cand,) for key in param_keys + score_keys) def test_grid_search_cv_results():","0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4","0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As","expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree',","= make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf,","one fold and a score of 1/3 on # the","i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score =","est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing():","def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV","the predict_proba works when loss is specified # as one","'best_index_' is out of bounds. \"\"\" return out_bound_value X, y","Scipy 0.12's stats dists do not accept seed, hence we","GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y)","n_search_iter = 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma':","regressor_results[1] is not grid_search.best_estimator_ # check that we didn't modify","clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove","the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a","test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an error with","FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be removed on","def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None,","with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y) def","= {\"foo\": [4, 2], \"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2 =","want to break without an explicit deprecation cycle and API","cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) #","(grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ # Check tie","for param in param_keys) assert all(cv_results[key].dtype == object for key","err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X,","[[1], [2], [3], [4], [5]], [0, 0, 0, 0, 1]","y = [0, 1, 1, 0] gs = GridSearchCV(svc, {'C':","as np import scipy.sparse as sp import pytest from sklearn.utils.fixes","y) == grid_search_no_score.score(X, y) # giving no scoring function raises","y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,","= ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in sampler]","without following # proper deprecation cycle. fit_params = { 'tuple_of_arrays':","search part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',","if score == \"f1\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score","raises a ValueError on fit()\"\"\" FAILING_PARAMETER = 2 def __init__(self,","scalar values in fit_params, for instance in LightGBM # that","sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]},","y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): # test grid-search","not # caught by grid_search (expected behavior), and this would","= GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter should not","a model that has the least `mean_test_prec`. \"\"\" assert 'mean_test_prec'","with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test, scorer,","42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha':","parameter=None): self.parameter = parameter def fit(self, X, y): assert not","of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,","# Check if a one time iterable is accepted as","searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list,","[1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test","caught and warnings raised instead. If # refit was done,","def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation catches the errors when","4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test","1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma',","= search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure the","hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba`","import partial import pickle import sys from types import GeneratorType","[{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]},","for the following parameters. But this abuse is common in","an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV])","== [{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def","param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] )","GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier","in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if the","assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']: assert","!= 0` for this dummy clf assert clf.best_index_ != 0","\"\"\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y =","3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict(): #","n_samples = 100 n_splits = 5 X, y = make_classification(n_samples=n_samples,","so this is what we look for. assert_raises(ValueError, gs.fit, X,","* 5 + [1] * 5) Cs = [.1, 1,","import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC,","removed on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc","self.foo_param predict_proba = predict predict_log_proba = predict decision_function = predict","assert (points == set((\"bar\", x, \"foo\", y) for x, y","SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that","Test refit=callable, which adds flexibility in identifying the \"best\" estimator.","not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) ==","= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 =","random_state=0) assert [x for x in sampler] == [x for","Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert","X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params","method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X,","y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def","GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit,","y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not have","50 n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params","= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred =","y) score_auc = search_auc.score(X, y) # ensure the test is","3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search():","param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y)","params, train, test, scorer, verbose=False) def test_pickle(): # Test that","has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_","param_dict) param_dict = {\"C\": []} clf = SVC() assert_raise_message( ValueError,","expected_warning, list, sampler) # degenerates to GridSearchCV if n_iter the","Should raise an error upon multimetric scorer assert_raise_message(ValueError, \"For evaluating","np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1]","cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3)","(\"fit_grid_point is deprecated in version 0.23 \" \"and will be","fit_grid_point( X, y, clone(svc), params, train, test, scorer, verbose=False) est","is correctly passed to the cv object rng = np.random.RandomState(0)","CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3)","test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0, 0, 0,","is deprecated in version 0.23 \" \"and will be removed","-f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C':","= y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >=","= random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train,","from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting","training kernel matrix K_train = np.zeros((10, 20)) y_train = np.ones((10,","of bound result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\" A dummy function","fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin):","= GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) #","test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X, y =","cv=3) # When the estimator is not fitted, `predict_proba` is","assert search.best_score_ == 42 def test_param_sampler(): # test basic properties","_FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X,","6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly',","match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\d+ '","s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf","Check if a one time iterable is accepted as a","GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from","make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision',","5 * 3 * 2).reshape(10, 5, 3, 2) y_3d =","to get a list of keys in # clf.cv_results_. X,","== pytest.approx(np.std(test_cv_scores)) # For the train scores, we do not","[1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises","__init__(self, a=None): self.a = a def fit(self, X, y, r=None):","y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv =","[0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ ==","# Ensures that a warning is raised and score reset","Iterable, Sized from io import StringIO from itertools import chain,","2]}, {'min_samples_split': [5, 10]}])) # Using regressor to make sure","cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message","X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for","(all(cv_results[k] >= 0) for k in score_keys if k !=","\"\"\" def refit_callable_out_bound(cv_results): \"\"\" A dummy function tests when returned","as list in GridSearchCV X = np.arange(100).reshape(10, 10) y =","clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not","with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X,","\"Fit parameter spam has length 1; expected\" with pytest.raises(AssertionError, match=err_msg):","list in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0]","the expected ValueError [u for u in cv] # cv","0.23 \" \"and will be removed in version 0.25\") params","= RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not","from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster","{'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})]","ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): #","sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in","hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0])","a weighted mean irrespective of # i.i.d. or not assert","{'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure","parameter def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise","errors caused by fits # to individual folds will be","SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X,","= make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for score","# XXX: results['params'] is a list :| results[k] = np.asanyarray(results[k])","test loss where greater is worse def f1_loss(y_true_, y_pred_): return","is what we look for. assert_raises(ValueError, gs.fit, X, y) def","= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:])","list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler,","n_features=100, random_state=0) param_dict = {\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]}","be used for model selection alone without refitting clf =","sure `predict_proba` is not available when setting loss=['hinge'] # in","training kernel matrix corresponding to the linear kernel K_train =","* 5 + [1] * 5) clf = CheckingClassifier( check_y=lambda","{'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr","3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score',","SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert","cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for","raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator,","that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is","structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check","-1) X[2, :] = np.nan y = [0, 0, 1,","folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1,","n_samples = 50 n_splits = 2 X, y = make_classification(n_samples=n_samples,","1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError","# estimator requires score method to compete with scoring parameter","clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(),","ensure the test is sane assert score_auc < 1.0 assert","hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X","estimator n_features = 4 X, y = make_classification(n_features=n_features) gbdt =","that we do # not want to break without an","for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV,","of parameters %d is smaller ' 'than n_iter=%d. Running %d","search_auc.score(X, y) # ensure the test is sane assert score_auc","is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss =","in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y)","2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates =","cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores","in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits =","def test_grid_search_precomputed_kernel(): # Test that grid search works when the","# that are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score'","multimetric scorer assert_raise_message(ValueError, \"For evaluating multiple scores, use \" \"sklearn.model_selection.cross_validate","KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from","with only one point. clf = MockClassifier() grid_search = GridSearchCV(clf,","res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i,","than total parameter space params = [{'first': [0, 1], 'second':","refit=True) clf.fit(X, y) # Ensure that `best_index_ != 0` for","10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using","# split dataset into two folds that are not iid","any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):","linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf","= 1. else: score = 0. return score def get_params(self,","that `best_index_ != 0` for this dummy clf assert clf.best_index_","{} for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' %","# The CheckingClassifier generates an assertion error if # a","tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass model =","tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0","sure the predict_proba works when loss is specified # as","in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf","([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a dict","this used to break with # sparse SVMs. X =","'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples)","for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std =","a large grid params = {'a': range(10), 'b': range(10), 'c':","estimator. Non-regression test for issue #13920. \"\"\" est = BaseEstimator()","y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input(): # check cross_val_score","# If True, for multi-metric pass refit='accuracy' if refit: probability","among multi-metric and # single metric grid search results. np.testing.assert_equal","n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf =","test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ], [2, ],","grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert","check support for scalar values in fit_params, for instance in","an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent","KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import recall_score from","= SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for parameter (C) needs","import scipy.sparse as sp import pytest from sklearn.utils.fixes import sp_version","5 + [1] * 5) clf = CheckingClassifier( check_X=lambda x:","sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import","0) assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']:","train scores must also be # same as long as","error messages for refit in [\"\", 5, True, 'recall', 'accuracy']:","param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI","grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert","test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO: Clearly this","class MockClassifier: \"\"\"Dummy classifier to test the parameter search algorithms\"\"\"","GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip","self.foo_param > 1: score = 1. else: score = 0.","for those fits # that are expected to fail. def","y) # Ensure that `best_index_ != 0` for this dummy","param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert","predict predict_log_proba = predict decision_function = predict def score(self, X=None,","setting for score_type in ('train', 'test'): per_param_scores = {} for","fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples ==","searcher = SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\"raise\"","for search in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_","Test scorer names result_keys = list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\")","'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score',","correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \"roc_auc\": dec =","space params = [{'first': [0, 1], 'second': ['a', 'b', 'c']},","= Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param':","'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False,","dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if not","in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv,","implementation of BaseSearchCV has the _pairwise property which matches the","[3], [4], [5]], [0, 0, 0, 0, 1] estimators =","both dense and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100,","\\(0\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a","y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid","Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results =","assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise():","roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from","shuffle=True), return_train_score=True) gs.fit(X, y) # As the first two param","def set_params(self, **params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC):","errors when 'best_index_' returns an invalid result. \"\"\" def refit_callable_invalid_type(cv_results):","y[:180])) # test loss where greater is worse def f1_loss(y_true_,","for est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3)","from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection","total space of parameters %d is smaller ' 'than n_iter=%d.","of the two cv_results dicts np.testing.assert_equal({k: v for k, v","property must match estimator\" for _pairwise_setting in [True, False]: setattr(est,","n_iter=7) samples = list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param():","provided # validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train,","# Test that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10,","will be generated for the 2nd and subsequent cv.split calls.","getattr(grid_search, fn_name), X) # Test that an invalid refit param","is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv',","in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k, v","a list of keys in # clf.cv_results_. X, y =","= StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param ==","are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we","GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv)","8 for values in ParameterGrid(params): assert values in samples #","and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] ==","when 'best_index_' returns an out of bound result. \"\"\" def","searcher has no classes_ attribute before it's fit grid_search =","assert clf.best_index_ == 0 # Ensure `best_score_` is disabled when","for train, test in cv.split(X, y): this_scores, this_params, n_test_samples =","bound result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\" A dummy function tests","y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv =","check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and","predict_proba works when loss is specified # as one of","np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned to the","_fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search ) # NOTE:","param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs","or numpy array, but got (<class 'str'>).\" \" Single values","to make sure cv.split is not called once per param","import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut","'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params,","if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i]","= list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i","\"\"\"Compare refit multi-metric search methods with single metric methods\"\"\" assert","base estimator. Non-regression test for issue #13920. \"\"\" est =","expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for","search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \"refit_time_\")","\"eggs\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6 # loop","gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded","per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':","is not grid_search.best_estimator_ # check that we didn't modify the","'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search","attr in dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and","assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not have a","cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_,","def test_trivial_cv_results_attr(): # Test search over a \"grid\" with only","only # once if ``cv.split`` is called once per param","check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_ contains all","C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys =","in # popular third-party libraries and we should tolerate this","grid_search.best_params_[\"n_clusters\"] == 3 # Now without a score, and without","Check that succeeded estimators have lower ranks assert ranks[0] <=","'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than time","'_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert","list \\(0\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is not","y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self,","def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_ contains","match estimator\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting)","p['c']) for p in samples] assert len(set(hashable_samples)) == 99 #","list(sampler) assert len(samples) == 8 for values in ParameterGrid(params): assert","y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring","3]}, cv=2, error_score=\"raise\" ) # The CheckingClassifier generates an assertion","# When the estimator is not fitted, `predict_proba` is not","param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_","# check that parameters that are estimators are cloned before","check that parameters that are estimators are cloned before fitting","{'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert","this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params, train, test,","scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search","not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y","y = np.array([0] * 5 + [1] * 5) clf","best estimator contains the right value for foo_param clf =","= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning,","= {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True)","do not exactly respect the scikit-learn API contract but that","== 99 hashable_samples = [\"a%db%dc%d\" % (p['a'], p['b'], p['c']) for","the behaviour of the grid search part gs = GridSearchCV(clf,","key in param_keys + score_keys) def test_grid_search_cv_results(): X, y =","= GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 )","_pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return","== .1 assert search.best_score_ == 42 def test_param_sampler(): # test","3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used assert","from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier","y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test","def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =","the first two param settings (C=0.1) and the next two","10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_)","assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y = [[1],","from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics","method.\"\"\" @property def score(self): raise AttributeError X = np.array([[-1, -1],","clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf,","p in samples] assert len(set(hashable_samples)) == 99 # doesn't go","this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): #","X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ ==","make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search =","{'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned","an out of bound result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\" A","sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import","self.a = a def fit(self, X, y, r=None): self.r_ =","[.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf,","'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time',","predict_proba = predict predict_log_proba = predict decision_function = predict def","grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y)","refit in (True, False): random_searches = [] for scoring in","s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for","test.size # Should raise an error upon multimetric scorer assert_raise_message(ValueError,","for key in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin()","[[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0)","{'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that","def __init__(self, a=None): self.a = a def fit(self, X, y,","# to test hyperparameter search on user-defined classifiers. class MockClassifier:","% key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): #","refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X,","equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can","cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X)","def test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe types","3rd parameter will not be evaluated as no train/test indices","[True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]})","X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C':","the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params ==","F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]},","respect the scikit-learn API contract but that we do #","in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is","{'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 =","# As the first two param settings (C=0.1) and the","cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in","from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors","BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search","r def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search)","that grid search works with both dense and sparse matrices","'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand","clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value,","NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)","cv where split can be called only # once if","r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha':","samples = list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): #","uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance","rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def","X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,","for scalar values in fit_params # non-regression test for: #","error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'):","= lambda x: x.shape[1:] == (5, 3, 2) check_y =","for k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi,","scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert","param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_ n_candidates","test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,","KFold() for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv,","[-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1,","if the keys # are present assert all((np.all(cv_results_multi[k] <= 1)","fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results,","[4, 2], \"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2 = ParameterGrid(params2) assert","for cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) #","= 0. return score def get_params(self, deep=False): return {'foo_param': self.foo_param}","shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in","CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1,","chain, product from functools import partial import pickle import sys","def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO: Clearly","score and that it gives the correct result assert grid_search.score(X,","random_state=42) # The two C values are close enough to","'accuracy', 'recall'): # If True, for multi-metric pass refit='accuracy' if","{'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make sure it","structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates =","GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned","`refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test implementation","assert params == this_params assert n_test_samples == test.size # Should","['log'], } X = np.arange(24).reshape(6, -1) y = [0, 0,","# test loss where greater is worse def f1_loss(y_true_, y_pred_):","non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs =","== 2)[0][::2]] = 0 # this leads to perfect classification","empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert","array, but got (<class 'str'>).\" \" Single values need to","3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs","# assert that this raises an error with pytest.raises(ValueError, match='cv.split","cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0,","candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y =","DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from","rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE","on_error == 'raise' raises the error X, y = make_classification(n_samples=20,","n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True)","that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y","[\"a%db%dc%d\" % (p['a'], p['b'], p['c']) for p in samples] assert","clf.fit(X[train], y[train]) if score == \"f1\": correct_score = f1_score(y[test], clf.predict(X[test]))","numpy as np import scipy.sparse as sp import pytest from","not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype ==","0, 3, base=0.1)) for refit in (True, False): random_searches =","common in # popular third-party libraries and we should tolerate","long as the same train/test indices are generated for all","have lower ranks assert ranks[0] <= 2 and ranks[1] <=","1 # create bad cv cv = BrokenKFold(n_splits=3) train_size =","GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y)","return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in","gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],","('The total space of parameters %d is smaller ' 'than","multiple scores, use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y, svc,","assert cv.best_score_ >= 0 # compute the test kernel matrix","cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_y_as_list(): #","create bad cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge","Use global X, y # create cv cv = KFold(n_splits=3).split(X)","ValueError(\"Failing classifier failed as required\") def predict(self, X): return np.zeros(X.shape[0])","as a cv parameter. n_samples = 100 n_splits = 5","v for k, v in cv_results_multi.items() if not k.endswith('_time')}, {k:","in score_keys) assert all(cv_results[key].dtype == np.float64 for key in score_keys","make_blobs(n_samples=50, random_state=42) # The two C values are close enough","dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and attr not","2, 3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)]","= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test","test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3","key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time","'hinge'. assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) #","y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with","assert list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid,","assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV if n_iter","TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x,","grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict(): # test grid-search with an","precomputed metric\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\",","Check warning only occurs in situation where behavior changed: #","grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring grid_search.scoring","GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):","@ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe","ranks assert ranks[0] <= 2 and ranks[1] <= 2 #","grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) #","def get_n_splits(self, *args, **kw): return 1 # create bad cv","parameter space params = [{'first': [0, 1], 'second': ['a', 'b',","clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\")",":) assert grid_search.best_params_[\"n_clusters\"] == 3 # Single metric evaluation unsupervised","grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can call score","on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def","range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_","score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def","ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower","correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when","**kwargs) def fit(self, X, y=None, groups=None, **fit_params): return self #","validate _pairwise is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed')","in version 0.25\") params = {'C': 0.1} train, test =","irrespective of # i.i.d. or not assert train_mean == pytest.approx(1)","evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5,","X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points","assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X","== \"roc_auc\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score,","y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C","grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test)","test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer =","[5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr in","in case of ties old_stdout = sys.stdout sys.stdout = StringIO()","pickle import sys from types import GeneratorType import re import","correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) #","cv=cv) # Should not raise an error gs.fit(X, y) def","y) cv_results = search.cv_results_ # Check if score and timing","mean cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search =","be careful not to break support for those without following","def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit multi-metric search methods with","= GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def","cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as a cv","= search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert","grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid,","Pass X as list in GridSearchCV X = np.arange(100).reshape(10, 10)","= make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid =","X, y = make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0)","as long as the same train/test indices are generated for","[0, 1, 1, 0] gs = GridSearchCV(svc, {'C': [0, 1]},","assert 0 <= sample[\"C\"] <= 1 # test that repeated","can be pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':","RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for","= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X,","y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ ==","p = 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params,","score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc =","make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False)","search.fit(X, y) cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search,","assert C == C2 # Smoke test the score #","est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with","{\"C\": [0, 1, 2, 3, 4, 5, 6, 7, 8,","TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1,","{\"C\": \"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for","recommendations for implementing early stopping with a user provided #","of the following two estimators inherit from BaseEstimator, # to","used n_splits = 3 clf = LinearSVC(random_state=0) X, y =","# Test with grid search cv for est in estimators:","# make sure it selects the smallest parameter in case","the parameter grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_')","else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype","def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search ) #","'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not","% s][0] for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0]","from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone from","0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a","= a def fit(self, X, y, r=None): self.r_ = r","multi-metric pass refit='accuracy' if refit: probability = True refit =","# Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,","cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of BaseSearchCV has","np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i]","messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError,","n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y)","score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and","y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(),","/ 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used","this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params, train,","def predict(self, T): return T.shape[0] def transform(self, X): return X","for p in samples] assert len(set(hashable_samples)) == 99 # doesn't","range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier()","point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3)","y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning","K_train = np.zeros((10, 20)) y_train = np.ones((10, )) clf =","Ensure that `best_index_ != 0` for this dummy clf assert","* 5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs'])","cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric():","1] clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y)","not hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure","[1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in","kernel matrix K_train = np.zeros((10, 20)) y_train = np.ones((10, ))","GridSearchCV, clf, param_dict) param_dict = {\"C\": \"1,2,3\"} clf = SVC(gamma='auto')","that has no score function. clf = LinearSVC(random_state=0) X, y","a model that has the least `mean_test_score`. \"\"\" # Fit","3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_X_as_list(): #","# as one of the parameters in the param_grid. param_grid","contract but that we do # not want to break","hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features","disabled on estimator. X = np.arange(20).reshape(5, -1) y = [0,","np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] ) train_mean","cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def","assert np.mean(y_pred == y_test) >= 0 # test error is","if the search `cv_results`'s array are of correct types cv_results","CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate):","search grid_search_no_score.fit(X, y) # check that best params are equal","tests when returned 'best_index_' is out of bounds. \"\"\" return","identical with precomputed metric\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize(","# check that we didn't modify the parameter grid that","estimator.bandwidth == .1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1,","the \" \"parameter refit must be set to a scorer","fitted, `predict_proba` is not available as the # loss is","[0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X =","1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}","LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\"fit_grid_point is deprecated in","check if groups is correctly passed to the cv object","error if # a parameter is missing or has length","enough to give similar models # which would result in","from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation","42 def test_param_sampler(): # test basic properties of param sampler","= parameter def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER:","grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results =","== FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed as required\") def predict(self,","trivial X and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key]","y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C ==","predict_proba when disabled on estimator. X = np.arange(20).reshape(5, -1) y","warnings raised instead. If # refit was done, then an","assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim ==","should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def","if ``cv.split`` is called once per param setting in GridSearchCV.fit","be removed in version 0.25\") params = {'C': 0.1} train,","MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)},","refit=True) clf.fit(X, y) def test_refit_callable(): \"\"\" Test refit=callable, which adds","X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType)","est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i","test_fit_grid_point as the function will be removed on 0.25 @ignore_warnings(category=FutureWarning)","= roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as","in the # form of a precomputed kernel matrix X_,","= cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 # test","1e-1]}, cv=cv, n_jobs=4) # assert that this raises an error","[0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C","1 # test that repeated calls yield identical parameters param_distributions","make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C':","y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\"C\": [1.0], \"kernel\":","all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for k in","Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i]","any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype == np.float64","uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for","of time.time in windows is not high # enough for","X, y, clone(svc), params, train, test, scorer, verbose=False) est =","in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys)","grid search returns an error with a non-square precomputed #","not the case for the following parameters. But this abuse","each score differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100,","Test that grid search returns an error with a non-square","[1, 2, 3]}, cv=2, error_score=\"raise\" ) # The CheckingClassifier generates","]] y = [0, 1, 1, 0] gs = GridSearchCV(svc,","[0, 0, 1, 1, 1] clf = SVC(probability=False) gs =","~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score':","the training kernel matrix corresponding to the linear kernel K_train","**kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}])","cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter should not be None.\", gs.fit,","(<class 'str'>).\" \" Single values need to be wrapped in","s][0] for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std","random_state=0) # compute the training kernel matrix corresponding to the",":] = np.nan y = [0, 0, 1, 1, 1]","sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import","one element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": []} clf","len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] ==","range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s","cv_results = search.cv_results_ # Check tie breaking strategy - #","n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type)","that grid scores were set to zero as required for","= KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed","assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can call","estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth':","for search in (gs, rs): search.fit(X, y) for key in","[] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If","1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x","test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for","p in grid2) assert (points == set((\"bar\", x, \"foo\", y)","etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling","def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in range(len(grid))]","assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,)","[\"\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring, the","[1, 2, 3]) # Smoke test the score etc: grid_search.score(X,","'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter =","can find the right number :) assert grid_search.best_params_[\"n_clusters\"] == 3","enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \"f1\": correct_score =","# test grid-search with an estimator without predict. # slight","as no train/test indices # will be generated for the","assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1,","cv.score(X_[:180], y[:180])) # test loss where greater is worse def","import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer","make sure each score differs clf = DecisionTreeRegressor(random_state=0) X, y","candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' %","2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]}","verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be","a list or numpy array, but got (<class 'str'>).\" \"","grid params = {'a': range(10), 'b': range(10), 'c': range(10)} sampler","0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert","clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_` is","test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The two C","pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas import","if sp_version >= (0, 16): param_distributions = {\"C\": uniform(0, 1)}","KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from","multiple single metric cv_results from single metric grid/random search\"\"\" assert","in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys,","Regression test for bug in refitting # Simulates re-fitting a","return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},","clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y)","estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert","= len(gs.cv_results_['params']) # Ensure that grid scores were set to","the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X,","shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':","works when the input features are given in the #","random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X,","tuple + chain transforms {\"a\": 1, \"b\": 2} to (\"a\",","grid search will capture errors on data with different length","y) assert hasattr(grid_search, \"cv_results_\") def test_no_refit(): # Test that GSCV","'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X,","clf = MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param':","score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score',","scalar_param > 0 assert callable(callable_param) # The tuple of arrays","# once if ``cv.split`` is called once per param setting","version 0.25\") params = {'C': 0.1} train, test = next(StratifiedKFold().split(X,","score differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4,","return self def predict(self, T): return T.shape[0] def transform(self, X):","1]]) y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert","# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss where","= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf',","X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv):","be wrapped in a list\" \" with one element.\", GridSearchCV,","grid_params = {'n_neighbors': [10]} # defaults to euclidean metric (minkowski","== 2 def test_grid_search_bad_param_grid(): param_dict = {\"C\": 1} clf =","loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler","sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from","in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV,","2).reshape(10, 5, 3, 2) y_3d = np.arange(10 * 7 *","that GSCV can be used for model selection alone without","8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x","values in ParameterGrid(params): assert values in samples # test sampling","r\"Expected fit parameter\\(s\\) \\['eggs'\\] not seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X,","= r def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(),","not high # enough for the fit/score times to be","bigger than total parameter space params = [{'first': [0, 1],","random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ], C=[1, 10],","invalid result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A dummy function tests","cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),","raised instead. If # refit was done, then an exception","= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test","as grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert","score(self, X=None, Y=None): if self.foo_param > 1: score = 1.","and subsequent cv.split calls. # This is a check to","'std_fit_time'))) # Compare the keys, other than time keys, among","where split can be called only # once if ``cv.split``","a tie of their mean cv-scores param_grid = {'C': [1,","{\"a\": 1, \"b\": 2} to (\"a\", 1, \"b\", 2) points","test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc,","assert values in samples # test sampling without replacement in","# Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert","assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke test the","that the grid searcher has no classes_ attribute before it's","Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),","in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train, test","pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},","The CheckingClassifier generates an assertion error if # a parameter","and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if","in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \"f1\": correct_score","ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in sampler] ==","= np.array([0] * 5 + [1] * 5) clf =","same as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1,","= np.nan y = [0, 0, 1, 1, 1] p","Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std","Neither of the following two estimators inherit from BaseEstimator, #","disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type():","go into infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a',",">= .9 assert C == C2 def test_grid_search_sparse_scoring(): X_, y_","from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from","def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ], [2,","evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1,","test_grid_search(): # Test that the best estimator contains the right","\\ hasattr(grid_search, \"best_params_\") # Make sure the functions predict/transform etc","'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert","y, spam=np.ones(10)) err_msg = \"Fit parameter spam has length 1;","'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after","an error with pytest.raises(ValueError, match='No fits were performed. ' 'Was","= n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y)","param_dict = {\"C\": []} clf = SVC() assert_raise_message( ValueError, \"Parameter","True, for multi-metric pass refit='accuracy' if refit: probability = True","y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def","make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},","@ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ],","score_keys) assert all(cv_results[key].dtype == np.float64 for key in score_keys if","set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid","== np.float64 for key in score_keys if not key.startswith('rank')) scorer_keys","y_pred2) assert C == C2 # Smoke test the score","# single metric grid search results. np.testing.assert_equal performs a #","present assert all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time',","# Pass X as list in GridSearchCV X_4d = np.arange(10","test_fit_grid_point_deprecated as # fit_grid_point will be removed on 0.25 def","random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params =","assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def","r'Parameter .* is not a dict or a list \\(0\\)'),","to zero as required for those fits # that are","iterate over the grid multiple times for i in range(2):","= {'n_neighbors': [10]} # defaults to euclidean metric (minkowski p","GridSearchCV with on_error != 'raise' # Ensures that a warning","all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks","assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) assert not hasattr(clf, \"predict_proba\")","should not be None.\", gs.fit, X, y) gs.fit(X, y, groups=groups)","assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float)","KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X,","with one element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": np.ones((3,","keys # are present assert all((np.all(cv_results_multi[k] <= 1) for k","an error with a non-square precomputed # training kernel matrix","the scikit-learn API contract but that we do # not","_pairwise property of its estimator. This test make sure _pairwise","integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def","X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\"C\": [1.0],","SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): #","RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X,","GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_')","is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores))","1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]},","{'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search","property which matches the _pairwise property of its estimator. This","[GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y =","assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation","that are not iid # first one contains data of","= SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range():","= GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def","= SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not","== 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER))","assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups():","values in fit_params, for instance in LightGBM # that do","from scipy.stats import bernoulli, expon, uniform from sklearn.base import BaseEstimator,","clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): #","= CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv =","'test'): per_param_scores = {} for param_i in range(4): per_param_scores[param_i] =","' 'Was the CV iterator empty\\\\? ' 'Were there no","param raises appropriate error messages for refit in [\"\", 5,","== n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of BaseSearchCV has","and attr[-1:] == '_' and attr not in {'cv_results_', 'best_estimator_',","failed as required\") def predict(self, X): return np.zeros(X.shape[0]) def score(self,","slight duplication of a test from KDE def custom_scoring(estimator, X):","= 2 def __init__(self, parameter=None): self.parameter = parameter def fit(self,","# Check if score and timing are reasonable, also checks","1, 1, 0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2,","n_jobs=4) # assert that this raises an error with pytest.raises(ValueError,","[0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") def test_no_refit():","def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X = np.arange(20,","0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) #","2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings","import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score","be a list or numpy array, but got (<class 'int'>).\"","[\"rbf\", \"linear\"] assert 0 <= sample[\"C\"] <= 1 # test","n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def","search_multi.refit return # search cannot predict/score without refit X, y","[{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2,","# which would result in a tie of their mean","values in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class","def test_grid_search_cv_splits_consistency(): # Check if a one time iterable is","= [[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv':","sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import","x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if","be pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,","= [0, 0, 1, 1, 1] clf = SVC(probability=False) gs","= scorer(est, X[test], y[test]) # Test the return values of","grid scores were set to zero as required for those","score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when","in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y","np.float64 for key in score_keys if not key.startswith('rank')) scorer_keys =","for x in sampler] if sp_version >= (0, 16): param_distributions","y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba',","\"f1\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \"roc_auc\": dec","the least `mean_test_prec`. \"\"\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin()","= fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_","\" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y, svc, params, train, test,","def test_X_as_list(): # Pass X as list in GridSearchCV X","y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an","score = 0. return score def get_params(self, deep=False): return {'foo_param':","= 'accuracy' if isinstance(scoring, tuple) else refit else: probability =","def check_cv_results_array_types(search, param_keys, score_keys): # Check if the search `cv_results`'s","refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error():","3]) def test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4],","the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]},","- self.foo_param predict_proba = predict predict_log_proba = predict decision_function =","in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(),","of its estimator. This test make sure _pairwise is delegated","StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in","in refitting # Simulates re-fitting a broken estimator; this used","parameter spam has length 1; expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X,","scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42","def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =","LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that has no score method.\"\"\" @property","== 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 #","test_param_sampler(): # test basic properties of param sampler param_distributions =","sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import","keys, among multi-metric and # single metric grid search results.","groups is correctly passed to the cv object rng =","best parameters' % fn_name), getattr(grid_search, fn_name), X) # Test that","v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit):",") grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X,","n_iter the same as grid_size sampler = ParameterSampler(params, n_iter=8) samples","search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k, v","to euclidean metric (minkowski p = 2) clf = KNeighborsClassifier()","shuffle=False, n_samples=80) # split dataset into two folds that are","GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_,","for attr in dir(gscv): if (attr[0].islower() and attr[-1:] == '_'","one contains data of all 4 blobs, second only from","2 def __init__(self, parameter=None): self.parameter = parameter def fit(self, X,","2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search))","n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we want","make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}):","\"\"\" def refit_callable(cv_results): \"\"\" A dummy function tests `refit=callable` interface.","in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\",","def test_grid_search_pipeline_steps(): # check that parameters that are estimators are","= KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric evaluation unsupervised scoring =","y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params =","in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for","clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError,","% s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0)","clf.fit(X, y) def test_refit_callable(): \"\"\" Test refit=callable, which adds flexibility","[dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])]","timing are reasonable, also checks if the keys # are","X, Y): assert len(X) == len(Y) self.classes_ = np.unique(Y) return","= np.zeros((10, 20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed')","# FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be removed","non-re-entrant cv where split can be called only # once","groups is None) propagates to GridSearchCV # And also check","features are given in the # form of a precomputed","'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter,","[search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] ) test_mean =","= euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits)","if score and timing are reasonable, also checks if the","were set to zero as required for those fits #","in a list\" \" with one element.\", GridSearchCV, clf, param_dict)","enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import","= make_blobs(n_samples=50, random_state=42) # The two C values are close","LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score","dummy clf with `refit=True` to get a list of keys","estimator; this used to break with # sparse SVMs. X","for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip():","self def predict(self, T): return T.shape[0] def transform(self, X): return","+ score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)","instance in LightGBM # that do not exactly respect the","= RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results =","import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold","correct scores are used n_splits = 3 clf = LinearSVC(random_state=0)","if # a parameter is missing or has length !=","contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys +","= {\"random_state\": [0, None]} cv = KFold() for est in","BaseEstimator, # to test hyperparameter search on user-defined classifiers. class","0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0],","assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4, 2],","# Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180],","= {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution,","sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions","dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] =","clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X, y =","raises an error with pytest.raises(ValueError, match='No fits were performed. '","1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2,","param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2)","not fitted, `predict_proba` is not available as the # loss","as the function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def","BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2,","Test the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys","compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit multi-metric search methods with single","key in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4,","for parameter (C) needs to\" \" be a list or","[1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth':","folds will be caught and warnings raised instead. If #","= search.scorer_.keys() if search.multimetric_ else ['score'] for key in scorer_keys:","= [\"a%db%dc%d\" % (p['a'], p['b'], p['c']) for p in samples]","= 9 grid_size = 8 expected_warning = ('The total space","evaluation setting \"\"\" def refit_callable(cv_results): \"\"\" A dummy function tests","score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss","+ tuple(\"split%d_test_score\" % cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys,","import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal","search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index","= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True)","check_y=check_y, methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})","assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from","of bounds. \"\"\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4,","error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results.","As the first two param settings (C=0.1) and the next","y = [0, 0, 1, 1, 1] clf = SVC(probability=False)","random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]})","it, this should cause the expected ValueError [u for u","refit=False, error_score='raise') # FailingClassifier issues a ValueError so this is","== 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)","- # Check that there is a tie in the","But this abuse is common in # popular third-party libraries","from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model","C values are close enough to give similar models #","= grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train,","np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) ==","fn_name), getattr(grid_search, fn_name), X) # Test that an invalid refit","y = [0, 0, 0, 1, 1, 1] clf =","refit_callable_out_bound(cv_results): \"\"\" A dummy function tests when returned 'best_index_' is","to be wrapped in a list\" \" with one element.\",","def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =","classifiers. class MockClassifier: \"\"\"Dummy classifier to test the parameter search","for foo_param clf = MockClassifier() grid_search = None grid_search =","err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' +","correctly passed to the cv object rng = np.random.RandomState(0) X,","['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results","len(grid2) == 6 # loop to assert we can iterate","a non-square precomputed # training kernel matrix K_train = np.zeros((10,","assert all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time', 'std_score_time',","4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and FMS","from types import GeneratorType import re import numpy as np","\"\"\" A dummy function tests when returned 'best_index_' is out","search.best_score_ == 42 def test_param_sampler(): # test basic properties of","(s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3])","fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight)","__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None,","but got (<class 'int'>).\" \" Single values need to be","np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]]","== 42 @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV,","= cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv =","[1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises","not integer. \"\"\" return None X, y = make_classification(n_samples=100, n_features=4,","n_iter=%d. Running %d iterations. For ' 'exhaustive searches, use GridSearchCV.'","sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import","delegated to the base estimator. Non-regression test for issue #13920.","lambda x: x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X,","keys in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42)","attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr)","0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv =","for i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input,","= { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, }","partial import pickle import sys from types import GeneratorType import","than time keys, among multi-metric and # single metric grid","and timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k]","n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches)","HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following","= Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search =","[u for u in cv] # cv is empty now","GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point():","duplication of a test from KDE def custom_scoring(estimator, X): return","iterations. For ' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter,","that a fit search can be pickled clf = MockClassifier()","`refit=callable` interface. Return the index of a model that has","param_dict) def test_grid_search_sparse(): # Test that grid search works with","[0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 =","in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] *","# Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True,","key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0]","windows is not high # enough for the fit/score times","hasattr(grid_search, \"best_params_\") # Make sure the functions predict/transform etc raise","LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from","in sampler] assert len(samples) == 10 for sample in samples:","0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X,","not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for","import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier,","3, 2) y_3d = np.arange(10 * 7 * 11).reshape(10, 7,","do not take a weighted mean irrespective of # i.i.d.","MockClassifier: \"\"\"Dummy classifier to test the parameter search algorithms\"\"\" def","Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand)","import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal","be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y =","random_state=42) n_splits = 3 n_search_iter = 30 params = [{'kernel':","when loss is specified # as one of the parameters","{\"C\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for","# unofficially sanctioned tolerance for scalar values in fit_params #","of a model that has the least `mean_test_score`. \"\"\" #","0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0)","n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for","# FIXME remove test_fit_grid_point as the function will be removed","X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0,","test_n_features_in(): # make sure grid search and random search delegate","= {\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf = SVC(gamma='auto')","C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def","# Should not raise an error gs.fit(X, y) def test_classes__property():","greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180])","ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) #","in grid2) assert (points == set((\"bar\", x, \"foo\", y) for","not called once per param # setting. np.testing.assert_equal({k: v for","0 # Ensure `best_score_` is disabled when using `refit=callable` assert","Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe,","classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs},",">= 0) for k in score_keys if k != 'rank_test_score')","' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y)","# Scipy 0.12's stats dists do not accept seed, hence","that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1],","of param sampler param_distributions = {\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0,","now and be careful not to break support for those","metric methods\"\"\" assert search_acc.refit == refit if refit: assert search_multi.refit","all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates)","returns an error with a non-square precomputed # training kernel","or not assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0)","from BaseEstimator, # to test hyperparameter search on user-defined classifiers.","= GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1)","1, 1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf, {},","('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if","random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled","], [3, ], [4, ]] y = [0, 1, 1,","params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ],","1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates","k in results: if not k.endswith('_time'): # XXX: results['params'] is","v in cv_results_multi.items() if not k.endswith('_time')}, {k: v for k,","= 30 # Scipy 0.12's stats dists do not accept","MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame, Series)) except","the _pairwise property of its estimator. This test make sure","gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower ranks assert","test ensures the equivalence of 'precomputed'. Non-regression test for issue","Test search over a \"grid\" with only one point. clf","types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame","train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4,","0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar","per_param_scores = {} for param_i in range(4): per_param_scores[param_i] = list(","= SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf =","u in cv] # cv is empty now train_size =","single metric cv_results from single metric grid/random search\"\"\" assert search_multi.multimetric_","[10]} # defaults to euclidean metric (minkowski p = 2)","('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k):","score_type in ('train', 'test'): per_param_scores = {} for param_i in","as one of the parameters in the param_grid. param_grid =","``cv.split`` is called once per param setting in GridSearchCV.fit #","y) # Check warning only occurs in situation where behavior","check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower() and attr[-1:]","scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search =","`mean_test_prec`. \"\"\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y","not seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg =","tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return self def","= fit_grid_point( X, y, clone(svc), params, train, test, scorer, verbose=False)","for refit in (True, False): random_searches = [] for scoring","i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\",","= GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings()","to the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train =","X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])","y) assert not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): # Test GridSearchCV","{'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point","exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,","which matches the _pairwise property of its estimator. This test","import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal","got (<class 'str'>).\" \" Single values need to be wrapped","key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50,","{'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') #","-1) y = [0, 0, 0, 1, 1, 1] clf","y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]})","single metric grid search results. np.testing.assert_equal performs a # deep","s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0]","KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(),","the search `cv_results`'s array are of correct types cv_results =","input features are given in the # form of a","this dummy clf assert clf.best_index_ != 0 # Assert every","and 3rd parameter will not be evaluated as no train/test","object for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for","k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for","[1, 2, 3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0),","cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds that","{'C': 0.01}, {'C': 0.001}): for train, test in cv.split(X, y):","refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in","cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(),","from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection","return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf,","its estimator. This test ensures the equivalence of 'precomputed'. Non-regression","tolerance for scalar values in fit_params # non-regression test for:","def test_y_as_list(): # Pass y as list in GridSearchCV X","InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series)","from collections.abc import Iterable, Sized from io import StringIO from","np.testing.assert_equal performs a # deep nested comparison of the two","not search_multi.refit return # search cannot predict/score without refit X,","range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\", [(0,","hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is","split can be called only # once if ``cv.split`` is","param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2","only want to check that errors caused by fits #","it gives the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X,","from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking","random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,","ensemble of multiple single metric cv_results from single metric grid/random","classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})","{'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5},","s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in","[search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] ) train_mean =","is not iterable .*\" r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass, input,","searcher has no classes_ attribute without a refit grid_search =","that has the least `mean_test_prec`. \"\"\" assert 'mean_test_prec' in cv_results","isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1","X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10]","K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test)","refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search","y = make_blobs(n_samples=50, random_state=42) # The two C values are","in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator,","support for scalar values in fit_params, for instance in LightGBM","= GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param","in GridSearchCV X_4d = np.arange(10 * 5 * 3 *","[{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search():","predict decision_function = predict def score(self, X=None, Y=None): if self.foo_param","to give similar models # which would result in a","[0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute","random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1,","= GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only occurs","param_distributions = {\"C\": [0, 1, 2, 3, 4, 5, 6,","a def fit(self, X, y, r=None): self.r_ = r def","iterator empty\\\\? ' 'Were there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def","import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split","grid_search.best_estimator_ # check that we didn't modify the parameter grid","out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\" Test refit=callable","'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(),","import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity","from sklearn.base import clone from sklearn.exceptions import NotFittedError from sklearn.datasets","Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from","_pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise,","= ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search =","range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train],","search_acc, search_rec): \"\"\"Compare multi-metric cv_results with the ensemble of multiple","The tuple of arrays should be preserved as tuple. assert","'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4,","grid_search.transform(X) # Test exception handling on scoring grid_search.scoring = 'sklearn'","test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \"f1\":","= ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) == 7","2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError so","n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores","assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid","= pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2,","classifier that has no score method.\"\"\" @property def score(self): raise","Check if ValueError (when groups is None) propagates to GridSearchCV","\"\"\" Test refit=callable, which adds flexibility in identifying the \"best\"","est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test])","FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,","euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed,","its estimator. This test make sure _pairwise is delegated to","np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(","return_train_score=True) gs2.fit(X, y) # Give generator as a cv parameter","from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection","with a randomized search for est in estimators: random_search =","x, y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty","for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score'","refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X))","kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred","grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \"GridSearchCV","1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C","for s in range(n_splits))) for i, (train, test) in enumerate(cv.split(X,","isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf,","5 + [1] * 5) Cs = [.1, 1, 10]","grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3","= clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) #","'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores =","fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self,","GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed():","global X, y # create cv cv = KFold(n_splits=3).split(X) #","the param_grid. param_grid = { 'loss': ['log'], } X =","search.multimetric_ else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s' %","param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_)","KFold(n_splits=3).split(X) # pop all of it, this should cause the","score of 1/3 on # the other # create \"cv\"","y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input(): # check",">= 1) assert (all(cv_results[k] >= 0) for k in score_keys","return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise'","y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults","= list(sampler) assert len(samples) == 99 hashable_samples = [\"a%db%dc%d\" %","{'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search,","in types: # X dataframe, y series X_df, y_ser =","basic properties of ParameterGrid. params1 = {\"foo\": [1, 2, 3]}","global X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return","not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is","<= 2 # Check that failed estimator has the highest","assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return self","test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation catches the errors when 'best_index_'","assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True,","grid for parameter (C) needs to\" \" be a list","changed: # estimator requires score method to compete with scoring","the case for the following parameters. But this abuse is","assert not hasattr(grid_search, 'classes_') # Test that the grid searcher","search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores","i.i.d. or not assert train_mean == pytest.approx(1) assert train_std ==","y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test)","compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits","same train/test indices are generated for all # the cv","= np.arange(24).reshape(6, -1) y = [0, 0, 0, 1, 1,","cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in search_rec.cv_results_.items()}) #","params = [{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third':","k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k):","'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits","= len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for","i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X,","gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error ==","= GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \"predict_proba\") clf.fit(X, y)","for issue #13920. \"\"\" est = BaseEstimator() attr_message = \"BaseSearchCV","not hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search,","for k in score_keys if k != 'rank_test_score') assert (all(cv_results[k]","y series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return","n_iter=9) n_iter = 9 grid_size = 8 expected_warning = ('The","called once per param # setting. np.testing.assert_equal({k: v for k,","= [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy',","to be non-zero for trivial X and y assert np.all(search.cv_results_[key]","score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc =","scorer names result_keys = list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\") +","GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) #","GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] * 5","from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from","= SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit,","must also be # same as long as the same","scores were set to zero as required for those fits","best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check","y[:train_size]) def test_n_features_in(): # make sure grid search and random","y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_)","0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters =","= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180],","y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert","Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy')","= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >=","classifier that cannot be fit twice\"\"\" def __init__(self, parameter=None): self.parameter","on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc =","[DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv for est","+ [1] * 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x,","range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check","in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)):","= 0 mask[np.where(y == 2)[0][::2]] = 0 # this leads","from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors","in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y),","svc, params, train, test, {'score': scorer}, verbose=True) # FIXME remove","with pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X, y)","cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates)","list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for i, (train,","not be evaluated as no train/test indices # will be","1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1,","isinstance(scoring, tuple) else refit else: probability = False clf =","on refit and not # caught by grid_search (expected behavior),","is 'hinge'. assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X)","do not have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha':","for k in score_keys if 'time' not in k and","a dummy clf with `refit=True` to get a list of","{'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs},","'inconsistent results. Expected \\\\d+ ' 'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size])","1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 #","if n_iter is bigger than total parameter space params =","scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X,","== pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y","gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for","only occurs in situation where behavior changed: # estimator requires","assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 =","assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':","X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter':","gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_","{'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params):","cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred","[x for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): #","and this would cause an # error in this test.","= {\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert","== [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)])","preds_precomputed = cv.predict(X_precomputed) attr_message = \"GridSearchCV not identical with precomputed","# Use global X, y class BrokenKFold(KFold): def get_n_splits(self, *args,","to get default estimator settings) empty = ParameterGrid({}) assert len(empty)","assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50,","'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results =","= search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' %","# form of a precomputed kernel matrix X_, y_ =","@pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01,","SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\"raise\" ) #","over the grid multiple times for i in range(2): #","generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y),","GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from","we only want to check that errors caused by fits","isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\":","* 5) Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0),","def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y=None):","array, but got (<class 'int'>).\" \" Single values need to","Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4,","sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for","C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search","cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): #","def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param","scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs})","y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores were","GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1","GridSearchCV with on_error == 'raise' raises the error X, y","svc = LinearSVC(random_state=0) X = [[1, ], [2, ], [3,","1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 # this","in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results,","that repeated calls yield identical parameters param_distributions = {\"C\": [0,","param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search","np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)","all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time',","the same as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores,","GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert","# https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a =","available when setting loss=['hinge'] # in param_grid param_grid = {","assert sample[\"kernel\"] in [\"rbf\", \"linear\"] assert 0 <= sample[\"C\"] <=","grid_search.fit(X, y) # Both ARI and FMS can find the","metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score')","scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure","assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator","cannot be fit twice\"\"\" def __init__(self, parameter=None): self.parameter = parameter","{'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def","a list or numpy array, but got (<class 'int'>).\" \"","assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key]","the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ !=","5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0,","base=0.1)) for refit in (True, False): random_searches = [] for","== '_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}):","= grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores =","n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV","multi-metric and # single metric grid search results. np.testing.assert_equal performs","matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0] *","sure the functions predict/transform etc raise meaningful # error messages","sampler param_distributions = {\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0, 1)} sampler","check that we can call score and that it gives","k): v for k, v in search_rec.cv_results_.items()}) # Check if","0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as","Non-regression test for issue #13920. \"\"\" est = BaseEstimator() attr_message","X, y, svc, params, train, test, {'score': scorer}, verbose=True) #","make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv =","@pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\", [(0, TypeError,","Ensures that a warning is raised and score reset where","clf.predict(X[test])) elif score == \"roc_auc\": dec = clf.decision_function(X[test]) correct_score =","for splits cv = [[mask, ~mask], [~mask, mask]] common_params =","not hasattr(clf, \"predict_proba\") clf.fit(X, y) assert not hasattr(clf, \"predict_proba\") def","InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return","scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] == 3 # Now without","0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\":","and warnings raised instead. If # refit was done, then","assert all(cv_results[key].dtype == np.float64 for key in score_keys if not","exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in","__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"):","5 + [1] * 5) clf = CheckingClassifier( check_y=lambda x:","\" be a list or numpy array, but got (<class","= make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric","an # error in this test. gs = GridSearchCV(clf, [{'parameter':","range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42)","results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]},","for key in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys()","FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter = parameter def","group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter","= [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf,","time.time in windows is not high # enough for the","= cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >=","degenerates to GridSearchCV if n_iter the same as grid_size sampler","sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import","search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec","gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i","import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point","make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults to euclidean","1, \"b\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)","the estimator is not fitted, `predict_proba` is not available as","points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points ==","samples: assert sample[\"kernel\"] in [\"rbf\", \"linear\"] assert 0 <= sample[\"C\"]","np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' +","0 # test error is raised when the precomputed kernel","['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size","= 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv,","# slight duplication of a test from KDE def custom_scoring(estimator,","scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est,","rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert","specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1,","cv_results = search.cv_results_ # Check if score and timing are","ImportError: pass X = np.arange(100).reshape(10, 10) y = np.array([0] *","# deep nested comparison of the two cv_results dicts np.testing.assert_equal({k:","LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred","return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)","param sampler param_distributions = {\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0, 1)}","refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) #","2, 3]}, cv=3, verbose=3) # make sure it selects the","requires score method to compete with scoring parameter score_no_scoring =","are generated for all # the cv splits, for both","pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \"Fit parameter spam","'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not","import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import","\"grid\" with only one point. clf = MockClassifier() grid_search =","to assert we can iterate over the grid multiple times","search cv for est in estimators: grid_search = GridSearchCV(est, est_parameters,","that grid search works when the input features are given","np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for key in","(n_cand,) for key in param_keys + score_keys) def test_grid_search_cv_results(): X,","(p['a'], p['b'], p['c']) for p in samples] assert len(set(hashable_samples)) ==","= {'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning,","gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert","scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1],","{'C': 0.001}): for train, test in cv.split(X, y): this_scores, this_params,","passed to the cv object rng = np.random.RandomState(0) X, y","search.scorer_.keys() if search.multimetric_ else ['score'] for key in scorer_keys: assert","that the best estimator contains the right value for foo_param","grid_search.best_params_[\"n_clusters\"] == 3 # Single metric evaluation unsupervised grid_search =","we use param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5,","scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and FMS can","GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X,","clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv","search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def","6 # loop to assert we can iterate over the","make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score)","And also check if groups is correctly passed to the","f1_score(y[test], clf.predict(X[test])) elif score == \"roc_auc\": dec = clf.decision_function(X[test]) correct_score","# this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class","cv=3) assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) assert not hasattr(clf,","can be used for model selection alone without refitting clf","give similar models # which would result in a tie","from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection","[0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier","ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) ==","'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples","sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import","5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring, the \"","def test_param_sampler(): # test basic properties of param sampler param_distributions","empty = ParameterGrid({}) assert len(empty) == 1 assert list(empty) ==","hasattr(grid_search, \"cv_results_\") def test_X_as_list(): # Pass X as list in","etc raise meaningful # error messages for fn_name in ('predict',","match=error_message): klass(input) def test_parameter_grid(): # Test basic properties of ParameterGrid.","Y=None): if self.foo_param > 1: score = 1. else: score","param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) cv_results","2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_X_as_list():","search_acc.refit == refit if refit: assert search_multi.refit == 'accuracy' else:","not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank","self.foo_param = foo_param def fit(self, X, Y): assert len(X) ==","grid2 = ParameterGrid(params2) assert len(grid2) == 6 # loop to","= mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower()","2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1,","test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold): def get_n_splits(self,","GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert","ValueError (when groups is None) propagates to GridSearchCV # And","assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for","= RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y)","Now without a score, and without y grid_search = GridSearchCV(km,","random_state=0) clf = FailingClassifier() # refit=False because we want to","there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global","y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self,","0 <= sample[\"C\"] <= 1 # test that repeated calls","'recall'): # If True, for multi-metric pass refit='accuracy' if refit:","1, 1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C':","do not accept seed, hence we use param grid params","@ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier that has","should cause the expected ValueError [u for u in cv]","enough for the fit/score times to be non-zero for trivial","self # this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y)","FailingClassifier issues a ValueError so this is what we look","cv.predict(X_precomputed) attr_message = \"GridSearchCV not identical with precomputed metric\" assert","TypeError, r'Parameter .* is not a dict or a list","= np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1]","API # recommendations for implementing early stopping with a user","sanctioned tolerance for scalar values in fit_params # non-regression test","y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results","import BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions import","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df)","raised on refit and not # caught by grid_search (expected","tie breaking strategy - # Check that there is a","pytest.approx(np.std(test_cv_scores)) # For the train scores, we do not take","clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params,","[(0, TypeError, r'Parameter .* is not a dict or a","X): return X + self.foo_param def inverse_transform(self, X): return X","The precision of time.time in windows is not high #","sane assert score_auc < 1.0 assert score_accuracy < 1.0 assert","GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert","{'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this","RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that","range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train])","assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not","= {\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions,","k, v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency","# recommendations for implementing early stopping with a user provided","= GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not","model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \"SearchCV, param_search\",","class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 # create","1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for parameter",") train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] ==","check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}])","assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C':","range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def","sure grid search and random search delegate n_features_in to the","and the next two param # settings (C=0.2) are same,","y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge)","= StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params","y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15)","= KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original","1]}, cv=2, error_score=0, n_iter=2) for search in (gs, rs): search.fit(X,","grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score =","Check if the search `cv_results`'s array are of correct types","np import scipy.sparse as sp import pytest from sklearn.utils.fixes import","def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0] *","Fit a dummy clf with `refit=True` to get a list","GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def","n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X,","error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier()","test_grid_search_sparse(): # Test that grid search works with both dense","# Pass y as list in GridSearchCV X = np.arange(100).reshape(10,","NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import make_blobs from","def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X, y","once if ``cv.split`` is called once per param setting in","assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from","cv] # cv is empty now train_size = 100 ridge","import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans","n_features=4, random_state=42) n_splits = 3 n_grid_points = 6 params =","import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa","{'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy =","cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf,","{'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors","y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed as","and train scores must also be # same as long","7, 11) check_X = lambda x: x.shape[1:] == (5, 3,","cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc","# i.i.d. or not assert train_mean == pytest.approx(1) assert train_std","with on_error == 'raise' raises the error X, y =","for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv =","f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv","1, \"b\": 2} to (\"a\", 1, \"b\", 2) points =","% (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates","n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results","search over a \"grid\" with only one point. clf =","for parameter (C) need to be a non-empty sequence.\", GridSearchCV,","Pass y as list in GridSearchCV X = np.arange(100).reshape(10, 10)","2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter","in samples] assert len(set(hashable_samples)) == 99 # doesn't go into","random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare multi-metric cv_results","GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y)","0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_","no scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C':","= (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\": [0, None]} cv =","'_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting ==","from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC from","1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler =","X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed","AttributeError X = np.array([[-1, -1], [-2, -1], [1, 1], [2,","scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params'])","grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not","length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()","[.5]}]) assert len(has_empty) == 4 assert list(has_empty) == [{'C': 1},","scorer, verbose=False) def test_pickle(): # Test that a fit search","value for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':","refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare multi-metric cv_results with","0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs","will be removed on 0.25 def test_fit_grid_point_deprecated(): X, y =","k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items() if not","of multiple single metric cv_results from single metric grid/random search\"\"\"","0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): #","{'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip =","msg = (\"fit_grid_point is deprecated in version 0.23 \" \"and","\"parameter refit must be set to a scorer key\", GridSearchCV(clf,","from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection","error_type, error_message\", [(0, TypeError, r'Parameter .* is not a dict","= 8 expected_warning = ('The total space of parameters %d","returned ' 'inconsistent results. Expected \\\\d+ ' 'splits, got \\\\d+'):","not exactly respect the scikit-learn API contract but that we","sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import","== pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)","[0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y)","for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results():","bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base","hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def","Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2","is a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind ==","all of it, this should cause the expected ValueError [u","of ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y)","if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1,","import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold","if refit: assert search_multi.refit == 'accuracy' else: assert not search_multi.refit","assert that this raises an error with pytest.raises(ValueError, match='No fits","import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning","2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X,","hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y =","the errors when 'best_index_' returns an invalid result. \"\"\" def","test the behaviour of the grid search part gs =","'best_index_' is not integer. \"\"\" return None X, y =","this_params assert n_test_samples == test.size # Should raise an error","len(samples) == 8 for values in ParameterGrid(params): assert values in","# Test that the best estimator contains the right value","= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y)","(RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check","1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two","* 7 * 11).reshape(10, 7, 11) check_X = lambda x:","there is a tie in the mean scores between #","model that has the least `mean_test_prec`. \"\"\" assert 'mean_test_prec' in","'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than time keys,","\"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for parameter","scalar values in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805","assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0","' 'refitting on the best parameters' % fn_name), getattr(grid_search, fn_name),","the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180]","# check cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame,","X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score),","an invalid result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A dummy function","TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a def fit(self,","# Test search over a \"grid\" with only one point.","(0, 16): param_distributions = {\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions,","fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated(): X, y","params = {'a': range(10), 'b': range(10), 'c': range(10)} sampler =","!= 'raise' # Ensures that a warning is raised and","np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y =","the right number :) assert grid_search.best_params_[\"n_clusters\"] == 3 # Single","clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError,","X and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] <","hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y)","== sorted(exp_results) for k in results: if not k.endswith('_time'): #","grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y)","precompute euclidean metric to validate _pairwise is working X_precomputed =","iid # first one contains data of all 4 blobs,","X=None, Y=None): if self.foo_param > 1: score = 1. else:","key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for key","parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc","y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits,","of its estimator. This test ensures the equivalence of 'precomputed'.","cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf,","y) def test_grid_search_cv_splits_consistency(): # Check if a one time iterable","for i in range(2): # tuple + chain transforms {\"a\":","if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed as required\")","ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from","X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() #","Pass X as list in GridSearchCV X_4d = np.arange(10 *","Test exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit,","make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric evaluation","scorer assert_raise_message(ValueError, \"For evaluating multiple scores, use \" \"sklearn.model_selection.cross_validate instead.\",","assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2])","test_grid_search_bad_param_grid(): param_dict = {\"C\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError,","k, v in cv_results_multi.items() if not k.endswith('_time')}, {k: v for","assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from","settings (C=0.2) are same, the test and train scores must","n_splits = 3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0,","!= 'rank_test_score') assert (all(cv_results[k] <= 1) for k in score_keys","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train)","range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y =","GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def","y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01,","\"BaseSearchCV _pairwise property must match estimator\" for _pairwise_setting in [True,","list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0],","6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0)","# test that repeated calls yield identical parameters param_distributions =","== C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)","clf with `refit=True` to get a list of keys in","# scores are the same as above assert_allclose(test_cv_scores, [1, 1.","{\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf = SVC(gamma='auto') cv","x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search =","# Now without a score, and without y grid_search =","if score and timing are reasonable assert all(cv_results['rank_test_score'] >= 1)","LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not","are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid =","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X,","cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i]","= list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make","score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results =","not take a weighted mean irrespective of # i.i.d. or","search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf,","test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)]","is not fitted, `predict_proba` is not available as the #","reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for","# in param_grid param_grid = { 'loss': ['hinge'], } clf","cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits","**kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): # this","assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError","break support for those without following # proper deprecation cycle.","== len(Y) self.classes_ = np.unique(Y) return self def predict(self, T):","search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys,","!= clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise'","10] for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C':","import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold","re import numpy as np import scipy.sparse as sp import","random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search","metric grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi =","returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2])","(when groups is None) propagates to GridSearchCV # And also","gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter should","KMeans(random_state=0, init=\"random\", n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score',","clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X,","tie in the mean scores between # candidates 1 and","return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =","check to make sure cv.split is not called once per","list in GridSearchCV X_4d = np.arange(10 * 5 * 3","3 n_search_iter = 30 # Scipy 0.12's stats dists do",".1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_","est_parameters = {\"max_depth\": [1, 2, 3, 4]} cv = KFold()","{'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\"","make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs","sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import","check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor","assert len(samples) == 8 for values in ParameterGrid(params): assert values","+ [1] * 5) for InputFeatureType, TargetType in types: #","def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def","cv_results_multi.items() if not k.endswith('_time')}, {k: v for k, v in","0, 1, 1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf,","GridSearchCV, clf, param_dict) param_dict = {\"C\": []} clf = SVC()","= [0, 0, 0, 1, 1, 1] clf = LinearSVC(random_state=0)","= ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples) ==","== 7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works","0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':","[1] * 5) for InputFeatureType, TargetType in types: # X","# check support for scalar values in fit_params, for instance","3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized)","GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is not fitted,","getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) #","cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should","Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X,","for InputFeatureType, TargetType in types: # X dataframe, y series","refit and not # caught by grid_search (expected behavior), and","'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score',","grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]},","param_dict = {\"C\": \"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter","n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]}","3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with","the cv splits, for both param setting for score_type in","Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer","\"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A dummy function tests when returned","'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV,","in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors':","assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best estimator contains","fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42,","# will be generated for the 2nd and subsequent cv.split","BaseSearchCV has the _pairwise property which matches the _pairwise property","refit=callable in multiple metric evaluation setting \"\"\" def refit_callable(cv_results): \"\"\"","probability = False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf,","LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from","pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): # this should raise a NotImplementedError","GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from","score method to compete with scoring parameter score_no_scoring = search_no_scoring.score(X,","subsequent cv.split calls. # This is a check to make","of it, this should cause the expected ValueError [u for","\"\"\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf","assert score_auc < 1.0 assert score_accuracy < 1.0 assert score_auc","from itertools import chain, product from functools import partial import","LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y)","= KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)","cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring","(train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score ==","= np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] )","to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s","cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9","{'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0,","a=None): self.a = a def fit(self, X, y, r=None): self.r_","sure it selects the smallest parameter in case of ties","def fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ =","# Test that an invalid refit param raises appropriate error","uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x","random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with","from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following two","((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i for cv_i in range(n_splits)))","estimators are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid","clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,",">= 0 def test_grid_search_correct_score_results(): # test that correct scores are","list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty)","# Test search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True,","candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert","inherit from BaseEstimator, # to test hyperparameter search on user-defined","[LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs =","clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\"raise\" ) # The","# Check warning only occurs in situation where behavior changed:","= SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should be","('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def","in (True, False): random_searches = [] for scoring in (('accuracy',","same as long as the same train/test indices are generated","a tie in the mean scores between # candidates 1","# Make sure the predict_proba works when loss is specified","def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0, 0,","= FailingClassifier() # refit=False because we only want to check","== 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}),","clf, param_dict) param_dict = {\"C\": \"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message(","if not k.endswith('_time'): # XXX: results['params'] is a list :|","collections.abc import Iterable, Sized from io import StringIO from itertools","failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3","get a list of keys in # clf.cv_results_. X, y","for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a","sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import","[0, 1]}], scoring=\"precision\", refit=True) clf.fit(X, y) def test_refit_callable(): \"\"\" Test","two cv_results dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items()","2 # Check that failed estimator has the highest rank","rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of BaseSearchCV","else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1,","params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that has","('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score',","len(samples) == 99 hashable_samples = [\"a%db%dc%d\" % (p['a'], p['b'], p['c'])","param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check results","\"\"\"Dummy classifier to test the parameter search algorithms\"\"\" def __init__(self,","test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5,","Check that failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER]","= evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split':","single metric methods\"\"\" assert search_acc.refit == refit if refit: assert","equivalence of 'precomputed'. Non-regression test for issue #13920. \"\"\" n_samples","accept seed, hence we use param grid params = dict(C=np.logspace(-4,","check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,","# Test the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()),","0, 1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)),","GridSearchCV if n_iter the same as grid_size sampler = ParameterSampler(params,","test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises the error","StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from","results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking","not hasattr(grid_search, 'classes_') # Test that the grid searcher has","v for k, v in gs2.cv_results_.items() if not k.endswith('_time')}) #","np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def","what we look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement():","cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0),","_pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of","= LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid,","on one fold and a score of 1/3 on #","score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if","1.0 assert score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy,","= np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y =","is not high # enough for the fit/score times to","assert_raise_message( ValueError, \"Parameter grid for parameter (C) needs to\" \"","explicit deprecation cycle and API # recommendations for implementing early","assert key in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100,","X): return 42 if estimator.bandwidth == .1 else 0 X,","delegate n_features_in to the # best estimator n_features = 4","= GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid,","are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0)","random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable)","sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x","when 'best_index_' returns an invalid result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\"","def refit_callable_out_bound(cv_results): \"\"\" A dummy function tests when returned 'best_index_'","param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score',","{'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_","hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over a \"grid\"","'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score',","cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a ValueError on","param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check if","y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot be fit twice\"\"\"","2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i","= [0, 1, 1, 0] gs = GridSearchCV(svc, {'C': [0,","svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\"fit_grid_point is","assert score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring)","return 1 # create bad cv cv = BrokenKFold(n_splits=3) train_size","shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,","in a tie of their mean cv-scores param_grid = {'C':","method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_',","n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X,","= predict decision_function = predict def score(self, X=None, Y=None): if","= 0 # this leads to perfect classification on one","'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X,","'std_score_time'): cv_results.pop(key) return cv_results # Check if generators are supported","range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores =","grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i])","assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For","for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,","5) Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':","Special case: empty grid (useful to get default estimator settings)","gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k, v in","TargetType in types: # X dataframe, y series X_df, y_ser","can call score and that it gives the correct result","est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y)","gs.fit(X, y) def test_classes__property(): # Test that classes_ property matches","mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]},","= make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]},","v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v","y[:train_size]) def test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold):",") def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar","total parameter space params = [{'first': [0, 1], 'second': ['a',","assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from","np.unique(Y) return self def predict(self, T): return T.shape[0] def transform(self,","io import StringIO from itertools import chain, product from functools","= np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y ==","== y_pred2) >= .9 assert C == C2 def test_grid_search_sparse_scoring():","spam has length 1; expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y,","grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4 def test_gridsearch_no_predict(): # test grid-search","centers=2) Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search","sample[\"C\"] <= 1 # test that repeated calls yield identical","self.foo_param def inverse_transform(self, X): return X - self.foo_param predict_proba =","clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y)","search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters","search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def test_param_sampler(): #","np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred","X, y, r=None): self.r_ = r def predict(self, X): return","and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1],","Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10,","params == this_params assert n_test_samples == test.size # Should raise","np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] *","'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search =","is a tie in the mean scores between # candidates","= make_scorer(accuracy_score) msg = (\"fit_grid_point is deprecated in version 0.23","y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for","ranks[0] <= 2 and ranks[1] <= 2 # Check that","y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 #","sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import",".* is not a dict or a list \\(0\\)'), ([{'foo':","check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df,","cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict","proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param':","y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) #","splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant","be called only # once if ``cv.split`` is called once","that failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] ==","def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression","{'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter':","parameters in the param_grid. param_grid = { 'loss': ['log'], }","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) #","a dict or a list \\(0\\)'), ([{'foo': [0]}, 0], TypeError,","grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit, [[1]])","assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y)","result_keys = list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" %","_pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est,","parameter TODO: Clearly this test does something else??? # noise-free","s][candidate_i] for s in range(n_splits))) for i, (train, test) in","gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of folds across","= GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert","the grid searcher has no classes_ attribute before it's fit","return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check if score","1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()),","centers=2) Cs = [.1, 1, 10] for score in ['f1',","# giving no scoring function raises an error grid_search_no_score =","raises appropriate error messages for refit in [\"\", 5, True,","{'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken","[0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that","working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf,","model selection alone without refitting clf = MockClassifier() for scoring","if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50,","a cv parameter. n_samples = 100 n_splits = 5 X,","rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for","assert (all(cv_results[k] >= 0) for k in score_keys if k","= search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k,","be evaluated as no train/test indices # will be generated","{\"max_depth\": [1, 2, 3, 4]} cv = KFold() estimators =","y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\"C\":","C == C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100,","+ [1] * 5) Cs = [.1, 1, 10] grid_search","res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i,","the test and train scores must also be # same","= sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout","getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_',","grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor']","= train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X,","5 + [1] * 5) for InputFeatureType, TargetType in types:",".5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best estimator","y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\": [1, 2, 3,","y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4,","1) assert (all(cv_results[k] >= 0) for k in score_keys if","{'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7)","scorer = make_scorer(accuracy_score) msg = (\"fit_grid_point is deprecated in version","cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean metric to","X, y) def test_grid_search_pipeline_steps(): # check that parameters that are","from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection","[0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of","3 * 2).reshape(10, 5, 3, 2) y_3d = np.arange(10 *","'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time')","the \"best\" estimator. \"\"\" def refit_callable(cv_results): \"\"\" A dummy function","= {'a': range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params,","{\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x","y) def test_refit_callable_multi_metric(): \"\"\" Test refit=callable in multiple metric evaluation","test_refit_callable(): \"\"\" Test refit=callable, which adds flexibility in identifying the","\"Parameter values for parameter (C) need to be a non-empty","for x, y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case:","in (gs, rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']:","[0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search):","and a score of 1/3 on # the other #","out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42),","= list(sampler) assert len(samples) == 8 for values in ParameterGrid(params):","y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf,","y) def test_parameters_sampler_replacement(): # raise warning if n_iter is bigger","params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to get","make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},","all 4 blobs, second only from two. mask = np.ones(X.shape[0],","n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation","param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0],","# Test that the grid searcher has no classes_ attribute","grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf,","functions predict/transform etc raise meaningful # error messages for fn_name","# Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1)","without replacement in a large grid params = {'a': range(10),","= 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors':","correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point","'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X,","attr[-1:] == '_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_',","values need to be wrapped in a list\" \" with","None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1,","foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,","= np.arange(6).reshape(6, -1) y = [0, 0, 0, 1, 1,","instead.\", fit_grid_point, X, y, svc, params, train, test, {'score': scorer},","data dependent (e.g. `sample_weight`) which # is not the case","[1, 1]) # Unweighted mean/std is used assert test_mean ==","with # sparse SVMs. X = np.arange(100).reshape(10, 10) y =","= SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring,","grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def","test for bug in refitting # Simulates re-fitting a broken","sp_version >= (0, 16): param_distributions = {\"C\": uniform(0, 1)} sampler","# noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1,","Assert every key matches those in `cv_results` for key in","grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1,","y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_X_as_list(): # Pass X","grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test that the","corresponding to the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train","from functools import partial import pickle import sys from types","clf.predict_log_proba(X) # Make sure `predict_proba` is not available when setting","the least `mean_test_score`. \"\"\" # Fit a dummy clf with","GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) #","y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg","error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no scoring\", grid_search_no_score.fit,","y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3,","all(cv_results[key].dtype == np.float64 for key in score_keys if not key.startswith('rank'))","def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values in","matches those in `cv_results` for key in clf.cv_results_.keys(): assert key","\" with one element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\":","def test_predict_proba_disabled(): # Test predict_proba when disabled on estimator. X","('train', 'test'): per_param_scores = {} for param_i in range(4): per_param_scores[param_i]","dummy function tests when returned 'best_index_' is not integer. \"\"\"","clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3)","import bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from","1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier',","bug in refitting # Simulates re-fitting a broken estimator; this","(('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for multi-metric pass","random_state=42) n_splits = 3 n_search_iter = 30 # Scipy 0.12's","for k, v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check","Test that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10)","% (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2],","assert_raise_message(NotFittedError, ('refit=False. %s is available only after ' 'refitting on","[None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2,","2, 3, 4, 5, 6, 7, 8, 9, 10]} sampler","metric grid search results. np.testing.assert_equal performs a # deep nested","score method.\"\"\" @property def score(self): raise AttributeError X = np.array([[-1,","range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\" Test refit=callable in multiple","# Check if generators are supported as cv and #","in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] ==","> 1: score = 1. else: score = 0. return","classifier failed as required\") def predict(self, X): return np.zeros(X.shape[0]) def","a ValueError on fit()\"\"\" FAILING_PARAMETER = 2 def __init__(self, parameter=None):","% cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv","sampler] == [x for x in sampler] if sp_version >=","be None.\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs =","grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50,","'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): # Test","and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1)",").fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc =","__init__(self, parameter=None): self.parameter = parameter def fit(self, X, y=None): if","0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0),","setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if not","1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): #","LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf,","BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 # create bad","def test_grid_search_sparse(): # Test that grid search works with both","assert not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): # Test GridSearchCV with","def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =","[0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]},","# or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): #","FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators have","= GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search,","== test.size # Should raise an error upon multimetric scorer","samples = [x for x in sampler] assert len(samples) ==","_pairwise is delegated to the base estimator. Non-regression test for","\" Single values need to be wrapped in a list\"","with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected","1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\": [0, None]}","be # same as long as the same train/test indices","used to break with # sparse SVMs. X = np.arange(100).reshape(10,","y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True)","will capture errors on data with different length X_, y_","'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking():","and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv,","test_grid_search_correct_score_results(): # test that correct scores are used n_splits =",".* is not a dict \\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.*","grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) # Smoke test","GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y)","[\"rbf\"], \"gamma\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict)","10) y = np.array([0] * 5 + [1] * 5)","y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test","samples = list(sampler) assert len(samples) == 99 hashable_samples = [\"a%db%dc%d\"","None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y)","for x in sampler] assert len(samples) == 10 for sample","= StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs):","gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0),","k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys)","parameter\\(s\\) \\['eggs'\\] not seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10))","\"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search, \"best_params_\") #","= pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with","a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3","{'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma',","{k: v for k, v in gs2.cv_results_.items() if not k.endswith('_time')})","search cannot predict/score without refit X, y = make_blobs(n_samples=100, n_features=4,","== set((\"bar\", x, \"foo\", y) for x, y in product(params2[\"bar\"],","= make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\": [1, 2, 3, 4]}","grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y)","from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression","(RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV,","for those without following # proper deprecation cycle. fit_params =","y_pred2) >= .9 assert C == C2 def test_grid_search_sparse_scoring(): X_,","gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True)","k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc,","Expected \\\\d+ ' 'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in():","ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X, y class","{'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\")","to test hyperparameter search on user-defined classifiers. class MockClassifier: \"\"\"Dummy","# the cv splits, for both param setting for score_type","5, 6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3,","grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input(): #","import f1_score from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score","test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains the right","\"best_params_\") # Make sure the functions predict/transform etc raise meaningful","CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5,","[x for x in sampler] assert len(samples) == 10 for","[0, 1, 2, 3, 4, 5, 6, 7, 8, 9,","X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert","Use global X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw):","2], \"bar\": [\"ham\", \"spam\", \"eggs\"]} grid2 = ParameterGrid(params2) assert len(grid2)","when setting loss=['hinge'] # in param_grid param_grid = { 'loss':","assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}]","= make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'):","'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler)","which would result in a tie of their mean cv-scores","RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y)","0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the","if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score']","np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam',","0) for k in score_keys if k != 'rank_test_score') assert","which adds flexibility in identifying the \"best\" estimator. \"\"\" def","CheckingClassifier generates an assertion error if # a parameter is","not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over a","sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in","assert sorted(results.keys()) == sorted(exp_results) for k in results: if not","correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving","= {\"C\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid","this would cause an # error in this test. gs","in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf =","be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3])","are not iid # first one contains data of all","Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180]))","per param setting in GridSearchCV.fit # the 2nd and 3rd","_pairwise property of its estimator. This test ensures the equivalence","X = [[1, ], [2, ], [3, ], [4, ]]","LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X,","random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i])","2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split':","<= 2 and ranks[1] <= 2 # Check that failed","a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X,","estimator settings) empty = ParameterGrid({}) assert len(empty) == 1 assert","from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search","\"cv_results_\") def test_no_refit(): # Test that GSCV can be used","LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier","if (attr[0].islower() and attr[-1:] == '_' and attr not in","X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for","and ranks[1] <= 2 # Check that failed estimator has","est = BaseEstimator() attr_message = \"BaseSearchCV _pairwise property must match","assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no scoring","# refit=False because we want to test the behaviour of","seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \"Fit","without a score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,","in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_","# first one contains data of all 4 blobs, second","this behavior for # now and be careful not to","param_grid param_grid = { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),","Test that GSCV can be used for model selection alone","identical parameters param_distributions = {\"C\": [0, 1, 2, 3, 4,","= r\"Expected fit parameter\\(s\\) \\['eggs'\\] not seen.\" with pytest.raises(AssertionError, match=err_msg):","with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic properties","'Were there no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use","= make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1,","grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): #","and that it gives the correct result assert grid_search.score(X, y)","in param_keys) assert all(cv_results[key].dtype == object for key in param_keys)","input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): #","grid-search with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km","assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass model","{}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the","= n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X,","X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X,","assert getattr(gscv, attr) == getattr(mycv, attr), \\ \"Attribute %s not","once per param setting in GridSearchCV.fit # the 2nd and","assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100,","== 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV","Series)) except ImportError: pass X = np.arange(100).reshape(10, 10) y =","the same train/test indices are generated for all # the","searcher.fit(X, y, spam=np.ones(10)) err_msg = \"Fit parameter spam has length","Simulates re-fitting a broken estimator; this used to break with","= ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score',","== n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test","no classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':","all # the cv splits, for both param setting for","k in score_keys if k != 'rank_test_score') assert (all(cv_results[k] <=","import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message","check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for","[-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test","clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def","\"b\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert","test_refit_callable_invalid_type(): \"\"\" Test implementation catches the errors when 'best_index_' returns","import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler","= y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1,","GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:])","to break with # sparse SVMs. X = np.arange(100).reshape(10, 10)","None.\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(),","'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,","reasonable, also checks if the keys # are present assert","scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an integer'):","= 3 n_search_iter = 30 params = [{'kernel': ['rbf'], 'C':","SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for parameter (C) needs to\"","validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train,","clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in","XXX: results['params'] is a list :| results[k] = np.asanyarray(results[k]) if","for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for","test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4], [5]], [0,","with different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf","\"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i for cv_i in range(n_splits))) assert","train/test indices # will be generated for the 2nd and","\"GridSearchCV not identical with precomputed metric\" assert (preds_original == preds_precomputed).all(),","grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search)","TypeError, \"Parameter.* value is not iterable .*\" r\"\\(key='foo', value=0\\)\")] )","grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV if","of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\" Test refit=callable in","multiple metric evaluation setting \"\"\" def refit_callable(cv_results): \"\"\" A dummy","FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed as required\") def predict(self, X):","GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs,","`predict_proba` is not available when setting loss=['hinge'] # in param_grid","({'foo': 0}, TypeError, \"Parameter.* value is not iterable .*\" r\"\\(key='foo',","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_","enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score,","2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X,","y, r=None): self.r_ = r def predict(self, X): return np.zeros(shape=(len(X)))","in samples # test sampling without replacement in a large","that we didn't modify the parameter grid that was passed","{'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled","are reasonable, also checks if the keys # are present","CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3)","FailingClassifier() # refit=False because we only want to check that","clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),","\"input, error_type, error_message\", [(0, TypeError, r'Parameter .* is not a","return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring =","pytest.raises(ValueError, match='No fits were performed. ' 'Was the CV iterator","this raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned","= np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X =","-1], [1, 1], [2, 1]]) y = np.array([1, 1, 2,","results = mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if","sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) ==","from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection","def transform(self, X): return X + self.foo_param def inverse_transform(self, X):","# the 2nd and 3rd parameter will not be evaluated","matrix K_train = np.zeros((10, 20)) y_train = np.ones((10, )) clf","from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection","y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec':","scores are used n_splits = 3 clf = LinearSVC(random_state=0) X,","GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i","raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned '","cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand):","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3","error gs.fit(X, y) def test_classes__property(): # Test that classes_ property","# not want to break without an explicit deprecation cycle","search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s","product from functools import partial import pickle import sys from","called only # once if ``cv.split`` is called once per","1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_,","If # refit was done, then an exception would be","probability = True refit = 'accuracy' if isinstance(scoring, tuple) else","search works when the input features are given in the","test hyperparameter search on user-defined classifiers. class MockClassifier: \"\"\"Dummy classifier","estimator contains the right value for foo_param clf = MockClassifier()","the _pairwise property which matches the _pairwise property of its","predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test","# Test that regressors do not have a classes_ attribute","attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of BaseSearchCV has the","gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0)","[0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ ==","BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC","when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): #","For the train scores, we do not take a weighted","y).cv_results_ # Test scorer names result_keys = list(cv_results.keys()) expected_keys =","warning is raised and score reset where appropriate. X, y","import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline","precision of time.time in windows is not high # enough","are the same as above assert_allclose(test_cv_scores, [1, 1. / 3.])","dict or a list \\(0\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter","(expected behavior), and this would cause an # error in","= make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]}","Test that grid search will capture errors on data with","noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0],","one of the parameters in the param_grid. param_grid = {","in situation where behavior changed: # estimator requires score method","grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y)","== 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k],","import StringIO from itertools import chain, product from functools import","both param setting for score_type in ('train', 'test'): per_param_scores =","# loop to assert we can iterate over the grid","test_grid_search_no_score(): # Test grid-search on classifier that has no score","= make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0,","evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results =","make sure grid search and random search delegate n_features_in to","@pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y","compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy =","Make sure `predict_proba` is not available when setting loss=['hinge'] #","the precomputed kernel is not array-like # or sparse assert_raises(ValueError,","3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_y_as_list():","This test ensures the equivalence of 'precomputed'. Non-regression test for","y) assert hasattr(grid_search, \"cv_results_\") def test_y_as_list(): # Pass y as","KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed =","y): this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params,","split dataset into two folds that are not iid #","%s is available only after ' 'refitting on the best","(5, 3, 2) check_y = lambda x: x.shape[1:] == (7,","n_iter=10, random_state=0) assert [x for x in sampler] == [x","to make sure each score differs clf = DecisionTreeRegressor(random_state=0) X,","two C values are close enough to give similar models","y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1,","random_state=0) groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid","an estimator without predict. # slight duplication of a test","spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier","pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y =","cv = [[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(),","if generators are supported as cv and # that the","range(2): # tuple + chain transforms {\"a\": 1, \"b\": 2}","% i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled on","ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x in sampler]","def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key)","['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]},","cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search =","pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X","[StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf, grid,","import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings","def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of BaseSearchCV has the _pairwise","# Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert","[1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test","C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches =","{'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is","X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} #","a list \\(0\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is","[1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]),","pass refit='accuracy' if refit: probability = True refit = 'accuracy'","{}, cv=2).fit(X, y) assert not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): #","1: score = 1. else: score = 0. return score","search.cv_results_ # Check tie breaking strategy - # Check that","def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf","when disabled on estimator. X = np.arange(20).reshape(5, -1) y =","the grid multiple times for i in range(2): # tuple","as required\") def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None,","of BaseSearchCV has the _pairwise property which matches the _pairwise","'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates =","param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,","get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert","1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X,","it selects the smallest parameter in case of ties old_stdout","available only after ' 'refitting on the best parameters' %","selects the smallest parameter in case of ties old_stdout =","key in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X,","= KFold(n_splits=3).split(X) # pop all of it, this should cause","from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] =","in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key)","in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X, y,","cv_results.pop(key) return cv_results # Check if generators are supported as","test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score =","grid-search on classifier that has no score function. clf =","param setting in GridSearchCV.fit # the 2nd and 3rd parameter","make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\": [1, 2, 3, 4]} cv","dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in","cv = BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha':","test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3","random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples),","= GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\")","np.arange(6).reshape(6, -1) y = [0, 0, 0, 1, 1, 1]","compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare","{'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test that the","foo_param def fit(self, X, Y): assert len(X) == len(Y) self.classes_","clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError,","try: from pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError:","= make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg =","best estimator n_features = 4 X, y = make_classification(n_features=n_features) gbdt","GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True)","if refit: probability = True refit = 'accuracy' if isinstance(scoring,","= RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search,","pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric():","== 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1])","estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params","= KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid","not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test implementation catches the","1 return self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(),","attr_message = \"GridSearchCV not identical with precomputed metric\" assert (preds_original","'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params,","not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer","16): param_distributions = {\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10,","[0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y)","Test refit=callable in multiple metric evaluation setting \"\"\" def refit_callable(cv_results):","20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv =","*args, **kw): return 1 # create bad cv cv =","# Ensure that `best_index_ != 0` for this dummy clf","1; expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X,","cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i]","{'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3","refit else: probability = False clf = SVC(probability=probability, random_state=42) random_search","indices # will be generated for the 2nd and subsequent","clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \"predict_proba\") clf.fit(X,","length 1; expected\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10))","once per param # setting. np.testing.assert_equal({k: v for k, v","X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C':","cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert","FMS can find the right number :) assert grid_search.best_params_[\"n_clusters\"] ==","{'min_samples_split': [5, 10]}])) # Using regressor to make sure each","tuple) else refit else: probability = False clf = SVC(probability=probability,","len(set(hashable_samples)) == 99 # doesn't go into infinite loops params_distribution","assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used assert test_mean","hasattr(grid_search, \"cv_results_\") def test_no_refit(): # Test that GSCV can be","y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): # test","random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None,","x: x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y,","= DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv =","['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0","check_y = lambda x: x.shape[1:] == (7, 11) clf =","score == \"f1\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score ==","times for i in range(2): # tuple + chain transforms","GSCV can be used for model selection alone without refitting","3]) # Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X)","[4], [5]], [0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(),","(preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1,","evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X,","import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit","== 3 # Now without a score, and without y","parameter should not be None.\", gs.fit, X, y) gs.fit(X, y,","compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec):","3, base=0.1)) for refit in (True, False): random_searches = []","(gs, rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: #","r=None): self.r_ = r def predict(self, X): return np.zeros(shape=(len(X))) model","random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds","is raised and score reset where appropriate. X, y =","in identifying the \"best\" estimator. \"\"\" def refit_callable(cv_results): \"\"\" A","parameters that are estimators are cloned before fitting pipe =","{'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv","# cv.score(X_[:180], y[:180])) # test loss where greater is worse","= make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv","BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions import NotFittedError","assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for key","1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When","0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for","fit(self, X, Y): assert len(X) == len(Y) self.classes_ = np.unique(Y)","value=0\\)\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message):","# error in this test. gs = GridSearchCV(clf, [{'parameter': [0,","are same, the test and train scores must also be","tolerate this behavior for # now and be careful not","to test the behaviour of the grid search part gs","the # loss is 'hinge'. assert not hasattr(clf, \"predict_proba\") clf.fit(X,","\"\"\"Compare multi-metric cv_results with the ensemble of multiple single metric","issue #13920. \"\"\" n_samples = 50 n_splits = 2 X,","X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv","n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\", [(0, TypeError, r'Parameter .* is","grid multiple times for i in range(2): # tuple +","('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available","search_cv): \"\"\" Test implementation catches the errors when 'best_index_' returns","types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10, 10) y","[1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search =","# popular third-party libraries and we should tolerate this behavior","GridSearchCV.fit # the 2nd and 3rd parameter will not be","X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)","random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test)","cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC()","search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score'","GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation catches the errors","import pickle import sys from types import GeneratorType import re","smallest parameter in case of ties old_stdout = sys.stdout sys.stdout","np.array([0] * 5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(),","assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_')","y) cv_results = search.cv_results_ # Check tie breaking strategy -","search module\"\"\" from collections.abc import Iterable, Sized from io import","the cv object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15,","assert hasattr(grid_search, \"cv_results_\") def test_y_as_list(): # Pass y as list","test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid():","%d iterations. For ' 'exhaustive searches, use GridSearchCV.' % (grid_size,","x in sampler] == [x for x in sampler] if","== refit if refit: assert search_multi.refit == 'accuracy' else: assert","y[train]) if score == \"f1\": correct_score = f1_score(y[test], clf.predict(X[test])) elif","test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\"C\":","cv = KFold() for est in estimators: grid_search = GridSearchCV(est,","a broken estimator; this used to break with # sparse","key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def","index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \"\"\" Test","gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \"predict_proba\")","class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot be fit twice\"\"\" def","getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y =","n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1,","test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe types =","result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C",">= 0) assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time',","\"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf,","X as list in GridSearchCV X = np.arange(100).reshape(10, 10) y","= InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x):","make sure _pairwise is delegated to the base estimator. Non-regression","' 'than n_iter=%d. Running %d iterations. For ' 'exhaustive searches,","assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV,","check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv = KFold(n_splits=3) grid_search","instead. If # refit was done, then an exception would","search results. np.testing.assert_equal performs a # deep nested comparison of","fit search can be pickled clf = MockClassifier() grid_search =","predict. # slight duplication of a test from KDE def","param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i]","fit_grid_point, X, y, svc, params, train, test, {'score': scorer}, verbose=True)","Check consistency of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0),","cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X,","X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y =","import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer","y) # giving no scoring function raises an error grid_search_no_score","got (<class 'int'>).\" \" Single values need to be wrapped","score_auc < 1.0 assert score_accuracy < 1.0 assert score_auc !=","behavior for # now and be careful not to break","isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df,","3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X))","of a precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100,","group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs:","grid/random search\"\"\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_","= np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] )","v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k,","correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized search","( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other","test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3","= GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message","pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from","the # form of a precomputed kernel matrix X_, y_","gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs =","len(X). err_msg = r\"Expected fit parameter\\(s\\) \\['eggs'\\] not seen.\" with","the base estimator. Non-regression test for issue #13920. \"\"\" est","in multiple metric evaluation setting \"\"\" def refit_callable(cv_results): \"\"\" A","__init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X, Y): assert","= make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score =","== grid_search.best_params_ # check that we can call score and","with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): # this should raise a","ValueError, \"Parameter grid for parameter (C) needs to\" \" be","= make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]},","return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42)","matches the _pairwise property of its estimator. This test make","search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth']","3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] #","[1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4 assert","from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing","X = np.arange(6).reshape(6, -1) y = [0, 0, 0, 1,","required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape","grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2,","is specified # as one of the parameters in the","== 'raise' raises the error X, y = make_classification(n_samples=20, n_features=10,","MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3)","for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning","error with pytest.raises(ValueError, match='No fits were performed. ' 'Was the","is common in # popular third-party libraries and we should","expected_warning = ('The total space of parameters %d is smaller","in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits)","key in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if","0.01}, {'C': 0.001}): for train, test in cv.split(X, y): this_scores,","np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X,","set to zero as required for those fits # that","fn_name), X) # Test that an invalid refit param raises","# Using regressor to make sure each score differs clf","np.zeros((10, 20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv","Make sure the predict_proba works when loss is specified #","match=msg): fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False) def","{'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search,","check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking","{'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search in (gs,","GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter should not be","if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit multi-metric","X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in","cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits)))","assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0]","results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates","[\"rbf\", \"linear\"], \"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0)","cv cv = KFold(n_splits=3).split(X) # pop all of it, this","# X dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y)","same as grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler)","for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) ==","y).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_y_as_list(): # Pass y","the 2nd and subsequent cv.split calls. # This is a","different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =","verbose=3) # make sure it selects the smallest parameter in","scores, we do not take a weighted mean irrespective of","that errors caused by fits # to individual folds will","Cs}) assert not hasattr(grid_search, 'classes_') # Test that the grid","cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k, v in","y[train]) expected_score = scorer(est, X[test], y[test]) # Test the return","clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(),","assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search","sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import","+ chain transforms {\"a\": 1, \"b\": 2} to (\"a\", 1,","search methods with single metric methods\"\"\" assert search_acc.refit == refit","when the input features are given in the # form","didn't modify the parameter grid that was passed assert not","list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure","param_dict = {\"C\": [1.0], \"kernel\": [\"rbf\"], \"gamma\": [0.1]} clf =","score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else","np.array([0] * 5 + [1] * 5) for InputFeatureType, TargetType","% fn_name), getattr(grid_search, fn_name), X) # Test that an invalid","cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert C ==","= np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3,","test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) #","cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def","kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf =","cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] ==","is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check","take a weighted mean irrespective of # i.i.d. or not","hyperparameter search on user-defined classifiers. class MockClassifier: \"\"\"Dummy classifier to","2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X),","y) assert not hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\") and","refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_`","\"\"\" n_samples = 50 n_splits = 2 X, y =","GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for","list of keys in # clf.cv_results_. X, y = make_classification(n_samples=100,","error_score=0, n_iter=2) for search in (gs, rs): search.fit(X, y) for","in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates)","n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)):","not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear')","module\"\"\" from collections.abc import Iterable, Sized from io import StringIO","2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \"cv_results_\") def","ParameterGrid(params): assert values in samples # test sampling without replacement","highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER","the grid searcher has no classes_ attribute without a refit","test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def","matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the","deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param']","= search.cv_results_ # Check if score and timing are reasonable","[2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys =","regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert","estimator X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\"random\",","assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4, 2], \"bar\": [\"ham\", \"spam\", \"eggs\"]}","issues a ValueError so this is what we look for.","pop all of it, this should cause the expected ValueError","differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0)","fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_')","GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_')","2])] grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall':","K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns","y[test]) # Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score)","cv=2, error_score=\"raise\" ) # The CheckingClassifier generates an assertion error","gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in","return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check results structure","{'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg):","cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute the test","10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = []","= np.array([0] * 5 + [1] * 5) Cs =","we didn't modify the parameter grid that was passed assert","types: # X dataframe, y series X_df, y_ser = InputFeatureType(X),","\"predict_proba\") clf.fit(X, y) assert not hasattr(clf, \"predict_proba\") def test_search_train_scores_set_to_false(): X","following # proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid,","GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] ==","errors on data with different length X_, y_ = make_classification(n_samples=200,","from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection","euclidean metric to validate _pairwise is working X_precomputed = euclidean_distances(X)","= 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)},","import re import numpy as np import scipy.sparse as sp","# setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if","test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores, we do","= BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3,","reset where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf","first one contains data of all 4 blobs, second only","between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0],","'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C',","is smaller ' 'than n_iter=%d. Running %d iterations. For '","params, train, test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated","('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2,","x.shape[1:] == (5, 3, 2) check_y = lambda x: x.shape[1:]","= [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame,","i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score","verbose=False) def test_pickle(): # Test that a fit search can","LightGBM # that do not exactly respect the scikit-learn API","for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X,","grid_search.classes_) # Test that regressors do not have a classes_","= search.cv_results_ # Check tie breaking strategy - # Check","scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y","assert (all(cv_results[k] <= 1) for k in score_keys if 'time'","following parameters. But this abuse is common in # popular","def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y,","[{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}]","of a model that has the least `mean_test_prec`. \"\"\" assert","= predict predict_log_proba = predict decision_function = predict def score(self,","case for the following parameters. But this abuse is common","'accuracy' else: assert not search_multi.refit return # search cannot predict/score","assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test","else??? # noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0],","(7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search","clf, param_dict) def test_grid_search_sparse(): # Test that grid search works","sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import","fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s","predict/score without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for","train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X,","of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples","is not available when setting loss=['hinge'] # in param_grid param_grid","pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value',","(all(cv_results[k] <= 1) for k in score_keys if 'time' not","assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the function will","5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param':","eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test","should tolerate this behavior for # now and be careful","in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'],","is None) propagates to GridSearchCV # And also check if","for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check","search will capture errors on data with different length X_,","np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1,","search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys","9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for","re-fitting a broken estimator; this used to break with #","= make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults to","assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator","cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\"rbf\",","used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) #","(attr[0].islower() and attr[-1:] == '_' and attr not in {'cv_results_',","without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert","= search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and","10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that","model = SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should","dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for scoring in","raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \"no","search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y)","the functions predict/transform etc raise meaningful # error messages for","sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import","'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params,","for # now and be careful not to break support","assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): #","make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision',","for k, v in cv_results_multi.items() if not k.endswith('_time')}, {k: v","scores between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1])","grid_search.fit(X, y) assert not hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\")","assert n_test_samples == test.size # Should raise an error upon","fit parameter\\(s\\) \\['eggs'\\] not seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y,","def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def","GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:])","other # create \"cv\" for splits cv = [[mask, ~mask],","non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should not raise","grid, cv=cv) # Should not raise an error gs.fit(X, y)","= {\"C\": []} clf = SVC() assert_raise_message( ValueError, \"Parameter values","array are of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param],","roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the","predict/transform etc raise meaningful # error messages for fn_name in","refit=refit) grid_search.fit(X, y) # Both ARI and FMS can find","import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier","ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in sampler] ==","n_iter = 9 grid_size = 8 expected_warning = ('The total","in ('train', 'test'): per_param_scores = {} for param_i in range(4):","and k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys,","== 10 for sample in samples: assert sample[\"kernel\"] in [\"rbf\",","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred =","kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute","return None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =","following two estimators inherit from BaseEstimator, # to test hyperparameter","init=\"random\", n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score']","# test basic properties of param sampler param_distributions = {\"kernel\":","sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import","of a test from KDE def custom_scoring(estimator, X): return 42","assert grid_search.best_params_[\"n_clusters\"] == 3 # Now without a score, and","of 1/3 on # the other # create \"cv\" for","returned 'best_index_' is out of bounds. \"\"\" return out_bound_value X,","[0, 1]}, cv=2, error_score=0, n_iter=2) for search in (gs, rs):","an invalid refit param raises appropriate error messages for refit","test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) #","# Make sure `predict_proba` is not available when setting loss=['hinge']","use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler)","scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') #","v in search_rec.cv_results_.items()}) # Check if score and timing are","search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype","not want to break without an explicit deprecation cycle and","methods with single metric methods\"\"\" assert search_acc.refit == refit if","def test_refit_callable_multi_metric(): \"\"\" Test refit=callable in multiple metric evaluation setting","parameter def fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_","# this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error():","assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'):","= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y)","clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y)","LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1,","{}, {'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty) ==","that it gives the correct result assert grid_search.score(X, y) ==","in range(2): # tuple + chain transforms {\"a\": 1, \"b\":","to the cv object rng = np.random.RandomState(0) X, y =","\"\"\" return None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf","which # is not the case for the following parameters.","Sized from io import StringIO from itertools import chain, product","OneTimeSplitter # Neither of the following two estimators inherit from","= [.1, 1, 10] for score in ['f1', 'roc_auc']: grid_search","sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree","samples # test sampling without replacement in a large grid","scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names","k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k,","v for k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def","bad cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge =","def test_gridsearch_nd(): # Pass X as list in GridSearchCV X_4d","# Test exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError,","assert C == C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200,","grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') #","Test that a fit search can be pickled clf =","CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform from sklearn.base","and \\ hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search, \"best_params_\") # Make","scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for key in","common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search =","the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test =","= GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] ==","[1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80)","pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output","X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert","works with both dense and sparse matrices X_, y_ =","GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that","GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) assert","support for those without following # proper deprecation cycle. fit_params","pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False)","refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \"best_estimator_\") and","cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \"best_estimator_\") and \\","check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params'])","upon multimetric scorer assert_raise_message(ValueError, \"For evaluating multiple scores, use \"","to GridSearchCV if n_iter the same as grid_size sampler =","for k, v in search_rec.cv_results_.items()}) # Check if score and","will not be evaluated as no train/test indices # will","the right value for foo_param clf = MockClassifier() grid_search =","score def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params):","object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0)","for key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of","cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \"GridSearchCV not","[ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\", [(0, TypeError, r'Parameter","\"cv_results_\") def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X,","= list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5))","make_scorer(accuracy_score) msg = (\"fit_grid_point is deprecated in version 0.23 \"","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y)","= ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i for cv_i in","11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:] == (5,","got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid","assert len(X) == len(Y) self.classes_ = np.unique(Y) return self def","estimator. This test make sure _pairwise is delegated to the","fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in","grid searcher has no classes_ attribute without a refit grid_search","alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert","r'Parameter .* is not a dict \\(0\\)'), ({'foo': 0}, TypeError,","gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid","# smoketest grid search grid_search_no_score.fit(X, y) # check that best","assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking","for trivial X and y assert np.all(search.cv_results_[key] >= 0) assert","clf = MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search","fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True","clf = FailingClassifier() # refit=False because we only want to","broken estimator; this used to break with # sparse SVMs.","0.12's stats dists do not accept seed, hence we use","k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit multi-metric search methods","def inverse_transform(self, X): return X - self.foo_param predict_proba = predict","next two param # settings (C=0.2) are same, the test","grid search works when the input features are given in","fold and a score of 1/3 on # the other","# Test predict_proba when disabled on estimator. X = np.arange(20).reshape(5,","# Test that grid search works with both dense and","X, y) def test_grid_search_error(): # Test that grid search will","specialized_params): # Test the IID parameter TODO: Clearly this test","= clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME","\"cv_results_\") def test_y_as_list(): # Pass y as list in GridSearchCV","metric evaluation setting \"\"\" def refit_callable(cv_results): \"\"\" A dummy function","y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search","y) for x, y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) # Special","clf = FailingClassifier() # refit=False because we want to test","# to individual folds will be caught and warnings raised","test_gridsearch_nd(): # Pass X as list in GridSearchCV X_4d =","make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi,","import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import","on classifier that has no score function. clf = LinearSVC(random_state=0)","SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from","def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search","n_iter=3, random_state=0) assert [x for x in sampler] == [x","assert ranks[0] <= 2 and ranks[1] <= 2 # Check","out of bounds. \"\"\" return out_bound_value X, y = make_classification(n_samples=100,","set to a scorer key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy',","kernel matrix corresponding to the linear kernel K_train = np.dot(X_[:180],","to compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy","smoketest grid search grid_search_no_score.fit(X, y) # check that best params","cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with","be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim ==","as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert","is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv =","NOTE The precision of time.time in windows is not high","# Should raise an error upon multimetric scorer assert_raise_message(ValueError, \"For","when the precomputed kernel is not array-like # or sparse","candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X, y","* 3 * 2).reshape(10, 5, 3, 2) y_3d = np.arange(10","method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in","metric\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV,","make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf =","cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 =","must match estimator\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise',","= dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit","scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y)","{'foo_param': [1, 2, 3]}, cv=2, error_score=\"raise\" ) # The CheckingClassifier","and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X)","identifying the \"best\" estimator. \"\"\" def refit_callable(cv_results): \"\"\" A dummy","@pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1,","because we want to test the behaviour of the grid","param # settings (C=0.2) are same, the test and train","= np.arange(20).reshape(5, -1) y = [0, 0, 1, 1, 1]","Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params","where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf =","not available as the # loss is 'hinge'. assert not","n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i])","4 def test_gridsearch_no_predict(): # test grid-search with an estimator without","with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters =","in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42)","returns an out of bound result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\"","the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test","[(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def","[[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv,","'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring,","[0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not","assert len(samples) == 99 hashable_samples = [\"a%db%dc%d\" % (p['a'], p['b'],","* 5 + [1] * 5) clf = CheckingClassifier( check_X=lambda","and FMS can find the right number :) assert grid_search.best_params_[\"n_clusters\"]","import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV","'rank_test_score') assert (all(cv_results[k] <= 1) for k in score_keys if","tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim","call score and that it gives the correct result assert","sure each score differs clf = DecisionTreeRegressor(random_state=0) X, y =","ValueError so this is what we look for. assert_raises(ValueError, gs.fit,","cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key","preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV,","train, test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as","import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform from","A dummy function tests when returned 'best_index_' is not integer.","score == \"roc_auc\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec)","Check if generators are supported as cv and # that","BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X, y #",") def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values","it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search,","the error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf =","sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import","random_state=42) n_splits = 3 n_grid_points = 6 params = [dict(kernel=['rbf',","# Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys,","assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to get default","from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute",".*\" r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with","can be called only # once if ``cv.split`` is called","'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(),","assert_raise_message(ValueError, \"For evaluating multiple scores, use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point,","of 'precomputed'. Non-regression test for issue #13920. \"\"\" n_samples =","= cv.predict(X) # precompute euclidean metric to validate _pairwise is","0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' #","MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert","assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from","2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1],","ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4","match=\"_run_search not implemented.\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X,","y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision","be removed on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0)","random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42),","# the other # create \"cv\" for splits cv =","0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 =","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make","= ParameterGrid(params2) assert len(grid2) == 6 # loop to assert","= {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01,","{'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support","grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \"cv_results_\") def test_y_as_list(): # Pass","train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y,","{'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C","2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a","if n_iter the same as grid_size sampler = ParameterSampler(params, n_iter=8)","'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys","random_state=0) clf = FailingClassifier() # refit=False because we only want","y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\"random\", n_init=1) #","def test_gridsearch_no_predict(): # test grid-search with an estimator without predict.","= True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit():","parameter (C) need to be a non-empty sequence.\", GridSearchCV, clf,","that a warning is raised and score reset where appropriate.","[grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize(","on estimator. X = np.arange(20).reshape(5, -1) y = [0, 0,","to\" \" be a list or numpy array, but got","catches the errors when 'best_index_' returns an out of bound","random_state=0) est_parameters = {\"max_depth\": [1, 2, 3, 4]} cv =","parameter=None): self.parameter = parameter def fit(self, X, y=None): if self.parameter","GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2","def test_pickle(): # Test that a fit search can be","test_empty_cv_iterator_error(): # Use global X, y # create cv cv","`best_index_ != 0` for this dummy clf assert clf.best_index_ !=","in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params","def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =","score_keys if k != 'rank_test_score') assert (all(cv_results[k] <= 1) for","GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier","= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180])","make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0)","cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data():","hasattr(grid_search, \"cv_results_\") def test_y_as_list(): # Pass y as list in","values are close enough to give similar models # which","est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) # Test the","{'a': range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99,","self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def","np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if not k.endswith('_time')},","\\['eggs'\\] not seen.\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg","1 # scores are the same as above assert_allclose(test_cv_scores, [1,","assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't","= search_auc.score(X, y) # ensure the test is sane assert","assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): # test grid-search with unsupervised","`sample_weight`) which # is not the case for the following","1]) # Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores))","also checks if the keys # are present assert all((np.all(cv_results_multi[k]","@pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\"","that parameters that are estimators are cloned before fitting pipe","test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar values in","y) # ensure the test is sane assert score_auc <","y, clone(svc), params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params)","1]}], scoring=\"precision\", refit=True) clf.fit(X, y) def test_refit_callable(): \"\"\" Test refit=callable,","non-empty sequence.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": \"1,2,3\"} clf","replacement in a large grid params = {'a': range(10), 'b':","assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\"klass\",","gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True),","None) propagates to GridSearchCV # And also check if groups","`refit=True` to get a list of keys in # clf.cv_results_.","NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which #","cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) #","= True refit = 'accuracy' if isinstance(scoring, tuple) else refit","function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs","== cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of BaseSearchCV","3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X),","cv and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_))","only one point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':","# test that correct scores are used n_splits = 3","search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y)","parameter (C) needs to\" \" be a list or numpy","param_grid=param_grid, cv=3) # When the estimator is not fitted, `predict_proba`","for p in grid2) assert (points == set((\"bar\", x, \"foo\",","random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc,","doesn't go into infinite loops params_distribution = {'first': bernoulli(.5), 'second':","assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype","and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\d+ ' 'splits,","estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\"random_state\": [0, None]} cv","assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf,","cv = KFold(n_splits=3).split(X) # pop all of it, this should","'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure","clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], ) grid_search = GridSearchCV(clf,","scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_ != 0`","of ParameterGrid. params1 = {\"foo\": [1, 2, 3]} grid1 =","a # deep nested comparison of the two cv_results dicts","test for issue #13920. \"\"\" n_samples = 50 n_splits =","in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in","search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] <","A dummy function tests when returned 'best_index_' is out of","'_accuracy', k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall',","= GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_","and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf')","= (\"fit_grid_point is deprecated in version 0.23 \" \"and will","grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)",".1 assert search.best_score_ == 42 def test_param_sampler(): # test basic","with pytest.raises(ValueError, match='No fits were performed. ' 'Was the CV","= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4","grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser)","enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s","9 grid_size = 8 expected_warning = ('The total space of","on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv","implementation catches the errors when 'best_index_' returns an invalid result.","Test with a randomized search for est in estimators: random_search","repeated calls yield identical parameters param_distributions = {\"C\": [0, 1,","test_predict_proba_disabled(): # Test predict_proba when disabled on estimator. X =","# compute the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T)","check that best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_","if k != 'rank_test_score') assert (all(cv_results[k] <= 1) for k",") # The CheckingClassifier generates an assertion error if #","required for those fits # that are expected to fail.","= params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that","= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180])","range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if","the train scores, we do not take a weighted mean","all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype == object","a randomized search for est in estimators: random_search = RandomizedSearchCV(est,","assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3]) # Smoke","cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert","random_state=42) samples = list(sampler) assert len(samples) == 99 hashable_samples =","setting loss=['hinge'] # in param_grid param_grid = { 'loss': ['hinge'],","sklearn.base import clone from sklearn.exceptions import NotFittedError from sklearn.datasets import","return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier():","indices are generated for all # the cv splits, for","be caught and warnings raised instead. If # refit was","= ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in sampler]","caused by fits # to individual folds will be caught","as the same train/test indices are generated for all #","# are present assert all((np.all(cv_results_multi[k] <= 1) for k in","metric to validate _pairwise is working X_precomputed = euclidean_distances(X) clf","first two param settings (C=0.1) and the next two param","y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for","v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of","{'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test","0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the first","error_message\", [(0, TypeError, r'Parameter .* is not a dict or","on fit()\"\"\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter =","0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0","3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def","RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search in","is called once per param setting in GridSearchCV.fit # the","no train/test indices # will be generated for the 2nd","'refitting on the best parameters' % fn_name), getattr(grid_search, fn_name), X)","behavior changed: # estimator requires score method to compete with","set((\"bar\", x, \"foo\", y) for x, y in product(params2[\"bar\"], params2[\"foo\"])))","import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances","in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time in","GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C':","\"foo\", y) for x, y in product(params2[\"bar\"], params2[\"foo\"]))) assert_grid_iter_equals_getitem(grid2) #","test sampling without replacement in a large grid params =","not equal\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self,","[0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C =","} X = np.arange(24).reshape(6, -1) y = [0, 0, 0,","sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import","sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import","sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import","test_X_as_list(): # Pass X as list in GridSearchCV X =","# Test that GSCV can be used for model selection","X as list in GridSearchCV X_4d = np.arange(10 * 5",".1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1],","results: if not k.endswith('_time'): # XXX: results['params'] is a list","y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 #","= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:])","fits were performed. ' 'Was the CV iterator empty\\\\? '","def score(self): raise AttributeError X = np.array([[-1, -1], [-2, -1],","'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for","== [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1,","properties of param sampler param_distributions = {\"kernel\": [\"rbf\", \"linear\"], \"C\":","1, 0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0)","= cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C ==","perfect classification on one fold and a score of 1/3","ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8 expected_warning =","GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") random_search","search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only","n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X),","def test_grid_search(): # Test that the best estimator contains the","X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class","sp import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import","search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE The","callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param)","== pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train","# ensure the test is sane assert score_auc < 1.0","evaluating multiple scores, use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y,","n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores were set","n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth':","'int'>).\" \" Single values need to be wrapped in a","Check that there is a tie in the mean scores","isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert","# Test that grid search works when the input features","refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): #","# Regression test for bug in refitting # Simulates re-fitting","mean scores between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0],","assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized","make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ],",").fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check","warning if n_iter is bigger than total parameter space params","len(Y) self.classes_ = np.unique(Y) return self def predict(self, T): return","assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {},","preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2","np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype == np.float64 for","deprecation cycle and API # recommendations for implementing early stopping","def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises the","1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split':","# make sure grid search and random search delegate n_features_in","with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10),","= [0, 0, 1, 1, 1] p = Pipeline([ ('imputer',","], degree=[1, 2])] grid_searches = [] for scoring in ({'accuracy':","{\"random_state\": [0, None]} cv = KFold() for est in estimators:","= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') #","y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1,","ValueError, \"Parameter values for parameter (C) need to be a","['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time in windows","X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores","sampler) # degenerates to GridSearchCV if n_iter the same as","kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test","estimator. This test ensures the equivalence of 'precomputed'. Non-regression test","checks if the keys # are present assert all((np.all(cv_results_multi[k] <=","_run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth':","disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd():","test that repeated calls yield identical parameters param_distributions = {\"C\":","== .1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0,","GeneratorType import re import numpy as np import scipy.sparse as","error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure","== (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\"fit\"], )","Ensure `best_score_` is disabled when using `refit=callable` assert not hasattr(clf,","predict(self, T): return T.shape[0] def transform(self, X): return X +",":| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k],","grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data,","numpy array, but got (<class 'int'>).\" \" Single values need","the best estimator contains the right value for foo_param clf","= GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True)","check that errors caused by fits # to individual folds","# precompute euclidean metric to validate _pairwise is working X_precomputed","return self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that has no","'groups' parameter should not be None.\", gs.fit, X, y) gs.fit(X,","param_search): # unofficially sanctioned tolerance for scalar values in fit_params","train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score","0. return score def get_params(self, deep=False): return {'foo_param': self.foo_param} def","def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_)))","4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid =","of the parameters in the param_grid. param_grid = { 'loss':","self.classes_ = np.unique(Y) return self def predict(self, T): return T.shape[0]","X = np.arange(24).reshape(6, -1) y = [0, 0, 0, 1,","{'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search,","clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is","cv=2).fit(X, y) assert not hasattr(gs, \"predict_proba\") def test_grid_search_allows_nans(): # Test","MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3)","Test grid-search on classifier that has no score function. clf","properties of ParameterGrid. params1 = {\"foo\": [1, 2, 3]} grid1","large grid params = {'a': range(10), 'b': range(10), 'c': range(10)}","(train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test],","= cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf =","f1_score from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from","[]} clf = SVC() assert_raise_message( ValueError, \"Parameter values for parameter","getattr(mycv, attr), \\ \"Attribute %s not equal\" % attr def","cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check","n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound)","assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def test_param_sampler():","similar models # which would result in a tie of","that this raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits","multi-metric search methods with single metric methods\"\"\" assert search_acc.refit ==","of # i.i.d. or not assert train_mean == pytest.approx(1) assert","have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})","for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert","dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y = [0, 0,","cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a one","as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1])","0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as a","1, 10] for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf,","for values in ParameterGrid(params): assert values in samples # test","= MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1,","param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression)","k.endswith('_time'): # XXX: results['params'] is a list :| results[k] =","'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time',","['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')","ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout","cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param':","spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): #","we should tolerate this behavior for # now and be","is a non-re-entrant cv where split can be called only","def refit_callable_invalid_type(cv_results): \"\"\" A dummy function tests when returned 'best_index_'","key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test","expected_keys = ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i for cv_i","\"\"\"An LinearSVC classifier that has no score method.\"\"\" @property def","predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0.","remove test_fit_grid_point_deprecated as # fit_grid_point will be removed on 0.25","== [x for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys):","# a parameter is missing or has length != len(X).","== this_params assert n_test_samples == test.size # Should raise an","n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we only","**params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC","10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x","score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score,","types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in","= CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], ) cv =","* 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\", refit=True)","[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X,","1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs},","dummy function tests `refit=callable` interface. Return the index of a","RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs,","[LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results","NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X, y","scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C':","return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i)","import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge,","tuple(\"split%d_test_score\" % cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys))","grid search works with both dense and sparse matrices X_,","test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of BaseSearchCV has the _pairwise property","should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1,","from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing","in fit_params, for instance in LightGBM # that do not","'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter","assert not hasattr(clf, \"predict_proba\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make","0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): #","{'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2","y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,","n_iter=10, random_state=0) samples = [x for x in sampler] assert","# GridSearchCV with on_error == 'raise' raises the error X,","Clearly this test does something else??? # noise-free simple 2d-data","assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search works","\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid search","refit=callable, which adds flexibility in identifying the \"best\" estimator. \"\"\"","assert that this raises an error with pytest.raises(ValueError, match='cv.split and","dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')","{\"C\": []} clf = SVC() assert_raise_message( ValueError, \"Parameter values for","hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10,","train, test, scorer, verbose=False) def test_pickle(): # Test that a","assert_raise_message( ValueError, \"Parameter values for parameter (C) need to be","for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in","test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the","and be careful not to break support for those without","in a large grid params = {'a': range(10), 'b': range(10),","n_candidates) # Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params'])","n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 params =","after ' 'refitting on the best parameters' % fn_name), getattr(grid_search,","def test_parameter_grid(): # Test basic properties of ParameterGrid. params1 =","' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k)","assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from","<= 1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time')))","x in sampler] assert len(samples) == 10 for sample in","methods\"\"\" assert search_acc.refit == refit if refit: assert search_multi.refit ==","Y): assert len(X) == len(Y) self.classes_ = np.unique(Y) return self","gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning if n_iter","= search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s","grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean","'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter","CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df,","def test_grid_search_correct_score_results(): # test that correct scores are used n_splits","for instance in LightGBM # that do not exactly respect","(points == set((\"bar\", x, \"foo\", y) for x, y in","= ParameterGrid({}) assert len(empty) == 1 assert list(empty) == [{}]","assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric():","train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]},","'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k],","fit_params, for instance in LightGBM # that do not exactly","= LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1,","\" \"and will be removed in version 0.25\") params =","clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid for parameter (C)","def test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold() svc","== grid_search_no_score.score(X, y) # giving no scoring function raises an","def test_n_features_in(): # make sure grid search and random search","# Check if the search `cv_results`'s array are of correct","0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0","X) # Test that an invalid refit param raises appropriate","unofficially sanctioned tolerance for scalar values in fit_params # non-regression",") grid_search.fit(X, y) assert not hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search,","X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\"random\", n_init=1)","est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y)","= parameter def fit(self, X, y): assert not hasattr(self, 'has_been_fit_')","returns an invalid result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A dummy","2 and ranks[1] <= 2 # Check that failed estimator","1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises an","this test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',","# Ensure that grid scores were set to zero as","def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results","clf.best_index_ == 0 # Ensure `best_score_` is disabled when using","1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator","assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that parameters","2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def","estimator. \"\"\" def refit_callable(cv_results): \"\"\" A dummy function tests `refit=callable`","to check that errors caused by fits # to individual","search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results","1, 3]) def test_search_cv_results_none_param(): X, y = [[1], [2], [3],","= np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking '","n_cand): # Test the search.cv_results_ contains all the required results","= cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke","transform(self, X): return X + self.foo_param def inverse_transform(self, X): return","2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points","KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1","'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y))","make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits,","_pairwise property must match estimator\" for _pairwise_setting in [True, False]:","4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1)","raises the error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf","dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) #","y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results,","len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda:","precomputed # training kernel matrix K_train = np.zeros((10, 20)) y_train","no candidates\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X,","\"For evaluating multiple scores, use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X,","the mean scores between # candidates 1 and 2 alone","def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict =","ensures the equivalence of 'precomputed'. Non-regression test for issue #13920.","= [{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two',","(True, False): random_searches = [] for scoring in (('accuracy', 'recall'),","gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that","groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid =","property of its estimator. This test make sure _pairwise is","integer. \"\"\" return None X, y = make_classification(n_samples=100, n_features=4, random_state=42)","sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param","not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self,","cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ =","# Use global X, y # create cv cv =","GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C':","a precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)","`cv_results`'s array are of correct types cv_results = search.cv_results_ assert","10]}])) # Using regressor to make sure each score differs","y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param':","3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip)","that has the least `mean_test_score`. \"\"\" # Fit a dummy","as required for those fits # that are expected to","= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y)","= BaseEstimator() attr_message = \"BaseSearchCV _pairwise property must match estimator\"","('params',))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys +","= MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search =","behavior), and this would cause an # error in this","2, 3]}, cv=2, error_score=\"raise\" ) # The CheckingClassifier generates an","'best_index_' returns an invalid result. \"\"\" def refit_callable_invalid_type(cv_results): \"\"\" A","Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2,","verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search():","def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X,","with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] =","= np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C':","i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i]","uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone","[1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf,","make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 #","parameter is missing or has length != len(X). err_msg =","2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation","fit(self, X, y, r=None): self.r_ = r def predict(self, X):","n_samples=80) # split dataset into two folds that are not","1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that",")) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]})","search works with both dense and sparse matrices X_, y_","for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks =","return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf =","3 # Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,","check_X = lambda x: x.shape[1:] == (5, 3, 2) check_y","1) assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >=","the # best estimator n_features = 4 X, y =","return # search cannot predict/score without refit X, y =","param_grid = {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid,","Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do","n_splits = 3 n_grid_points = 6 params = [dict(kernel=['rbf', ],","cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize(","not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X,","'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v","If True, for multi-metric pass refit='accuracy' if refit: probability =","< 1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >=","or numpy array, but got (<class 'int'>).\" \" Single values","ParameterGrid(params2) assert len(grid2) == 6 # loop to assert we","accepted as a cv parameter. n_samples = 100 n_splits =","test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test])","} clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \"predict_proba\")","the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test","X, y=None, groups=None, **fit_params): return self # this should not","random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},","refit = 'accuracy' if isinstance(scoring, tuple) else refit else: probability","seed, hence we use param grid params = dict(C=np.logspace(-4, 1,","in cv_results_multi.items() if not k.endswith('_time')}, {k: v for k, v","invalid refit param raises appropriate error messages for refit in","not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i]","y) preds_precomputed = cv.predict(X_precomputed) attr_message = \"GridSearchCV not identical with","case of ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X,","return 42 if estimator.bandwidth == .1 else 0 X, _","[1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \"cv_results_\") def","= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) #","we do # not want to break without an explicit","__init__(self, parameter=None): self.parameter = parameter def fit(self, X, y): assert","= [] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): #","sampler] == [x for x in sampler] def check_cv_results_array_types(search, param_keys,","sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import","assert search.cv_results_['param_C'][0] == 1 # scores are the same as","KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search","infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']}","raised when the precomputed kernel is not array-like # or","implementing early stopping with a user provided # validation set.","error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2)","partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type, error_message\", [(0, TypeError, r'Parameter .*","search in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ #","'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys","evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score',","Test that the best estimator contains the right value for","hasattr(grid_search, 'classes_') # Test that the grid searcher has no","element.\", GridSearchCV, clf, param_dict) param_dict = {\"C\": []} clf =","the index of a model that has the least `mean_test_score`.","the following two estimators inherit from BaseEstimator, # to test","= {\"C\": \"1,2,3\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \"Parameter grid","results['params'] is a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind","= GridSearchCV(clf, grid, cv=cv) # Should not raise an error","= [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree':","estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0,","cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100,","ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from","# Simulates re-fitting a broken estimator; this used to break","GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that grid search","y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel","assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_","True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): #","4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid():","99 hashable_samples = [\"a%db%dc%d\" % (p['a'], p['b'], p['c']) for p","params = {'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with","assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned","with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \"Fit parameter","scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params'])","np.mean(y_pred == y_pred2) >= .9 assert C == C2 def","0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs","\"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})]","grid2) assert (points == set((\"bar\", x, \"foo\", y) for x,","[RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation catches the","y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2)","in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42)","= RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search","# is not the case for the following parameters. But","[1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred","\"The 'groups' parameter should not be None.\", gs.fit, X, y)","if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k)","for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs =","part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,","property of its estimator. This test ensures the equivalence of","search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid,","y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1,","ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples) == 99","function tests `refit=callable` interface. Return the index of a model","consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where","= 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits)","def fit(self, X, y, r=None): self.r_ = r def predict(self,","{\"kernel\": [\"rbf\", \"linear\"], \"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10,",") # NOTE: `fit_params` should be data dependent (e.g. `sample_weight`)","estimator\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv","== 1 return self def _fit_param_callable(): pass model = SearchCV(","gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in results: if","# degenerates to GridSearchCV if n_iter the same as grid_size","hasattr(grid_search, \"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X,","cv_scores[i]) # FIXME remove test_fit_grid_point as the function will be","['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler)","1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_","# https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42","y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score","\"\"\" A dummy function tests when returned 'best_index_' is not","= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y)","ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) == 7 def","assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy',","(\"a\", 1, \"b\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in","k.endswith('_time')}) # Check consistency of folds across the parameters gs","[\"ham\", \"spam\", \"eggs\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6","== FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',","property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0]","y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv","grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not","with a user provided # validation set. non-regression test for:",") def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO:","DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from","clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises","0` for this dummy clf assert clf.best_index_ != 0 #","gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time',","grid search results. np.testing.assert_equal performs a # deep nested comparison","import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score","n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ #","test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,","for implementing early stopping with a user provided # validation","value for foo_param clf = MockClassifier() grid_search = None grid_search","tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y","this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV):","< 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc)","an error upon multimetric scorer assert_raise_message(ValueError, \"For evaluating multiple scores,","grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)},","@ignore_warnings def test_refit(): # Regression test for bug in refitting","GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make sure","% i][cand_i]) # Test with a randomized search for est","generated for the 2nd and subsequent cv.split calls. # This","= make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'}","= 3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),","make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 params","if a one time iterable is accepted as a cv","test_search_cv__pairwise_property_equivalence_of_precomputed(): \"\"\" Test implementation of BaseSearchCV has the _pairwise property",") def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input)","we can iterate over the grid multiple times for i","grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\") def test_unsupervised_grid_search(): # test grid-search with","k in score_keys if 'time' not in k and k","for bug in refitting # Simulates re-fitting a broken estimator;","def test_refit_callable(): \"\"\" Test refit=callable, which adds flexibility in identifying","value is not iterable .*\" r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass,","assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame","unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y)","* 5) for InputFeatureType, TargetType in types: # X dataframe,","= {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs =","_ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]])","that has no score method.\"\"\" @property def score(self): raise AttributeError","in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for multi-metric","cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke test","to (\"a\", 1, \"b\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p","= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True)","\"predict_proba\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0,","create cv cv = KFold(n_splits=3).split(X) # pop all of it,","\"\"\" Test implementation of BaseSearchCV has the _pairwise property which","deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable,","C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf","{'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) #","refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert","assert search_acc.refit == refit if refit: assert search_multi.refit == 'accuracy'","params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'],","scorer key\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit,","K_train, y_train) class BrokenClassifier(BaseEstimator): \"\"\"Broken classifier that cannot be fit","**specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for","param setting for score_type in ('train', 'test'): per_param_scores = {}","== 'accuracy' else: assert not search_multi.refit return # search cannot","parameter in case of ties old_stdout = sys.stdout sys.stdout =","y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert","# pop all of it, this should cause the expected","assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)","sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =","\"no scoring\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100,","using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test","GridSearchCV, clf, param_dict) param_dict = {\"C\": np.ones((3, 2))} clf =","error_score=\"raise\" ) # The CheckingClassifier generates an assertion error if","specified # as one of the parameters in the param_grid.","# that do not exactly respect the scikit-learn API contract","StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2","== 3 # Single metric evaluation unsupervised grid_search = GridSearchCV(km,","cv parameter. n_samples = 100 n_splits = 5 X, y","random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True)","scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): #","expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys =","a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y)","'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if generators are","for scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf,","expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import","fit(self, X, y=None, groups=None, **fit_params): return self # this should","sys from types import GeneratorType import re import numpy as","estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): #","is not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)","CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2, 3]},","GridSearchCV # And also check if groups is correctly passed","search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that correct scores","assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty =","param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\"n_clusters\"] == 3","clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1,","performed. ' 'Was the CV iterator empty\\\\? ' 'Were there","test_refit_callable_multi_metric(): \"\"\" Test refit=callable in multiple metric evaluation setting \"\"\"","FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor","== preds_precomputed).all(), attr_message @pytest.mark.parametrize( \"SearchCV, param_search\", [(GridSearchCV, {'a': [0.1, 0.01]}),","# Make sure the functions predict/transform etc raise meaningful #","params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for","iterable .*\" r\"\\(key='foo', value=0\\)\")] ) def test_validate_parameter_input(klass, input, error_type, error_message):","assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for k","2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates =","= CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})","[~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True}","key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc,","grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3)","multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\":","scorer(est, X[test], y[test]) # Test the return values of fit_grid_point","dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series,","y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i])","Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import","Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr():","second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y ==","TypeError, r'Parameter .* is not a dict \\(0\\)'), ({'foo': 0},","range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid':","param_keys, score_keys): # Check if the search `cv_results`'s array are","ClassifierMixin): def __init__(self, a=None): self.a = a def fit(self, X,","GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only occurs in","make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from","is accepted as a cv parameter. n_samples = 100 n_splits","want to check that errors caused by fits # to","= ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x in","[0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search):","from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing","range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples =","for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if","of keys in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4,","caught by grid_search (expected behavior), and this would cause an","== 4 assert list(has_empty) == [{'C': 1}, {'C': 10}, {},","setting in GridSearchCV.fit # the 2nd and 3rd parameter will","param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X,","s in range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)):","assert len(set(hashable_samples)) == 99 # doesn't go into infinite loops","Test implementation catches the errors when 'best_index_' returns an out","'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y)","+ [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}],","in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores","and not # caught by grid_search (expected behavior), and this","parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits,","sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following two estimators","GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C","np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] ) test_mean","no score function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0,","for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False.","({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train, test in","IID parameter TODO: Clearly this test does something else??? #","n_test_samples = fit_grid_point( X, y, clone(svc), params, train, test, scorer,","messages for refit in [\"\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError,","the ensemble of multiple single metric cv_results from single metric","GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert","{'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) #","estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\": [1,","self.r_ = r def predict(self, X): return np.zeros(shape=(len(X))) model =","search.fit(X, y) cv_results = search.cv_results_ # Check if score and","raised and score reset where appropriate. X, y = make_classification(n_samples=20,","{'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out","'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert","= make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss)","[1, 1], [2, 1]]) y = np.array([1, 1, 2, 2])","train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1","= RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X,","1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0","missing or has length != len(X). err_msg = r\"Expected fit","pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,","k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid):","*make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None,","test grid-search with an estimator without predict. # slight duplication","[1, 2, 3]}, cv=3, verbose=3) # make sure it selects","y=None, groups=None, **fit_params): return self # this should not raise","raise warning if n_iter is bigger than total parameter space","**kwargs) with pytest.raises(NotImplementedError, match=\"_run_search not implemented.\"): # this should raise",">= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1)","y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X","given in the # form of a precomputed kernel matrix","[x for x in sampler] if sp_version >= (0, 16):","'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time',","search for est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv,","hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def","the best parameters' % fn_name), getattr(grid_search, fn_name), X) # Test","make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel matrix corresponding","\"\"\" A dummy function tests `refit=callable` interface. Return the index","= {re.sub('_score$', '_accuracy', k): v for k, v in search_acc.cv_results_.items()}","gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\"","y_train) assert cv.best_score_ >= 0 # compute the test kernel","searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search","**kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1},","gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X,","C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv","99 # doesn't go into infinite loops params_distribution = {'first':","right number :) assert grid_search.best_params_[\"n_clusters\"] == 3 # Single metric","in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter':","least `mean_test_prec`. \"\"\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X,","y)): clf.fit(X[train], y[train]) if score == \"f1\": correct_score = f1_score(y[test],",".9 assert C == C2 def test_grid_search_sparse_scoring(): X_, y_ =","'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\ \"Attribute","results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]}))","results. Expected \\\\d+ ' 'splits, got \\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def","cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \"GridSearchCV not identical","assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that grid","assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning if","random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time',","parameter. n_samples = 100 n_splits = 5 X, y =","def score(self, X=None, Y=None): if self.foo_param > 1: score =","= ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score',","params1 = {\"foo\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert","[(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]},","y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates)","list\" \" with one element.\", GridSearchCV, clf, param_dict) param_dict =","{\"foo\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable)","a fit search can be pickled clf = MockClassifier() grid_search","self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier failed as required\") def","returned 'best_index_' is not integer. \"\"\" return None X, y","[2, 1]]) y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid):","est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' %","1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 #","'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV):","0, 1, 1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf,","look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise","0 def test_grid_search_correct_score_results(): # test that correct scores are used","# now and be careful not to break support for","list(cv_results.keys()) expected_keys = ((\"mean_test_score\", \"rank_test_score\") + tuple(\"split%d_test_score\" % cv_i for","y_test) >= 0 # test error is raised when the","scoring, the \" \"parameter refit must be set to a","the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) #","s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0]","# create bad cv cv = BrokenKFold(n_splits=3) train_size = 100","= grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not","cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C","random_state=0) grid_params = {'n_neighbors': [10]} # defaults to euclidean metric","[(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def","ValueError on fit()\"\"\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter","test in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X,","= {} for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score'","score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with","[x for x in sampler] == [x for x in","check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf","[0]}, 0], TypeError, r'Parameter .* is not a dict \\(0\\)'),","then an exception would be raised on refit and not","cv splits, for both param setting for score_type in ('train',","[1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor to make","= CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split':","splits, for both param setting for score_type in ('train', 'test'):","Test implementation of BaseSearchCV has the _pairwise property which matches","3 assert_grid_iter_equals_getitem(grid1) params2 = {\"foo\": [4, 2], \"bar\": [\"ham\", \"spam\",","dense and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)","kernel is not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(),","= f1_score(y[test], clf.predict(X[test])) elif score == \"roc_auc\": dec = clf.decision_function(X[test])","not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ ==","transforms {\"a\": 1, \"b\": 2} to (\"a\", 1, \"b\", 2)","parameter search algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param = foo_param def","= old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2, 3])","cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \"The","clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the","that an invalid refit param raises appropriate error messages for","clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\"precision\", refit=True) clf.fit(X, y)","before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not","\"\"\" # Fit a dummy clf with `refit=True` to get","generators are supported as cv and # that the splits","y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s in","== C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),","# FailingClassifier issues a ValueError so this is what we","match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \"Fit parameter spam has","regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ #","API contract but that we do # not want to","eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier that","pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10))","this should cause the expected ValueError [u for u in","in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring,","y) assert grid_search.best_params_[\"n_clusters\"] == 3 # Now without a score,","make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params,","only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]]","score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))","those fits # that are expected to fail. def get_cand_scores(i):","fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False) def test_pickle():","super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) #","assert all(cv_results[key].dtype == object for key in param_keys) assert not","def test_classes__property(): # Test that classes_ property matches best_estimator_.classes_ X","def test_parameters_sampler_replacement(): # raise warning if n_iter is bigger than","y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi,","y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points =","def test_refit_callable_invalid_type(): \"\"\" Test implementation catches the errors when 'best_index_'","are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv","key matches those in `cv_results` for key in clf.cv_results_.keys(): assert","on_error != 'raise' # Ensures that a warning is raised","search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results =","gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True, False): random_searches","\"cv_results_\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y)","grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\"C\": 1} clf","gs = GridSearchCV(clf, grid, cv=cv) # Should not raise an","if search.multimetric_ else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s'","param_distributions = {\"C\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0)","on user-defined classifiers. class MockClassifier: \"\"\"Dummy classifier to test the","handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y)","# Test that grid search returns an error with a","\"spam\", \"eggs\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6 #","in range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train],","GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both","are given in the # form of a precomputed kernel","= {\"max_depth\": [1, 2, 3, 4]} cv = KFold() estimators","default estimator settings) empty = ParameterGrid({}) assert len(empty) == 1","that raises a ValueError on fit()\"\"\" FAILING_PARAMETER = 2 def","'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that","* 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf,","result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no","[1, 1, 3]) def test_search_cv_results_none_param(): X, y = [[1], [2],","from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection","'best_index_' returns an out of bound result. \"\"\" def refit_callable_out_bound(cv_results):","cv.split calls. # This is a check to make sure","SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert","param_grid. param_grid = { 'loss': ['log'], } X = np.arange(24).reshape(6,","are estimators are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())])","%d is smaller ' 'than n_iter=%d. Running %d iterations. For","DecisionTreeClassifier(random_state=0)] # Test with grid search cv for est in","y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a","clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError,","was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_')","random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): #","tuple of arrays should be preserved as tuple. assert isinstance(tuple_of_arrays,","1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert","data of all 4 blobs, second only from two. mask","'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand =","when returned 'best_index_' is not integer. \"\"\" return None X,","sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\"param_foo_param\"].data, [1, 2,","hashable_samples = [\"a%db%dc%d\" % (p['a'], p['b'], p['c']) for p in","a dict \\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.* value is not","grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not","GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :]","compute the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test","with both dense and sparse matrices X_, y_ = make_classification(n_samples=200,","= FailingClassifier() # refit=False because we want to test the","grid_search_no_score.score(X, y) # giving no scoring function raises an error","train scores, we do not take a weighted mean irrespective","score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i]","into infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b',","StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv)","= cv.predict(X_precomputed) attr_message = \"GridSearchCV not identical with precomputed metric\"","<= 1 # test that repeated calls yield identical parameters","'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert","== 2 assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable():","fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\"Failing classifier","matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()","hasattr(grid_search, \"best_estimator_\") and \\ hasattr(grid_search, \"best_index_\") and \\ hasattr(grid_search, \"best_params_\")","= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y),","function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X,","'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y)","the parameter search algorithms\"\"\" def __init__(self, foo_param=0): self.foo_param = foo_param","= RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert","match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings","to the base estimator. Non-regression test for issue #13920. \"\"\"","Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error !=","[1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search))","that correct scores are used n_splits = 3 clf =","return score def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self,","assert len(samples) == 10 for sample in samples: assert sample[\"kernel\"]","1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True,","1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates","assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X","= np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert","cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]},","where behavior changed: # estimator requires score method to compete","assert grid_search.best_params_[\"n_clusters\"] == 3 # Single metric evaluation unsupervised grid_search","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 =","warning only occurs in situation where behavior changed: # estimator","100 n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs","to break support for those without following # proper deprecation","= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search,","== \"f1\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \"roc_auc\":","are of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray)","random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3)","3, 2) check_y = lambda x: x.shape[1:] == (7, 11)","of arrays should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple)","2nd and subsequent cv.split calls. # This is a check","space of parameters %d is smaller ' 'than n_iter=%d. Running","SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0]","# candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1])","score reset where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0)","{'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): #","assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \"\"\" Test implementation catches","# NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which","for score_type in ('train', 'test'): per_param_scores = {} for param_i","v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v","def fit(self, X, Y): assert len(X) == len(Y) self.classes_ =","'precision'} ).fit, X, y) def test_grid_search_error(): # Test that grid","= cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert C","BaseEstimator() attr_message = \"BaseSearchCV _pairwise property must match estimator\" for","the 2nd and 3rd parameter will not be evaluated as","out of bound result. \"\"\" def refit_callable_out_bound(cv_results): \"\"\" A dummy","y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]],","return self # this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X,","spam=np.ones(10)) err_msg = \"Fit parameter spam has length 1; expected\"","class LinearSVCNoScore(LinearSVC): \"\"\"An LinearSVC classifier that has no score method.\"\"\"","recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from","check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_","'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size =","('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score',","= ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8 expected_warning","score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for key in","\"Parameter.* value is not iterable .*\" r\"\\(key='foo', value=0\\)\")] ) def","error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic","= 3 n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1,","0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False,","search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0]","X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error","= search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_","times to be non-zero for trivial X and y assert","error upon multimetric scorer assert_raise_message(ValueError, \"For evaluating multiple scores, use","{\"C\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict)","sorted(exp_results) for k in results: if not k.endswith('_time'): # XXX:","= GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X,","return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time',","estimator without predict. # slight duplication of a test from","scores must also be # same as long as the","dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the function","masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and","sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import","make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from","class FailingClassifier(BaseEstimator): \"\"\"Classifier that raises a ValueError on fit()\"\"\" FAILING_PARAMETER","scoring=\"precision\", refit=True) clf.fit(X, y) def test_refit_callable(): \"\"\" Test refit=callable, which","attr_message = \"BaseSearchCV _pairwise property must match estimator\" for _pairwise_setting","for k in results: if not k.endswith('_time'): # XXX: results['params']","in score_keys if 'time' not in k and k !=","because we only want to check that errors caused by","regressors do not have a classes_ attribute grid_search = GridSearchCV(Ridge(),","n_features_in to the # best estimator n_features = 4 X,","# enough for the fit/score times to be non-zero for","regressor to make sure each score differs clf = DecisionTreeRegressor(random_state=0)","True refit = 'accuracy' if isinstance(scoring, tuple) else refit else:","for all # the cv splits, for both param setting","uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support for","per param # setting. np.testing.assert_equal({k: v for k, v in","clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not available when","grid search cv for est in estimators: grid_search = GridSearchCV(est,","assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that correct","grid, cv=cv) assert_raise_message(ValueError, \"The 'groups' parameter should not be None.\",","assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X):","return T.shape[0] def transform(self, X): return X + self.foo_param def","_pairwise is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv","refit_callable(cv_results): \"\"\" A dummy function tests `refit=callable` interface. Return the","grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert","2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score',","list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C':","== FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators","7 * 11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:]","@pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \"\"\" Test implementation catches","gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results =","GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search,","`fit_params` should be data dependent (e.g. `sample_weight`) which # is","= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning,","This is a check to make sure cv.split is not","next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train,","1], [2, 1]]) y = np.array([1, 1, 2, 2]) def","\"roc_auc\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i])","2} to (\"a\", 1, \"b\", 2) points = set(tuple(chain(*(sorted(p.items())))) for","sure cv.split is not called once per param # setting.","score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits","n_splits = 3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1,","y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter =","'coef_') @pytest.mark.parametrize(\"SearchCV\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10)","GridSearchCV(clf, grid, cv=cv) # Should not raise an error gs.fit(X,","np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for bug in","y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0)","True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric scoring, the \" \"parameter","[5]], [0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier())","when returned 'best_index_' is out of bounds. \"\"\" return out_bound_value","y_) clf = SVC(C=1.0, kernel=\"rbf\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_)","return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) cv_results =","search on user-defined classifiers. class MockClassifier: \"\"\"Dummy classifier to test","# test error is raised when the precomputed kernel is","in sampler] == [x for x in sampler] def check_cv_results_array_types(search,","import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid","refit='accuracy' if refit: probability = True refit = 'accuracy' if","# Neither of the following two estimators inherit from BaseEstimator,","# defaults to euclidean metric (minkowski p = 2) clf","1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'):","def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False)","GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param ==","the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert","= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points = 6","= SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train)","assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \"\"\" Test implementation of","test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3","for key in param_keys + score_keys) def test_grid_search_cv_results(): X, y","import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental","test_classes__property(): # Test that classes_ property matches best_estimator_.classes_ X =","5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\"fit\"], )","def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,","test_parameter_grid(): # Test basic properties of ParameterGrid. params1 = {\"foo\":","# same as long as the same train/test indices are","= SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' %","def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains the","X dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y) def","Single values need to be wrapped in a list\" \"","a score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,","0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results):","the fit/score times to be non-zero for trivial X and","'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates","want to test the behaviour of the grid search part","expected_score = scorer(est, X[test], y[test]) # Test the return values","False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert","n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not","'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples =","'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points","setting \"\"\" def refit_callable(cv_results): \"\"\" A dummy function tests `refit=callable`","X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1,","np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for i,","appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier()","cv_results['mean_train_score'][2]) # 'min' rank should be assigned to the tied","# Special case: empty grid (useful to get default estimator","3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and","sparse SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0] *","i in range(len(grid))] @pytest.mark.parametrize(\"klass\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \"input, error_type,","= ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) == 8","make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we","[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y)","n_iter=2) for search in (gs, rs): search.fit(X, y) for key","hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not","= lambda x: x.shape[1:] == (7, 11) clf = CheckingClassifier(","raise an error upon multimetric scorer assert_raise_message(ValueError, \"For evaluating multiple","svc, params, train, test, scorer, verbose=False) def test_pickle(): # Test","= GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for","for k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v","GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf, grid,","doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from","grid search grid_search_no_score.fit(X, y) # check that best params are","scores, use \" \"sklearn.model_selection.cross_validate instead.\", fit_grid_point, X, y, svc, params,","the parameters in the param_grid. param_grid = { 'loss': ['log'],","GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\"n_clusters\"] == 4 def","sorted(results.keys()) == sorted(exp_results) for k in results: if not k.endswith('_time'):","those in `cv_results` for key in clf.cv_results_.keys(): assert key in","InputFeatureType, TargetType in types: # X dataframe, y series X_df,","assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf,","by grid_search (expected behavior), and this would cause an #","from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise","i][cand_i]) # Test with a randomized search for est in","in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X, y","not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return","dependent (e.g. `sample_weight`) which # is not the case for","[1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \"cv_results_\")","= [0, 0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),","param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert","\"SearchCV, specialized_params\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C':","mean irrespective of # i.i.d. or not assert train_mean ==","0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \"refit_time_\") assert isinstance(search.refit_time_,","% s][candidate_i] for s in range(n_splits))) for i, (train, test)","= GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X,","== getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42)","refit in [\"\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \"For multi-metric","np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y = [0,","need to be a non-empty sequence.\", GridSearchCV, clf, param_dict) param_dict","= 6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),","of the grid search part gs = GridSearchCV(clf, [{'parameter': [0,","# The tuple of arrays should be preserved as tuple.","{'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1,","not have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0,","import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut","X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\"max_depth\": [1, 2,","X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params,","decision_function = predict def score(self, X=None, Y=None): if self.foo_param >","works when loss is specified # as one of the","cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \"cv_results_\") @ignore_warnings def test_pandas_input():","Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in","score_keys if 'time' not in k and k != 'rank_test_score')","loop to assert we can iterate over the grid multiple","+ [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher =","1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ])","for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std =","and random search delegate n_features_in to the # best estimator","assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator():","0 mask[np.where(y == 2)[0][::2]] = 0 # this leads to","classifier to test the parameter search algorithms\"\"\" def __init__(self, foo_param=0):","= make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf","situation where behavior changed: # estimator requires score method to","cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_)","contains the right value for foo_param clf = MockClassifier() grid_search","1.0]}, scoring=\"f1\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C","the search module\"\"\" from collections.abc import Iterable, Sized from io","random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(),","{'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \"cv_results_\") def","calls. # This is a check to make sure cv.split","\" \"parameter refit must be set to a scorer key\",","], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys","in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1,","range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert","3 n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1, 10],","every key matches those in `cv_results` for key in clf.cv_results_.keys():","sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats","# check that we can call score and that it","estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv","Non-regression test for issue #13920. \"\"\" n_samples = 50 n_splits","grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None])","'min' rank should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'],","grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3,","from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics","deep nested comparison of the two cv_results dicts np.testing.assert_equal({k: v","an exception would be raised on refit and not #","for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key","compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \"\"\"Compare multi-metric cv_results with the ensemble","assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that","n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if","score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure","2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2])","precomputed kernel is not array-like # or sparse assert_raises(ValueError, cv.fit,","settings) empty = ParameterGrid({}) assert len(empty) == 1 assert list(empty)","modify the parameter grid that was passed assert not hasattr(param_grid['regressor'][0],","1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for","X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the training","if groups is correctly passed to the cv object rng","# Check that succeeded estimators have lower ranks assert ranks[0]","= np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) ==","'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for","[1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv)","assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3","'time' not in k and k != 'rank_test_score') # Check","are supported as cv and # that the splits are","dict \\(0\\)'), ({'foo': 0}, TypeError, \"Parameter.* value is not iterable","are close enough to give similar models # which would","X[test], y[test]) # Test the return values of fit_grid_point assert_almost_equal(this_scores,","(<class 'int'>).\" \" Single values need to be wrapped in","on # the other # create \"cv\" for splits cv","[(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame, Series))","with single metric methods\"\"\" assert search_acc.refit == refit if refit:","({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits,","assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec =","cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \"\"\"Compare refit","# raise warning if n_iter is bigger than total parameter","does something else??? # noise-free simple 2d-data X, y =","10 for sample in samples: assert sample[\"kernel\"] in [\"rbf\", \"linear\"]","in `cv_results` for key in clf.cv_results_.keys(): assert key in cv_results","\"cv_results_\") def test_X_as_list(): # Pass X as list in GridSearchCV","= LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y)","import OneTimeSplitter # Neither of the following two estimators inherit","The two C values are close enough to give similar","= GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc,","# assert that this raises an error with pytest.raises(ValueError, match='No","assert search_multi.refit == 'accuracy' else: assert not search_multi.refit return #","not assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def","X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf,","DecisionTreeClassifier()) est_parameters = {\"random_state\": [0, None]} cv = KFold() for"],"string":"[\n \"0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3)\",\n \"by fits # to individual folds will be caught and\",\n \"predict def score(self, X=None, Y=None): if self.foo_param > 1: score\",\n \"def test_grid_search_groups(): # Check if ValueError (when groups is None)\",\n \"train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)]\",\n \"parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0),\",\n \"parameter grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert\",\n \"scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param':\",\n \"sampler] assert len(samples) == 10 for sample in samples: assert\",\n \"loss is 'hinge'. assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X)\",\n \"must be set to a scorer key\\\", GridSearchCV(clf, {}, refit=refit,\",\n \"\\\"\\\"\\\" Test implementation catches the errors when 'best_index_' returns an\",\n \"= {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy\",\n \"= search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X,\",\n \"test is sane assert score_auc < 1.0 assert score_accuracy <\",\n \"for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search\",\n \"1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1,\",\n \"!= 0 # Assert every key matches those in `cv_results`\",\n \"km = KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric evaluation unsupervised scoring\",\n \"that succeeded estimators have lower ranks assert ranks[0] <= 2\",\n \"X + self.foo_param def inverse_transform(self, X): return X - self.foo_param\",\n \"{'n_neighbors': [10]} # defaults to euclidean metric (minkowski p =\",\n \"in ParameterGrid(params): assert values in samples # test sampling without\",\n \"return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf,\",\n \"simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0,\",\n \"number :) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single metric evaluation\",\n \"\\\"Attribute %s not equal\\\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV):\",\n \"2nd and 3rd parameter will not be evaluated as no\",\n \"cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2)\",\n \"10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the\",\n \"X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score)\",\n \"comparison of the two cv_results dicts np.testing.assert_equal({k: v for k,\",\n \"Ensure that grid scores were set to zero as required\",\n \"{ 'loss': ['log'], } X = np.arange(24).reshape(6, -1) y =\",\n \"= \\\"GridSearchCV not identical with precomputed metric\\\" assert (preds_original ==\",\n \"'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train, y_train,\",\n \"scorer = make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01},\",\n \"cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check\",\n \"est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in\",\n \"np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0,\",\n \"assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a':\",\n \"of their mean cv-scores param_grid = {'C': [1, 1.001, 0.001]}\",\n \"**fit_params): return self # this should not raise any exceptions\",\n \"for u in cv] # cv is empty now train_size\",\n \"centers=[[0, 1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01,\",\n \"would be raised on refit and not # caught by\",\n \"clf.fit(X, y) assert not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X =\",\n \"10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv):\",\n \"estimators have lower ranks assert ranks[0] <= 2 and ranks[1]\",\n \"\\\"\\\"\\\" est = BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise property must\",\n \"Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search =\",\n \"'raise' raises the error X, y = make_classification(n_samples=20, n_features=10, random_state=0)\",\n \"sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import\",\n \"30 # Scipy 0.12's stats dists do not accept seed,\",\n \"test_grid_search_cv_splits_consistency(): # Check if a one time iterable is accepted\",\n \"[1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV(\",\n \"train/test indices are generated for all # the cv splits,\",\n \"@pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions':\",\n \"else: probability = False clf = SVC(probability=probability, random_state=42) random_search =\",\n \"a test from KDE def custom_scoring(estimator, X): return 42 if\",\n \"X - self.foo_param predict_proba = predict predict_log_proba = predict decision_function\",\n \"n_features = 4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier()\",\n \"class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self,\",\n \"random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0)\",\n \"to break without an explicit deprecation cycle and API #\",\n \"in [\\\"\\\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring,\",\n \"5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X,\",\n \"n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params =\",\n \"score and timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert\",\n \"= [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X,\",\n \"make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf\",\n \"# cv is empty now train_size = 100 ridge =\",\n \"test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None):\",\n \"that the grid searcher has no classes_ attribute without a\",\n \") test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array(\",\n \"all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] ==\",\n \"clf assert clf.best_index_ != 0 # Assert every key matches\",\n \"cv=3, verbose=3) # make sure it selects the smallest parameter\",\n \"also be # same as long as the same train/test\",\n \"scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2,\",\n \"def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y):\",\n \"= grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results\",\n \"adds flexibility in identifying the \\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results):\",\n \"y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on\",\n \"= \\\"BaseSearchCV _pairwise property must match estimator\\\" for _pairwise_setting in\",\n \"as cv and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_),\",\n \"test_pickle(): # Test that a fit search can be pickled\",\n \"= np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv\",\n \"scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for\",\n \"[[1, ], [2, ], [3, ], [4, ]] y =\",\n \"for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits)\",\n \"= make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize(\",\n \"x: x.shape[1:] == (5, 3, 2) check_y = lambda x:\",\n \"'precomputed'. Non-regression test for issue #13920. \\\"\\\"\\\" n_samples = 50\",\n \"assert all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys)\",\n \"get_n_splits(self, *args, **kw): return 1 # create bad cv cv\",\n \"0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3,\",\n \"be non-zero for trivial X and y assert np.all(search.cv_results_[key] >=\",\n \"assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure the\",\n \"= [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs\",\n \"return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =\",\n \"in sampler] if sp_version >= (0, 16): param_distributions = {\\\"C\\\":\",\n \"k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the\",\n \"x in sampler] == [x for x in sampler] def\",\n \"an assertion error if # a parameter is missing or\",\n \"clf.best_index_ != 0 # Assert every key matches those in\",\n \"in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert\",\n \"random_search): search.fit(X, y) cv_results = search.cv_results_ # Check tie breaking\",\n \"parameters param_distributions = {\\\"C\\\": [0, 1, 2, 3, 4, 5,\",\n \"this test does something else??? # noise-free simple 2d-data X,\",\n \"scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) # check that\",\n \"the keys # are present assert all((np.all(cv_results_multi[k] <= 1) for\",\n \"is sane assert score_auc < 1.0 assert score_accuracy < 1.0\",\n \"into two folds that are not iid # first one\",\n \"\\\"b\\\": 2} to (\\\"a\\\", 1, \\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items()))))\",\n \"# Test grid-search on classifier that has no score function.\",\n \"if estimator.bandwidth == .1 else 0 X, _ = make_blobs(cluster_std=.1,\",\n \"bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples\",\n \"and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf\",\n \"over a \\\"grid\\\" with only one point. clf = MockClassifier()\",\n \"error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test\",\n \"{'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV,\",\n \"DataFrame types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10, 10)\",\n \"mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std ==\",\n \"succeeded estimators have lower ranks assert ranks[0] <= 2 and\",\n \"correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param\",\n \"'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than\",\n \"unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0,\",\n \"cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1],\",\n \"False): random_searches = [] for scoring in (('accuracy', 'recall'), 'accuracy',\",\n \"= est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test\",\n \"passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\",\",\n \"methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d,\",\n \"[1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf = SVC(gamma='auto') cv =\",\n \"{'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV,\",\n \"to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param():\",\n \"C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert\",\n \"4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict(): # test\",\n \"check_cv_results_array_types(search, param_keys, score_keys): # Check if the search `cv_results`'s array\",\n \"n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y)\",\n \"y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'):\",\n \"'recall'), 'accuracy', 'recall'): # If True, for multi-metric pass refit='accuracy'\",\n \"is bigger than total parameter space params = [{'first': [0,\",\n \"should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim\",\n \"key in score_keys) assert all(cv_results[key].dtype == np.float64 for key in\",\n \"for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X,\",\n \"error in this test. gs = GridSearchCV(clf, [{'parameter': [0, 1,\",\n \"n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): #\",\n \"test_y_as_list(): # Pass y as list in GridSearchCV X =\",\n \"the test is sane assert score_auc < 1.0 assert score_accuracy\",\n \"test_gridsearch_no_predict(): # test grid-search with an estimator without predict. #\",\n \"to a scorer key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec':\",\n \"GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X,\",\n \"+ self.foo_param def inverse_transform(self, X): return X - self.foo_param predict_proba\",\n \"pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0\",\n \"= [x for x in sampler] assert len(samples) == 10\",\n \"classifier that has no score function. clf = LinearSVC(random_state=0) X,\",\n \"param_keys + score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4,\",\n \"on data with different length X_, y_ = make_classification(n_samples=200, n_features=100,\",\n \"will be caught and warnings raised instead. If # refit\",\n \"clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X,\",\n \"# validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid,\",\n \"else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return\",\n \"samples = list(sampler) assert len(samples) == 8 for values in\",\n \"= set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points == set((\\\"bar\\\",\",\n \"from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection\",\n \"degree=[1, 2])] grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score),\",\n \"['score'] for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype ==\",\n \"0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert\",\n \"p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p,\",\n \"mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower() and\",\n \"search returns an error with a non-square precomputed # training\",\n \"make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points = 6 params\",\n \"parameters' % fn_name), getattr(grid_search, fn_name), X) # Test that an\",\n \"clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180],\",\n \"np.all(search.cv_results_[key] < 1) assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert\",\n \"x, \\\"foo\\\", y) for x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2)\",\n \"assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) # Smoke test the score etc:\",\n \"scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_\",\n \"SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier,\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3)\",\n \"the errors when 'best_index_' returns an out of bound result.\",\n \"but got (<class 'str'>).\\\" \\\" Single values need to be\",\n \"refit param raises appropriate error messages for refit in [\\\"\\\",\",\n \"as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType)\",\n \"scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure\",\n \"for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"== 42 def test_param_sampler(): # test basic properties of param\",\n \"a list\\\" \\\" with one element.\\\", GridSearchCV, clf, param_dict) param_dict\",\n \"hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator\",\n \"issue #13920. \\\"\\\"\\\" est = BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise\",\n \"X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C':\",\n \"2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse():\",\n \"grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not\",\n \"clf, param_dict) param_dict = {\\\"C\\\": []} clf = SVC() assert_raise_message(\",\n \"# Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit\",\n \"LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import\",\n \"# OneTimeSplitter is a non-re-entrant cv where split can be\",\n \"from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing\",\n \"(RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially\",\n \"grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest\",\n \"[3, ], [4, ]] y = [0, 1, 1, 0]\",\n \"scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will\",\n \"destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas\",\n \"# loss is 'hinge'. assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y)\",\n \"cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for\",\n \"dummy clf assert clf.best_index_ != 0 # Assert every key\",\n \"make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180],\",\n \"in cv] # cv is empty now train_size = 100\",\n \"that best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ #\",\n \"their mean cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search\",\n \"test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score'\",\n \"y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv):\",\n \"3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for\",\n \"n_features=100, random_state=0) # compute the training kernel matrix corresponding to\",\n \"== y_test) >= 0 # test error is raised when\",\n \"return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return\",\n \"n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,\",\n \"this is what we look for. assert_raises(ValueError, gs.fit, X, y)\",\n \"'loss': ['log'], } X = np.arange(24).reshape(6, -1) y = [0,\",\n \"dists do not accept seed, hence we use param grid\",\n \"attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert\",\n \"= {\\\"C\\\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf,\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled\",\n \"search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s in\",\n \"score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): #\",\n \"\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples =\",\n \"def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when loss\",\n \"'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search\",\n \"defaults to euclidean metric (minkowski p = 2) clf =\",\n \"test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' # Ensures that\",\n \"@pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0, TypeError, r'Parameter .* is not\",\n \"x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"],\",\n \"for refit in [\\\"\\\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For\",\n \"clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv\",\n \"find the right number :) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 #\",\n \"class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a def\",\n \"{'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y)\",\n \"cv.best_score_ >= 0 # compute the test kernel matrix K_test\",\n \"that this raises an error with pytest.raises(ValueError, match='No fits were\",\n \"groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs\",\n \"not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1]\",\n \"n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ], C=[1,\",\n \"clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X,\",\n \"'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits,\",\n \"this leads to perfect classification on one fold and a\",\n \"y) # Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits,\",\n \"search delegate n_features_in to the # best estimator n_features =\",\n \"Test that regressors do not have a classes_ attribute grid_search\",\n \"grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that parameters that\",\n \"LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180],\",\n \"== C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid\",\n \"y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1,\",\n \"ParameterGrid({}) assert len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty)\",\n \"LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3),\",\n \"y) assert clf.best_index_ == 0 # Ensure `best_score_` is disabled\",\n \"4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now\",\n \"X, y) def test_parameters_sampler_replacement(): # raise warning if n_iter is\",\n \"[0, None]} cv = KFold() for est in estimators: grid_search\",\n \"method to compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y)\",\n \"= 4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid\",\n \"removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0)\",\n \"# Test basic properties of ParameterGrid. params1 = {\\\"foo\\\": [1,\",\n \"score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when groups is\",\n \"Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates)\",\n \"'accuracy' if isinstance(scoring, tuple) else refit else: probability = False\",\n \"behaviour of the grid search part gs = GridSearchCV(clf, [{'parameter':\",\n \"has the least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in cv_results return\",\n \"degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys =\",\n \"10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty)\",\n \"Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y\",\n \"def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with the\",\n \"blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y\",\n \"FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER =\",\n \"# proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid),\",\n \"search.fit(X, y) cv_results = search.cv_results_ # Check tie breaking strategy\",\n \"3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a ValueError\",\n \"\\\\\\\\d+ ' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): #\",\n \"y = np.array([0] * 5 + [1] * 5) Cs\",\n \"y_train = y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C':\",\n \"search_multi.refit == 'accuracy' else: assert not search_multi.refit return # search\",\n \"gives the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y)\",\n \"alone without refitting clf = MockClassifier() for scoring in [None,\",\n \"be data dependent (e.g. `sample_weight`) which # is not the\",\n \"param_grid = { 'loss': ['log'], } X = np.arange(24).reshape(6, -1)\",\n \"[0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) #\",\n \"n_iter is bigger than total parameter space params = [{'first':\",\n \"X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0)\",\n \"in param_grid param_grid = { 'loss': ['hinge'], } clf =\",\n \"gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]\",\n \"C == C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]),\",\n \"weighted mean irrespective of # i.i.d. or not assert train_mean\",\n \"scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys,\",\n \"param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler =\",\n \"cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)):\",\n \"return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},\",\n \"assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype ==\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): # Test that GSCV can\",\n \"This test make sure _pairwise is delegated to the base\",\n \"isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_')\",\n \"a warning is raised and score reset where appropriate. X,\",\n \"import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises\",\n \"= grid_search.fit(X, y).cv_results_ # Test scorer names result_keys = list(cv_results.keys())\",\n \"search_rec.cv_results_.items()}) # Check if score and timing are reasonable, also\",\n \"for the 2nd and subsequent cv.split calls. # This is\",\n \"is not the case for the following parameters. But this\",\n \"= search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X,\",\n \"<= sample[\\\"C\\\"] <= 1 # test that repeated calls yield\",\n \"cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains\",\n \"list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param':\",\n \"for scalar values in fit_params, for instance in LightGBM #\",\n \"sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if the search\",\n \"for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare\",\n \"scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert\",\n \"is raised when the precomputed kernel is not array-like #\",\n \"test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0] * 5\",\n \"= gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower ranks\",\n \"'str'>).\\\" \\\" Single values need to be wrapped in a\",\n \"without an explicit deprecation cycle and API # recommendations for\",\n \"old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) #\",\n \"Test predict_proba when disabled on estimator. X = np.arange(20).reshape(5, -1)\",\n \"v for k, v in search_rec.cv_results_.items()}) # Check if score\",\n \"3, 4, 5, 6, 7, 8, 9, 10]} sampler =\",\n \"sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import\",\n \"= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True,\",\n \"in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ # Check\",\n \"from pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass\",\n \"import GeneratorType import re import numpy as np import scipy.sparse\",\n \"assert [x for x in sampler] == [x for x\",\n \"score = 1. else: score = 0. return score def\",\n \"a check to make sure cv.split is not called once\",\n \"from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing\",\n \"in search_rec.cv_results_.items()}) # Check if score and timing are reasonable,\",\n \"make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1,\",\n \"is not called once per param # setting. np.testing.assert_equal({k: v\",\n \"random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with\",\n \"'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search =\",\n \"svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C':\",\n \"assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1],\",\n \"np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype == object for\",\n \"compute the training kernel matrix corresponding to the linear kernel\",\n \"param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results\",\n \"search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the same\",\n \"the keys, other than time keys, among multi-metric and #\",\n \"and score reset where appropriate. X, y = make_classification(n_samples=20, n_features=10,\",\n \"% attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):\",\n \"sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import\",\n \"required\\\") def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None):\",\n \"those without following # proper deprecation cycle. fit_params = {\",\n \"1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for scoring\",\n \"numpy array, but got (<class 'str'>).\\\" \\\" Single values need\",\n \"# compute the training kernel matrix corresponding to the linear\",\n \"leads to perfect classification on one fold and a score\",\n \"make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for score in\",\n \"('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X,\",\n \"cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy':\",\n \"train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are\",\n \"not a dict \\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.* value is\",\n \"sampling without replacement in a large grid params = {'a':\",\n \"hence we use param grid params = dict(C=np.logspace(-4, 1, 3),\",\n \"[2], [3], [4], [5]], [0, 0, 0, 0, 1] estimators\",\n \"LinearSVC(random_state=0) X = [[1, ], [2, ], [3, ], [4,\",\n \"np.arange(24).reshape(6, -1) y = [0, 0, 0, 1, 1, 1]\",\n \"evaluated as no train/test indices # will be generated for\",\n \"make_classification from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from\",\n \"a user provided # validation set. non-regression test for: #\",\n \"= [[1, ], [2, ], [3, ], [4, ]] y\",\n \"= SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\\\"raise\\\" )\",\n \"# test sampling without replacement in a large grid params\",\n \"clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs =\",\n \"random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X,\",\n \"a parameter is missing or has length != len(X). err_msg\",\n \"assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error\",\n \"the equivalence of 'precomputed'. Non-regression test for issue #13920. \\\"\\\"\\\"\",\n \"= CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2,\",\n \"import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV\",\n \"from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing\",\n \"if 'time' not in k and k != 'rank_test_score') #\",\n \">= 0 # compute the test kernel matrix K_test =\",\n \"\\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is\",\n \"assert regressor_results[1] is not grid_search.best_estimator_ # check that we didn't\",\n \"fits # to individual folds will be caught and warnings\",\n \"== np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the\",\n \"class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None):\",\n \"2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search,\",\n \"make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\":\",\n \"10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def\",\n \"1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is\",\n \"model that has the least `mean_test_score`. \\\"\\\"\\\" # Fit a\",\n \"isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param':\",\n \"sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert 0 <= sample[\\\"C\\\"] <= 1\",\n \"\\\"linear\\\"] assert 0 <= sample[\\\"C\\\"] <= 1 # test that\",\n \"'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score',\",\n \"1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for\",\n \"refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y)\",\n \"2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for\",\n \"search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid,\",\n \"v for k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k:\",\n \"# noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter\",\n \"(e.g. `sample_weight`) which # is not the case for the\",\n \"r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type,\",\n \"list or numpy array, but got (<class 'str'>).\\\" \\\" Single\",\n \"1) for k in score_keys if 'time' not in k\",\n \"% s][0] for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0]\",\n \"scores are the same as above assert_allclose(test_cv_scores, [1, 1. /\",\n \"if not k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items()\",\n \"assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys,\",\n \"gscv) for attr in dir(gscv): if (attr[0].islower() and attr[-1:] ==\",\n \"array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare():\",\n \"# doesn't go into infinite loops params_distribution = {'first': bernoulli(.5),\",\n \"# Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X)\",\n \"== [x for x in sampler] if sp_version >= (0,\",\n \"sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import\",\n \"sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) ==\",\n \"* 5 + [1] * 5) for InputFeatureType, TargetType in\",\n \"make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C':\",\n \"attr), \\\\ \\\"Attribute %s not equal\\\" % attr def test__custom_fit_no_run_search():\",\n \"X, y # create cv cv = KFold(n_splits=3).split(X) # pop\",\n \"if not k.endswith('_time')}) # Check consistency of folds across the\",\n \"2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in\",\n \"SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should be data\",\n \"' 'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): #\",\n \"other than time keys, among multi-metric and # single metric\",\n \"best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0] * 5\",\n \"score(self): raise AttributeError X = np.array([[-1, -1], [-2, -1], [1,\",\n \"cause the expected ValueError [u for u in cv] #\",\n \"test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()\",\n \"3, 15) clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs\",\n \"'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits,\",\n \"sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert\",\n \"def custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1 else\",\n \"0 # Assert every key matches those in `cv_results` for\",\n \"import sys from types import GeneratorType import re import numpy\",\n \"5, 3, 2) y_3d = np.arange(10 * 7 * 11).reshape(10,\",\n \"assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot\",\n \"exception would be raised on refit and not # caught\",\n \"-1) y = [0, 0, 1, 1, 1] clf =\",\n \"sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree\",\n \"['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit)\",\n \"assert not search_multi.refit return # search cannot predict/score without refit\",\n \"= make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search\",\n \"not iid # first one contains data of all 4\",\n \"scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred,\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:])\",\n \"\\\"Parameter grid for parameter (C) needs to\\\" \\\" be a\",\n \"case: empty grid (useful to get default estimator settings) empty\",\n \"Test that the grid searcher has no classes_ attribute before\",\n \"1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ =\",\n \"smaller ' 'than n_iter=%d. Running %d iterations. For ' 'exhaustive\",\n \"param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X,\",\n \"gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k\",\n \"sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform\",\n \"(grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to\",\n \"test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf =\",\n \"metric cv_results from single metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_),\",\n \"a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O':\",\n \"= ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) ==\",\n \"GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans():\",\n \"is missing or has length != len(X). err_msg = r\\\"Expected\",\n \"GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True,\",\n \"self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier\",\n \"TODO: Clearly this test does something else??? # noise-free simple\",\n \"y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid\",\n \"cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot be\",\n \"only after ' 'refitting on the best parameters' % fn_name),\",\n \"# search cannot predict/score without refit X, y = make_blobs(n_samples=100,\",\n \"selection alone without refitting clf = MockClassifier() for scoring in\",\n \"k, v in search_rec.cv_results_.items()}) # Check if score and timing\",\n \"classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y)\",\n \"n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision',\",\n \"# Ensure `best_score_` is disabled when using `refit=callable` assert not\",\n \"score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc,\",\n \"\\\"and will be removed in version 0.25\\\") params = {'C':\",\n \"y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score'\",\n \"2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test that\",\n \"param_search): # check support for scalar values in fit_params, for\",\n \"[0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 =\",\n \"cv_results from single metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy',\",\n \"clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X,\",\n \"2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) # Smoke test the score\",\n \"timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >=\",\n \"1. else: score = 0. return score def get_params(self, deep=False):\",\n \"error is raised when the precomputed kernel is not array-like\",\n \"self.parameter = parameter def fit(self, X, y=None): if self.parameter ==\",\n \"assert len(grid2) == 6 # loop to assert we can\",\n \"search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi,\",\n \"'than n_iter=%d. Running %d iterations. For ' 'exhaustive searches, use\",\n \"GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X,\",\n \"cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param':\",\n \"precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) #\",\n \"for key in score_keys) assert all(cv_results[key].dtype == np.float64 for key\",\n \"a non-empty sequence.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"}\",\n \"range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0]\",\n \"in this test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],\",\n \"2 def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1} clf = SVC(gamma='auto')\",\n \"param_dict = {\\\"C\\\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter\",\n \"cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search\",\n \"across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2,\",\n \"sampler] if sp_version >= (0, 16): param_distributions = {\\\"C\\\": uniform(0,\",\n \"pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits\",\n \"param settings (C=0.1) and the next two param # settings\",\n \"\\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert len(grid2) ==\",\n \"implementation catches the errors when 'best_index_' returns an out of\",\n \"def test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold): def\",\n \"multiple times for i in range(2): # tuple + chain\",\n \"super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth':\",\n \"* 5 * 3 * 2).reshape(10, 5, 3, 2) y_3d\",\n \"should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use\",\n \"3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly',\",\n \"GridSearchCV X_4d = np.arange(10 * 5 * 3 * 2).reshape(10,\",\n \"y # create cv cv = KFold(n_splits=3).split(X) # pop all\",\n \"param_search ) # NOTE: `fit_params` should be data dependent (e.g.\",\n \"inverse_transform(self, X): return X - self.foo_param predict_proba = predict predict_log_proba\",\n \"dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x):\",\n \"are used n_splits = 3 clf = LinearSVC(random_state=0) X, y\",\n \"BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot be fit twice\\\"\\\"\\\" def __init__(self,\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y)\",\n \"with precomputed metric\\\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV,\",\n \"assert np.mean(y_pred == y_pred2) >= .9 assert C == C2\",\n \"__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results =\",\n \"dataset into two folds that are not iid # first\",\n \"assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y =\",\n \"'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable)\",\n \"test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as #\",\n \"len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba\",\n \"from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline\",\n \"!= len(X). err_msg = r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\"\",\n \"X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y\",\n \"gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits,\",\n \"y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred ==\",\n \"0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring)\",\n \"[1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled =\",\n \">= (0, 16): param_distributions = {\\\"C\\\": uniform(0, 1)} sampler =\",\n \"cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2\",\n \"y) # As the first two param settings (C=0.1) and\",\n \"index of a model that has the least `mean_test_prec`. \\\"\\\"\\\"\",\n \"match='best_index_ index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\"\",\n \"assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i\",\n \"from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection\",\n \"'raise' # Ensures that a warning is raised and score\",\n \"key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The\",\n \"y) assert not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6,\",\n \"methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"giving no scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score,\",\n \"param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_')\",\n \"cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys)\",\n \"n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs =\",\n \"= GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc'\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 =\",\n \"k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items() if not\",\n \"not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV])\",\n \"Test search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0)\",\n \"parameters. But this abuse is common in # popular third-party\",\n \"predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y\",\n \"n_search_iter = 30 # Scipy 0.12's stats dists do not\",\n \"import chain, product from functools import partial import pickle import\",\n \"the input features are given in the # form of\",\n \"right value for foo_param clf = MockClassifier() grid_search = None\",\n \"a ValueError so this is what we look for. assert_raises(ValueError,\",\n \"y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42\",\n \"not grid_search.best_estimator_ # check that we didn't modify the parameter\",\n \"param_keys, score_keys, n_cand): # Test the search.cv_results_ contains all the\",\n \"KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from\",\n \"assert len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError,\",\n \"= { 'loss': ['log'], } X = np.arange(24).reshape(6, -1) y\",\n \"not available when setting loss=['hinge'] # in param_grid param_grid =\",\n \"assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from\",\n \"2 assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass\",\n \"ParameterGrid. params1 = {\\\"foo\\\": [1, 2, 3]} grid1 = ParameterGrid(params1)\",\n \"in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups'\",\n \"= make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"],\",\n \"sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import\",\n \"GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X,\",\n \"{ 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train,\",\n \"['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km,\",\n \"= {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X,\",\n \"be a list or numpy array, but got (<class 'str'>).\\\"\",\n \"0 # this leads to perfect classification on one fold\",\n \"= np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups =\",\n \"third-party libraries and we should tolerate this behavior for #\",\n \"not be None.\\\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs\",\n \"for cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError,\",\n \"def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results)\",\n \"refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit)\",\n \"from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither\",\n \"assert clf.best_index_ != 0 # Assert every key matches those\",\n \"is not a dict \\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.* value\",\n \"X, y = [[1], [2], [3], [4], [5]], [0, 0,\",\n \"import KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit\",\n \"random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert\",\n \"grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()]\",\n \"y[:180]), # cv.score(X_[:180], y[:180])) # test loss where greater is\",\n \"np.mean(y_pred == y_test) >= 0 # test error is raised\",\n \"were performed. ' 'Was the CV iterator empty\\\\\\\\? ' 'Were\",\n \"grid_search (expected behavior), and this would cause an # error\",\n \"'std_fit_time']: # NOTE The precision of time.time in windows is\",\n \"assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples == test.size\",\n \"with `refit=True` to get a list of keys in #\",\n \"def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results,\",\n \"raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global\",\n \"#13920. \\\"\\\"\\\" est = BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise property\",\n \"in dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and attr\",\n \"when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\"\",\n \"else: assert not search_multi.refit return # search cannot predict/score without\",\n \"False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits,\",\n \"assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i]\",\n \"fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]}\",\n \"non-square precomputed # training kernel matrix K_train = np.zeros((10, 20))\",\n \"Check if score and timing are reasonable assert all(cv_results['rank_test_score'] >=\",\n \"[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator\",\n \"capture errors on data with different length X_, y_ =\",\n \"0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0,\",\n \"For ' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size))\",\n \"cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor':\",\n \"X, y = make_blobs(n_samples=50, random_state=42) # The two C values\",\n \"we do not take a weighted mean irrespective of #\",\n \"euclidean metric (minkowski p = 2) clf = KNeighborsClassifier() cv\",\n \"y = [0, 0, 1, 1, 1] p = Pipeline([\",\n \"matches the _pairwise property of its estimator. This test ensures\",\n \"refit must be set to a scorer key\\\", GridSearchCV(clf, {},\",\n \"assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores, we\",\n \"[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X,\",\n \"estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params =\",\n \"return X + self.foo_param def inverse_transform(self, X): return X -\",\n \"raise an error gs.fit(X, y) def test_classes__property(): # Test that\",\n \"C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that\",\n \"def fit(self, X, y=None, groups=None, **fit_params): return self # this\",\n \"are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i]\",\n \"5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher\",\n \"assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over\",\n \"score_keys): # Check if the search `cv_results`'s array are of\",\n \"[1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std\",\n \"would result in a tie of their mean cv-scores param_grid\",\n \"Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) # check\",\n \"np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss where greater\",\n \"{'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search =\",\n \"GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message =\",\n \"occurs in situation where behavior changed: # estimator requires score\",\n \"lambda x: x.shape[1:] == (5, 3, 2) check_y = lambda\",\n \"== 4 def test_gridsearch_no_predict(): # test grid-search with an estimator\",\n \"scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc\",\n \"\\\"\\\"\\\"Broken classifier that cannot be fit twice\\\"\\\"\\\" def __init__(self, parameter=None):\",\n \"= {\\\"foo\\\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1,\",\n \"random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single(\",\n \"\\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples\",\n \"clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X,\",\n \"the _pairwise property of its estimator. This test ensures the\",\n \"' 'inconsistent results. Expected \\\\\\\\d+ ' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size],\",\n \"= 100 n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0)\",\n \"results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid,\",\n \"+ k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def\",\n \"in windows is not high # enough for the fit/score\",\n \"== 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \\\"refit_time_\\\") assert\",\n \"return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for bug\",\n \"will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y\",\n \"2) y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)\",\n \"now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2,\",\n \"in results: if not k.endswith('_time'): # XXX: results['params'] is a\",\n \"= GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_\",\n \"and API # recommendations for implementing early stopping with a\",\n \"= LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C': 0.1},\",\n \"[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def\",\n \"< 1.0 assert score_accuracy < 1.0 assert score_auc != score_accuracy\",\n \"[0, 0, 0, 1, 1, 1] clf = LinearSVC(random_state=0) gs\",\n \"0.001}): for train, test in cv.split(X, y): this_scores, this_params, n_test_samples\",\n \"== 0 # Ensure `best_score_` is disabled when using `refit=callable`\",\n \"mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y\",\n \"fits # that are expected to fail. def get_cand_scores(i): return\",\n \"param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3,\",\n \"the smallest parameter in case of ties old_stdout = sys.stdout\",\n \"[4, ]] y = [0, 1, 1, 0] gs =\",\n \"k != 'rank_test_score') assert (all(cv_results[k] <= 1) for k in\",\n \"GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0],\",\n \"def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an error\",\n \"cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys)\",\n \"to test the parameter search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param\",\n \"super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params): return self\",\n \"grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1))\",\n \"self.parameter = parameter def fit(self, X, y): assert not hasattr(self,\",\n \"X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred ==\",\n \"y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): # Pass X as\",\n \"= next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params,\",\n \"clf, param_dict) param_dict = {\\\"C\\\": np.ones((3, 2))} clf = SVC()\",\n \"calls yield identical parameters param_distributions = {\\\"C\\\": [0, 1, 2,\",\n \"= sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1,\",\n \"isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1)\",\n \"y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key)\",\n \"be raised on refit and not # caught by grid_search\",\n \"not identical with precomputed metric\\\" assert (preds_original == preds_precomputed).all(), attr_message\",\n \"for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i\",\n \"for sample in samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert\",\n \"clf = SVC() assert_raise_message( ValueError, \\\"Parameter values for parameter (C)\",\n \"'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): # Test that\",\n \"Make sure the functions predict/transform etc raise meaningful # error\",\n \"{'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt,\",\n \"test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) ==\",\n \"= False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter,\",\n \"random_searches = [] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'):\",\n \"Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X)\",\n \"1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ ==\",\n \"predict_log_proba = predict decision_function = predict def score(self, X=None, Y=None):\",\n \"'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings\",\n \"y) # Both ARI and FMS can find the right\",\n \"2) check_y = lambda x: x.shape[1:] == (7, 11) clf\",\n \"['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}]\",\n \"refitting # Simulates re-fitting a broken estimator; this used to\",\n \"# refit=False because we only want to check that errors\",\n \"y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def\",\n \"cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try:\",\n \"['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9)\",\n \"stopping with a user provided # validation set. non-regression test\",\n \"params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train])\",\n \"grid_search.fit(X, y).cv_results_ # Test scorer names result_keys = list(cv_results.keys()) expected_keys\",\n \"GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) #\",\n \"y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for\",\n \"list, sampler) # degenerates to GridSearchCV if n_iter the same\",\n \"return cv_results # Check if generators are supported as cv\",\n \"ValueError [u for u in cv] # cv is empty\",\n \"random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc,\",\n \"[1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] )\",\n \"estimators inherit from BaseEstimator, # to test hyperparameter search on\",\n \"results. np.testing.assert_equal performs a # deep nested comparison of the\",\n \"[.1, 1, 10] for score in ['f1', 'roc_auc']: grid_search =\",\n \"pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores,\",\n \"in sampler] == [x for x in sampler] if sp_version\",\n \"fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results,\",\n \"cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1,\",\n \"\\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests\",\n \"[1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X))\",\n \"test_parameters_sampler_replacement(): # raise warning if n_iter is bigger than total\",\n \"for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not\",\n \"'_recall', k): v for k, v in search_rec.cv_results_.items()}) # Check\",\n \"# this leads to perfect classification on one fold and\",\n \"hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false():\",\n \"of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for\",\n \"foo_param=0): self.foo_param = foo_param def fit(self, X, Y): assert len(X)\",\n \"\\\"\\\"\\\" Test refit=callable in multiple metric evaluation setting \\\"\\\"\\\" def\",\n \"empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3,\",\n \"from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets\",\n \"+ k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class\",\n \"non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid =\",\n \"key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X,\",\n \"available as the # loss is 'hinge'. assert not hasattr(clf,\",\n \"import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common\",\n \"if ValueError (when groups is None) propagates to GridSearchCV #\",\n \"abuse is common in # popular third-party libraries and we\",\n \"' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make\",\n \"'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring, the \\\" \\\"parameter refit\",\n \"], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches\",\n \"if not k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items()\",\n \"[1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"with on_error != 'raise' # Ensures that a warning is\",\n \"settings (C=0.1) and the next two param # settings (C=0.2)\",\n \"Should not raise an error gs.fit(X, y) def test_classes__property(): #\",\n \"n_iter=8) samples = list(sampler) assert len(samples) == 8 for values\",\n \"hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy\",\n \"loss=['hinge'] # in param_grid param_grid = { 'loss': ['hinge'], }\",\n \"where greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_)\",\n \"param # setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items()\",\n \"assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y\",\n \"sample in samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert 0\",\n \"[10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test\",\n \"Using regressor to make sure each score differs clf =\",\n \"popular third-party libraries and we should tolerate this behavior for\",\n \"import numpy as np import scipy.sparse as sp import pytest\",\n \"def test_grid_search_no_score(): # Test grid-search on classifier that has no\",\n \"= GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches)\",\n \"i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled on estimator.\",\n \"from single metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall'))\",\n \"LinearSVC classifier that has no score method.\\\"\\\"\\\" @property def score(self):\",\n \"GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ #\",\n \"[0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 =\",\n \"= 3 n_search_iter = 30 # Scipy 0.12's stats dists\",\n \"import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV\",\n \"grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now without a\",\n \"[1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not\",\n \"refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y =\",\n \"in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should not\",\n \"from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics\",\n \"= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit,\",\n \"{'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 #\",\n \"isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test\",\n \"clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train,\",\n \"tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return\",\n \"that do not exactly respect the scikit-learn API contract but\",\n \"grid searcher has no classes_ attribute before it's fit grid_search\",\n \"= 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check\",\n \"sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import\",\n \"greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss\",\n \"noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter #\",\n \"data with different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\",\n \"= [[1], [2], [3], [4], [5]], [0, 0, 0, 0,\",\n \"y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test,\",\n \"n_test_samples == test.size # Should raise an error upon multimetric\",\n \"import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X =\",\n \"7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert\",\n \"def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return\",\n \"to the # best estimator n_features = 4 X, y\",\n \"= LinearSVC(random_state=0) X = [[1, ], [2, ], [3, ],\",\n \"# tuple + chain transforms {\\\"a\\\": 1, \\\"b\\\": 2} to\",\n \"= 3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2)\",\n \"old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout =\",\n \"self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that has no score\",\n \"# Check consistency of folds across the parameters gs =\",\n \"rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid = {'C': [1]}\",\n \"parameter will not be evaluated as no train/test indices #\",\n \"check cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)]\",\n \"callable(callable_param) # The tuple of arrays should be preserved as\",\n \"'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2,\",\n \"chain transforms {\\\"a\\\": 1, \\\"b\\\": 2} to (\\\"a\\\", 1, \\\"b\\\",\",\n \"was done, then an exception would be raised on refit\",\n \"k and k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search,\",\n \"# fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated(): X,\",\n \"or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test\",\n \"Cs = [.1, 1, 10] for score in ['f1', 'roc_auc']:\",\n \"time iterable is accepted as a cv parameter. n_samples =\",\n \"use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y, svc, params, train,\",\n \"**kw): return 1 # create bad cv cv = BrokenKFold(n_splits=3)\",\n \"as sp import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing\",\n \"* 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"],\",\n \"grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples)\",\n \"in GridSearchCV.fit # the 2nd and 3rd parameter will not\",\n \"grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict =\",\n \"searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def\",\n \"has length 1; expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1),\",\n \"[1] * 5) Cs = [.1, 1, 10] grid_search =\",\n \"ARI and FMS can find the right number :) assert\",\n \"train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y,\",\n \"self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search )\",\n \"SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs,\",\n \"# Test scorer names result_keys = list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\",\",\n \"for both param setting for score_type in ('train', 'test'): per_param_scores\",\n \"'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after '\",\n \"the IID parameter TODO: Clearly this test does something else???\",\n \"assert_raise_message(ValueError, \\\"The 'groups' parameter should not be None.\\\", gs.fit, X,\",\n \"grid search and random search delegate n_features_in to the #\",\n \"in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] #\",\n \"tests `refit=callable` interface. Return the index of a model that\",\n \"10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C',\",\n \"est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' %\",\n \"], [2, ], [3, ], [4, ]] y = [0,\",\n \"1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) #\",\n \"C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i]\",\n \"individual folds will be caught and warnings raised instead. If\",\n \"refit multi-metric search methods with single metric methods\\\"\\\"\\\" assert search_acc.refit\",\n \"Check if score and timing are reasonable, also checks if\",\n \"cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the first two\",\n \"splits cv = [[mask, ~mask], [~mask, mask]] common_params = {'estimator':\",\n \"5 X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':\",\n \"y) def test_empty_cv_iterator_error(): # Use global X, y # create\",\n \"assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1}\",\n \"grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no scoring function\",\n \"], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys\",\n \"+ [1] * 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x,\",\n \"for the fit/score times to be non-zero for trivial X\",\n \"# create \\\"cv\\\" for splits cv = [[mask, ~mask], [~mask,\",\n \"search can be pickled clf = MockClassifier() grid_search = GridSearchCV(clf,\",\n \"sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting #\",\n \"random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]},\",\n \"return X - self.foo_param predict_proba = predict predict_log_proba = predict\",\n \"all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i,\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X,\",\n \"a scorer key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'}\",\n \"def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the _pairwise\",\n \"use param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0,\",\n \"[0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters\",\n \"ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli,\",\n \"'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert\",\n \"\\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results():\",\n \"len(has_empty) == 4 assert list(has_empty) == [{'C': 1}, {'C': 10},\",\n \"match='best_index_ returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1,\",\n \"= {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for\",\n \"for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score\",\n \"tie of their mean cv-scores param_grid = {'C': [1, 1.001,\",\n \"setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting\",\n \"result in a tie of their mean cv-scores param_grid =\",\n \"time keys, among multi-metric and # single metric grid search\",\n \"assert_raise_message(ValueError, \\\"For multi-metric scoring, the \\\" \\\"parameter refit must be\",\n \"== 3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\",\",\n \"we look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): #\",\n \"y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict\",\n \"y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc\",\n \"catches the errors when 'best_index_' returns an invalid result. \\\"\\\"\\\"\",\n \"sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples)\",\n \"samples] assert len(set(hashable_samples)) == 99 # doesn't go into infinite\",\n \"scoring='roc_auc').fit(X, y) # Check warning only occurs in situation where\",\n \"3 # Now without a score, and without y grid_search\",\n \"K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed')\",\n \"i in range(2): # tuple + chain transforms {\\\"a\\\": 1,\",\n \"estimator requires score method to compete with scoring parameter score_no_scoring\",\n \"list or numpy array, but got (<class 'int'>).\\\" \\\" Single\",\n \"equal\\\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator,\",\n \"y) def test_grid_search_pipeline_steps(): # check that parameters that are estimators\",\n \"get default estimator settings) empty = ParameterGrid({}) assert len(empty) ==\",\n \"A dummy function tests `refit=callable` interface. Return the index of\",\n \"param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_')\",\n \"{'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best\",\n \"= LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2),\",\n \"https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 )\",\n \"np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should\",\n \"GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def test_refit_callable():\",\n \"unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']:\",\n \"SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0] * 5\",\n \"make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\\\"fit_grid_point\",\n \"a non-re-entrant cv where split can be called only #\",\n \"= GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency():\",\n \"else refit else: probability = False clf = SVC(probability=probability, random_state=42)\",\n \"# GridSearchCV with on_error != 'raise' # Ensures that a\",\n \"SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]},\",\n \"sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8\",\n \"= np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y\",\n \"generated for all # the cv splits, for both param\",\n \"the other # create \\\"cv\\\" for splits cv = [[mask,\",\n \"key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in\",\n \"error_score='raise') # FailingClassifier issues a ValueError so this is what\",\n \"ClassifierMixin from sklearn.base import clone from sklearn.exceptions import NotFittedError from\",\n \"# create cv cv = KFold(n_splits=3).split(X) # pop all of\",\n \"cv=3) random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): # Test\",\n \"make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from\",\n \"has no score method.\\\"\\\"\\\" @property def score(self): raise AttributeError X\",\n \"assert scalar_param > 0 assert callable(callable_param) # The tuple of\",\n \"`refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X\",\n \"fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter = parameter\",\n \"score and timing are reasonable, also checks if the keys\",\n \"test does something else??? # noise-free simple 2d-data X, y\",\n \"# Test the IID parameter TODO: Clearly this test does\",\n \"= ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1)\",\n \"random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search,\",\n \"[.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C':\",\n \"# This is a check to make sure cv.split is\",\n \"1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not\",\n \"y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) # The\",\n \"Test the IID parameter TODO: Clearly this test does something\",\n \"random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): # Test that\",\n \"errors when 'best_index_' returns an out of bound result. \\\"\\\"\\\"\",\n \"close enough to give similar models # which would result\",\n \"empty grid (useful to get default estimator settings) empty =\",\n \"clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf\",\n \"lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}])\",\n \"!= score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check\",\n \"from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing\",\n \"1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty\",\n \"= np.unique(Y) return self def predict(self, T): return T.shape[0] def\",\n \"exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X,\",\n \"RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search)\",\n \"y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1,\",\n \"not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as list\",\n \"def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType)\",\n \"T): return T.shape[0] def transform(self, X): return X + self.foo_param\",\n \"np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if not k.endswith('_time')},\",\n \"n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the\",\n \"cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc),\",\n \"and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) #\",\n \"estimator is not fitted, `predict_proba` is not available as the\",\n \"grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search\",\n \"return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params\",\n \"y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False\",\n \"return self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search\",\n \"sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import\",\n \"sklearn.datasets import make_classification from sklearn.datasets import make_blobs from sklearn.datasets import\",\n \"None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),\",\n \"search.cv_results_['param_C'][0] == 1 # scores are the same as above\",\n \"sequence.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf =\",\n \"non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self,\",\n \"version 0.23 \\\" \\\"and will be removed in version 0.25\\\")\",\n \"test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split(\",\n \"[0, 0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid,\",\n \"2, 3]) # Smoke test the score etc: grid_search.score(X, y)\",\n \"not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check that\",\n \"sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import\",\n \"2)[0][::2]] = 0 # this leads to perfect classification on\",\n \"classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y =\",\n \"1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the\",\n \"clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv\",\n \"results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else:\",\n \"3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 #\",\n \"# training kernel matrix K_train = np.zeros((10, 20)) y_train =\",\n \"that there is a tie in the mean scores between\",\n \"def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X, Y):\",\n \"y = np.array([0] * 5 + [1] * 5) for\",\n \"cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc(\",\n \"all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in\",\n \"param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y)\",\n \"np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_\",\n \"flexibility in identifying the \\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\"\",\n \"def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1} clf = SVC(gamma='auto') assert_raise_message(\",\n \"{}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def\",\n \"clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) # Test\",\n \"# Check that failed estimator has the highest rank assert\",\n \"sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import\",\n \"= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"]\",\n \"X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier):\",\n \"sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from\",\n \"parameters %d is smaller ' 'than n_iter=%d. Running %d iterations.\",\n \"user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier to test the parameter\",\n \"[0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a\",\n \"[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues\",\n \"in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of folds\",\n \"[1] * 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list),\",\n \"== 8 for values in ParameterGrid(params): assert values in samples\",\n \"= np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for\",\n \"RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_\",\n \"cv_results with the ensemble of multiple single metric cv_results from\",\n \"score_auc = search_auc.score(X, y) # ensure the test is sane\",\n \"rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features\",\n \"y as list in GridSearchCV X = np.arange(100).reshape(10, 10) y\",\n \"import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns\",\n \"np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where split\",\n \"estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params):\",\n \"= make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},\",\n \"assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_\",\n \"matrix corresponding to the linear kernel K_train = np.dot(X_[:180], X_[:180].T)\",\n \"gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a one time\",\n \"search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric search methods with single metric\",\n \"set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points == set((\\\"bar\\\", x,\",\n \"an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\",\",\n \"0}, TypeError, \\\"Parameter.* value is not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")]\",\n \"single metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi\",\n \"euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from\",\n \"def test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4], [5]],\",\n \"1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an\",\n \"no classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':\",\n \"= {\\\"C\\\": [0, 1, 2, 3, 4, 5, 6, 7,\",\n \"from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection\",\n \"'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True)\",\n \"test from KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth\",\n \"`mean_test_score`. \\\"\\\"\\\" # Fit a dummy clf with `refit=True` to\",\n \"params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that\",\n \"param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message( ValueError,\",\n \"X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def\",\n \"Return the index of a model that has the least\",\n \"\\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy pandas\",\n \"= np.array([0] * 5 + [1] * 5) for InputFeatureType,\",\n \"of all 4 blobs, second only from two. mask =\",\n \"# NOTE The precision of time.time in windows is not\",\n \"sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of\",\n \"not implemented.\\\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y)\",\n \"y = make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer\",\n \"param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert\",\n \"one time iterable is accepted as a cv parameter. n_samples\",\n \"'Was the CV iterator empty\\\\\\\\? ' 'Were there no candidates\\\\\\\\?'):\",\n \"and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if\",\n \"not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned to\",\n \"has no classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0),\",\n \"get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param =\",\n \"X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1\",\n \"== 1 # scores are the same as above assert_allclose(test_cv_scores,\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X,\",\n \"not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric search\",\n \"estimator. X = np.arange(20).reshape(5, -1) y = [0, 0, 1,\",\n \"appropriate error messages for refit in [\\\"\\\", 5, True, 'recall',\",\n \"yield identical parameters param_distributions = {\\\"C\\\": [0, 1, 2, 3,\",\n \"is not integer. \\\"\\\"\\\" return None X, y = make_classification(n_samples=100,\",\n \"\\\"For multi-metric scoring, the \\\" \\\"parameter refit must be set\",\n \"results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape ==\",\n \"y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid)\",\n \"y, svc, params, train, test, {'score': scorer}, verbose=True) # FIXME\",\n \"= make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer =\",\n \"model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y,\",\n \"SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train,\",\n \"def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The two\",\n \"not raise an error gs.fit(X, y) def test_classes__property(): # Test\",\n \"# 'min' rank should be assigned to the tied candidates\",\n \"ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) == 8 for\",\n \"and # single metric grid search results. np.testing.assert_equal performs a\",\n \"X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3,\",\n \"make sure cv.split is not called once per param #\",\n \"cv object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2,\",\n \"gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits),\",\n \"has the least `mean_test_score`. \\\"\\\"\\\" # Fit a dummy clf\",\n \"rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0) groups\",\n \"used for model selection alone without refitting clf = MockClassifier()\",\n \"# error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform',\",\n \"two param settings (C=0.1) and the next two param #\",\n \"assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as\",\n \"else: score = 0. return score def get_params(self, deep=False): return\",\n \"cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4,\",\n \"grid_search.best_params_ # check that we can call score and that\",\n \"assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert\",\n \"grid-search with an estimator without predict. # slight duplication of\",\n \"for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs},\",\n \"y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for\",\n \"def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys())\",\n \"cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score']\",\n \"'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the IID\",\n \"len(samples) == 10 for sample in samples: assert sample[\\\"kernel\\\"] in\",\n \"Test that an invalid refit param raises appropriate error messages\",\n \"has length != len(X). err_msg = r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\]\",\n \"attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,\",\n \"SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit,\",\n \"scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc =\",\n \"cv.get_n_splits returned ' 'inconsistent results. Expected \\\\\\\\d+ ' 'splits, got\",\n \"https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a\",\n \"y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a ValueError on fit()\\\"\\\"\\\"\",\n \"# non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def\",\n \"names result_keys = list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\"\",\n \"], [4, ]] y = [0, 1, 1, 0] gs\",\n \"two param # settings (C=0.2) are same, the test and\",\n \"4 blobs, second only from two. mask = np.ones(X.shape[0], dtype=np.bool)\",\n \"for multi-metric pass refit='accuracy' if refit: probability = True refit\",\n \"' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning,\",\n \"n_splits = 3 n_search_iter = 30 params = [{'kernel': ['rbf'],\",\n \"a one time iterable is accepted as a cv parameter.\",\n \"cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(),\",\n \"search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in search_rec.cv_results_.items()})\",\n \"LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180])\",\n \"[0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split\",\n \"raise meaningful # error messages for fn_name in ('predict', 'predict_proba',\",\n \"y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True,\",\n \"pass model = SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params`\",\n \"is not a dict or a list \\\\(0\\\\)'), ([{'foo': [0]},\",\n \"verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test],\",\n \"or a list \\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .*\",\n \"assertion error if # a parameter is missing or has\",\n \"Test that grid search works with both dense and sparse\",\n \"y) def test_grid_search_error(): # Test that grid search will capture\",\n \"'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after ' 'refitting\",\n \"s][0] for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std\",\n \"check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search\",\n \"= GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is not\",\n \"refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function tests when returned 'best_index_' is\",\n \"3 n_search_iter = 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10),\",\n \"def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)\",\n \"= GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_\",\n \"a score of 1/3 on # the other # create\",\n \"this abuse is common in # popular third-party libraries and\",\n \"not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X =\",\n \"Test with grid search cv for est in estimators: grid_search\",\n \"\\\\ \\\"Attribute %s not equal\\\" % attr def test__custom_fit_no_run_search(): class\",\n \"3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \\\"best_estimator_\\\")\",\n \"with an estimator without predict. # slight duplication of a\",\n \"same, the test and train scores must also be #\",\n \"0 assert callable(callable_param) # The tuple of arrays should be\",\n \"nested comparison of the two cv_results dicts np.testing.assert_equal({k: v for\",\n \"gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1])\",\n \"sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import\",\n \"y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best\",\n \"without predict. # slight duplication of a test from KDE\",\n \"'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys,\",\n \"# For the train scores, we do not take a\",\n \"but that we do # not want to break without\",\n \"removed in version 0.25\\\") params = {'C': 0.1} train, test\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): # Pass X as list\",\n \"refitting clf = MockClassifier() for scoring in [None, ['accuracy', 'precision']]:\",\n \"est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train])\",\n \"grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): #\",\n \"strategy - # Check that there is a tie in\",\n \"not to break support for those without following # proper\",\n \"15) clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs =\",\n \"GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): #\",\n \"sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import\",\n \"{'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr),\",\n \"2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1,\",\n \"[1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"\\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\") # Make sure\",\n \"in samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert 0 <=\",\n \"the same as grid_size sampler = ParameterSampler(params, n_iter=8) samples =\",\n \"def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests `refit=callable` interface. Return\",\n \"length != len(X). err_msg = r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not\",\n \"est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): #\",\n \"sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import\",\n \"called once per param setting in GridSearchCV.fit # the 2nd\",\n \"gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise')\",\n \"with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km =\",\n \"grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check that we\",\n \"multi-metric cv_results with the ensemble of multiple single metric cv_results\",\n \"scipy.stats import bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin\",\n \"product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to\",\n \"params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train,\",\n \"cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 # test error\",\n \"{'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_,\",\n \"= make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because\",\n \"rank should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1,\",\n \"stats dists do not accept seed, hence we use param\",\n \"param_grid=param_grid, cv=3) assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert not\",\n \"score function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2)\",\n \"values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert\",\n \"loss where greater is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_,\",\n \"# test grid-search with unsupervised estimator X, y = make_blobs(n_samples=50,\",\n \"Test that grid search works when the input features are\",\n \"param_keys) assert all(cv_results[key].dtype == object for key in param_keys) assert\",\n \"series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x,\",\n \"7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class\",\n \"cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names result_keys\",\n \"{\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2)\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator):\",\n \"assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when groups\",\n \"param_dict = {\\\"C\\\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV,\",\n \"self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return self class\",\n \"params2 = {\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2\",\n \"param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] )\",\n \"4 assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C':\",\n \"def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer\",\n \"+ ('params',))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys\",\n \"= \\\"Fit parameter spam has length 1; expected\\\" with pytest.raises(AssertionError,\",\n \"sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that\",\n \"y) def test_classes__property(): # Test that classes_ property matches best_estimator_.classes_\",\n \"is a check to make sure cv.split is not called\",\n \"ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid search and\",\n \"implemented.\\\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def\",\n \"folds that are not iid # first one contains data\",\n \"key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results #\",\n \"grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test\",\n \"per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in\",\n \"in the param_grid. param_grid = { 'loss': ['log'], } X\",\n \"# Both ARI and FMS can find the right number\",\n \"y) def test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which adds flexibility in\",\n \"StringIO from itertools import chain, product from functools import partial\",\n \"assert we can iterate over the grid multiple times for\",\n \"= foo_param def fit(self, X, Y): assert len(X) == len(Y)\",\n \"do # not want to break without an explicit deprecation\",\n \"import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the\",\n \"refit: probability = True refit = 'accuracy' if isinstance(scoring, tuple)\",\n \"= LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X,\",\n \"[0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_)\",\n \"list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k],\",\n \"return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])\",\n \"HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid)\",\n \"cv for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv)\",\n \"n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0)\",\n \"assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted\",\n \"= search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores are the\",\n \"# clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =\",\n \"gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X,\",\n \"multi-metric scoring, the \\\" \\\"parameter refit must be set to\",\n \"len(gs.cv_results_['params']) # Ensure that grid scores were set to zero\",\n \"{'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X,\",\n \"sure _pairwise is delegated to the base estimator. Non-regression test\",\n \"not in k and k != 'rank_test_score') # Check cv_results\",\n \"grid_size = 8 expected_warning = ('The total space of parameters\",\n \"cv is empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(),\",\n \"in the mean scores between # candidates 1 and 2\",\n \"'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning,\",\n \"{'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty) == [{'C':\",\n \"generates an assertion error if # a parameter is missing\",\n \"y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"]\",\n \") class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None,\",\n \"3]}, cv=3, verbose=3) # make sure it selects the smallest\",\n \"the two cv_results dicts np.testing.assert_equal({k: v for k, v in\",\n \"not k.endswith('_time')}) # Check consistency of folds across the parameters\",\n \"+ score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for key\",\n \"the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys +\",\n \"in param_keys + score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50,\",\n \"from io import StringIO from itertools import chain, product from\",\n \"grid, scoring='roc_auc').fit(X, y) # Check warning only occurs in situation\",\n \"gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs =\",\n \"-1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1,\",\n \"is delegated to the base estimator. Non-regression test for issue\",\n \"expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y,\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y)\",\n \"assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_\",\n \"not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def\",\n \"per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search =\",\n \"as list in GridSearchCV X_4d = np.arange(10 * 5 *\",\n \"1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset\",\n \"class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self,\",\n \"assert len(has_empty) == 4 assert list(has_empty) == [{'C': 1}, {'C':\",\n \"make sure it selects the smallest parameter in case of\",\n \"test basic properties of param sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\",\",\n \"supported as cv and # that the splits are consistent\",\n \"'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring,\",\n \"refit=False because we want to test the behaviour of the\",\n \"0], TypeError, r'Parameter .* is not a dict \\\\(0\\\\)'), ({'foo':\",\n \"X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict',\",\n \"# Compare the keys, other than time keys, among multi-metric\",\n \"search and random search delegate n_features_in to the # best\",\n \"in [\\\"rbf\\\", \\\"linear\\\"] assert 0 <= sample[\\\"C\\\"] <= 1 #\",\n \") cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \"\\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.* value is not iterable .*\\\"\",\n \"SVC() assert_raise_message( ValueError, \\\"Parameter values for parameter (C) need to\",\n \"refit was done, then an exception would be raised on\",\n \"'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring, the \\\" \\\"parameter refit must\",\n \"y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i])\",\n \"import FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree import\",\n \"= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y)\",\n \"function tests when returned 'best_index_' is out of bounds. \\\"\\\"\\\"\",\n \"clone from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification from\",\n \"test for issue #13920. \\\"\\\"\\\" est = BaseEstimator() attr_message =\",\n \"no score method.\\\"\\\"\\\" @property def score(self): raise AttributeError X =\",\n \"test_stochastic_gradient_loss_param(): # Make sure the predict_proba works when loss is\",\n \"X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter\",\n \"{'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_\",\n \"the CV iterator empty\\\\\\\\? ' 'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size],\",\n \"= rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid = {'C':\",\n \"something else??? # noise-free simple 2d-data X, y = make_blobs(centers=[[0,\",\n \"right value for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf,\",\n \"sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import\",\n \"\\\"gamma\\\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_,\",\n \"wrapped in a list\\\" \\\" with one element.\\\", GridSearchCV, clf,\",\n \"'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if generators\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X,\",\n \"test error is raised when the precomputed kernel is not\",\n \"the function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point():\",\n \"elif score == \\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test],\",\n \"{'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param'] return self\",\n \"preds_original = cv.predict(X) # precompute euclidean metric to validate _pairwise\",\n \"= KFold() for est in estimators: grid_search = GridSearchCV(est, est_parameters,\",\n \"that grid search will capture errors on data with different\",\n \">= 0 # test error is raised when the precomputed\",\n \"gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def\",\n \"GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X,\",\n \"test grid-search with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0)\",\n \"# Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys,\",\n \"[2, ], [3, ], [4, ]] y = [0, 1,\",\n \"be fit twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter = parameter def\",\n \"\\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\") # Make sure the functions\",\n \"done, then an exception would be raised on refit and\",\n \"sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param >\",\n \"{k: v for k, v in cv_results_acc_rec.items() if not k.endswith('_time')})\",\n \"the grid search part gs = GridSearchCV(clf, [{'parameter': [0, 1,\",\n \"gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree',\",\n \"== getattr(mycv, attr), \\\\ \\\"Attribute %s not equal\\\" % attr\",\n \"= [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv for\",\n \"keys, other than time keys, among multi-metric and # single\",\n \"# Check tie breaking strategy - # Check that there\",\n \"grid_search_no_score.fit(X, y) # check that best params are equal assert\",\n \"len(X) == len(Y) self.classes_ = np.unique(Y) return self def predict(self,\",\n \"random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric\",\n \"\\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})]\",\n \"test and train scores must also be # same as\",\n \"y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3\",\n \"[5, 10]}])) # Using regressor to make sure each score\",\n \"def test_grid_search_error(): # Test that grid search will capture errors\",\n \"(DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0, None]} cv = KFold()\",\n \"if isinstance(scoring, tuple) else refit else: probability = False clf\",\n \"without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X,\",\n \"[0, 0, 1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean',\",\n \"[0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_\",\n \"custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1 else 0\",\n \"all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',)))\",\n \"mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search\",\n \"= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30\",\n \"values in samples # test sampling without replacement in a\",\n \"def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar values\",\n \"= [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf,\",\n \"function tests when returned 'best_index_' is not integer. \\\"\\\"\\\" return\",\n \"that cannot be fit twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter =\",\n \"refit: assert search_multi.refit == 'accuracy' else: assert not search_multi.refit return\",\n \"8 expected_warning = ('The total space of parameters %d is\",\n \"grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X,\",\n \"0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) #\",\n \"def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X, y\",\n \"super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): # this should\",\n \"\\\"\\\"\\\"Test the search module\\\"\\\"\\\" from collections.abc import Iterable, Sized from\",\n \"from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon,\",\n \"RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0]\",\n \"element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": np.ones((3, 2))} clf\",\n \"np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): #\",\n \"[0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler\",\n \"matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred =\",\n \"# Check if score and timing are reasonable assert all(cv_results['rank_test_score']\",\n \"MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV(\",\n \"for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True,\",\n \"y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0\",\n \"list(sampler) assert len(samples) == 99 hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'],\",\n \"random search delegate n_features_in to the # best estimator n_features\",\n \"be a non-empty sequence.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\":\",\n \"\\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X =\",\n \"meaningful # error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba',\",\n \"# Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert\",\n \"scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps():\",\n \"param_dict) param_dict = {\\\"C\\\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError,\",\n \"loss is specified # as one of the parameters in\",\n \"for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X))\",\n \"Running %d iterations. For ' 'exhaustive searches, use GridSearchCV.' %\",\n \"arrays should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert\",\n \"careful not to break support for those without following #\",\n \"to be a non-empty sequence.\\\", GridSearchCV, clf, param_dict) param_dict =\",\n \"not k.endswith('_time')}, {k: v for k, v in cv_results_acc_rec.items() if\",\n \"if self.foo_param > 1: score = 1. else: score =\",\n \"grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): # Pass\",\n \"performs a # deep nested comparison of the two cv_results\",\n \"y) assert not hasattr(grid_search, 'classes_') # Test that the grid\",\n \"refit if refit: assert search_multi.refit == 'accuracy' else: assert not\",\n \"raise ValueError(\\\"Failing classifier failed as required\\\") def predict(self, X): return\",\n \"for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32\",\n \"not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV,\",\n \"score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception\",\n \"form of a precomputed kernel matrix X_, y_ = make_classification(n_samples=200,\",\n \"param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check\",\n \"# Test that a fit search can be pickled clf\",\n \"gs.fit(X, y) # As the first two param settings (C=0.1)\",\n \"GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit,\",\n \"refit=False because we only want to check that errors caused\",\n \"_pairwise property which matches the _pairwise property of its estimator.\",\n \"that are estimators are cloned before fitting pipe = Pipeline([('regressor',\",\n \"n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1,\",\n \"libraries and we should tolerate this behavior for # now\",\n \"for x in sampler] == [x for x in sampler]\",\n \"cv=2, error_score=0, n_iter=2) for search in (gs, rs): search.fit(X, y)\",\n \"test_trivial_cv_results_attr(): # Test search over a \\\"grid\\\" with only one\",\n \"[0.1, 0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if\",\n \"= make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel matrix\",\n \"True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array(\",\n \"LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid,\",\n \"GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute\",\n \"hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches the errors\",\n \"cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf\",\n \"breaking strategy - # Check that there is a tie\",\n \"y = [[1], [2], [3], [4], [5]], [0, 0, 0,\",\n \"test_grid_search_error(): # Test that grid search will capture errors on\",\n \"assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba\",\n \"y) preds_original = cv.predict(X) # precompute euclidean metric to validate\",\n \"# Check that there is a tie in the mean\",\n \"random_state=0) km = KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric evaluation unsupervised\",\n \"Test basic properties of ParameterGrid. params1 = {\\\"foo\\\": [1, 2,\",\n \"clf.fit(X, y) # Ensure that `best_index_ != 0` for this\",\n \"'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches the errors when\",\n \"= GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def\",\n \"for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results\",\n \"y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param\",\n \"None]} cv = KFold() for est in estimators: grid_search =\",\n \"cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean metric\",\n \"fit twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter = parameter def fit(self,\",\n \"y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score():\",\n \"search in (gs, rs): search.fit(X, y) for key in ['mean_fit_time',\",\n \"grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array(\",\n \"also check if groups is correctly passed to the cv\",\n \"Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})\",\n \"is out of bounds. \\\"\\\"\\\" return out_bound_value X, y =\",\n \"# Pass X as list in GridSearchCV X = np.arange(100).reshape(10,\",\n \"[3, 4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid,\",\n \"fit/score times to be non-zero for trivial X and y\",\n \"and we should tolerate this behavior for # now and\",\n \"= LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\\\"fit_grid_point is deprecated\",\n \"scikit-learn API contract but that we do # not want\",\n \"# settings (C=0.2) are same, the test and train scores\",\n \"for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42),\",\n \"return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator,\",\n \"* 11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:] ==\",\n \"train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold from\",\n \"cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed)\",\n \"= RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params']\",\n \"def test_refit(): # Regression test for bug in refitting #\",\n \"from KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth ==\",\n \"search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not\",\n \"test the parameter search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param =\",\n \"user provided # validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805\",\n \"[1] * 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list),\",\n \"break without an explicit deprecation cycle and API # recommendations\",\n \"from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics\",\n \"a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X,\",\n \"C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), #\",\n \"# that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter\",\n \"RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from\",\n \"for issue #13920. \\\"\\\"\\\" n_samples = 50 n_splits = 2\",\n \"foo_param clf = MockClassifier() grid_search = None grid_search = GridSearchCV(clf,\",\n \"y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid\",\n \"cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in\",\n \"functools import partial import pickle import sys from types import\",\n \"import clone from sklearn.exceptions import NotFittedError from sklearn.datasets import make_classification\",\n \"'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\\\\",\n \"mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0\",\n \"#13920. \\\"\\\"\\\" n_samples = 50 n_splits = 2 X, y\",\n \"{'estimator': SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params)\",\n \"4, 5, 6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions,\",\n \"would cause an # error in this test. gs =\",\n \"two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0\",\n \"1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute the\",\n \"clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in multiple metric\",\n \"import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose\",\n \"be generated for the 2nd and subsequent cv.split calls. #\",\n \"'best_score_') def test_gridsearch_nd(): # Pass X as list in GridSearchCV\",\n \"in score_keys if k != 'rank_test_score') assert (all(cv_results[k] <= 1)\",\n \"1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C\",\n \"KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original =\",\n \"0 # compute the test kernel matrix K_test = np.dot(X_[180:],\",\n \"getattr(gscv, attr) == getattr(mycv, attr), \\\\ \\\"Attribute %s not equal\\\"\",\n \"grid search part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],\",\n \"Both ARI and FMS can find the right number :)\",\n \"in k and k != 'rank_test_score') # Check cv_results structure\",\n \"break with # sparse SVMs. X = np.arange(100).reshape(10, 10) y\",\n \"= [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1,\",\n \"be set to a scorer key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc':\",\n \"groups=None, **fit_params): return self # this should not raise any\",\n \"(C=0.2) are same, the test and train scores must also\",\n \"11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search =\",\n \"with the ensemble of multiple single metric cv_results from single\",\n \"100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4)\",\n \"= predict def score(self, X=None, Y=None): if self.foo_param > 1:\",\n \"with grid search cv for est in estimators: grid_search =\",\n \"clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert\",\n \"%s not equal\\\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def\",\n \"grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] ==\",\n \"accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from\",\n \"grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring grid_search.scoring =\",\n \"before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(),\",\n \"for model selection alone without refitting clf = MockClassifier() for\",\n \"check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search =\",\n \"== object for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray)\",\n \"= GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score,\",\n \"[.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y)\",\n \"# Assert every key matches those in `cv_results` for key\",\n \"cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv =\",\n \"bounds. \\\"\\\"\\\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42)\",\n \"RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful\",\n \"[1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\",\",\n \"X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5,\",\n \"needs to\\\" \\\" be a list or numpy array, but\",\n \"(C) need to be a non-empty sequence.\\\", GridSearchCV, clf, param_dict)\",\n \"two estimators inherit from BaseEstimator, # to test hyperparameter search\",\n \"(minkowski p = 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf,\",\n \"np.arange(20).reshape(5, -1) y = [0, 0, 1, 1, 1] clf\",\n \"11) check_X = lambda x: x.shape[1:] == (5, 3, 2)\",\n \"to validate _pairwise is working X_precomputed = euclidean_distances(X) clf =\",\n \"= cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred,\",\n \"grid (useful to get default estimator settings) empty = ParameterGrid({})\",\n \"= { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3)\",\n \"30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel':\",\n \"== 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i])\",\n \"in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and\",\n \"Test that the grid searcher has no classes_ attribute without\",\n \"not a dict or a list \\\\(0\\\\)'), ([{'foo': [0]}, 0],\",\n \"y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search works when\",\n \"except ImportError: pass X = np.arange(100).reshape(10, 10) y = np.array([0]\",\n \"should be data dependent (e.g. `sample_weight`) which # is not\",\n \"are present assert all((np.all(cv_results_multi[k] <= 1) for k in (\",\n \"DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf,\",\n \"algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X,\",\n \"klass(input) def test_parameter_grid(): # Test basic properties of ParameterGrid. params1\",\n \"OneTimeSplitter is a non-re-entrant cv where split can be called\",\n \"with a non-square precomputed # training kernel matrix K_train =\",\n \"GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert\",\n \"on the best parameters' % fn_name), getattr(grid_search, fn_name), X) #\",\n \"model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}),\",\n \"can iterate over the grid multiple times for i in\",\n \"_FitParamClassifier(), param_search ) # NOTE: `fit_params` should be data dependent\",\n \"in version 0.23 \\\" \\\"and will be removed in version\",\n \"test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:]\",\n \"sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) # The tuple\",\n \"len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i\",\n \"= GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def\",\n \"least `mean_test_score`. \\\"\\\"\\\" # Fit a dummy clf with `refit=True`\",\n \"search.cv_results_ # Check if score and timing are reasonable assert\",\n \"classification on one fold and a score of 1/3 on\",\n \"= gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in results:\",\n \"the following parameters. But this abuse is common in #\",\n \"(X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, } model.fit(X_train, y_train, **fit_params)\",\n \"= 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0),\",\n \"expected ValueError [u for u in cv] # cv is\",\n \"the index of a model that has the least `mean_test_prec`.\",\n \"for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s,\",\n \"= make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01}, {'C':\",\n \"cv_results dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if\",\n \"as # fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated():\",\n \"`cv_results` for key in clf.cv_results_.keys(): assert key in cv_results return\",\n \"['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf,\",\n \"missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X,\",\n \"values for parameter (C) need to be a non-empty sequence.\\\",\",\n \"grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized search for\",\n \"import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message\",\n \"not accept seed, hence we use param grid params =\",\n \"err_msg = \\\"Fit parameter spam has length 1; expected\\\" with\",\n \"MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class\",\n \"two folds that are not iid # first one contains\",\n \"grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params']\",\n \"in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for\",\n \"cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and\",\n \"y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc =\",\n \"= None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X,\",\n \"def test_empty_cv_iterator_error(): # Use global X, y # create cv\",\n \"sklearn.metrics import f1_score from sklearn.metrics import recall_score from sklearn.metrics import\",\n \"def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing\",\n \"if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that\",\n \"random_state=0) samples = [x for x in sampler] assert len(samples)\",\n \"`best_score_` is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_')\",\n \"42 if estimator.bandwidth == .1 else 0 X, _ =\",\n \"GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test that\",\n \"hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass X as list in\",\n \"Compare the keys, other than time keys, among multi-metric and\",\n \"refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert\",\n \"for this dummy clf assert clf.best_index_ != 0 # Assert\",\n \"not k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items() if\",\n \"score_keys, n_cand): # Test the search.cv_results_ contains all the required\",\n \"# Test with a randomized search for est in estimators:\",\n \"NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X,\",\n \"!= 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results,\",\n \"deprecated in version 0.23 \\\" \\\"and will be removed in\",\n \"< 1) assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert search.refit_time_\",\n \"that regressors do not have a classes_ attribute grid_search =\",\n \"= CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf, {'foo_param':\",\n \"> 0 assert callable(callable_param) # The tuple of arrays should\",\n \"iterable is accepted as a cv parameter. n_samples = 100\",\n \"= MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3,\",\n \"= SVC() assert_raise_message( ValueError, \\\"Parameter values for parameter (C) need\",\n \"y, svc, params, train, test, scorer, verbose=False) def test_pickle(): #\",\n \"FailingClassifier() # refit=False because we want to test the behaviour\",\n \"p['b'], p['c']) for p in samples] assert len(set(hashable_samples)) == 99\",\n \"has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty)\",\n \"an explicit deprecation cycle and API # recommendations for implementing\",\n \"in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare\",\n \"sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from\",\n \"to perfect classification on one fold and a score of\",\n \"% (p['a'], p['b'], p['c']) for p in samples] assert len(set(hashable_samples))\",\n \"{'foo_param': [1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled =\",\n \"(useful to get default estimator settings) empty = ParameterGrid({}) assert\",\n \"# refit was done, then an exception would be raised\",\n \"assert not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1)\",\n \"grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1],\",\n \"and \\\\ hasattr(grid_search, \\\"best_params_\\\") # Make sure the functions predict/transform\",\n \"flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring\",\n \"MockDataFrame from scipy.stats import bernoulli, expon, uniform from sklearn.base import\",\n \"\\\"cv\\\" for splits cv = [[mask, ~mask], [~mask, mask]] common_params\",\n \"= len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i]\",\n \"gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan'))\",\n \"non-zero for trivial X and y assert np.all(search.cv_results_[key] >= 0)\",\n \"empty\\\\\\\\? ' 'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv():\",\n \"make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV,\",\n \"X): return X - self.foo_param predict_proba = predict predict_log_proba =\",\n \"FIXME remove test_fit_grid_point as the function will be removed on\",\n \"need to be wrapped in a list\\\" \\\" with one\",\n \"we can call score and that it gives the correct\",\n \"random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def\",\n \"test_refit(): # Regression test for bug in refitting # Simulates\",\n \"1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def\",\n \"contains data of all 4 blobs, second only from two.\",\n \"to GridSearchCV # And also check if groups is correctly\",\n \"import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import recall_score\",\n \"assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key\",\n \"def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not\",\n \"SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble\",\n \"will be removed in version 0.25\\\") params = {'C': 0.1}\",\n \"# Fit a dummy clf with `refit=True` to get a\",\n \"grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc\",\n \"test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values in fit_params,\",\n \"\\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y, svc, params, train, test, {'score':\",\n \"range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0,\",\n \"for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for\",\n \"y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf\",\n \"with one element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": []}\",\n \"exactly respect the scikit-learn API contract but that we do\",\n \"3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True, False):\",\n \"y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV,\",\n \"function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError,\",\n \"from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm\",\n \"1/3 on # the other # create \\\"cv\\\" for splits\",\n \"{'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9\",\n \"= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y)\",\n \"hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\") # Make sure the\",\n \"[] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'):\",\n \"\\\"\\\"\\\"Classifier that raises a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2\",\n \"`predict_proba` is not available as the # loss is 'hinge'.\",\n \"np.nan y = [0, 0, 1, 1, 1] p =\",\n \"X = np.arange(100).reshape(10, 10) y = np.array([0] * 5 +\",\n \"in LightGBM # that do not exactly respect the scikit-learn\",\n \"{'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2\",\n \"@property def score(self): raise AttributeError X = np.array([[-1, -1], [-2,\",\n \"not k.endswith('_time'): # XXX: results['params'] is a list :| results[k]\",\n \"assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict(): # test grid-search with\",\n \"models # which would result in a tie of their\",\n \"= cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel():\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert\",\n \"search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with the ensemble of\",\n \"cv.predict(X) # precompute euclidean metric to validate _pairwise is working\",\n \"test_grid_search_with_multioutput_data(): # Test search with multi-output estimator X, y =\",\n \"remove test_fit_grid_point as the function will be removed on 0.25\",\n \"this raises an error with pytest.raises(ValueError, match='No fits were performed.\",\n \"result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function tests when\",\n \"cannot predict/score without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42)\",\n \"test_grid_search_precomputed_kernel(): # Test that grid search works when the input\",\n \"# And also check if groups is correctly passed to\",\n \"n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 # Scipy\",\n \"]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator):\",\n \"using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): # Pass\",\n \"cv.split is not called once per param # setting. np.testing.assert_equal({k:\",\n \"== 6 # loop to assert we can iterate over\",\n \"metric (minkowski p = 2) clf = KNeighborsClassifier() cv =\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y)\",\n \"# The two C values are close enough to give\",\n \"match='No fits were performed. ' 'Was the CV iterator empty\\\\\\\\?\",\n \"grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on\",\n \"y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score'\",\n \"grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0],\",\n \"that grid search returns an error with a non-square precomputed\",\n \"a \\\"grid\\\" with only one point. clf = MockClassifier() grid_search\",\n \"search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$',\",\n \"5) clf = CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], )\",\n \"5) for InputFeatureType, TargetType in types: # X dataframe, y\",\n \"scipy.sparse as sp import pytest from sklearn.utils.fixes import sp_version from\",\n \"test that correct scores are used n_splits = 3 clf\",\n \"if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C':\",\n \"est_parameters = {\\\"random_state\\\": [0, None]} cv = KFold() for est\",\n \"= evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results\",\n \"== (5, 3, 2) check_y = lambda x: x.shape[1:] ==\",\n \"param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give\",\n \"= RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search):\",\n \"{'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C =\",\n \"without refitting clf = MockClassifier() for scoring in [None, ['accuracy',\",\n \"[{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\"\",\n \"high # enough for the fit/score times to be non-zero\",\n \"import Iterable, Sized from io import StringIO from itertools import\",\n \"= SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test\",\n \"ranks[1] <= 2 # Check that failed estimator has the\",\n \"param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict():\",\n \"correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def\",\n \"in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal(\",\n \"0.1}, {'C': 0.01}, {'C': 0.001}): for train, test in cv.split(X,\",\n \"param in param_keys) assert all(cv_results[key].dtype == object for key in\",\n \"* 5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"],\",\n \"that are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' %\",\n \"param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i]\",\n \"sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]})\",\n \"1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_ !=\",\n \"test, scorer, verbose=False) def test_pickle(): # Test that a fit\",\n \"cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names result_keys =\",\n \"we want to test the behaviour of the grid search\",\n \"from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing\",\n \"'classes_') def test_trivial_cv_results_attr(): # Test search over a \\\"grid\\\" with\",\n \"scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) #\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2\",\n \"worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss,\",\n \"all(cv_results[key].dtype == object for key in param_keys) assert not any(isinstance(cv_results[key],\",\n \"n_iter=99, random_state=42) samples = list(sampler) assert len(samples) == 99 hashable_samples\",\n \"isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,\",\n \").fit, X, y) def test_grid_search_error(): # Test that grid search\",\n \"y) # check that best params are equal assert grid_search_no_score.best_params_\",\n \"error with a non-square precomputed # training kernel matrix K_train\",\n \"GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ #\",\n \"= SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42)\",\n \"* 2).reshape(10, 5, 3, 2) y_3d = np.arange(10 * 7\",\n \"refit): \\\"\\\"\\\"Compare refit multi-metric search methods with single metric methods\\\"\\\"\\\"\",\n \"('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y)\",\n \"= {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params,\",\n \"to individual folds will be caught and warnings raised instead.\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search,\",\n \"assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in\",\n \"one element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": np.ones((3, 2))}\",\n \"When the estimator is not fitted, `predict_proba` is not available\",\n \"Test implementation catches the errors when 'best_index_' returns an invalid\",\n \"param_grid = { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid,\",\n \"StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C)\",\n \"cause an # error in this test. gs = GridSearchCV(clf,\",\n \"has the _pairwise property which matches the _pairwise property of\",\n \"zero as required for those fits # that are expected\",\n \"early stopping with a user provided # validation set. non-regression\",\n \"randomized search for est in estimators: random_search = RandomizedSearchCV(est, est_parameters,\",\n \"'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\\\ \\\"Attribute %s\",\n \"**kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None, **fit_params): return\",\n \"attr) == getattr(mycv, attr), \\\\ \\\"Attribute %s not equal\\\" %\",\n \"[1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert\",\n \"clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]},\",\n \"= GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \\\"predict_proba\\\") def\",\n \"= ('The total space of parameters %d is smaller '\",\n \"LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2\",\n \"params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler =\",\n \"import make_classification from sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification\",\n \"in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check\",\n \"pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\\\\\d+\",\n \"train, test in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point(\",\n \"pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic properties of\",\n \"import NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import make_blobs\",\n \"{ 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert\",\n \"check that we didn't modify the parameter grid that was\",\n \"y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def\",\n \"class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError,\",\n \"search `cv_results`'s array are of correct types cv_results = search.cv_results_\",\n \"@ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold()\",\n \"itertools import chain, product from functools import partial import pickle\",\n \"test_no_refit(): # Test that GSCV can be used for model\",\n \"set_params(self, **params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An\",\n \"is not available as the # loss is 'hinge'. assert\",\n \"search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure the test\",\n \"cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be\",\n \"the next two param # settings (C=0.2) are same, the\",\n \"basic properties of param sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"],\",\n \"gs2.fit(X, y) # Give generator as a cv parameter assert\",\n \"that we can call score and that it gives the\",\n \"test make sure _pairwise is delegated to the base estimator.\",\n \"without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method\",\n \"sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import\",\n \"from sklearn.metrics import f1_score from sklearn.metrics import recall_score from sklearn.metrics\",\n \"one point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]},\",\n \"* 5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter':\",\n \"test_grid_search_groups(): # Check if ValueError (when groups is None) propagates\",\n \"an error gs.fit(X, y) def test_classes__property(): # Test that classes_\",\n \"= est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled():\",\n \"cycle and API # recommendations for implementing early stopping with\",\n \"est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X,\",\n \"from sklearn.datasets import make_classification from sklearn.datasets import make_blobs from sklearn.datasets\",\n \"assert callable(callable_param) # The tuple of arrays should be preserved\",\n \"create \\\"cv\\\" for splits cv = [[mask, ~mask], [~mask, mask]]\",\n \"# Check if ValueError (when groups is None) propagates to\",\n \"0.25\\\") params = {'C': 0.1} train, test = next(StratifiedKFold().split(X, y))\",\n \"or has length != len(X). err_msg = r\\\"Expected fit parameter\\\\(s\\\\)\",\n \"expected_score) assert params == this_params assert n_test_samples == test.size #\",\n \"1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(),\",\n \"y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y)\",\n \"# sparse SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0]\",\n \"fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from\",\n \"pass X = np.arange(100).reshape(10, 10) y = np.array([0] * 5\",\n \"SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan\",\n \"(C) needs to\\\" \\\" be a list or numpy array,\",\n \"not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input, error_type,\",\n \"assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min'\",\n \"attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a':\",\n \"random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring =\",\n \"'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only\",\n \"assert not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\\",\n \"err_msg = r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError,\",\n \"'classes_') # Test that the grid searcher has no classes_\",\n \"test_grid_search_pipeline_steps(): # check that parameters that are estimators are cloned\",\n \"lower ranks assert ranks[0] <= 2 and ranks[1] <= 2\",\n \"index of a model that has the least `mean_test_score`. \\\"\\\"\\\"\",\n \"sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import\",\n \"search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with the ensemble of multiple single\",\n \"range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not\",\n \"above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1]) #\",\n \"== 99 # doesn't go into infinite loops params_distribution =\",\n \"def test_no_refit(): # Test that GSCV can be used for\",\n \"T.shape[0] def transform(self, X): return X + self.foo_param def inverse_transform(self,\",\n \"assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search,\",\n \"# best estimator n_features = 4 X, y = make_classification(n_features=n_features)\",\n \"X = np.arange(20).reshape(5, -1) y = [0, 0, 1, 1,\",\n \"all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys) def\",\n \"y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs:\",\n \"assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for\",\n \"SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0,\",\n \"np.array([0] * 5 + [1] * 5) Cs = [.1,\",\n \"is disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def\",\n \"results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking '\",\n \"('refit=False. %s is available only after ' 'refitting on the\",\n \"raise AttributeError X = np.array([[-1, -1], [-2, -1], [1, 1],\",\n \"dummy function tests when returned 'best_index_' is out of bounds.\",\n \"GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method():\",\n \"LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C':\",\n \"as the # loss is 'hinge'. assert not hasattr(clf, \\\"predict_proba\\\")\",\n \"cv_results # Check if generators are supported as cv and\",\n \"[0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X)\",\n \"C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 #\",\n \"score_keys, n_candidates) # Check masking cv_results = search.cv_results_ n_candidates =\",\n \"has no classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0),\",\n \"n_splits = 3 n_search_iter = 30 # Scipy 0.12's stats\",\n \"len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4, 2], \\\"bar\\\":\",\n \"cv=cv, n_jobs=4) # assert that this raises an error with\",\n \"# check that best params are equal assert grid_search_no_score.best_params_ ==\",\n \"= 50 n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0)\",\n \"propagates to GridSearchCV # And also check if groups is\",\n \"attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert\",\n \"and timing are reasonable, also checks if the keys #\",\n \"refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),\",\n \"twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter = parameter def fit(self, X,\",\n \"X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def\",\n \"x in sampler] if sp_version >= (0, 16): param_distributions =\",\n \"CV iterator empty\\\\\\\\? ' 'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size])\",\n \"# Test that grid search will capture errors on data\",\n \"mask[np.where(y == 2)[0][::2]] = 0 # this leads to perfect\",\n \"{'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test\",\n \"clone(svc), params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train],\",\n \"(C=0.1) and the next two param # settings (C=0.2) are\",\n \"= HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt,\",\n \"== 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) # Smoke test the\",\n \"float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that\",\n \"[0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C\",\n \"_pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where split can\",\n \"np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv =\",\n \"= search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 #\",\n \"types import GeneratorType import re import numpy as np import\",\n \"is empty now train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha':\",\n \"def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' # Ensures\",\n \"assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200,\",\n \"{re.sub('_score$', '_accuracy', k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$',\",\n \"attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False)\",\n \"search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param = foo_param def fit(self,\",\n \"interface. Return the index of a model that has the\",\n \"test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold() svc =\",\n \"error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i)))\",\n \"fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor to\",\n \"Check tie breaking strategy - # Check that there is\",\n \"5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]))\",\n \"tests when returned 'best_index_' is not integer. \\\"\\\"\\\" return None\",\n \"0, 0, 1, 1, 1] clf = LinearSVC(random_state=0) gs =\",\n \"has no score function. clf = LinearSVC(random_state=0) X, y =\",\n \"is available only after ' 'refitting on the best parameters'\",\n \"cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,\",\n \"consistency of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':\",\n \"y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not available\",\n \"StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from\",\n \"X[2, :] = np.nan y = [0, 0, 1, 1,\",\n \"k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV):\",\n \"<= 1) for k in score_keys if 'time' not in\",\n \"random_state=0) param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf\",\n \"= search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results,\",\n \"np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X = lambda\",\n \"y) cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys,\",\n \"result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function tests when\",\n \"return_train_score=True) gs.fit(X, y) # As the first two param settings\",\n \"10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy')\",\n \"Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1,\",\n \"'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_\",\n \"# caught by grid_search (expected behavior), and this would cause\",\n \"scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError so this\",\n \"== (n_cand,) for key in param_keys + score_keys) def test_grid_search_cv_results():\",\n \"0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4\",\n \"0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As\",\n \"expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree',\",\n \"= make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf,\",\n \"one fold and a score of 1/3 on # the\",\n \"i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score =\",\n \"est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing():\",\n \"def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV\",\n \"the predict_proba works when loss is specified # as one\",\n \"'best_index_' is out of bounds. \\\"\\\"\\\" return out_bound_value X, y\",\n \"Scipy 0.12's stats dists do not accept seed, hence we\",\n \"GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y)\",\n \"n_search_iter = 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma':\",\n \"regressor_results[1] is not grid_search.best_estimator_ # check that we didn't modify\",\n \"clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove\",\n \"the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a\",\n \"test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an error with\",\n \"FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be removed on\",\n \"def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None,\",\n \"with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y) def\",\n \"= {\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 =\",\n \"want to break without an explicit deprecation cycle and API\",\n \"cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) #\",\n \"(grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ # Check tie\",\n \"for param in param_keys) assert all(cv_results[key].dtype == object for key\",\n \"err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X,\",\n \"[[1], [2], [3], [4], [5]], [0, 0, 0, 0, 1]\",\n \"y = [0, 1, 1, 0] gs = GridSearchCV(svc, {'C':\",\n \"as np import scipy.sparse as sp import pytest from sklearn.utils.fixes\",\n \"y) == grid_search_no_score.score(X, y) # giving no scoring function raises\",\n \"y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,\",\n \"= ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in sampler]\",\n \"without following # proper deprecation cycle. fit_params = { 'tuple_of_arrays':\",\n \"search part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',\",\n \"if score == \\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score\",\n \"raises a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def __init__(self,\",\n \"scalar values in fit_params, for instance in LightGBM # that\",\n \"sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]},\",\n \"y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): # test grid-search\",\n \"not # caught by grid_search (expected behavior), and this would\",\n \"= GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter should not\",\n \"a model that has the least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec'\",\n \"with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test, scorer,\",\n \"42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha':\",\n \"parameter=None): self.parameter = parameter def fit(self, X, y): assert not\",\n \"of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,\",\n \"# Check if a one time iterable is accepted as\",\n \"searches, use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list,\",\n \"[1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test\",\n \"caught and warnings raised instead. If # refit was done,\",\n \"def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation catches the errors when\",\n \"4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test\",\n \"1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma',\",\n \"= search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure the\",\n \"hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba`\",\n \"import partial import pickle import sys from types import GeneratorType\",\n \"[{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]},\",\n \"for the following parameters. But this abuse is common in\",\n \"an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV])\",\n \"== [{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def\",\n \"param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] )\",\n \"GridSearchCV(p, {'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier\",\n \"in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if the\",\n \"assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']: assert\",\n \"!= 0` for this dummy clf assert clf.best_index_ != 0\",\n \"\\\"\\\"\\\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y =\",\n \"3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict(): #\",\n \"n_samples = 100 n_splits = 5 X, y = make_classification(n_samples=n_samples,\",\n \"so this is what we look for. assert_raises(ValueError, gs.fit, X,\",\n \"* 5 + [1] * 5) Cs = [.1, 1,\",\n \"import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC,\",\n \"removed on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc\",\n \"self.foo_param predict_proba = predict predict_log_proba = predict decision_function = predict\",\n \"assert (points == set((\\\"bar\\\", x, \\\"foo\\\", y) for x, y\",\n \"SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that\",\n \"Test refit=callable, which adds flexibility in identifying the \\\"best\\\" estimator.\",\n \"not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) ==\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 =\",\n \"random_state=0) assert [x for x in sampler] == [x for\",\n \"Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert\",\n \"X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params\",\n \"method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X,\",\n \"y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def\",\n \"GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit,\",\n \"y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not have\",\n \"50 n_splits = 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred =\",\n \"y) score_auc = search_auc.score(X, y) # ensure the test is\",\n \"3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search():\",\n \"param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y)\",\n \"params, train, test, scorer, verbose=False) def test_pickle(): # Test that\",\n \"has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_\",\n \"param_dict) param_dict = {\\\"C\\\": []} clf = SVC() assert_raise_message( ValueError,\",\n \"expected_warning, list, sampler) # degenerates to GridSearchCV if n_iter the\",\n \"Should raise an error upon multimetric scorer assert_raise_message(ValueError, \\\"For evaluating\",\n \"np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1]\",\n \"cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3)\",\n \"(\\\"fit_grid_point is deprecated in version 0.23 \\\" \\\"and will be\",\n \"fit_grid_point( X, y, clone(svc), params, train, test, scorer, verbose=False) est\",\n \"is correctly passed to the cv object rng = np.random.RandomState(0)\",\n \"CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3)\",\n \"test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0, 0, 0,\",\n \"is deprecated in version 0.23 \\\" \\\"and will be removed\",\n \"-f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C':\",\n \"= y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >=\",\n \"= random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train,\",\n \"from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting\",\n \"training kernel matrix K_train = np.zeros((10, 20)) y_train = np.ones((10,\",\n \"of bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function\",\n \"fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin):\",\n \"= GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) #\",\n \"test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X, y =\",\n \"cv=3) # When the estimator is not fitted, `predict_proba` is\",\n \"assert search.best_score_ == 42 def test_param_sampler(): # test basic properties\",\n \"_FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X,\",\n \"6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly',\",\n \"match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\\\\\d+ '\",\n \"s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf\",\n \"Check if a one time iterable is accepted as a\",\n \"GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from\",\n \"make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision',\",\n \"5 * 3 * 2).reshape(10, 5, 3, 2) y_3d =\",\n \"to get a list of keys in # clf.cv_results_. X,\",\n \"== pytest.approx(np.std(test_cv_scores)) # For the train scores, we do not\",\n \"[1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises\",\n \"__init__(self, a=None): self.a = a def fit(self, X, y, r=None):\",\n \"y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv =\",\n \"[0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ ==\",\n \"# Ensures that a warning is raised and score reset\",\n \"Iterable, Sized from io import StringIO from itertools import chain,\",\n \"2]}, {'min_samples_split': [5, 10]}])) # Using regressor to make sure\",\n \"cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message\",\n \"X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for\",\n \"(all(cv_results[k] >= 0) for k in score_keys if k !=\",\n \"\\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function tests when returned\",\n \"as list in GridSearchCV X = np.arange(100).reshape(10, 10) y =\",\n \"clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not\",\n \"with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X,\",\n \"\\\"Fit parameter spam has length 1; expected\\\" with pytest.raises(AssertionError, match=err_msg):\",\n \"list in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0]\",\n \"the expected ValueError [u for u in cv] # cv\",\n \"0.23 \\\" \\\"and will be removed in version 0.25\\\") params\",\n \"= RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not\",\n \"from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster\",\n \"{'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})]\",\n \"ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): #\",\n \"sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in\",\n \"hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0])\",\n \"a weighted mean irrespective of # i.i.d. or not assert\",\n \"{'C': [0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure\",\n \"parameter def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise\",\n \"errors caused by fits # to individual folds will be\",\n \"SVC(), 'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X,\",\n \"= make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for score\",\n \"# XXX: results['params'] is a list :| results[k] = np.asanyarray(results[k])\",\n \"test loss where greater is worse def f1_loss(y_true_, y_pred_): return\",\n \"is what we look for. assert_raises(ValueError, gs.fit, X, y) def\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:])\",\n \"list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler,\",\n \"n_features=100, random_state=0) param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]}\",\n \"be used for model selection alone without refitting clf =\",\n \"sure `predict_proba` is not available when setting loss=['hinge'] # in\",\n \"training kernel matrix corresponding to the linear kernel K_train =\",\n \"* 5 + [1] * 5) clf = CheckingClassifier( check_y=lambda\",\n \"{'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr\",\n \"3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score',\",\n \"SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert\",\n \"cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for\",\n \"raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator,\",\n \"that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is\",\n \"structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check\",\n \"-1) X[2, :] = np.nan y = [0, 0, 1,\",\n \"folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1,\",\n \"n_samples = 50 n_splits = 2 X, y = make_classification(n_samples=n_samples,\",\n \"1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError\",\n \"# estimator requires score method to compete with scoring parameter\",\n \"clf = LinearSVC(random_state=0) grid = {'C': [1]} group_cvs = [LeaveOneGroupOut(),\",\n \"ensure the test is sane assert score_auc < 1.0 assert\",\n \"hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X\",\n \"estimator n_features = 4 X, y = make_classification(n_features=n_features) gbdt =\",\n \"that we do # not want to break without an\",\n \"for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV,\",\n \"of parameters %d is smaller ' 'than n_iter=%d. Running %d\",\n \"search_auc.score(X, y) # ensure the test is sane assert score_auc\",\n \"is worse def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss =\",\n \"in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y)\",\n \"2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates =\",\n \"cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores\",\n \"in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits =\",\n \"def test_grid_search_precomputed_kernel(): # Test that grid search works when the\",\n \"# that are expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score'\",\n \"multimetric scorer assert_raise_message(ValueError, \\\"For evaluating multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate\",\n \"KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from\",\n \"with only one point. clf = MockClassifier() grid_search = GridSearchCV(clf,\",\n \"res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i,\",\n \"than total parameter space params = [{'first': [0, 1], 'second':\",\n \"refit=True) clf.fit(X, y) # Ensure that `best_index_ != 0` for\",\n \"10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using\",\n \"# split dataset into two folds that are not iid\",\n \"any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):\",\n \"linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf\",\n \"= 1. else: score = 0. return score def get_params(self,\",\n \"that `best_index_ != 0` for this dummy clf assert clf.best_index_\",\n \"{} for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' %\",\n \"# The CheckingClassifier generates an assertion error if # a\",\n \"tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass model =\",\n \"tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0\",\n \"sure the predict_proba works when loss is specified # as\",\n \"in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf\",\n \"([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a dict\",\n \"this used to break with # sparse SVMs. X =\",\n \"'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples)\",\n \"for s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std =\",\n \"a large grid params = {'a': range(10), 'b': range(10), 'c':\",\n \"estimator. Non-regression test for issue #13920. \\\"\\\"\\\" est = BaseEstimator()\",\n \"y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): # check cross_val_score\",\n \"# If True, for multi-metric pass refit='accuracy' if refit: probability\",\n \"among multi-metric and # single metric grid search results. np.testing.assert_equal\",\n \"n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf =\",\n \"test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ], [2, ],\",\n \"grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert\",\n \"check support for scalar values in fit_params, for instance in\",\n \"an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent\",\n \"KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import recall_score from\",\n \"= SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for parameter (C) needs\",\n \"import scipy.sparse as sp import pytest from sklearn.utils.fixes import sp_version\",\n \"5 + [1] * 5) clf = CheckingClassifier( check_X=lambda x:\",\n \"sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import\",\n \"0) assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time', 'std_score_time']:\",\n \"train scores must also be # same as long as\",\n \"error messages for refit in [\\\"\\\", 5, True, 'recall', 'accuracy']:\",\n \"param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI\",\n \"grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert\",\n \"test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO: Clearly this\",\n \"class MockClassifier: \\\"\\\"\\\"Dummy classifier to test the parameter search algorithms\\\"\\\"\\\"\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip\",\n \"self.foo_param > 1: score = 1. else: score = 0.\",\n \"for those fits # that are expected to fail. def\",\n \"y) # Ensure that `best_index_ != 0` for this dummy\",\n \"param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert\",\n \"predict predict_log_proba = predict decision_function = predict def score(self, X=None,\",\n \"setting for score_type in ('train', 'test'): per_param_scores = {} for\",\n \"fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples ==\",\n \"searcher = SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\\\"raise\\\"\",\n \"for search in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_\",\n \"Test scorer names result_keys = list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\")\",\n \"'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score',\",\n \"correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \\\"roc_auc\\\": dec =\",\n \"space params = [{'first': [0, 1], 'second': ['a', 'b', 'c']},\",\n \"= Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param':\",\n \"'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False,\",\n \"dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items() if not\",\n \"in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv,\",\n \"implementation of BaseSearchCV has the _pairwise property which matches the\",\n \"[3], [4], [5]], [0, 0, 0, 0, 1] estimators =\",\n \"both dense and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100,\",\n \"\\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is not a\",\n \"y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid\",\n \"Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results =\",\n \"assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise():\",\n \"roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from\",\n \"shuffle=True), return_train_score=True) gs.fit(X, y) # As the first two param\",\n \"def set_params(self, **params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC):\",\n \"errors when 'best_index_' returns an invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results):\",\n \"y[:180])) # test loss where greater is worse def f1_loss(y_true_,\",\n \"for est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3)\",\n \"from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection\",\n \"total space of parameters %d is smaller ' 'than n_iter=%d.\",\n \"of the two cv_results dicts np.testing.assert_equal({k: v for k, v\",\n \"property must match estimator\\\" for _pairwise_setting in [True, False]: setattr(est,\",\n \"n_iter=7) samples = list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param():\",\n \"provided # validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train,\",\n \"# Test that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10,\",\n \"will be generated for the 2nd and subsequent cv.split calls.\",\n \"getattr(grid_search, fn_name), X) # Test that an invalid refit param\",\n \"is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv',\",\n \"in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k, v\",\n \"a list of keys in # clf.cv_results_. X, y =\",\n \"= StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param ==\",\n \"are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we\",\n \"GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv)\",\n \"8 for values in ParameterGrid(params): assert values in samples #\",\n \"and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] ==\",\n \"when 'best_index_' returns an out of bound result. \\\"\\\"\\\" def\",\n \"searcher has no classes_ attribute before it's fit grid_search =\",\n \"assert clf.best_index_ == 0 # Ensure `best_score_` is disabled when\",\n \"for train, test in cv.split(X, y): this_scores, this_params, n_test_samples =\",\n \"bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function tests\",\n \"y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv =\",\n \"check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and\",\n \"predict_proba works when loss is specified # as one of\",\n \"np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned to the\",\n \"_fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search ) # NOTE:\",\n \"param_grid = {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs\",\n \"or numpy array, but got (<class 'str'>).\\\" \\\" Single values\",\n \"to make sure cv.split is not called once per param\",\n \"import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut\",\n \"'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params,\",\n \"if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i]\",\n \"= list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i\",\n \"\\\"\\\"\\\"Compare refit multi-metric search methods with single metric methods\\\"\\\"\\\" assert\",\n \"base estimator. Non-regression test for issue #13920. \\\"\\\"\\\" est =\",\n \"expected to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for\",\n \"search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \\\"refit_time_\\\")\",\n \"\\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6 # loop\",\n \"gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded\",\n \"per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':\",\n \"is not grid_search.best_estimator_ # check that we didn't modify the\",\n \"'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search\",\n \"attr in dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and\",\n \"assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do not have a\",\n \"cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_,\",\n \"def test_trivial_cv_results_attr(): # Test search over a \\\"grid\\\" with only\",\n \"only # once if ``cv.split`` is called once per param\",\n \"check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_ contains all\",\n \"C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys =\",\n \"in # popular third-party libraries and we should tolerate this\",\n \"grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now without a score, and without\",\n \"Check that succeeded estimators have lower ranks assert ranks[0] <=\",\n \"'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than time\",\n \"'_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert\",\n \"list \\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is not\",\n \"y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self,\",\n \"def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test the search.cv_results_ contains\",\n \"match estimator\\\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting)\",\n \"p['c']) for p in samples] assert len(set(hashable_samples)) == 99 #\",\n \"list(sampler) assert len(samples) == 8 for values in ParameterGrid(params): assert\",\n \"y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring\",\n \"3]}, cv=2, error_score=\\\"raise\\\" ) # The CheckingClassifier generates an assertion\",\n \"# When the estimator is not fitted, `predict_proba` is not\",\n \"param_search) X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_\",\n \"# check that parameters that are estimators are cloned before\",\n \"check that parameters that are estimators are cloned before fitting\",\n \"{'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y) assert\",\n \"this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params, train, test,\",\n \"scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search\",\n \"not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y\",\n \"y = np.array([0] * 5 + [1] * 5) clf\",\n \"best estimator contains the right value for foo_param clf =\",\n \"= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning,\",\n \"= {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True)\",\n \"do not exactly respect the scikit-learn API contract but that\",\n \"== 99 hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c']) for\",\n \"the behaviour of the grid search part gs = GridSearchCV(clf,\",\n \"key in param_keys + score_keys) def test_grid_search_cv_results(): X, y =\",\n \"= GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 )\",\n \"_pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return\",\n \"== .1 assert search.best_score_ == 42 def test_param_sampler(): # test\",\n \"3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used assert\",\n \"from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier\",\n \"y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test\",\n \"def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =\",\n \"the first two param settings (C=0.1) and the next two\",\n \"10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_)\",\n \"assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y = [[1],\",\n \"from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics\",\n \"method.\\\"\\\"\\\" @property def score(self): raise AttributeError X = np.array([[-1, -1],\",\n \"clf = LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf,\",\n \"p in samples] assert len(set(hashable_samples)) == 99 # doesn't go\",\n \"this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): #\",\n \"X, y = make_classification(random_state=42) model.fit(X, y, r=42) assert model.best_estimator_.r_ ==\",\n \"make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search =\",\n \"{'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned\",\n \"an out of bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A\",\n \"sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import\",\n \"self.a = a def fit(self, X, y, r=None): self.r_ =\",\n \"[.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf,\",\n \"'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time',\",\n \"predict_proba = predict predict_log_proba = predict decision_function = predict def\",\n \"grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y)\",\n \"refit in (True, False): random_searches = [] for scoring in\",\n \"s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for\",\n \"test.size # Should raise an error upon multimetric scorer assert_raise_message(ValueError,\",\n \"for key in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin()\",\n \"[[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0)\",\n \"{'classifier__foo_param': [1, 2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that\",\n \"def __init__(self, a=None): self.a = a def fit(self, X, y,\",\n \"# to test hyperparameter search on user-defined classifiers. class MockClassifier:\",\n \"% key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): #\",\n \"refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X,\",\n \"equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can\",\n \"cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X)\",\n \"def test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe types\",\n \"3rd parameter will not be evaluated as no train/test indices\",\n \"[True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]})\",\n \"X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C':\",\n \"the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params ==\",\n \"F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]},\",\n \"respect the scikit-learn API contract but that we do #\",\n \"in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is\",\n \"{'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 =\",\n \"# As the first two param settings (C=0.1) and the\",\n \"cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in\",\n \"from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors\",\n \"BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search\",\n \"r def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search)\",\n \"that grid search works with both dense and sparse matrices\",\n \"'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand\",\n \"clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value,\",\n \"NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)\",\n \"cv where split can be called only # once if\",\n \"r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha':\",\n \"samples = list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): #\",\n \"uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance\",\n \"rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER def\",\n \"X, y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,\",\n \"for scalar values in fit_params # non-regression test for: #\",\n \"error messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'):\",\n \"= lambda x: x.shape[1:] == (5, 3, 2) check_y =\",\n \"for k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi,\",\n \"scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert\",\n \"param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_ n_candidates\",\n \"test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \"KFold() for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv,\",\n \"[-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1,\",\n \"if the keys # are present assert all((np.all(cv_results_multi[k] <= 1)\",\n \"fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results,\",\n \"[4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert\",\n \"for cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) #\",\n \"= 0. return score def get_params(self, deep=False): return {'foo_param': self.foo_param}\",\n \"shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in\",\n \"CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"chain, product from functools import partial import pickle import sys\",\n \"def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO: Clearly\",\n \"score and that it gives the correct result assert grid_search.score(X,\",\n \"random_state=42) # The two C values are close enough to\",\n \"'accuracy', 'recall'): # If True, for multi-metric pass refit='accuracy' if\",\n \"{'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make sure it\",\n \"structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates =\",\n \"GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned\",\n \"`refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation\",\n \"assert params == this_params assert n_test_samples == test.size # Should\",\n \"['log'], } X = np.arange(24).reshape(6, -1) y = [0, 0,\",\n \"# test loss where greater is worse def f1_loss(y_true_, y_pred_):\",\n \"non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs =\",\n \"== 2)[0][::2]] = 0 # this leads to perfect classification\",\n \"empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert\",\n \"array, but got (<class 'str'>).\\\" \\\" Single values need to\",\n \"3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs\",\n \"# assert that this raises an error with pytest.raises(ValueError, match='cv.split\",\n \"cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0,\",\n \"candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y =\",\n \"DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans from\",\n \"rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE\",\n \"on_error == 'raise' raises the error X, y = make_classification(n_samples=20,\",\n \"n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True)\",\n \"that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y\",\n \"[\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c']) for p in samples] assert\",\n \"clf.fit(X[train], y[train]) if score == \\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test]))\",\n \"numpy as np import scipy.sparse as sp import pytest from\",\n \"not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype ==\",\n \"0, 3, base=0.1)) for refit in (True, False): random_searches =\",\n \"common in # popular third-party libraries and we should tolerate\",\n \"long as the same train/test indices are generated for all\",\n \"have lower ranks assert ranks[0] <= 2 and ranks[1] <=\",\n \"1 # create bad cv cv = BrokenKFold(n_splits=3) train_size =\",\n \"GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y)\",\n \"return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in\",\n \"gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}],\",\n \"('The total space of parameters %d is smaller ' 'than\",\n \"multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y, svc,\",\n \"assert cv.best_score_ >= 0 # compute the test kernel matrix\",\n \"cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): #\",\n \"create bad cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge\",\n \"Use global X, y # create cv cv = KFold(n_splits=3).split(X)\",\n \"ValueError(\\\"Failing classifier failed as required\\\") def predict(self, X): return np.zeros(X.shape[0])\",\n \"as a cv parameter. n_samples = 100 n_splits = 5\",\n \"v for k, v in cv_results_multi.items() if not k.endswith('_time')}, {k:\",\n \"in score_keys) assert all(cv_results[key].dtype == np.float64 for key in score_keys\",\n \"make_blobs(n_samples=50, random_state=42) # The two C values are close enough\",\n \"dir(gscv): if (attr[0].islower() and attr[-1:] == '_' and attr not\",\n \"2, 3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)]\",\n \"= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test\",\n \"test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3\",\n \"key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time\",\n \"'hinge'. assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) #\",\n \"y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with\",\n \"assert list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid,\",\n \"assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV if n_iter\",\n \"TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x,\",\n \"grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict(): # test grid-search with an\",\n \"precomputed metric\\\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\",\",\n \"Check warning only occurs in situation where behavior changed: #\",\n \"grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling on scoring grid_search.scoring\",\n \"GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs):\",\n \"@ignore_warnings def test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe\",\n \"ranks assert ranks[0] <= 2 and ranks[1] <= 2 #\",\n \"grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) #\",\n \"def get_n_splits(self, *args, **kw): return 1 # create bad cv\",\n \"parameter space params = [{'first': [0, 1], 'second': ['a', 'b',\",\n \"clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\")\",\n \":) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single metric evaluation unsupervised\",\n \"grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can call score\",\n \"on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def\",\n \"range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_\",\n \"score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def\",\n \"ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower\",\n \"correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when\",\n \"**kwargs) def fit(self, X, y=None, groups=None, **fit_params): return self #\",\n \"validate _pairwise is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed')\",\n \"in version 0.25\\\") params = {'C': 0.1} train, test =\",\n \"irrespective of # i.i.d. or not assert train_mean == pytest.approx(1)\",\n \"evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5,\",\n \"X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points\",\n \"assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X\",\n \"== \\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score,\",\n \"y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C\",\n \"grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test)\",\n \"test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer =\",\n \"[5, 10]}]) results = mycv.cv_results_ check_results(results, gscv) for attr in\",\n \"in case of ties old_stdout = sys.stdout sys.stdout = StringIO()\",\n \"pickle import sys from types import GeneratorType import re import\",\n \"correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) #\",\n \"cv=cv) # Should not raise an error gs.fit(X, y) def\",\n \"y) cv_results = search.cv_results_ # Check if score and timing\",\n \"mean cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search =\",\n \"be careful not to break support for those without following\",\n \"def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric search methods with\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def\",\n \"cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as a cv\",\n \"= search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert\",\n \"grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid,\",\n \"Pass X as list in GridSearchCV X = np.arange(100).reshape(10, 10)\",\n \"= make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid =\",\n \"X, y = make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0)\",\n \"as long as the same train/test indices are generated for\",\n \"[0, 1, 1, 0] gs = GridSearchCV(svc, {'C': [0, 1]},\",\n \"assert 0 <= sample[\\\"C\\\"] <= 1 # test that repeated\",\n \"can be pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':\",\n \"RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for\",\n \"= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X,\",\n \"y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ ==\",\n \"p = 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params,\",\n \"score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc =\",\n \"make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False)\",\n \"search.fit(X, y) cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search,\",\n \"assert C == C2 # Smoke test the score #\",\n \"est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with\",\n \"{\\\"C\\\": [0, 1, 2, 3, 4, 5, 6, 7, 8,\",\n \"TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"{\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for\",\n \"recommendations for implementing early stopping with a user provided #\",\n \"of the following two estimators inherit from BaseEstimator, # to\",\n \"used n_splits = 3 clf = LinearSVC(random_state=0) X, y =\",\n \"# Test with grid search cv for est in estimators:\",\n \"# make sure it selects the smallest parameter in case\",\n \"the parameter grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_')\",\n \"else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype\",\n \"def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(), param_search ) #\",\n \"'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not\",\n \"% s][0] for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0]\",\n \"from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone from\",\n \"0.2]}, cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a\",\n \"= a def fit(self, X, y, r=None): self.r_ = r\",\n \"multi-metric pass refit='accuracy' if refit: probability = True refit =\",\n \"# Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,\",\n \"cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of BaseSearchCV has\",\n \"np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i]\",\n \"messages for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError,\",\n \"n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y)\",\n \"score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and\",\n \"y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(),\",\n \"/ 3.]) assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used\",\n \"this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params, train,\",\n \"def predict(self, T): return T.shape[0] def transform(self, X): return X\",\n \"for p in samples] assert len(set(hashable_samples)) == 99 # doesn't\",\n \"range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier()\",\n \"point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3)\",\n \"y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning\",\n \"K_train = np.zeros((10, 20)) y_train = np.ones((10, )) clf =\",\n \"Ensure that `best_index_ != 0` for this dummy clf assert\",\n \"* 5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs'])\",\n \"cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric():\",\n \"1] clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y)\",\n \"not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make sure\",\n \"[1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in\",\n \"kernel matrix K_train = np.zeros((10, 20)) y_train = np.ones((10, ))\",\n \"GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto')\",\n \"that has no score function. clf = LinearSVC(random_state=0) X, y\",\n \"a model that has the least `mean_test_score`. \\\"\\\"\\\" # Fit\",\n \"3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): #\",\n \"# as one of the parameters in the param_grid. param_grid\",\n \"contract but that we do # not want to break\",\n \"hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features\",\n \"disabled on estimator. X = np.arange(20).reshape(5, -1) y = [0,\",\n \"np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] ) train_mean\",\n \"cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def\",\n \"assert np.mean(y_pred == y_test) >= 0 # test error is\",\n \"if the search `cv_results`'s array are of correct types cv_results\",\n \"CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate):\",\n \"search grid_search_no_score.fit(X, y) # check that best params are equal\",\n \"tests when returned 'best_index_' is out of bounds. \\\"\\\"\\\" return\",\n \"identical with precomputed metric\\\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize(\",\n \"# check that we didn't modify the parameter grid that\",\n \"estimator.bandwidth == .1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1,\",\n \"the \\\" \\\"parameter refit must be set to a scorer\",\n \"fitted, `predict_proba` is not available as the # loss is\",\n \"[0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X =\",\n \"1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}\",\n \"LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\\\"fit_grid_point is deprecated in\",\n \"check if groups is correctly passed to the cv object\",\n \"error if # a parameter is missing or has length\",\n \"enough to give similar models # which would result in\",\n \"from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation\",\n \"42 def test_param_sampler(): # test basic properties of param sampler\",\n \"= parameter def fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER:\",\n \"grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results =\",\n \"== FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed as required\\\") def predict(self,\",\n \"trivial X and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key]\",\n \"y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C ==\",\n \"predict_proba when disabled on estimator. X = np.arange(20).reshape(5, -1) y\",\n \"warnings raised instead. If # refit was done, then an\",\n \"assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim ==\",\n \"should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def\",\n \"if ``cv.split`` is called once per param setting in GridSearchCV.fit\",\n \"be removed in version 0.25\\\") params = {'C': 0.1} train,\",\n \"MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)},\",\n \"refit=True) clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which adds\",\n \"X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType)\",\n \"est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i\",\n \"test_fit_grid_point as the function will be removed on 0.25 @ignore_warnings(category=FutureWarning)\",\n \"= roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as\",\n \"in the # form of a precomputed kernel matrix X_,\",\n \"= cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 # test\",\n \"1e-1]}, cv=cv, n_jobs=4) # assert that this raises an error\",\n \"[0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C\",\n \"1 # test that repeated calls yield identical parameters param_distributions\",\n \"make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C':\",\n \"y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\":\",\n \"all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for k in\",\n \"Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i]\",\n \"any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype == np.float64\",\n \"uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for\",\n \"of time.time in windows is not high # enough for\",\n \"X, y, clone(svc), params, train, test, scorer, verbose=False) est =\",\n \"in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys)\",\n \"grid search returns an error with a non-square precomputed #\",\n \"not the case for the following parameters. But this abuse\",\n \"each score differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100,\",\n \"Test that grid search returns an error with a non-square\",\n \"[1, 2, 3]}, cv=2, error_score=\\\"raise\\\" ) # The CheckingClassifier generates\",\n \"]] y = [0, 1, 1, 0] gs = GridSearchCV(svc,\",\n \"[0, 0, 1, 1, 1] clf = SVC(probability=False) gs =\",\n \"~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score':\",\n \"the training kernel matrix corresponding to the linear kernel K_train\",\n \"**kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}])\",\n \"cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter should not be None.\\\", gs.fit,\",\n \"(<class 'str'>).\\\" \\\" Single values need to be wrapped in\",\n \"s][0] for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std\",\n \"random_state=0) # compute the training kernel matrix corresponding to the\",\n \":] = np.nan y = [0, 0, 1, 1, 1]\",\n \"sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import\",\n \"one element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": []} clf\",\n \"len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] ==\",\n \"range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s\",\n \"cv_results = search.cv_results_ # Check tie breaking strategy - #\",\n \"n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type)\",\n \"that grid scores were set to zero as required for\",\n \"= KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed\",\n \"assert grid_search_no_score.best_params_ == grid_search.best_params_ # check that we can call\",\n \"estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth':\",\n \"for search in (gs, rs): search.fit(X, y) for key in\",\n \"[] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): # If\",\n \"1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x\",\n \"test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for\",\n \"p in grid2) assert (points == set((\\\"bar\\\", x, \\\"foo\\\", y)\",\n \"etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test exception handling\",\n \"def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in range(len(grid))]\",\n \"assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,)\",\n \"[\\\"\\\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring, the\",\n \"[1, 2, 3]) # Smoke test the score etc: grid_search.score(X,\",\n \"'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter =\",\n \"can find the right number :) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3\",\n \"enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \\\"f1\\\": correct_score =\",\n \"# test grid-search with an estimator without predict. # slight\",\n \"as no train/test indices # will be generated for the\",\n \"assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1,\",\n \"cv.score(X_[:180], y[:180])) # test loss where greater is worse def\",\n \"import recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer\",\n \"make sure each score differs clf = DecisionTreeRegressor(random_state=0) X, y\",\n \"candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' %\",\n \"2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]}\",\n \"verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be\",\n \"a list or numpy array, but got (<class 'str'>).\\\" \\\"\",\n \"grid params = {'a': range(10), 'b': range(10), 'c': range(10)} sampler\",\n \"0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert\",\n \"clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_` is\",\n \"test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) # The two C\",\n \"pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas import\",\n \"if sp_version >= (0, 16): param_distributions = {\\\"C\\\": uniform(0, 1)}\",\n \"KFold from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from\",\n \"multiple single metric cv_results from single metric grid/random search\\\"\\\"\\\" assert\",\n \"in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys,\",\n \"Regression test for bug in refitting # Simulates re-fitting a\",\n \"return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},\",\n \"clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3 ) grid_search.fit(X, y)\",\n \"estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert\",\n \"= len(gs.cv_results_['params']) # Ensure that grid scores were set to\",\n \"the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X,\",\n \"shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':\",\n \"works when the input features are given in the #\",\n \"random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X,\",\n \"tuple + chain transforms {\\\"a\\\": 1, \\\"b\\\": 2} to (\\\"a\\\",\",\n \"grid search will capture errors on data with different length\",\n \"y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): # Test that GSCV\",\n \"'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X,\",\n \"clf = MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param':\",\n \"score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score',\",\n \"scalar_param > 0 assert callable(callable_param) # The tuple of arrays\",\n \"# once if ``cv.split`` is called once per param setting\",\n \"version 0.25\\\") params = {'C': 0.1} train, test = next(StratifiedKFold().split(X,\",\n \"score differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4,\",\n \"return self def predict(self, T): return T.shape[0] def transform(self, X):\",\n \"1]]) y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert\",\n \"# np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss where\",\n \"= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf',\",\n \"X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv):\",\n \"be wrapped in a list\\\" \\\" with one element.\\\", GridSearchCV,\",\n \"grid_params = {'n_neighbors': [10]} # defaults to euclidean metric (minkowski\",\n \"== 2 def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1} clf =\",\n \"loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler\",\n \"sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from\",\n \"in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV,\",\n \"2).reshape(10, 5, 3, 2) y_3d = np.arange(10 * 7 *\",\n \"that GSCV can be used for model selection alone without\",\n \"8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x\",\n \"values in ParameterGrid(params): assert values in samples # test sampling\",\n \"r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X,\",\n \"= r def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(),\",\n \"not high # enough for the fit/score times to be\",\n \"bigger than total parameter space params = [{'first': [0, 1],\",\n \"random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ], C=[1, 10],\",\n \"invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function tests\",\n \"cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),\",\n \"raised instead. If # refit was done, then an exception\",\n \"= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test\",\n \"as grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler) assert\",\n \"score(self, X=None, Y=None): if self.foo_param > 1: score = 1.\",\n \"and subsequent cv.split calls. # This is a check to\",\n \"'std_fit_time'))) # Compare the keys, other than time keys, among\",\n \"where split can be called only # once if ``cv.split``\",\n \"a tie of their mean cv-scores param_grid = {'C': [1,\",\n \"{\\\"a\\\": 1, \\\"b\\\": 2} to (\\\"a\\\", 1, \\\"b\\\", 2) points\",\n \"test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc,\",\n \"assert values in samples # test sampling without replacement in\",\n \"# Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert\",\n \"assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke test the\",\n \"that the grid searcher has no classes_ attribute before it's\",\n \"Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]),\",\n \"in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train, test\",\n \"pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},\",\n \"The CheckingClassifier generates an assertion error if # a parameter\",\n \"and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if\",\n \"in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \\\"f1\\\": correct_score\",\n \"ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in sampler] ==\",\n \"= np.array([0] * 5 + [1] * 5) clf =\",\n \"same as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1,\",\n \"= np.nan y = [0, 0, 1, 1, 1] p\",\n \"Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std\",\n \"Neither of the following two estimators inherit from BaseEstimator, #\",\n \"disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type():\",\n \"go into infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a',\",\n \">= .9 assert C == C2 def test_grid_search_sparse_scoring(): X_, y_\",\n \"from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from\",\n \"def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ], [2,\",\n \"evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1,\",\n \"test_grid_search(): # Test that the best estimator contains the right\",\n \"\\\\ hasattr(grid_search, \\\"best_params_\\\") # Make sure the functions predict/transform etc\",\n \"'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert\",\n \"y, spam=np.ones(10)) err_msg = \\\"Fit parameter spam has length 1;\",\n \"'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available only after\",\n \"an error with pytest.raises(ValueError, match='No fits were performed. ' 'Was\",\n \"= n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y)\",\n \"param_dict = {\\\"C\\\": []} clf = SVC() assert_raise_message( ValueError, \\\"Parameter\",\n \"True, for multi-metric pass refit='accuracy' if refit: probability = True\",\n \"y) assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def\",\n \"make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},\",\n \"@ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1, ],\",\n \"score_keys) assert all(cv_results[key].dtype == np.float64 for key in score_keys if\",\n \"set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid\",\n \"== np.float64 for key in score_keys if not key.startswith('rank')) scorer_keys\",\n \"y_pred2) assert C == C2 # Smoke test the score\",\n \"# single metric grid search results. np.testing.assert_equal performs a #\",\n \"present assert all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time',\",\n \"# Pass X as list in GridSearchCV X_4d = np.arange(10\",\n \"test_fit_grid_point_deprecated as # fit_grid_point will be removed on 0.25 def\",\n \"random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params =\",\n \"assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def\",\n \"r'Parameter .* is not a dict or a list \\\\(0\\\\)'),\",\n \"to zero as required for those fits # that are\",\n \"iterate over the grid multiple times for i in range(2):\",\n \"= {'n_neighbors': [10]} # defaults to euclidean metric (minkowski p\",\n \"GridSearchCV with on_error != 'raise' # Ensures that a warning\",\n \"all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks\",\n \"assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert not hasattr(clf, \\\"predict_proba\\\")\",\n \"should not be None.\\\", gs.fit, X, y) gs.fit(X, y, groups=groups)\",\n \"assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float)\",\n \"KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X,\",\n \"with one element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": np.ones((3,\",\n \"keys # are present assert all((np.all(cv_results_multi[k] <= 1) for k\",\n \"an error with a non-square precomputed # training kernel matrix\",\n \"the scikit-learn API contract but that we do # not\",\n \"_pairwise property of its estimator. This test make sure _pairwise\",\n \"integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def\",\n \"X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\\\"C\\\": [1.0],\",\n \"SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): #\",\n \"RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X,\",\n \"GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_')\",\n \"is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores))\",\n \"1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]},\",\n \"{'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search\",\n \"property which matches the _pairwise property of its estimator. This\",\n \"[GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y =\",\n \"assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation\",\n \"that are not iid # first one contains data of\",\n \"= SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range():\",\n \"= GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def\",\n \"= SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not\",\n \"== 0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER))\",\n \"assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups():\",\n \"values in fit_params, for instance in LightGBM # that do\",\n \"from scipy.stats import bernoulli, expon, uniform from sklearn.base import BaseEstimator,\",\n \"clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): #\",\n \"= CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv =\",\n \"'test'): per_param_scores = {} for param_i in range(4): per_param_scores[param_i] =\",\n \"' 'Was the CV iterator empty\\\\\\\\? ' 'Were there no\",\n \"param raises appropriate error messages for refit in [\\\"\\\", 5,\",\n \"== n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of BaseSearchCV has\",\n \"and attr[-1:] == '_' and attr not in {'cv_results_', 'best_estimator_',\",\n \"failed as required\\\") def predict(self, X): return np.zeros(X.shape[0]) def score(self,\",\n \"slight duplication of a test from KDE def custom_scoring(estimator, X):\",\n \"= 2 def __init__(self, parameter=None): self.parameter = parameter def fit(self,\",\n \"# Check if score and timing are reasonable, also checks\",\n \"1, 1, 0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2,\",\n \"n_jobs=4) # assert that this raises an error with pytest.raises(ValueError,\",\n \"[0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit():\",\n \"def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X = np.arange(20,\",\n \"0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) #\",\n \"2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings\",\n \"import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score\",\n \"be a list or numpy array, but got (<class 'int'>).\\\"\",\n \"[\\\"rbf\\\", \\\"linear\\\"] assert 0 <= sample[\\\"C\\\"] <= 1 # test\",\n \"n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def\",\n \"search_multi.refit return # search cannot predict/score without refit X, y\",\n \"[{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2,\",\n \"# which would result in a tie of their mean\",\n \"values in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class\",\n \"def test_grid_search_cv_splits_consistency(): # Check if a one time iterable is\",\n \"= [[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv':\",\n \"sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import\",\n \"x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check if\",\n \"be pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"= [0, 0, 1, 1, 1] clf = SVC(probability=False) gs\",\n \"= scorer(est, X[test], y[test]) # Test the return values of\",\n \"grid scores were set to zero as required for those\",\n \"score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError (when\",\n \"in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y\",\n \"np.float64 for key in score_keys if not key.startswith('rank')) scorer_keys =\",\n \"for x in sampler] if sp_version >= (0, 16): param_distributions\",\n \"y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba',\",\n \"\\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \\\"roc_auc\\\": dec\",\n \"the least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin()\",\n \"= fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_\",\n \"\\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y, svc, params, train, test,\",\n \"def test_X_as_list(): # Pass X as list in GridSearchCV X\",\n \"y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns an\",\n \"score = 0. return score def get_params(self, deep=False): return {'foo_param':\",\n \"= 'accuracy' if isinstance(scoring, tuple) else refit else: probability =\",\n \"def check_cv_results_array_types(search, param_keys, score_keys): # Check if the search `cv_results`'s\",\n \"refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error():\",\n \"3]) def test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4],\",\n \"the parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]},\",\n \"- self.foo_param predict_proba = predict predict_log_proba = predict decision_function =\",\n \"in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(),\",\n \"of its estimator. This test make sure _pairwise is delegated\",\n \"StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params in\",\n \"in refitting # Simulates re-fitting a broken estimator; this used\",\n \"parameter spam has length 1; expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X,\",\n \"scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42\",\n \"def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =\",\n \"LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that has no score method.\\\"\\\"\\\" @property\",\n \"== 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 #\",\n \"test_param_sampler(): # test basic properties of param sampler param_distributions =\",\n \"sklearn.datasets import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import\",\n \"keys, among multi-metric and # single metric grid search results.\",\n \"groups is correctly passed to the cv object rng =\",\n \"best parameters' % fn_name), getattr(grid_search, fn_name), X) # Test that\",\n \"v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit):\",\n \") grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X,\",\n \"n_iter the same as grid_size sampler = ParameterSampler(params, n_iter=8) samples\",\n \"search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k, v\",\n \"to euclidean metric (minkowski p = 2) clf = KNeighborsClassifier()\",\n \"shuffle=False, n_samples=80) # split dataset into two folds that are\",\n \"GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_,\",\n \"for attr in dir(gscv): if (attr[0].islower() and attr[-1:] == '_'\",\n \"one contains data of all 4 blobs, second only from\",\n \"2 def __init__(self, parameter=None): self.parameter = parameter def fit(self, X,\",\n \"2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search))\",\n \"n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we want\",\n \"make_scorer(accuracy_score) for params in ({'C': 0.1}, {'C': 0.01}, {'C': 0.001}):\",\n \"\\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests `refit=callable` interface.\",\n \"in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\",\",\n \"def test_grid_search_pipeline_steps(): # check that parameters that are estimators are\",\n \"= KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric evaluation unsupervised scoring =\",\n \"y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params =\",\n \"in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for\",\n \"clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError,\",\n \"% s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0)\",\n \"clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which adds flexibility\",\n \"[dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])]\",\n \"timing are reasonable, also checks if the keys # are\",\n \"X, Y): assert len(X) == len(Y) self.classes_ = np.unique(Y) return\",\n \"= np.zeros((10, 20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed')\",\n \"# FIXME remove test_fit_grid_point_deprecated as # fit_grid_point will be removed\",\n \"non-re-entrant cv where split can be called only # once\",\n \"groups is None) propagates to GridSearchCV # And also check\",\n \"features are given in the # form of a precomputed\",\n \"'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter,\",\n \"[search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] ) test_mean =\",\n \"= euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits)\",\n \"if score and timing are reasonable, also checks if the\",\n \"were set to zero as required for those fits #\",\n \"in a list\\\" \\\" with one element.\\\", GridSearchCV, clf, param_dict)\",\n \"enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection.tests.common import\",\n \"= make_blobs(n_samples=50, random_state=42) # The two C values are close\",\n \"LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score\",\n \"dummy clf with `refit=True` to get a list of keys\",\n \"estimator; this used to break with # sparse SVMs. X\",\n \"for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip():\",\n \"self def predict(self, T): return T.shape[0] def transform(self, X): return\",\n \"+ score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)\",\n \"instance in LightGBM # that do not exactly respect the\",\n \"= RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results =\",\n \"import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import StratifiedKFold\",\n \"correct scores are used n_splits = 3 clf = LinearSVC(random_state=0)\",\n \"if # a parameter is missing or has length !=\",\n \"contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys +\",\n \"= {\\\"random_state\\\": [0, None]} cv = KFold() for est in\",\n \"BaseEstimator, # to test hyperparameter search on user-defined classifiers. class\",\n \"0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0],\",\n \"assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4, 2],\",\n \"# Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180],\",\n \"= {'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution,\",\n \"sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions\",\n \"dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] =\",\n \"clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X, y =\",\n \"raises an error with pytest.raises(ValueError, match='No fits were performed. '\",\n \"1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2,\",\n \"param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2)\",\n \"not fitted, `predict_proba` is not available as the # loss\",\n \"as the function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def\",\n \"BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2,\",\n \"Test the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys\",\n \"compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric search methods with single\",\n \"key in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4,\",\n \"for parameter (C) needs to\\\" \\\" be a list or\",\n \"[1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth':\",\n \"folds will be caught and warnings raised instead. If #\",\n \"= search.scorer_.keys() if search.multimetric_ else ['score'] for key in scorer_keys:\",\n \"= [\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c']) for p in samples]\",\n \"= 9 grid_size = 8 expected_warning = ('The total space\",\n \"evaluation setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests\",\n \"score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test loss\",\n \"+ tuple(\\\"split%d_test_score\\\" % cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys,\",\n \"import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal\",\n \"search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index\",\n \"= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True)\",\n \"check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})\",\n \"assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from\",\n \"of bounds. \\\"\\\"\\\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4,\",\n \"error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results.\",\n \"As the first two param settings (C=0.1) and the next\",\n \"y = [0, 0, 1, 1, 1] clf = SVC(probability=False)\",\n \"random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]})\",\n \"it, this should cause the expected ValueError [u for u\",\n \"refit=False, error_score='raise') # FailingClassifier issues a ValueError so this is\",\n \"== 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)\",\n \"- # Check that there is a tie in the\",\n \"But this abuse is common in # popular third-party libraries\",\n \"from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model\",\n \"C values are close enough to give similar models #\",\n \"= grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train,\",\n \"np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) ==\",\n \"fn_name), getattr(grid_search, fn_name), X) # Test that an invalid refit\",\n \"y = [0, 0, 0, 1, 1, 1] clf =\",\n \"refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function tests when returned 'best_index_' is\",\n \"to be wrapped in a list\\\" \\\" with one element.\\\",\",\n \"def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =\",\n \"classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier to test the parameter search\",\n \"for foo_param clf = MockClassifier() grid_search = None grid_search =\",\n \"err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' +\",\n \"correctly passed to the cv object rng = np.random.RandomState(0) X,\",\n \"['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results\",\n \"len(grid2) == 6 # loop to assert we can iterate\",\n \"a non-square precomputed # training kernel matrix K_train = np.zeros((10,\",\n \"assert all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time', 'std_score_time',\",\n \"4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and FMS\",\n \"from types import GeneratorType import re import numpy as np\",\n \"\\\"\\\"\\\" A dummy function tests when returned 'best_index_' is out\",\n \"search.best_score_ == 42 def test_param_sampler(): # test basic properties of\",\n \"(s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3])\",\n \"fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight)\",\n \"__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def fit(self, X, y=None, groups=None,\",\n \"but got (<class 'int'>).\\\" \\\" Single values need to be\",\n \"np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]]\",\n \"== 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV,\",\n \"= cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv =\",\n \"[1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises\",\n \"not integer. \\\"\\\"\\\" return None X, y = make_classification(n_samples=100, n_features=4,\",\n \"n_iter=%d. Running %d iterations. For ' 'exhaustive searches, use GridSearchCV.'\",\n \"sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import\",\n \"delegated to the base estimator. Non-regression test for issue #13920.\",\n \"lambda x: x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X,\",\n \"keys in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42)\",\n \"attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv, attr)\",\n \"0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv =\",\n \"for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input,\",\n \"= { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42, }\",\n \"partial import pickle import sys from types import GeneratorType import\",\n \"than time keys, among multi-metric and # single metric grid\",\n \"and timing are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k]\",\n \"n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches)\",\n \"HistGradientBoostingClassifier from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following\",\n \"= Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search =\",\n \"[u for u in cv] # cv is empty now\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_) def test_grid_search_one_grid_point():\",\n \"duplication of a test from KDE def custom_scoring(estimator, X): return\",\n \"iterations. For ' 'exhaustive searches, use GridSearchCV.' % (grid_size, n_iter,\",\n \"that a fit search can be pickled clf = MockClassifier()\",\n \"`refit=callable` interface. Return the index of a model that has\",\n \"param_dict) def test_grid_search_sparse(): # Test that grid search works with\",\n \"[0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 =\",\n \"in GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] *\",\n \"# Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True,\",\n \"key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0]\",\n \"windows is not high # enough for the fit/score times\",\n \"hasattr(grid_search, \\\"best_params_\\\") # Make sure the functions predict/transform etc raise\",\n \"LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from\",\n \"in sampler] assert len(samples) == 10 for sample in samples:\",\n \"0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X,\",\n \"not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for\",\n \"import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier,\",\n \"3, 2) y_3d = np.arange(10 * 7 * 11).reshape(10, 7,\",\n \"do not take a weighted mean irrespective of # i.i.d.\",\n \"MockClassifier: \\\"\\\"\\\"Dummy classifier to test the parameter search algorithms\\\"\\\"\\\" def\",\n \"Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand)\",\n \"import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal\",\n \"be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y =\",\n \"random_state=42) n_splits = 3 n_search_iter = 30 params = [{'kernel':\",\n \"when loss is specified # as one of the parameters\",\n \"{\\\"C\\\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for\",\n \"# unofficially sanctioned tolerance for scalar values in fit_params #\",\n \"of a model that has the least `mean_test_score`. \\\"\\\"\\\" #\",\n \"0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc = LinearSVC(random_state=0)\",\n \"n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for\",\n \"# FIXME remove test_fit_grid_point as the function will be removed\",\n \"X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0,\",\n \"test_n_features_in(): # make sure grid search and random search delegate\",\n \"= {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf = SVC(gamma='auto')\",\n \"C3 = cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def\",\n \"# Should not raise an error gs.fit(X, y) def test_classes__property():\",\n \"greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180])\",\n \"ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) #\",\n \"in grid2) assert (points == set((\\\"bar\\\", x, \\\"foo\\\", y) for\",\n \"not called once per param # setting. np.testing.assert_equal({k: v for\",\n \"0 # Ensure `best_score_` is disabled when using `refit=callable` assert\",\n \"Pipeline([('regressor', LinearRegression())]) param_grid = {'regressor': [LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe,\",\n \"classes_ attribute without a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs},\",\n \">= 0) for k in score_keys if k != 'rank_test_score')\",\n \"' + k) def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y)\",\n \"# Scipy 0.12's stats dists do not accept seed, hence\",\n \"that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1],\",\n \"of param sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0,\",\n \"now and be careful not to break support for those\",\n \"metric methods\\\"\\\"\\\" assert search_acc.refit == refit if refit: assert search_multi.refit\",\n \"all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates)\",\n \"returns an error with a non-square precomputed # training kernel\",\n \"or not assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0)\",\n \"from BaseEstimator, # to test hyperparameter search on user-defined classifiers.\",\n \"= GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1)\",\n \"1, 1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf, {},\",\n \"('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if\",\n \"random_search.cv_results_['split%d_test_score' % i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled\",\n \"], [3, ], [4, ]] y = [0, 1, 1,\",\n \"params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ],\",\n \"1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates\",\n \"k in results: if not k.endswith('_time'): # XXX: results['params'] is\",\n \"v in cv_results_multi.items() if not k.endswith('_time')}, {k: v for k,\",\n \"= 30 # Scipy 0.12's stats dists do not accept\",\n \"MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame, Series)) except\",\n \"the _pairwise property of its estimator. This test make sure\",\n \"gs.cv_results_['rank_test_score'] # Check that succeeded estimators have lower ranks assert\",\n \"test ensures the equivalence of 'precomputed'. Non-regression test for issue\",\n \"Test search over a \\\"grid\\\" with only one point. clf\",\n \"types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame\",\n \"train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4,\",\n \"0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar\",\n \"per_param_scores = {} for param_i in range(4): per_param_scores[param_i] = list(\",\n \"= SVC(gamma='auto') cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf =\",\n \"u in cv] # cv is empty now train_size =\",\n \"single metric cv_results from single metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_\",\n \"[10]} # defaults to euclidean metric (minkowski p = 2)\",\n \"('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k):\",\n \"score_type in ('train', 'test'): per_param_scores = {} for param_i in\",\n \"as one of the parameters in the param_grid. param_grid =\",\n \"``cv.split`` is called once per param setting in GridSearchCV.fit #\",\n \"y) # Check warning only occurs in situation where behavior\",\n \"check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower() and attr[-1:]\",\n \"scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search =\",\n \"`mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y\",\n \"not seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg =\",\n \"tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return self def\",\n \"= fit_grid_point( X, y, clone(svc), params, train, test, scorer, verbose=False)\",\n \"for refit in (True, False): random_searches = [] for scoring\",\n \"i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\",\",\n \"= GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings()\",\n \"to the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train =\",\n \"X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\",\n \"y) assert not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test GridSearchCV\",\n \"{'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as # fit_grid_point\",\n \"exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,\",\n \"which matches the _pairwise property of its estimator. This test\",\n \"import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal\",\n \"got (<class 'str'>).\\\" \\\" Single values need to be wrapped\",\n \"key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50,\",\n \"{'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') #\",\n \"-1) y = [0, 0, 0, 1, 1, 1] clf\",\n \"y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]})\",\n \"single metric grid search results. np.testing.assert_equal performs a # deep\",\n \"s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0]\",\n \"KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(),\",\n \"the search `cv_results`'s array are of correct types cv_results =\",\n \"input features are given in the # form of a\",\n \"this dummy clf assert clf.best_index_ != 0 # Assert every\",\n \"and 3rd parameter will not be evaluated as no train/test\",\n \"object for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for\",\n \"k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for\",\n \"[1, 2, 3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0),\",\n \"cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds that\",\n \"{'C': 0.01}, {'C': 0.001}): for train, test in cv.split(X, y):\",\n \"refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for method in\",\n \"cv-scores param_grid = {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(),\",\n \"from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection\",\n \"return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf,\",\n \"its estimator. This test ensures the equivalence of 'precomputed'. Non-regression\",\n \"tolerance for scalar values in fit_params # non-regression test for:\",\n \"def test_y_as_list(): # Pass y as list in GridSearchCV X\",\n \"InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series)\",\n \"from collections.abc import Iterable, Sized from io import StringIO from\",\n \"np.testing.assert_equal performs a # deep nested comparison of the two\",\n \"not search_multi.refit return # search cannot predict/score without refit X,\",\n \"range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0,\",\n \"hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is\",\n \"split can be called only # once if ``cv.split`` is\",\n \"param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2\",\n \"only want to check that errors caused by fits #\",\n \"it gives the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X,\",\n \"from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking\",\n \"random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1,\",\n \"ensemble of multiple single metric cv_results from single metric grid/random\",\n \"classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})\",\n \"{'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5},\",\n \"s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in\",\n \"[search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] ) train_mean =\",\n \"is not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input,\",\n \"searcher has no classes_ attribute without a refit grid_search =\",\n \"that has the least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in cv_results\",\n \"isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1\",\n \"X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10]\",\n \"K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test)\",\n \"refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search\",\n \"y = make_blobs(n_samples=50, random_state=42) # The two C values are\",\n \"in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator,\",\n \"support for scalar values in fit_params, for instance in LightGBM\",\n \"= GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param\",\n \"in GridSearchCV X_4d = np.arange(10 * 5 * 3 *\",\n \"[{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search():\",\n \"predict decision_function = predict def score(self, X=None, Y=None): if self.foo_param\",\n \"to give similar models # which would result in a\",\n \"[0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute\",\n \"random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1,\",\n \"= GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only occurs\",\n \"param_distributions = {\\\"C\\\": [0, 1, 2, 3, 4, 5, 6,\",\n \"a def fit(self, X, y, r=None): self.r_ = r def\",\n \"iterator empty\\\\\\\\? ' 'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def\",\n \"import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split\",\n \"grid_search.best_estimator_ # check that we didn't modify the parameter grid\",\n \"out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable\",\n \"'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(),\",\n \"import DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity\",\n \"from sklearn.base import clone from sklearn.exceptions import NotFittedError from sklearn.datasets\",\n \"Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa from\",\n \"_pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise,\",\n \"= ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search =\",\n \"range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)): est.fit(X[train],\",\n \"search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with the ensemble of multiple\",\n \"The tuple of arrays should be preserved as tuple. assert\",\n \"'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4,\",\n \"grid_search.transform(X) # Test exception handling on scoring grid_search.scoring = 'sklearn'\",\n \"test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score == \\\"f1\\\":\",\n \"= ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) == 7\",\n \"2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier issues a ValueError so\",\n \"n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores\",\n \"assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid\",\n \"= pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2,\",\n \"classifier that has no score method.\\\"\\\"\\\" @property def score(self): raise\",\n \"Check if ValueError (when groups is None) propagates to GridSearchCV\",\n \"\\\"\\\"\\\" Test refit=callable, which adds flexibility in identifying the \\\"best\\\"\",\n \"est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test])\",\n \"FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,\",\n \"euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed,\",\n \"its estimator. This test make sure _pairwise is delegated to\",\n \"np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(\",\n \"return_train_score=True) gs2.fit(X, y) # Give generator as a cv parameter\",\n \"from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection\",\n \"with a randomized search for est in estimators: random_search =\",\n \"x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty\",\n \"for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score'\",\n \"refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X))\",\n \"kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred\",\n \"grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \\\"GridSearchCV\",\n \"1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C\",\n \"for s in range(n_splits))) for i, (train, test) in enumerate(cv.split(X,\",\n \"isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search = GridSearchCV(clf,\",\n \"5 + [1] * 5) Cs = [.1, 1, 10]\",\n \"grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]}, refit=False, cv=3\",\n \"= clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) #\",\n \"'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores =\",\n \"fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self,\",\n \"GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed():\",\n \"global X, y # create cv cv = KFold(n_splits=3).split(X) #\",\n \"the param_grid. param_grid = { 'loss': ['log'], } X =\",\n \"search.multimetric_ else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s' %\",\n \"param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_)\",\n \"KFold(n_splits=3).split(X) # pop all of it, this should cause the\",\n \"score of 1/3 on # the other # create \\\"cv\\\"\",\n \"y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): # check\",\n \">= 1) assert (all(cv_results[k] >= 0) for k in score_keys\",\n \"return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise'\",\n \"y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults\",\n \"= list(sampler) assert len(samples) == 99 hashable_samples = [\\\"a%db%dc%d\\\" %\",\n \"{'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search,\",\n \"in types: # X dataframe, y series X_df, y_ser =\",\n \"basic properties of ParameterGrid. params1 = {\\\"foo\\\": [1, 2, 3]}\",\n \"global X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return\",\n \"not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is\",\n \"<= 2 # Check that failed estimator has the highest\",\n \"assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim == 1 return self\",\n \"test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation catches the errors when 'best_index_'\",\n \"assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True,\",\n \"grid for parameter (C) needs to\\\" \\\" be a list\",\n \"changed: # estimator requires score method to compete with scoring\",\n \"the case for the following parameters. But this abuse is\",\n \"assert not hasattr(grid_search, 'classes_') # Test that the grid searcher\",\n \"search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1 # scores\",\n \"i.i.d. or not assert train_mean == pytest.approx(1) assert train_std ==\",\n \"y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test)\",\n \"compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits\",\n \"same train/test indices are generated for all # the cv\",\n \"= np.arange(24).reshape(6, -1) y = [0, 0, 0, 1, 1,\",\n \"cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in search_rec.cv_results_.items()}) #\",\n \"params = [{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third':\",\n \"k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k):\",\n \"'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits\",\n \"= len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for\",\n \"i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X,\",\n \"gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error ==\",\n \"= GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y)\",\n \"for issue #13920. \\\"\\\"\\\" est = BaseEstimator() attr_message = \\\"BaseSearchCV\",\n \"not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search,\",\n \"for k in score_keys if k != 'rank_test_score') assert (all(cv_results[k]\",\n \"y series X_df, y_ser = InputFeatureType(X), TargetType(y) def check_df(x): return\",\n \"n_iter=9) n_iter = 9 grid_size = 8 expected_warning = ('The\",\n \"called once per param # setting. np.testing.assert_equal({k: v for k,\",\n \"= [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy',\",\n \"to be non-zero for trivial X and y assert np.all(search.cv_results_[key]\",\n \"score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc =\",\n \"scorer names result_keys = list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") +\",\n \"GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) #\",\n \"GridSearchCV X = np.arange(100).reshape(10, 10) y = np.array([0] * 5\",\n \"from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from\",\n \"= SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit,\",\n \"must also be # same as long as the same\",\n \"scores were set to zero as required for those fits\",\n \"best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_ # check\",\n \"y[:train_size]) def test_n_features_in(): # make sure grid search and random\",\n \"y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_)\",\n \"0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters =\",\n \"= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180],\",\n \"y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert\",\n \"Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy')\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_ >=\",\n \"classifier that cannot be fit twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter\",\n \"on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0) svc =\",\n \"[DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv for est\",\n \"+ [1] * 5) clf = CheckingClassifier( check_X=lambda x: isinstance(x,\",\n \"range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check\",\n \"in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X, y)):\",\n \"= 0 mask[np.where(y == 2)[0][::2]] = 0 # this leads\",\n \"from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors\",\n \"in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y),\",\n \"svc, params, train, test, {'score': scorer}, verbose=True) # FIXME remove\",\n \"with pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X, y)\",\n \"cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates)\",\n \"list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for i, (train,\",\n \"not be evaluated as no train/test indices # will be\",\n \"1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1,\",\n \"isinstance(scoring, tuple) else refit else: probability = False clf =\",\n \"on refit and not # caught by grid_search (expected behavior),\",\n \"is 'hinge'. assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X)\",\n \"do not have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha':\",\n \"for k in score_keys if 'time' not in k and\",\n \"a dummy clf with `refit=True` to get a list of\",\n \"{'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs},\",\n \"'inconsistent results. Expected \\\\\\\\d+ ' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size])\",\n \"1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 #\",\n \"if n_iter is bigger than total parameter space params =\",\n \"scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X,\",\n \"== pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y\",\n \"gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for\",\n \"only occurs in situation where behavior changed: # estimator requires\",\n \"assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 =\",\n \"assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':\",\n \"X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter':\",\n \"gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ == n_features assert rs.n_features_in_\",\n \"{'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params):\",\n \"cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred\",\n \"[x for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): #\",\n \"and this would cause an # error in this test.\",\n \"= {\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert\",\n \"== [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)])\",\n \"preds_precomputed = cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not identical with precomputed\",\n \"# Use global X, y class BrokenKFold(KFold): def get_n_splits(self, *args,\",\n \"to get default estimator settings) empty = ParameterGrid({}) assert len(empty)\",\n \"assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50,\",\n \"'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results =\",\n \"= search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' %\",\n \"# form of a precomputed kernel matrix X_, y_ =\",\n \"@pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01,\",\n \"SearchCV( clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\\\"raise\\\" ) #\",\n \"over the grid multiple times for i in range(2): #\",\n \"generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y),\",\n \"GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from\",\n \"we only want to check that errors caused by fits\",\n \"isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\":\",\n \"* 5) Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0),\",\n \"def __init__(self, parameter=None): self.parameter = parameter def fit(self, X, y=None):\",\n \"array, but got (<class 'int'>).\\\" \\\" Single values need to\",\n \"Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4,\",\n \"sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for\",\n \"C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search\",\n \"cv.best_estimator_.C assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): #\",\n \"def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param\",\n \"scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs})\",\n \"y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores were\",\n \"GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1\",\n \"GridSearchCV with on_error == 'raise' raises the error X, y\",\n \"svc = LinearSVC(random_state=0) X = [[1, ], [2, ], [3,\",\n \"1)[0][::2]] = 0 mask[np.where(y == 2)[0][::2]] = 0 # this\",\n \"in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results,\",\n \"that repeated calls yield identical parameters param_distributions = {\\\"C\\\": [0,\",\n \"param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for search\",\n \"np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)\",\n \"all((np.all(cv_results_multi[k] <= 1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time',\",\n \"the same as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores,\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert\",\n \"# https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a =\",\n \"available when setting loss=['hinge'] # in param_grid param_grid = {\",\n \"assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert 0 <= sample[\\\"C\\\"] <=\",\n \"grid_search.fit(X, y) # Both ARI and FMS can find the\",\n \"metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score')\",\n \"scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure\",\n \"assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator\",\n \"cannot be fit twice\\\"\\\"\\\" def __init__(self, parameter=None): self.parameter = parameter\",\n \"{'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def\",\n \"a list or numpy array, but got (<class 'int'>).\\\" \\\"\",\n \"assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key]\",\n \"the highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ !=\",\n \"5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0,\",\n \"base=0.1)) for refit in (True, False): random_searches = [] for\",\n \"== '_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}):\",\n \"= grid_search.n_splits_ for candidate_i, C in enumerate(Cs): clf.set_params(C=C) cv_scores =\",\n \"n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV\",\n \"multi-metric and # single metric grid search results. np.testing.assert_equal performs\",\n \"matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0] *\",\n \"sure the functions predict/transform etc raise meaningful # error messages\",\n \"sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler\",\n \"check that we can call score and that it gives\",\n \"k): v for k, v in search_rec.cv_results_.items()}) # Check if\",\n \"0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as\",\n \"Non-regression test for issue #13920. \\\"\\\"\\\" est = BaseEstimator() attr_message\",\n \"X, y, svc, params, train, test, {'score': scorer}, verbose=True) #\",\n \"make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv =\",\n \"@pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0, TypeError,\",\n \"Ensures that a warning is raised and score reset where\",\n \"clf.predict(X[test])) elif score == \\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score =\",\n \"for splits cv = [[mask, ~mask], [~mask, mask]] common_params =\",\n \"not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert not hasattr(clf, \\\"predict_proba\\\") def\",\n \"InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return\",\n \"scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now without\",\n \"0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\":\",\n \"and warnings raised instead. If # refit was done, then\",\n \"assert all(cv_results[key].dtype == np.float64 for key in score_keys if not\",\n \"exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in\",\n \"__init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"):\",\n \"5 + [1] * 5) clf = CheckingClassifier( check_y=lambda x:\",\n \"\\\" be a list or numpy array, but got (<class\",\n \"= make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric\",\n \"an # error in this test. gs = GridSearchCV(clf, [{'parameter':\",\n \"range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42)\",\n \"results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]},\",\n \"for key in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys()\",\n \"FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter = parameter def\",\n \"group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter\",\n \"= [StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf,\",\n \"time.time in windows is not high # enough for the\",\n \"= cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >=\",\n \"degenerates to GridSearchCV if n_iter the same as grid_size sampler\",\n \"sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import f1_score from sklearn.metrics import\",\n \"search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec\",\n \"gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i\",\n \"import make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point\",\n \"make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults to euclidean\",\n \"1, \\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2)\",\n \"the estimator is not fitted, `predict_proba` is not available as\",\n \"points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points ==\",\n \"samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"] assert 0 <= sample[\\\"C\\\"]\",\n \"np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' +\",\n \"0 # test error is raised when the precomputed kernel\",\n \"['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size\",\n \"= 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv,\",\n \"# slight duplication of a test from KDE def custom_scoring(estimator,\",\n \"scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score = scorer(est,\",\n \"rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert\",\n \"specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1,\",\n \"cv_results = search.cv_results_ # Check if score and timing are\",\n \"ImportError: pass X = np.arange(100).reshape(10, 10) y = np.array([0] *\",\n \"# deep nested comparison of the two cv_results dicts np.testing.assert_equal({k:\",\n \"LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred\",\n \"return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs)\",\n \"param sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)}\",\n \"refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) #\",\n \"2, 3]}, cv=3, verbose=3) # make sure it selects the\",\n \"requires score method to compete with scoring parameter score_no_scoring =\",\n \"are generated for all # the cv splits, for both\",\n \"pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \\\"Fit parameter spam\",\n \"'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not\",\n \"import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import\",\n \"\\\"grid\\\" with only one point. clf = MockClassifier() grid_search =\",\n \"to assert we can iterate over the grid multiple times\",\n \"search cv for est in estimators: grid_search = GridSearchCV(est, est_parameters,\",\n \"that grid search works when the input features are given\",\n \"np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for key in\",\n \"(n_cand,) for key in param_keys + score_keys) def test_grid_search_cv_results(): X,\",\n \"(p['a'], p['b'], p['c']) for p in samples] assert len(set(hashable_samples)) ==\",\n \"= {'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning,\",\n \"gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert\",\n \"scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1],\",\n \"{'C': 0.001}): for train, test in cv.split(X, y): this_scores, this_params,\",\n \"passed to the cv object rng = np.random.RandomState(0) X, y\",\n \"search.scorer_.keys() if search.multimetric_ else ['score'] for key in scorer_keys: assert\",\n \"that the best estimator contains the right value for foo_param\",\n \"grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single metric evaluation unsupervised grid_search =\",\n \"we use param grid params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5,\",\n \"scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and FMS can\",\n \"GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X,\",\n \"clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv\",\n \"search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def\",\n \"6 # loop to assert we can iterate over the\",\n \"make_classification(random_state=0) cv = StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score)\",\n \"And also check if groups is correctly passed to the\",\n \"f1_score(y[test], clf.predict(X[test])) elif score == \\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score\",\n \"# this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class\",\n \"cv=3) assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert not hasattr(clf,\",\n \"can be used for model selection alone without refitting clf\",\n \"give similar models # which would result in a tie\",\n \"from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection\",\n \"[0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier\",\n \"ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) ==\",\n \"'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples\",\n \"sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import\",\n \"5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring, the \\\"\",\n \"def test_param_sampler(): # test basic properties of param sampler param_distributions\",\n \"empty = ParameterGrid({}) assert len(empty) == 1 assert list(empty) ==\",\n \"hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): # Pass X as list in\",\n \"etc raise meaningful # error messages for fn_name in ('predict',\",\n \"match=error_message): klass(input) def test_parameter_grid(): # Test basic properties of ParameterGrid.\",\n \"Y=None): if self.foo_param > 1: score = 1. else: score\",\n \"param_distributions=param_grid, return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) cv_results\",\n \"2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list():\",\n \"search_acc.refit == refit if refit: assert search_multi.refit == 'accuracy' else:\",\n \"not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank\",\n \"self.foo_param = foo_param def fit(self, X, Y): assert len(X) ==\",\n \"grid2 = ParameterGrid(params2) assert len(grid2) == 6 # loop to\",\n \"= mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if (attr[0].islower()\",\n \"2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1,\",\n \"test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold): def get_n_splits(self,\",\n \"GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert\",\n \"ValueError (when groups is None) propagates to GridSearchCV # And\",\n \"assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for\",\n \"= RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y)\",\n \"Now without a score, and without y grid_search = GridSearchCV(km,\",\n \"random_state=0) clf = FailingClassifier() # refit=False because we want to\",\n \"there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global\",\n \"y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self,\",\n \"0 <= sample[\\\"C\\\"] <= 1 # test that repeated calls\",\n \"'recall'): # If True, for multi-metric pass refit='accuracy' if refit:\",\n \"1, 1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C':\",\n \"do not accept seed, hence we use param grid params\",\n \"@ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier that has\",\n \"should cause the expected ValueError [u for u in cv]\",\n \"enough for the fit/score times to be non-zero for trivial\",\n \"self # this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y)\",\n \"FailingClassifier issues a ValueError so this is what we look\",\n \"cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not identical with precomputed metric\\\" assert\",\n \"TypeError, r'Parameter .* is not a dict or a list\",\n \"= np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1]\",\n \"API # recommendations for implementing early stopping with a user\",\n \"sanctioned tolerance for scalar values in fit_params # non-regression test\",\n \"y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results\",\n \"import BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions import\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df)\",\n \"raised on refit and not # caught by grid_search (expected\",\n \"tie breaking strategy - # Check that there is a\",\n \"pytest.approx(np.std(test_cv_scores)) # For the train scores, we do not take\",\n \"clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params,\",\n \"[(0, TypeError, r'Parameter .* is not a dict or a\",\n \"X): return X + self.foo_param def inverse_transform(self, X): return X\",\n \"The precision of time.time in windows is not high #\",\n \"sane assert score_auc < 1.0 assert score_accuracy < 1.0 assert\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert\",\n \"{'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this\",\n \"RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that\",\n \"range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train])\",\n \"assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not\",\n \"= {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions,\",\n \"k, v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency\",\n \"# recommendations for implementing early stopping with a user provided\",\n \"= GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not\",\n \"model.fit(X, y, r=42) assert model.best_estimator_.r_ == 42 @pytest.mark.parametrize( \\\"SearchCV, param_search\\\",\",\n \"class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 # create\",\n \"1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for parameter\",\n \") train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] ==\",\n \"check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}])\",\n \"assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {}, {'C':\",\n \"range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def\",\n \"sure grid search and random search delegate n_features_in to the\",\n \"and the next two param # settings (C=0.2) are same,\",\n \"y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge)\",\n \"= StratifiedKFold() svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) for params\",\n \"y = make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15)\",\n \"= KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y) preds_original\",\n \"1]}, cv=2, error_score=0, n_iter=2) for search in (gs, rs): search.fit(X,\",\n \"grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score =\",\n \"Check if the search `cv_results`'s array are of correct types\",\n \"np import scipy.sparse as sp import pytest from sklearn.utils.fixes import\",\n \"def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y = np.array([0] *\",\n \"Fit a dummy clf with `refit=True` to get a list\",\n \"GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def\",\n \"n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X,\",\n \"error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier()\",\n \"test_grid_search_sparse(): # Test that grid search works with both dense\",\n \"# Pass y as list in GridSearchCV X = np.arange(100).reshape(10,\",\n \"NotFittedError from sklearn.datasets import make_classification from sklearn.datasets import make_blobs from\",\n \"def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X, y\",\n \"once if ``cv.split`` is called once per param setting in\",\n \"assert_array_almost_equal from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from\",\n \"cv] # cv is empty now train_size = 100 ridge\",\n \"import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from sklearn.cluster import KMeans\",\n \"n_features=4, random_state=42) n_splits = 3 n_grid_points = 6 params =\",\n \"import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import enable_hist_gradient_boosting # noqa\",\n \"{'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy =\",\n \"cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf,\",\n \"{'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors\",\n \"y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed as\",\n \"and train scores must also be # same as long\",\n \"7, 11) check_X = lambda x: x.shape[1:] == (5, 3,\",\n \"cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc\",\n \"# i.i.d. or not assert train_mean == pytest.approx(1) assert train_std\",\n \"with on_error == 'raise' raises the error X, y =\",\n \"for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv =\",\n \"f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False) cv\",\n \"1, \\\"b\\\": 2} to (\\\"a\\\", 1, \\\"b\\\", 2) points =\",\n \"% (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates\",\n \"n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results\",\n \"search over a \\\"grid\\\" with only one point. clf =\",\n \"for parameter (C) need to be a non-empty sequence.\\\", GridSearchCV,\",\n \"Pass y as list in GridSearchCV X = np.arange(100).reshape(10, 10)\",\n \"2})] ) def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter\",\n \"in samples] assert len(set(hashable_samples)) == 99 # doesn't go into\",\n \"random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results\",\n \"GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y)\",\n \"0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_\",\n \"no scoring function raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C':\",\n \"= (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0, None]} cv =\",\n \"'_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert _pairwise_setting ==\",\n \"from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC from\",\n \"1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler =\",\n \"X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed\",\n \"AttributeError X = np.array([[-1, -1], [-2, -1], [1, 1], [2,\",\n \"scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params'])\",\n \"grid that was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not\",\n \"length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()\",\n \"[.5]}]) assert len(has_empty) == 4 assert list(has_empty) == [{'C': 1},\",\n \"scorer, verbose=False) def test_pickle(): # Test that a fit search\",\n \"value for foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':\",\n \"refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with\",\n \"0.0) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs\",\n \"will be removed on 0.25 def test_fit_grid_point_deprecated(): X, y =\",\n \"k.endswith('_time')}, {k: v for k, v in gs2.cv_results_.items() if not\",\n \"of multiple single metric cv_results from single metric grid/random search\\\"\\\"\\\"\",\n \"0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): #\",\n \"{'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip =\",\n \"msg = (\\\"fit_grid_point is deprecated in version 0.23 \\\" \\\"and\",\n \"\\\"parameter refit must be set to a scorer key\\\", GridSearchCV(clf,\",\n \"from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection\",\n \"error_type, error_message\\\", [(0, TypeError, r'Parameter .* is not a dict\",\n \"= 8 expected_warning = ('The total space of parameters %d\",\n \"returned ' 'inconsistent results. Expected \\\\\\\\d+ ' 'splits, got \\\\\\\\d+'):\",\n \"not exactly respect the scikit-learn API contract but that we\",\n \"sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import\",\n \"== pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)\",\n \"[0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y)\",\n \"for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results():\",\n \"bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base\",\n \"hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def\",\n \"Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3 assert_grid_iter_equals_getitem(grid1) params2\",\n \"is a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind ==\",\n \"all of it, this should cause the expected ValueError [u\",\n \"of ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y)\",\n \"if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1,\",\n \"import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import GroupKFold\",\n \"if refit: assert search_multi.refit == 'accuracy' else: assert not search_multi.refit\",\n \"assert that this raises an error with pytest.raises(ValueError, match='No fits\",\n \"import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning\",\n \"2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X,\",\n \"hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y =\",\n \"the errors when 'best_index_' returns an invalid result. \\\"\\\"\\\" def\",\n \"test the behaviour of the grid search part gs =\",\n \"'best_index_' is not integer. \\\"\\\"\\\" return None X, y =\",\n \"this_params assert n_test_samples == test.size # Should raise an error\",\n \"len(samples) == 8 for values in ParameterGrid(params): assert values in\",\n \"# Test that the best estimator contains the right value\",\n \"= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y)\",\n \"(RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check\",\n \"1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two\",\n \"* 7 * 11).reshape(10, 7, 11) check_X = lambda x:\",\n \"there is a tie in the mean scores between #\",\n \"model that has the least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in\",\n \"'mean_fit_time', 'std_fit_time'))) # Compare the keys, other than time keys,\",\n \"\\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for parameter\",\n \"scalar values in fit_params # non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805\",\n \"assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0\",\n \"' 'refitting on the best parameters' % fn_name), getattr(grid_search, fn_name),\",\n \"the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180]\",\n \"# check cross_val_score doesn't destroy pandas dataframe types = [(MockDataFrame,\",\n \"X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score),\",\n \"an invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function\",\n \"TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a = a def fit(self,\",\n \"# Test search over a \\\"grid\\\" with only one point.\",\n \"(0, 16): param_distributions = {\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions,\",\n \"fit_grid_point will be removed on 0.25 def test_fit_grid_point_deprecated(): X, y\",\n \"params = {'a': range(10), 'b': range(10), 'c': range(10)} sampler =\",\n \"!= 'raise' # Ensures that a warning is raised and\",\n \"np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y =\",\n \"the right number :) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single\",\n \"clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError,\",\n \"X and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] <\",\n \"hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y)\",\n \"== sorted(exp_results) for k in results: if not k.endswith('_time'): #\",\n \"grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y)\",\n \"precompute euclidean metric to validate _pairwise is working X_precomputed =\",\n \"iid # first one contains data of all 4 blobs,\",\n \"X=None, Y=None): if self.foo_param > 1: score = 1. else:\",\n \"key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for key\",\n \"parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc\",\n \"y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits,\",\n \"of its estimator. This test ensures the equivalence of 'precomputed'.\",\n \"cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf,\",\n \"y) def test_grid_search_cv_splits_consistency(): # Check if a one time iterable\",\n \"for i in range(2): # tuple + chain transforms {\\\"a\\\":\",\n \"if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed as required\\\")\",\n \"ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import BaseSearchCV from\",\n \"X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() #\",\n \"Pass X as list in GridSearchCV X_4d = np.arange(10 *\",\n \"Test exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit,\",\n \"make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric evaluation\",\n \"scorer assert_raise_message(ValueError, \\\"For evaluating multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\",\",\n \"assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2])\",\n \"test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError,\",\n \"k, v in cv_results_multi.items() if not k.endswith('_time')}, {k: v for\",\n \"assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from\",\n \"settings (C=0.2) are same, the test and train scores must\",\n \"n_splits = 3 clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0,\",\n \"!= 'rank_test_score') assert (all(cv_results[k] <= 1) for k in score_keys\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train)\",\n \"range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y =\",\n \"GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def\",\n \"y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01,\",\n \"\\\"BaseSearchCV _pairwise property must match estimator\\\" for _pairwise_setting in [True,\",\n \"list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0],\",\n \"6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0)\",\n \"# test that repeated calls yield identical parameters param_distributions =\",\n \"== C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\",\n \"clf with `refit=True` to get a list of keys in\",\n \"# scores are the same as above assert_allclose(test_cv_scores, [1, 1.\",\n \"{\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf = SVC(gamma='auto') cv\",\n \"x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search =\",\n \"# Now without a score, and without y grid_search =\",\n \"if score and timing are reasonable assert all(cv_results['rank_test_score'] >= 1)\",\n \"LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert not\",\n \"are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid =\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X,\",\n \"cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i]\",\n \"= list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make\",\n \"score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results =\",\n \"not take a weighted mean irrespective of # i.i.d. or\",\n \"search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf,\",\n \"test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)]\",\n \"is not fitted, `predict_proba` is not available as the #\",\n \"reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for\",\n \"# in param_grid param_grid = { 'loss': ['hinge'], } clf\",\n \"cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits\",\n \"**kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): # this\",\n \"assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if ValueError\",\n \"break support for those without following # proper deprecation cycle.\",\n \"== len(Y) self.classes_ = np.unique(Y) return self def predict(self, T):\",\n \"search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys,\",\n \"!= clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise'\",\n \"10] for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C':\",\n \"import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection import KFold\",\n \"re import numpy as np import scipy.sparse as sp import\",\n \"random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search\",\n \"metric grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi =\",\n \"returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value', [-1, 2])\",\n \"(when groups is None) propagates to GridSearchCV # And also\",\n \"gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter should\",\n \"KMeans(random_state=0, init=\\\"random\\\", n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score',\",\n \"clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X,\",\n \"tie in the mean scores between # candidates 1 and\",\n \"return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =\",\n \"check to make sure cv.split is not called once per\",\n \"list in GridSearchCV X_4d = np.arange(10 * 5 * 3\",\n \"3 n_search_iter = 30 # Scipy 0.12's stats dists do\",\n \".1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] == .1 assert search.best_score_\",\n \"est_parameters = {\\\"max_depth\\\": [1, 2, 3, 4]} cv = KFold()\",\n \"{'n_neighbors': [10]}) assert _pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\"\",\n \"make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]} gs\",\n \"sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import\",\n \"check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor\",\n \"assert len(samples) == 8 for values in ParameterGrid(params): assert values\",\n \"+ [1] * 5) for InputFeatureType, TargetType in types: #\",\n \"def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def\",\n \"cv_results_multi.items() if not k.endswith('_time')}, {k: v for k, v in\",\n \"0, 1, 1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf,\",\n \"GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": []} clf = SVC()\",\n \"= [0, 0, 0, 1, 1, 1] clf = LinearSVC(random_state=0)\",\n \"= ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples) ==\",\n \"== 7 def test_stochastic_gradient_loss_param(): # Make sure the predict_proba works\",\n \"0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C':\",\n \"[1] * 5) for InputFeatureType, TargetType in types: # X\",\n \"# check support for scalar values in fit_params, for instance\",\n \"3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized)\",\n \"GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is not fitted,\",\n \"getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42) #\",\n \"cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should\",\n \"Give generator as a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X,\",\n \"for InputFeatureType, TargetType in types: # X dataframe, y series\",\n \"refit and not # caught by grid_search (expected behavior), and\",\n \"'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score',\",\n \"grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]},\",\n \"param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter\",\n \"n_classes=2, flip_y=.2, random_state=0) clf = LinearSVC(random_state=0) grid = {'C': [.1]}\",\n \"3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with\",\n \"the cv splits, for both param setting for score_type in\",\n \"Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer\",\n \"\\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function tests when returned\",\n \"'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV,\",\n \"in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors':\",\n \"assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best estimator contains\",\n \"fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param': 42,\",\n \"# will be generated for the 2nd and subsequent cv.split\",\n \"BaseSearchCV has the _pairwise property which matches the _pairwise property\",\n \"refit=callable in multiple metric evaluation setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\"\",\n \"probability = False clf = SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf,\",\n \"LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from\",\n \"pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): # this should raise a NotImplementedError\",\n \"GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from\",\n \"score method to compete with scoring parameter score_no_scoring = search_no_scoring.score(X,\",\n \"subsequent cv.split calls. # This is a check to make\",\n \"of it, this should cause the expected ValueError [u for\",\n \"\\\"\\\"\\\" return out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf\",\n \"assert score_auc < 1.0 assert score_accuracy < 1.0 assert score_auc\",\n \"from itertools import chain, product from functools import partial import\",\n \"LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3) gs.fit(X, y)\",\n \"= KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv)\",\n \"cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring\",\n \"(train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score ==\",\n \"= np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] )\",\n \"to fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s\",\n \"cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9\",\n \"{'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0,\",\n \"a=None): self.a = a def fit(self, X, y, r=None): self.r_\",\n \"sure it selects the smallest parameter in case of ties\",\n \"def fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ =\",\n \"# Test that an invalid refit param raises appropriate error\",\n \"uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x\",\n \"random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with\",\n \"from sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following two\",\n \"((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i for cv_i in range(n_splits)))\",\n \"estimators are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())]) param_grid\",\n \"clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \">= 0 def test_grid_search_correct_score_results(): # test that correct scores are\",\n \"list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty)\",\n \"# Test search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True,\",\n \"candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert\",\n \"inherit from BaseEstimator, # to test hyperparameter search on user-defined\",\n \"[LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs =\",\n \"clf, {'foo_param': [1, 2, 3]}, cv=2, error_score=\\\"raise\\\" ) # The\",\n \"# Check warning only occurs in situation where behavior changed:\",\n \"= SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should be\",\n \"('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def\",\n \"in (True, False): random_searches = [] for scoring in (('accuracy',\",\n \"same as long as the same train/test indices are generated\",\n \"a tie in the mean scores between # candidates 1\",\n \"# Make sure the predict_proba works when loss is specified\",\n \"def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0, 0,\",\n \"= FailingClassifier() # refit=False because we only want to check\",\n \"== 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}),\",\n \"clf, param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message(\",\n \"if not k.endswith('_time'): # XXX: results['params'] is a list :|\",\n \"collections.abc import Iterable, Sized from io import StringIO from itertools\",\n \"failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] == 3\",\n \"get a list of keys in # clf.cv_results_. X, y\",\n \"for: # https://github.com/scikit-learn/scikit-learn/issues/15805 class TestEstimator(BaseEstimator, ClassifierMixin): def __init__(self, a=None): self.a\",\n \"sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import\",\n \"[0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\" Test\",\n \"two cv_results dicts np.testing.assert_equal({k: v for k, v in cv_results_multi.items()\",\n \"2 # Check that failed estimator has the highest rank\",\n \"rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of BaseSearchCV\",\n \"else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1,\",\n \"params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that has\",\n \"('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score',\",\n \"len(samples) == 99 hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c'])\",\n \"param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check results\",\n \"\\\"\\\"\\\"Dummy classifier to test the parameter search algorithms\\\"\\\"\\\" def __init__(self,\",\n \"test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5,\",\n \"Check that failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER]\",\n \"= evaluate([{'min_samples_split': 5}, {'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split':\",\n \"single metric methods\\\"\\\"\\\" assert search_acc.refit == refit if refit: assert\",\n \"equivalence of 'precomputed'. Non-regression test for issue #13920. \\\"\\\"\\\" n_samples\",\n \"accept seed, hence we use param grid params = dict(C=np.logspace(-4,\",\n \"check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \"# Test the search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()),\",\n \"0, 1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)),\",\n \"GridSearchCV if n_iter the same as grid_size sampler = ParameterSampler(params,\",\n \"test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises the error\",\n \"StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from\",\n \"results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking\",\n \"not hasattr(grid_search, 'classes_') # Test that the grid searcher has\",\n \"v for k, v in gs2.cv_results_.items() if not k.endswith('_time')}) #\",\n \"np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict) def\",\n \"what we look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement():\",\n \"cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 = GridSearchCV(LinearSVC(random_state=0),\",\n \"_pairwise_setting == cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of\",\n \"= LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid,\",\n \"on one fold and a score of 1/3 on #\",\n \"score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc) def test_grid_search_groups(): # Check if\",\n \"1.0 assert score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy,\",\n \"= np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y =\",\n \"is not high # enough for the fit/score times to\",\n \"assert_raise_message( ValueError, \\\"Parameter grid for parameter (C) needs to\\\" \\\"\",\n \"explicit deprecation cycle and API # recommendations for implementing early\",\n \"assert key in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100,\",\n \"X): return 42 if estimator.bandwidth == .1 else 0 X,\",\n \"delegate n_features_in to the # best estimator n_features = 4\",\n \"= GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y) search_auc = GridSearchCV(clf, grid,\",\n \"are reasonable assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0)\",\n \"random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable)\",\n \"sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x\",\n \"when 'best_index_' returns an invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\"\",\n \"def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy function tests when returned 'best_index_'\",\n \"param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score',\",\n \"{'C': [0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_\",\n \"hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over a \\\"grid\\\"\",\n \"'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score',\",\n \"cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a ValueError on\",\n \"param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check if\",\n \"y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot be fit twice\\\"\\\"\\\"\",\n \"2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i\",\n \"= [0, 1, 1, 0] gs = GridSearchCV(svc, {'C': [0,\",\n \"svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg = (\\\"fit_grid_point is\",\n \"assert score_accuracy < 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring)\",\n \"return 1 # create bad cv cv = BrokenKFold(n_splits=3) train_size\",\n \"shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,\",\n \"in a tie of their mean cv-scores param_grid = {'C':\",\n \"method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_',\",\n \"n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X,\",\n \"= predict decision_function = predict def score(self, X=None, Y=None): if\",\n \"= 0 # this leads to perfect classification on one\",\n \"'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X,\",\n \"'std_score_time'): cv_results.pop(key) return cv_results # Check if generators are supported\",\n \"range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores =\",\n \"grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i])\",\n \"assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For\",\n \"for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,\",\n \"5) Cs = [.1, 1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':\",\n \"Special case: empty grid (useful to get default estimator settings)\",\n \"gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k, v in\",\n \"TargetType in types: # X dataframe, y series X_df, y_ser\",\n \"can call score and that it gives the correct result\",\n \"est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y)\",\n \"gs.fit(X, y) def test_classes__property(): # Test that classes_ property matches\",\n \"mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]},\",\n \"= make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]},\",\n \"v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v\",\n \"y[:train_size]) def test_random_search_bad_cv(): # Use global X, y class BrokenKFold(KFold):\",\n \") def test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar\",\n \"total parameter space params = [{'first': [0, 1], 'second': ['a',\",\n \"assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings from\",\n \"np.unique(Y) return self def predict(self, T): return T.shape[0] def transform(self,\",\n \"io import StringIO from itertools import chain, product from functools\",\n \"= np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] = 0 mask[np.where(y ==\",\n \"== y_pred2) >= .9 assert C == C2 def test_grid_search_sparse_scoring():\",\n \"spam has length 1; expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y,\",\n \"grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def test_gridsearch_no_predict(): # test grid-search\",\n \"centers=2) Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search\",\n \"sample[\\\"C\\\"] <= 1 # test that repeated calls yield identical\",\n \"self.foo_param def inverse_transform(self, X): return X - self.foo_param predict_proba =\",\n \"clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y)\",\n \"search with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters\",\n \"search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def test_param_sampler(): #\",\n \"np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert np.mean(y_pred\",\n \"X, y, r=None): self.r_ = r def predict(self, X): return\",\n \"and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1],\",\n \"Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10,\",\n \"params == this_params assert n_test_samples == test.size # Should raise\",\n \"np.arange(100).reshape(10, 10) y = np.array([0] * 5 + [1] *\",\n \"'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search =\",\n \"is a tie in the mean scores between # candidates\",\n \"= make_scorer(accuracy_score) msg = (\\\"fit_grid_point is deprecated in version 0.23\",\n \"y) res_params = grid_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for\",\n \"ranks[0] <= 2 and ranks[1] <= 2 # Check that\",\n \"y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 #\",\n \"sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from sklearn.utils._testing import\",\n \".* is not a dict or a list \\\\(0\\\\)'), ([{'foo':\",\n \"check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df,\",\n \"cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict\",\n \"proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param':\",\n \"y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) #\",\n \"splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant\",\n \"be called only # once if ``cv.split`` is called once\",\n \"that failed estimator has the highest rank assert ranks[clf.FAILING_PARAMETER] ==\",\n \"def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression\",\n \"{'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter':\",\n \"parameters in the param_grid. param_grid = { 'loss': ['log'], }\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) #\",\n \"a dict or a list \\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError,\",\n \"grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit, [[1]])\",\n \"assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y)\",\n \"result_keys = list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" %\",\n \"_pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est,\",\n \"parameter TODO: Clearly this test does something else??? # noise-free\",\n \"s][candidate_i] for s in range(n_splits))) for i, (train, test) in\",\n \"gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of folds across\",\n \"= GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert\",\n \"the grid searcher has no classes_ attribute before it's fit\",\n \"return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check if score\",\n \"1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()),\",\n \"centers=2) Cs = [.1, 1, 10] for score in ['f1',\",\n \"# giving no scoring function raises an error grid_search_no_score =\",\n \"raises appropriate error messages for refit in [\\\"\\\", 5, True,\",\n \"{'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken\",\n \"[0.01, 0.1, 1]}, scoring='precision', refit=True) clf.fit(X, y) # Ensure that\",\n \"working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf,\",\n \"model selection alone without refitting clf = MockClassifier() for scoring\",\n \"if cv_results['param_kernel'][i] == 'rbf') def test_random_search_cv_results(): X, y = make_classification(n_samples=50,\",\n \"a cv parameter. n_samples = 100 n_splits = 5 X,\",\n \"rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for\",\n \"assert (all(cv_results[k] >= 0) for k in score_keys if k\",\n \"= search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k,\",\n \"be evaluated as no train/test indices # will be generated\",\n \"{\\\"max_depth\\\": [1, 2, 3, 4]} cv = KFold() estimators =\",\n \"y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\":\",\n \"C == C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100,\",\n \"+ [1] * 5) Cs = [.1, 1, 10] grid_search\",\n \"res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i,\",\n \"the test and train scores must also be # same\",\n \"= sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout\",\n \"getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_',\",\n \"grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor']\",\n \"= train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X,\",\n \"5 + [1] * 5) for InputFeatureType, TargetType in types:\",\n \".5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the best estimator\",\n \"y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\": [1, 2, 3,\",\n \"y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4,\",\n \"1) assert (all(cv_results[k] >= 0) for k in score_keys if\",\n \"{'first': bernoulli(.5), 'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7)\",\n \"scorer = make_scorer(accuracy_score) msg = (\\\"fit_grid_point is deprecated in version\",\n \"cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean metric to\",\n \"X, y) def test_grid_search_pipeline_steps(): # check that parameters that are\",\n \"from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from sklearn.model_selection\",\n \"[0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of\",\n \"3 * 2).reshape(10, 5, 3, 2) y_3d = np.arange(10 *\",\n \"'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time')\",\n \"the \\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function\",\n \"= {'a': range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params,\",\n \"{\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x\",\n \"y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in multiple metric evaluation\",\n \"test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which adds flexibility in identifying the\",\n \"\\\"Parameter values for parameter (C) need to be a non-empty\",\n \"for x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case:\",\n \"in (gs, rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']:\",\n \"[0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search):\",\n \"and a score of 1/3 on # the other #\",\n \"out_bound_value X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42),\",\n \"= list(sampler) assert len(samples) == 8 for values in ParameterGrid(params):\",\n \"y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf,\",\n \"y) def test_parameters_sampler_replacement(): # raise warning if n_iter is bigger\",\n \"params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to get\",\n \"make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},\",\n \"all 4 blobs, second only from two. mask = np.ones(X.shape[0],\",\n \"n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation\",\n \"param_distributions=params, scoring=scoring, refit=refit, random_state=0) random_search.fit(X, y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0],\",\n \"# Test that the grid searcher has no classes_ attribute\",\n \"grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf,\",\n \"functions predict/transform etc raise meaningful # error messages for fn_name\",\n \"# Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1)\",\n \"without replacement in a large grid params = {'a': range(10),\",\n \"= 2 X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors':\",\n \"correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point\",\n \"'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X,\",\n \"attr[-1:] == '_' and attr not in {'cv_results_', 'best_estimator_', 'refit_time_',\",\n \"values need to be wrapped in a list\\\" \\\" with\",\n \"None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0) X = [[1,\",\n \"foo_param clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \"= np.arange(6).reshape(6, -1) y = [0, 0, 0, 1, 1,\",\n \"instead.\\\", fit_grid_point, X, y, svc, params, train, test, {'score': scorer},\",\n \"data dependent (e.g. `sample_weight`) which # is not the case\",\n \"[1, 1]) # Unweighted mean/std is used assert test_mean ==\",\n \"with # sparse SVMs. X = np.arange(100).reshape(10, 10) y =\",\n \"= SVC(probability=probability, random_state=42) random_search = RandomizedSearchCV(clf, n_iter=n_search_iter, cv=n_splits, param_distributions=params, scoring=scoring,\",\n \"grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def\",\n \"test for bug in refitting # Simulates re-fitting a broken\",\n \"sp_version >= (0, 16): param_distributions = {\\\"C\\\": uniform(0, 1)} sampler\",\n \"# noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1,\",\n \"Assert every key matches those in `cv_results` for key in\",\n \"grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1,\",\n \"y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_X_as_list(): # Pass X\",\n \"grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test that the\",\n \"corresponding to the linear kernel K_train = np.dot(X_[:180], X_[:180].T) y_train\",\n \"from functools import partial import pickle import sys from types\",\n \"clf.predict_log_proba(X) # Make sure `predict_proba` is not available when setting\",\n \"the least `mean_test_score`. \\\"\\\"\\\" # Fit a dummy clf with\",\n \"GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) #\",\n \"y = make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg\",\n \"error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no scoring\\\", grid_search_no_score.fit,\",\n \"y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3,\",\n \"all(cv_results[key].dtype == np.float64 for key in score_keys if not key.startswith('rank'))\",\n \"def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values in\",\n \"matches those in `cv_results` for key in clf.cv_results_.keys(): assert key\",\n \"\\\" with one element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\":\",\n \"def test_predict_proba_disabled(): # Test predict_proba when disabled on estimator. X\",\n \"('train', 'test'): per_param_scores = {} for param_i in range(4): per_param_scores[param_i]\",\n \"dummy function tests when returned 'best_index_' is not integer. \\\"\\\"\\\"\",\n \"clf = LinearSVC(random_state=0) gs = GridSearchCV(clf, param_grid={'C': [0.1, 0.2]}, cv=3)\",\n \"import bernoulli, expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from\",\n \"1, 1, 1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier',\",\n \"bug in refitting # Simulates re-fitting a broken estimator; this\",\n \"(('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for multi-metric pass\",\n \"random_state=42) n_splits = 3 n_search_iter = 30 # Scipy 0.12's\",\n \"for k, v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check\",\n \"Test that classes_ property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10)\",\n \"% (s, score_type)][param_i] for s in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2],\",\n \"assert_raise_message(NotFittedError, ('refit=False. %s is available only after ' 'refitting on\",\n \"[None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2,\",\n \"2, 3, 4, 5, 6, 7, 8, 9, 10]} sampler\",\n \"metric grid search results. np.testing.assert_equal performs a # deep nested\",\n \"score method.\\\"\\\"\\\" @property def score(self): raise AttributeError X = np.array([[-1,\",\n \"range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in multiple\",\n \"# Check if generators are supported as cv and #\",\n \"in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] ==\",\n \"> 1: score = 1. else: score = 0. return\",\n \"classifier failed as required\\\") def predict(self, X): return np.zeros(X.shape[0]) def\",\n \"a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def __init__(self, parameter=None):\",\n \"% cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys)) cv\",\n \"sampler] == [x for x in sampler] if sp_version >=\",\n \"be None.\\\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs =\",\n \"grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50,\",\n \"'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): # Test\",\n \"and y assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1)\",\n \").fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc =\",\n \"__init__(self, parameter=None): self.parameter = parameter def fit(self, X, y=None): if\",\n \"0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True) gs.fit(X, y) gs2 = GridSearchCV(LinearSVC(random_state=0),\",\n \"setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if not\",\n \"1}, {'C': 10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): #\",\n \"LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf,\",\n \"BrokenKFold(KFold): def get_n_splits(self, *args, **kw): return 1 # create bad\",\n \"def test_grid_search_sparse(): # Test that grid search works with both\",\n \"assert not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test GridSearchCV with\",\n \"def test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =\",\n \"[0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]},\",\n \"# or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): #\",\n \"FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators have\",\n \"= GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search,\",\n \"== test.size # Should raise an error upon multimetric scorer\",\n \"samples = [x for x in sampler] assert len(samples) ==\",\n \"_pairwise is delegated to the base estimator. Non-regression test for\",\n \"\\\" Single values need to be wrapped in a list\\\"\",\n \"with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned ' 'inconsistent results. Expected\",\n \"1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0, None]}\",\n \"be # same as long as the same train/test indices\",\n \"used to break with # sparse SVMs. X = np.arange(100).reshape(10,\",\n \"y) gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True)\",\n \"will capture errors on data with different length X_, y_\",\n \"'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking():\",\n \"and attr not in {'cv_results_', 'best_estimator_', 'refit_time_', 'classes_'}): assert getattr(gscv,\",\n \"test_grid_search_correct_score_results(): # test that correct scores are used n_splits =\",\n \".* is not a dict \\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.*\",\n \"grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) # Smoke test\",\n \"GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y)\",\n \"[\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf, param_dict)\",\n \"10) y = np.array([0] * 5 + [1] * 5)\",\n \"y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test\",\n \"samples = list(sampler) assert len(samples) == 99 hashable_samples = [\\\"a%db%dc%d\\\"\",\n \"None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y)\",\n \"for x in sampler] assert len(samples) == 10 for sample\",\n \"= StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C in enumerate(Cs):\",\n \"gs4 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0),\",\n \"k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys)\",\n \"parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10))\",\n \"\\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\") #\",\n \"= pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with\",\n \"a cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3\",\n \"{'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma',\",\n \"{k: v for k, v in gs2.cv_results_.items() if not k.endswith('_time')})\",\n \"search cannot predict/score without refit X, y = make_blobs(n_samples=100, n_features=4,\",\n \"== set((\\\"bar\\\", x, \\\"foo\\\", y) for x, y in product(params2[\\\"bar\\\"],\",\n \"= make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\": [1, 2, 3, 4]}\",\n \"grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X, y)\",\n \"from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression\",\n \"(RandomizedSearchCV, {'param_distributions': {'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV,\",\n \"for those without following # proper deprecation cycle. fit_params =\",\n \"y_pred2) >= .9 assert C == C2 def test_grid_search_sparse_scoring(): X_,\",\n \"gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples), return_train_score=True)\",\n \"k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc,\",\n \"Expected \\\\\\\\d+ ' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in():\",\n \"ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X, y class\",\n \"{'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"to test hyperparameter search on user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy\",\n \"# the cv splits, for both param setting for score_type\",\n \"5, 6, 7, 8, 9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3,\",\n \"grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): #\",\n \"import f1_score from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score\",\n \"test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains the right\",\n \"\\\"best_params_\\\") # Make sure the functions predict/transform etc raise meaningful\",\n \"CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5,\",\n \"[x for x in sampler] assert len(samples) == 10 for\",\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\",\n \"X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_ assert\",\n \"Use global X, y class BrokenKFold(KFold): def get_n_splits(self, *args, **kw):\",\n \"2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert len(grid2)\",\n \"when setting loss=['hinge'] # in param_grid param_grid = { 'loss':\",\n \"assert list(has_empty) == [{'C': 1}, {'C': 10}, {}, {'C': .5}]\",\n \"= make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'):\",\n \"'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler)\",\n \"which would result in a tie of their mean cv-scores\",\n \"RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y)\",\n \"0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the\",\n \"if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score']\",\n \"np.array([0] * 5 + [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam',\",\n \"0) for k in score_keys if k != 'rank_test_score') assert\",\n \"which adds flexibility in identifying the \\\"best\\\" estimator. \\\"\\\"\\\" def\",\n \"CheckingClassifier generates an assertion error if # a parameter is\",\n \"not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search over a\",\n \"sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in\",\n \"assert sorted(results.keys()) == sorted(exp_results) for k in results: if not\",\n \"correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving\",\n \"= {\\\"C\\\": 1} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid\",\n \"this would cause an # error in this test. gs\",\n \"in range(5)) assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf =\",\n \"be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3])\",\n \"are not iid # first one contains data of all\",\n \"Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180]))\",\n \"per param setting in GridSearchCV.fit # the 2nd and 3rd\",\n \"_pairwise property of its estimator. This test ensures the equivalence\",\n \"X = [[1, ], [2, ], [3, ], [4, ]]\",\n \"LinearSVC(random_state=0) grid = {'C': [.1]} search_no_scoring = GridSearchCV(clf, grid, scoring=None).fit(X,\",\n \"random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i])\",\n \"2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split': 5}, {'min_samples_split':\",\n \"<= 2 and ranks[1] <= 2 # Check that failed\",\n \"a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X,\",\n \"estimator settings) empty = ParameterGrid({}) assert len(empty) == 1 assert\",\n \"from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search\",\n \"\\\"cv_results_\\\") def test_no_refit(): # Test that GSCV can be used\",\n \"LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier\",\n \"if (attr[0].islower() and attr[-1:] == '_' and attr not in\",\n \"X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for\",\n \"and ranks[1] <= 2 # Check that failed estimator has\",\n \"est = BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise property must match\",\n \"assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no scoring\",\n \"# refit=False because we want to test the behaviour of\",\n \"seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \\\"Fit\",\n \"without a score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,\",\n \"in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_\",\n \"# first one contains data of all 4 blobs, second\",\n \"this behavior for # now and be careful not to\",\n \"param_grid param_grid = { 'loss': ['hinge'], } clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),\",\n \"Test that GSCV can be used for model selection alone\",\n \"identical parameters param_distributions = {\\\"C\\\": [0, 1, 2, 3, 4,\",\n \"= r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError, match=err_msg):\",\n \"with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic properties\",\n \"'Were there no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use\",\n \"= make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1,\",\n \"grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): #\",\n \"and that it gives the correct result assert grid_search.score(X, y)\",\n \"in param_keys) assert all(cv_results[key].dtype == object for key in param_keys)\",\n \"input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): #\",\n \"grid-search with unsupervised estimator X, y = make_blobs(n_samples=50, random_state=0) km\",\n \"assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable(): pass model\",\n \"{}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that the\",\n \"= n_search_iter search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X,\",\n \"X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X,\",\n \"assert getattr(gscv, attr) == getattr(mycv, attr), \\\\ \\\"Attribute %s not\",\n \"once per param setting in GridSearchCV.fit # the 2nd and\",\n \"assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100,\",\n \"== 3 assert gs.best_index_ != clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV\",\n \"Series)) except ImportError: pass X = np.arange(100).reshape(10, 10) y =\",\n \"the same train/test indices are generated for all # the\",\n \"searcher.fit(X, y, spam=np.ones(10)) err_msg = \\\"Fit parameter spam has length\",\n \"Simulates re-fitting a broken estimator; this used to break with\",\n \"= ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score',\",\n \"== n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test\",\n \"no classes_ attribute before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C':\",\n \"all # the cv splits, for both param setting for\",\n \"k in score_keys if k != 'rank_test_score') assert (all(cv_results[k] <=\",\n \"import assert_raises from sklearn.utils._testing import assert_warns from sklearn.utils._testing import assert_warns_message\",\n \"check_results(results, gscv): exp_results = gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for\",\n \"[-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test\",\n \"clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def\",\n \"\\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert\",\n \"test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches the errors when 'best_index_' returns\",\n \"import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler\",\n \"= y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1,\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:])\",\n \"to break with # sparse SVMs. X = np.arange(100).reshape(10, 10)\",\n \"None.\\\", gs.fit, X, y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(),\",\n \"'fowlkes_mallows_score'] for refit in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,\",\n \"reasonable, also checks if the keys # are present assert\",\n \"scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not an integer'):\",\n \"= 3 n_search_iter = 30 params = [{'kernel': ['rbf'], 'C':\",\n \"SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for parameter (C) needs to\\\"\",\n \"validation set. non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train,\",\n \"clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in\",\n \"XXX: results['params'] is a list :| results[k] = np.asanyarray(results[k]) if\",\n \"for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for\",\n \"test_search_cv_results_none_param(): X, y = [[1], [2], [3], [4], [5]], [0,\",\n \"with different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf\",\n \"\\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i for cv_i in range(n_splits))) assert\",\n \"train/test indices # will be generated for the 2nd and\",\n \"\\\"GridSearchCV not identical with precomputed metric\\\" assert (preds_original == preds_precomputed).all(),\",\n \"grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search)\",\n \"TypeError, \\\"Parameter.* value is not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] )\",\n \"grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler) # degenerates to GridSearchCV if\",\n \"of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in\",\n \"multiple metric evaluation setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy\",\n \"FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed as required\\\") def predict(self, X):\",\n \"GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs,\",\n \"`predict_proba` is not available when setting loss=['hinge'] # in param_grid\",\n \"({'foo': 0}, TypeError, \\\"Parameter.* value is not iterable .*\\\" r\\\"\\\\(key='foo',\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert cv.best_score_\",\n \"enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score,\",\n \"2, 3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X,\",\n \"y, r=None): self.r_ = r def predict(self, X): return np.zeros(shape=(len(X)))\",\n \"in samples # test sampling without replacement in a large\",\n \"that we didn't modify the parameter grid that was passed\",\n \"{'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled\",\n \"are reasonable, also checks if the keys # are present\",\n \"CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3)\",\n \"FailingClassifier() # refit=False because we only want to check that\",\n \"clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42),\",\n \"\\\"input, error_type, error_message\\\", [(0, TypeError, r'Parameter .* is not a\",\n \"return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42) scoring =\",\n \"pytest.raises(ValueError, match='No fits were performed. ' 'Was the CV iterator\",\n \"this raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned\",\n \"= np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X =\",\n \"-1], [1, 1], [2, 1]]) y = np.array([1, 1, 2,\",\n \"results = mycv.cv_results_ check_results(results, gscv) for attr in dir(gscv): if\",\n \"sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) ==\",\n \"from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection\",\n \"def transform(self, X): return X + self.foo_param def inverse_transform(self, X):\",\n \"# the 2nd and 3rd parameter will not be evaluated\",\n \"matrix K_train = np.zeros((10, 20)) y_train = np.ones((10, )) clf\",\n \"from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection\",\n \"y = make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec':\",\n \"scores are used n_splits = 3 clf = LinearSVC(random_state=0) X,\",\n \"GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for cand_i\",\n \"raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits returned '\",\n \"cv_results['rank_test_%s' % key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand):\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3\",\n \"error gs.fit(X, y) def test_classes__property(): # Test that classes_ property\",\n \"# not want to break without an explicit deprecation cycle\",\n \"search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s\",\n \"product from functools import partial import pickle import sys from\",\n \"called only # once if ``cv.split`` is called once per\",\n \"1, 10] grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_,\",\n \"If # refit was done, then an exception would be\",\n \"probability = True refit = 'accuracy' if isinstance(scoring, tuple) else\",\n \"search works when the input features are given in the\",\n \"test hyperparameter search on user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier\",\n \"estimator contains the right value for foo_param clf = MockClassifier()\",\n \"the _pairwise property which matches the _pairwise property of its\",\n \"predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test\",\n \"# Test that regressors do not have a classes_ attribute\",\n \"attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the\",\n \"gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0)\",\n \"[0.01, 0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ ==\",\n \"BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC\",\n \"when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd(): #\",\n \"For the train scores, we do not take a weighted\",\n \"y).cv_results_ # Test scorer names result_keys = list(cv_results.keys()) expected_keys =\",\n \"warning is raised and score reset where appropriate. X, y\",\n \"import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline\",\n \"precision of time.time in windows is not high # enough\",\n \"are the same as above assert_allclose(test_cv_scores, [1, 1. / 3.])\",\n \"dict or a list \\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter\",\n \"(expected behavior), and this would cause an # error in\",\n \"= make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid = {'max_iter': [3, 4]}\",\n \"Test that grid search will capture errors on data with\",\n \"noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0], [1, 0],\",\n \"one of the parameters in the param_grid. param_grid = {\",\n \"in situation where behavior changed: # estimator requires score method\",\n \"grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid, scoring='roc_auc' ).fit(X, y)\",\n \"== 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k],\",\n \"import StringIO from itertools import chain, product from functools import\",\n \"both param setting for score_type in ('train', 'test'): per_param_scores =\",\n \"# loop to assert we can iterate over the grid\",\n \"test_grid_search_no_score(): # Test grid-search on classifier that has no score\",\n \"= make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]], random_state=0,\",\n \"evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results =\",\n \"make sure grid search and random search delegate n_features_in to\",\n \"@pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10) y\",\n \"compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy =\",\n \"Make sure `predict_proba` is not available when setting loss=['hinge'] #\",\n \"the precomputed kernel is not array-like # or sparse assert_raises(ValueError,\",\n \"3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list():\",\n \"This test ensures the equivalence of 'precomputed'. Non-regression test for\",\n \"y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): # Pass y as\",\n \"KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed =\",\n \"y): this_scores, this_params, n_test_samples = fit_grid_point( X, y, clone(svc), params,\",\n \"split dataset into two folds that are not iid #\",\n \"%s is available only after ' 'refitting on the best\",\n \"(5, 3, 2) check_y = lambda x: x.shape[1:] == (7,\",\n \"n_iter=10, random_state=0) assert [x for x in sampler] == [x\",\n \"to make sure each score differs clf = DecisionTreeRegressor(random_state=0) X,\",\n \"two C values are close enough to give similar models\",\n \"y = make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1,\",\n \"random_state=0) groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid\",\n \"an estimator without predict. # slight duplication of a test\",\n \"spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier\",\n \"pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X, y =\",\n \"cv = [[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(),\",\n \"if generators are supported as cv and # that the\",\n \"range(2): # tuple + chain transforms {\\\"a\\\": 1, \\\"b\\\": 2}\",\n \"% i][cand_i]) def test_predict_proba_disabled(): # Test predict_proba when disabled on\",\n \"ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x in sampler]\",\n \"def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'): cv_results.pop(key)\",\n \"['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1, 2, 3]},\",\n \"cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search =\",\n \"pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError: pass X\",\n \"[StratifiedKFold(), StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf, grid,\",\n \"import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import ignore_warnings\",\n \"def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the _pairwise\",\n \"# Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert\",\n \"[1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') # Test\",\n \"C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches =\",\n \"{'C': [0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is\",\n \"X, y = make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} #\",\n \"a list \\\\(0\\\\)'), ([{'foo': [0]}, 0], TypeError, r'Parameter .* is\",\n \"[1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]),\",\n \"pass refit='accuracy' if refit: probability = True refit = 'accuracy'\",\n \"{}, cv=2).fit(X, y) assert not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): #\",\n \"1: score = 1. else: score = 0. return score\",\n \"search.cv_results_ # Check tie breaking strategy - # Check that\",\n \"def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2, random_state=0) clf\",\n \"when disabled on estimator. X = np.arange(20).reshape(5, -1) y =\",\n \"the grid multiple times for i in range(2): # tuple\",\n \"as required\\\") def predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None,\",\n \"of BaseSearchCV has the _pairwise property which matches the _pairwise\",\n \"'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates =\",\n \"param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator,\",\n \"get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert\",\n \"1, 1] clf = SVC(probability=False) gs = GridSearchCV(clf, {}, cv=2).fit(X,\",\n \"it selects the smallest parameter in case of ties old_stdout\",\n \"available only after ' 'refitting on the best parameters' %\",\n \"selects the smallest parameter in case of ties old_stdout =\",\n \"key in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X,\",\n \"= KFold(n_splits=3).split(X) # pop all of it, this should cause\",\n \"from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]] =\",\n \"in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc, key)\",\n \"in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X, y,\",\n \"cv_results.pop(key) return cv_results # Check if generators are supported as\",\n \"test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score =\",\n \"grid-search on classifier that has no score function. clf =\",\n \"param setting in GridSearchCV.fit # the 2nd and 3rd parameter\",\n \"make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\": [1, 2, 3, 4]} cv\",\n \"dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in\",\n \"cv = BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha':\",\n \"test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3\",\n \"random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits, n_samples=n_samples),\",\n \"= GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"np.arange(6).reshape(6, -1) y = [0, 0, 0, 1, 1, 1]\",\n \"compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare\",\n \"{'C': Cs}) assert not hasattr(grid_search, 'classes_') # Test that the\",\n \"foo_param def fit(self, X, Y): assert len(X) == len(Y) self.classes_\",\n \"clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError,\",\n \"try: from pandas import Series, DataFrame types.append((DataFrame, Series)) except ImportError:\",\n \"= make_classification(random_state=0) svc = LinearSVC(random_state=0) scorer = make_scorer(accuracy_score) msg =\",\n \"best estimator n_features = 4 X, y = make_classification(n_features=n_features) gbdt\",\n \"GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True)\",\n \"if refit: probability = True refit = 'accuracy' if isinstance(scoring,\",\n \"= RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search,\",\n \"pytest.raises(IndexError, match='best_index_ index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric():\",\n \"== 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1])\",\n \"estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv, n_iter=3) random_search.fit(X, y) res_params\",\n \"= KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid\",\n \"not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches the\",\n \"1 return self def _fit_param_callable(): pass model = SearchCV( _FitParamClassifier(),\",\n \"attr_message = \\\"GridSearchCV not identical with precomputed metric\\\" assert (preds_original\",\n \"'mean_score_time', 'std_score_time') n_candidates = n_grid_points search = GridSearchCV(SVC(), cv=n_splits, param_grid=params,\",\n \"not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test GridSearchCV with SimpleImputer\",\n \"16): param_distributions = {\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10,\",\n \"[0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y)\",\n \"Test refit=callable in multiple metric evaluation setting \\\"\\\"\\\" def refit_callable(cv_results):\",\n \"20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv =\",\n \"*args, **kw): return 1 # create bad cv cv =\",\n \"# Ensure that `best_index_ != 0` for this dummy clf\",\n \"1; expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X,\",\n \"cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i]\",\n \"{'C': [0.1, 1.0]}, scoring=F1Loss) cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3\",\n \"refit else: probability = False clf = SVC(probability=probability, random_state=42) random_search\",\n \"indices # will be generated for the 2nd and subsequent\",\n \"clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X,\",\n \"length 1; expected\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10))\",\n \"once per param # setting. np.testing.assert_equal({k: v for k, v\",\n \"X_ = sp.csr_matrix(X_) clf = LinearSVC() cv = GridSearchCV(clf, {'C':\",\n \"cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert\",\n \"FMS can find the right number :) assert grid_search.best_params_[\\\"n_clusters\\\"] ==\",\n \"{'min_samples_split': [5, 10]}])) # Using regressor to make sure each\",\n \"tuple) else refit else: probability = False clf = SVC(probability=probability,\",\n \"len(set(hashable_samples)) == 99 # doesn't go into infinite loops params_distribution\",\n \"assert_allclose(train_cv_scores, [1, 1]) # Unweighted mean/std is used assert test_mean\",\n \"hasattr(grid_search, \\\"cv_results_\\\") def test_no_refit(): # Test that GSCV can be\",\n \"y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): # test\",\n \"random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None, tuple_of_arrays=None,\",\n \"x: x.shape[1:] == (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y,\",\n \"= DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0) mycv =\",\n \"['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0\",\n \"check_y = lambda x: x.shape[1:] == (7, 11) clf =\",\n \"score == \\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score ==\",\n \"times for i in range(2): # tuple + chain transforms\",\n \"GSCV can be used for model selection alone without refitting\",\n \"3]) # Smoke test the score etc: grid_search.score(X, y) grid_search.predict_proba(X)\",\n \"[4], [5]], [0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(),\",\n \"(preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1,\",\n \"evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X,\",\n \"import LeavePGroupsOut from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit\",\n \"== 3 # Now without a score, and without y\",\n \"parameter should not be None.\\\", gs.fit, X, y) gs.fit(X, y,\",\n \"compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec):\",\n \"3, base=0.1)) for refit in (True, False): random_searches = []\",\n \"(gs, rs): search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: #\",\n \"r=None): self.r_ = r def predict(self, X): return np.zeros(shape=(len(X))) model\",\n \"random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into two folds\",\n \"is raised and score reset where appropriate. X, y =\",\n \"in identifying the \\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A\",\n \"parameters that are estimators are cloned before fitting pipe =\",\n \"{'C': [1]} group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv\",\n \"# cv.score(X_[:180], y[:180])) # test loss where greater is worse\",\n \"= make_classification(n_samples=100, n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv\",\n \"BaseEstimator, ClassifierMixin from sklearn.base import clone from sklearn.exceptions import NotFittedError\",\n \"assert np.all(search.cv_results_[key] >= 0) assert np.all(search.cv_results_[key] < 1) for key\",\n \"1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When\",\n \"0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for\",\n \"fit(self, X, Y): assert len(X) == len(Y) self.classes_ = np.unique(Y)\",\n \"value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message):\",\n \"# error in this test. gs = GridSearchCV(clf, [{'parameter': [0,\",\n \"are same, the test and train scores must also be\",\n \"tolerate this behavior for # now and be careful not\",\n \"to test the behaviour of the grid search part gs\",\n \"the # loss is 'hinge'. assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X,\",\n \"\\\"\\\"\\\"Compare multi-metric cv_results with the ensemble of multiple single metric\",\n \"issue #13920. \\\"\\\"\\\" n_samples = 50 n_splits = 2 X,\",\n \"X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC() cv\",\n \"n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0, TypeError, r'Parameter .* is\",\n \"grid multiple times for i in range(2): # tuple +\",\n \"('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s is available\",\n \"search_cv): \\\"\\\"\\\" Test implementation catches the errors when 'best_index_' returns\",\n \"types.append((DataFrame, Series)) except ImportError: pass X = np.arange(100).reshape(10, 10) y\",\n \"[1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search =\",\n \"# popular third-party libraries and we should tolerate this behavior\",\n \"GridSearchCV.fit # the 2nd and 3rd parameter will not be\",\n \"X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\",\n \"random_search.cv_results_['params'] for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test)\",\n \"cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC()\",\n \"search = SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score'\",\n \"GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation catches the errors\",\n \"import pickle import sys from types import GeneratorType import re\",\n \"smallest parameter in case of ties old_stdout = sys.stdout sys.stdout\",\n \"np.array([0] * 5 + [1] * 5) clf = GridSearchCV(BrokenClassifier(),\",\n \"assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_')\",\n \"y) cv_results = search.cv_results_ # Check tie breaking strategy -\",\n \"search module\\\"\\\"\\\" from collections.abc import Iterable, Sized from io import\",\n \"the cv object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15,\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): # Pass y as list\",\n \"test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid():\",\n \"%d iterations. For ' 'exhaustive searches, use GridSearchCV.' % (grid_size,\",\n \"x in sampler] == [x for x in sampler] if\",\n \"== refit if refit: assert search_multi.refit == 'accuracy' else: assert\",\n \"y[train]) if score == \\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test])) elif\",\n \"test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict = {\\\"C\\\":\",\n \"cv = KFold() for est in estimators: grid_search = GridSearchCV(est,\",\n \"a broken estimator; this used to break with # sparse\",\n \"key in scorer_keys: assert cv_results['rank_test_%s' % key].dtype == np.int32 def\",\n \"index out of range'): clf.fit(X, y) def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test\",\n \"gs = GridSearchCV(clf, {}, cv=2).fit(X, y) assert not hasattr(gs, \\\"predict_proba\\\")\",\n \"class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot be fit twice\\\"\\\"\\\" def\",\n \"getattr(search_multi, key) == getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y =\",\n \"n_features=100, random_state=0) clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1,\",\n \"test_pandas_input(): # check cross_val_score doesn't destroy pandas dataframe types =\",\n \"result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ for candidate_i, C\",\n \">= 0) assert np.all(search.cv_results_[key] < 1) for key in ['mean_score_time',\",\n \"\\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf = SVC(gamma='auto') cv = GridSearchCV(clf,\",\n \"X as list in GridSearchCV X = np.arange(100).reshape(10, 10) y\",\n \"= InputFeatureType(X), TargetType(y) def check_df(x): return isinstance(x, InputFeatureType) def check_series(x):\",\n \"make sure _pairwise is delegated to the base estimator. Non-regression\",\n \"' 'than n_iter=%d. Running %d iterations. For ' 'exhaustive searches,\",\n \"assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV,\",\n \"check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv = KFold(n_splits=3) grid_search\",\n \"instead. If # refit was done, then an exception would\",\n \"search results. np.testing.assert_equal performs a # deep nested comparison of\",\n \"fit search can be pickled clf = MockClassifier() grid_search =\",\n \"predict. # slight duplication of a test from KDE def\",\n \"param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i]\",\n \"fit_grid_point, X, y, svc, params, train, test, {'score': scorer}, verbose=True)\",\n \"Check consistency of folds across the parameters gs = GridSearchCV(LinearSVC(random_state=0),\",\n \"cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in enumerate(cv.split(X,\",\n \"X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y =\",\n \"import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer\",\n \"y) # giving no scoring function raises an error grid_search_no_score\",\n \"got (<class 'int'>).\\\" \\\" Single values need to be wrapped\",\n \"score_auc < 1.0 assert score_accuracy < 1.0 assert score_auc !=\",\n \"behavior for # now and be careful not to break\",\n \"isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df,\",\n \"3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X))\",\n \"of a precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100,\",\n \"group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs:\",\n \"grid/random search\\\"\\\"\\\" assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_\",\n \"= np.array( [search.cv_results_['split%d_train_score' % s][0] for s in range(search.n_splits_)] )\",\n \"v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v for k,\",\n \"correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized search\",\n \"( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time'))) # Compare the keys, other\",\n \"test_grid_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3\",\n \"= GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message\",\n \"pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import assert_raises from\",\n \"the # form of a precomputed kernel matrix X_, y_\",\n \"gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs =\",\n \"len(X). err_msg = r\\\"Expected fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with\",\n \"the base estimator. Non-regression test for issue #13920. \\\"\\\"\\\" est\",\n \"in multiple metric evaluation setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A\",\n \"__init__(self, foo_param=0): self.foo_param = foo_param def fit(self, X, Y): assert\",\n \"= make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score =\",\n \"== grid_search.best_params_ # check that we can call score and\",\n \"with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): # this should raise a\",\n \"ValueError, \\\"Parameter grid for parameter (C) needs to\\\" \\\" be\",\n \"= make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]},\",\n \"return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42)\",\n \"matches the _pairwise property of its estimator. This test make\",\n \"search = GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth']\",\n \"3, 4]} cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] #\",\n \"[1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4 assert\",\n \"from sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing\",\n \"X = np.arange(6).reshape(6, -1) y = [0, 0, 0, 1,\",\n \"required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert all(cv_results[key].shape\",\n \"grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2,\",\n \"is specified # as one of the parameters in the\",\n \"== 'raise' raises the error X, y = make_classification(n_samples=20, n_features=10,\",\n \"MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, cv=3)\",\n \"for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning\",\n \"error with pytest.raises(ValueError, match='No fits were performed. ' 'Was the\",\n \"is common in # popular third-party libraries and we should\",\n \"expected_warning = ('The total space of parameters %d is smaller\",\n \"in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits)\",\n \"key in score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if\",\n \"0.01}, {'C': 0.001}): for train, test in cv.split(X, y): this_scores,\",\n \"np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X, y = make_classification(random_state=42) model.fit(X,\",\n \"set to zero as required for those fits # that\",\n \"fn_name), X) # Test that an invalid refit param raises\",\n \"# Using regressor to make sure each score differs clf\",\n \"np.zeros((10, 20)) y_train = np.ones((10, )) clf = SVC(kernel='precomputed') cv\",\n \"Make sure the predict_proba works when loss is specified #\",\n \"match=msg): fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False) def\",\n \"{'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search,\",\n \"check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking\",\n \"{'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search in (gs,\",\n \"GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter should not be\",\n \"if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric\",\n \"X, y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in\",\n \"cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits)))\",\n \"assert not hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0]\",\n \"results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates\",\n \"[\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0)\",\n \"cv cv = KFold(n_splits=3).split(X) # pop all of it, this\",\n \"# X dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y)\",\n \"same as grid_size sampler = ParameterSampler(params, n_iter=8) samples = list(sampler)\",\n \"for key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) ==\",\n \"y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): # Pass y\",\n \"the 2nd and subsequent cv.split calls. # This is a\",\n \"different length X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =\",\n \"verbose=3) # make sure it selects the smallest parameter in\",\n \"scores, we do not take a weighted mean irrespective of\",\n \"that errors caused by fits # to individual folds will\",\n \"Cs}) assert not hasattr(grid_search, 'classes_') # Test that the grid\",\n \"cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v for k, v in\",\n \"y[train]) expected_score = scorer(est, X[test], y[test]) # Test the return\",\n \"clf = LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(),\",\n \"assert_array_almost_equal(per_param_scores[0], per_param_scores[1]) assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search\",\n \"sklearn.metrics.pairwise import euclidean_distances from sklearn.impute import SimpleImputer from sklearn.pipeline import\",\n \"+ chain transforms {\\\"a\\\": 1, \\\"b\\\": 2} to (\\\"a\\\", 1,\",\n \"search methods with single metric methods\\\"\\\"\\\" assert search_acc.refit == refit\",\n \"when the input features are given in the # form\",\n \"didn't modify the parameter grid that was passed assert not\",\n \"list(sampler) assert len(samples) == 7 def test_stochastic_gradient_loss_param(): # Make sure\",\n \"param_dict = {\\\"C\\\": [1.0], \\\"kernel\\\": [\\\"rbf\\\"], \\\"gamma\\\": [0.1]} clf =\",\n \"score_keys if not key.startswith('rank')) scorer_keys = search.scorer_.keys() if search.multimetric_ else\",\n \"np.array([0] * 5 + [1] * 5) for InputFeatureType, TargetType\",\n \"% fn_name), getattr(grid_search, fn_name), X) # Test that an invalid\",\n \"cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert C ==\",\n \"= np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3,\",\n \"test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) #\",\n \"cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def\",\n \"kernel K_train = np.dot(X_[:180], X_[:180].T) y_train = y_[:180] clf =\",\n \"cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] ==\",\n \"is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ # check\",\n \"take a weighted mean irrespective of # i.i.d. or not\",\n \"hyperparameter search on user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier to\",\n \"2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X),\",\n \"y) assert not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\") and\",\n \"refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 # Ensure `best_score_`\",\n \"\\\"\\\"\\\" n_samples = 50 n_splits = 2 X, y =\",\n \"GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True) for\",\n \"list of keys in # clf.cv_results_. X, y = make_classification(n_samples=100,\",\n \"error_score=0, n_iter=2) for search in (gs, rs): search.fit(X, y) for\",\n \"in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates)\",\n \"n_iter=3) random_search.fit(X, y) res_params = random_search.cv_results_['params'] for cand_i in range(len(res_params)):\",\n \"not cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear')\",\n \"module\\\"\\\"\\\" from collections.abc import Iterable, Sized from io import StringIO\",\n \"2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def\",\n \"ParameterGrid(params): assert values in samples # test sampling without replacement\",\n \"highest rank assert ranks[clf.FAILING_PARAMETER] == 3 assert gs.best_index_ != clf.FAILING_PARAMETER\",\n \"the grid searcher has no classes_ attribute without a refit\",\n \"test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def\",\n \"matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the\",\n \"deep=False): return {'foo_param': self.foo_param} def set_params(self, **params): self.foo_param = params['foo_param']\",\n \"= search.cv_results_ # Check if score and timing are reasonable\",\n \"[2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys =\",\n \"regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert\",\n \"estimator X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\\\"random\\\",\",\n \"assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]}\",\n \"issues a ValueError so this is what we look for.\",\n \"pop all of it, this should cause the expected ValueError\",\n \"differs clf = DecisionTreeRegressor(random_state=0) X, y = make_classification(n_samples=100, n_informative=4, random_state=0)\",\n \"fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_')\",\n \"GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_')\",\n \"2])] grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall':\",\n \"K_train.tolist(), y_train) def test_grid_search_precomputed_kernel_error_nonsquare(): # Test that grid search returns\",\n \"y[test]) # Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score)\",\n \"cv=2, error_score=\\\"raise\\\" ) # The CheckingClassifier generates an assertion error\",\n \"gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv in\",\n \"return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check results structure\",\n \"{'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg):\",\n \"cv.fit(K_train, y_train) assert cv.best_score_ >= 0 # compute the test\",\n \"10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] grid_searches = []\",\n \"= np.array([0] * 5 + [1] * 5) Cs =\",\n \"we didn't modify the parameter grid that was passed assert\",\n \"types: # X dataframe, y series X_df, y_ser = InputFeatureType(X),\",\n \"\\\"predict_proba\\\") clf.fit(X, y) assert not hasattr(clf, \\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X\",\n \"following # proper deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid,\",\n \"GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] ==\",\n \"errors on data with different length X_, y_ = make_classification(n_samples=200,\",\n \"from sklearn.model_selection import GroupKFold from sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection\",\n \"euclidean metric to validate _pairwise is working X_precomputed = euclidean_distances(X)\",\n \"= 30 params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)},\",\n \"import re import numpy as np import scipy.sparse as sp\",\n \"# setting. np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if\",\n \"test_std == pytest.approx(np.std(test_cv_scores)) # For the train scores, we do\",\n \"= BrokenKFold(n_splits=3) train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3,\",\n \"reset where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf\",\n \"first one contains data of all 4 blobs, second only\",\n \"between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0],\",\n \"'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys = ('param_C',\",\n \"is smaller ' 'than n_iter=%d. Running %d iterations. For '\",\n \"params, train, test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated\",\n \"('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ]) GridSearchCV(p, {'classifier__foo_param': [1, 2,\",\n \"x.shape[1:] == (5, 3, 2) check_y = lambda x: x.shape[1:]\",\n \"= [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame,\",\n \"i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if score\",\n \"verbose=False) def test_pickle(): # Test that a fit search can\",\n \"LightGBM # that do not exactly respect the scikit-learn API\",\n \"for est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X,\",\n \"grid_search.classes_) # Test that regressors do not have a classes_\",\n \"= search.cv_results_ # Check tie breaking strategy - # Check\",\n \"scoring=scoring, refit=False) grid_search.fit(X, y) grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y\",\n \"assert (all(cv_results[k] <= 1) for k in score_keys if 'time'\",\n \"following parameters. But this abuse is common in # popular\",\n \"def fit(self, X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y,\",\n \"[{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}]\",\n \"of a model that has the least `mean_test_prec`. \\\"\\\"\\\" assert\",\n \"= predict predict_log_proba = predict decision_function = predict def score(self,\",\n \"case for the following parameters. But this abuse is common\",\n \"'accuracy' else: assert not search_multi.refit return # search cannot predict/score\",\n \"assert C == C3 assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test\",\n \"else??? # noise-free simple 2d-data X, y = make_blobs(centers=[[0, 0],\",\n \"(7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search\",\n \"clf, param_dict) def test_grid_search_sparse(): # Test that grid search works\",\n \"sklearn.model_selection import train_test_split from sklearn.model_selection import KFold from sklearn.model_selection import\",\n \"fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False. %s\",\n \"predict/score without refit X, y = make_blobs(n_samples=100, n_features=4, random_state=42) for\",\n \"train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric(): X,\",\n \"of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params == this_params assert n_test_samples\",\n \"is not available when setting loss=['hinge'] # in param_grid param_grid\",\n \"pytest.raises(TypeError, match='best_index_ returned is not an integer'): clf.fit(X, y) @pytest.mark.parametrize('out_bound_value',\",\n \"(all(cv_results[k] <= 1) for k in score_keys if 'time' not\",\n \"assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the function will\",\n \"5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param':\",\n \"eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test\",\n \"should tolerate this behavior for # now and be careful\",\n \"in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'],\",\n \"is None) propagates to GridSearchCV # And also check if\",\n \"for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys): # Check\",\n \"search will capture errors on data with different length X_,\",\n \"np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1,\",\n \"search.cv_results_ contains all the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys\",\n \"9, 10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for\",\n \"re-fitting a broken estimator; this used to break with #\",\n \"= make_classification(n_samples=n_samples, random_state=0) grid_params = {'n_neighbors': [10]} # defaults to\",\n \"assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output estimator\",\n \"cv = GridSearchCV(clf, param_dict) cv.fit(X_, y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\",\",\n \"used assert test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) #\",\n \"(attr[0].islower() and attr[-1:] == '_' and attr not in {'cv_results_',\",\n \"without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert\",\n \"= search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and\",\n \"10}, {}, {'C': .5}] assert_grid_iter_equals_getitem(has_empty) def test_grid_search(): # Test that\",\n \"model = SearchCV( _FitParamClassifier(), param_search ) # NOTE: `fit_params` should\",\n \"dict(kernel=['poly', ], degree=[1, 2])] grid_searches = [] for scoring in\",\n \"raises an error grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}) assert_raise_message(TypeError, \\\"no\",\n \"search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y)\",\n \"the functions predict/transform etc raise meaningful # error messages for\",\n \"sklearn.model_selection import LeaveOneGroupOut from sklearn.model_selection import LeavePGroupsOut from sklearn.model_selection import\",\n \"'second': ['a', 'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params,\",\n \"for # now and be careful not to break support\",\n \"assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): #\",\n \"make_classification(n_samples=100, n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision',\",\n \"for k, v in cv_results_multi.items() if not k.endswith('_time')}, {k: v\",\n \"scores between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1])\",\n \"grid_search.fit(X, y) assert not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\")\",\n \"assert n_test_samples == test.size # Should raise an error upon\",\n \"fit parameter\\\\(s\\\\) \\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y,\",\n \"def fit_grid(param_grid): return GridSearchCV(clf, param_grid, return_train_score=True).fit(X, y) class CustomSearchCV(BaseSearchCV): def\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:])\",\n \"other # create \\\"cv\\\" for splits cv = [[mask, ~mask],\",\n \"non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv) # Should not raise\",\n \"grid, cv=cv) # Should not raise an error gs.fit(X, y)\",\n \"= {\\\"C\\\": []} clf = SVC() assert_raise_message( ValueError, \\\"Parameter values\",\n \"array are of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param],\",\n \"roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the\",\n \"predict/transform etc raise meaningful # error messages for fn_name in\",\n \"refit=refit) grid_search.fit(X, y) # Both ARI and FMS can find\",\n \"import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import KNeighborsClassifier\",\n \"ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) assert [x for x in sampler] ==\",\n \"n_iter = 9 grid_size = 8 expected_warning = ('The total\",\n \"in ('train', 'test'): per_param_scores = {} for param_i in range(4):\",\n \"and k != 'rank_test_score') # Check cv_results structure check_cv_results_array_types(search, param_keys,\",\n \"== 10 for sample in samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\",\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred =\",\n \"kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute\",\n \"return None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf =\",\n \"following two estimators inherit from BaseEstimator, # to test hyperparameter\",\n \"init=\\\"random\\\", n_init=1) # Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score']\",\n \"# test basic properties of param sampler param_distributions = {\\\"kernel\\\":\",\n \"sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid from sklearn.model_selection import\",\n \"of a test from KDE def custom_scoring(estimator, X): return 42\",\n \"assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now without a score, and\",\n \"of 1/3 on # the other # create \\\"cv\\\" for\",\n \"returned 'best_index_' is out of bounds. \\\"\\\"\\\" return out_bound_value X,\",\n \"[0, 1]}, cv=2, error_score=0, n_iter=2) for search in (gs, rs):\",\n \"an invalid refit param raises appropriate error messages for refit\",\n \"test the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) #\",\n \"# Make sure `predict_proba` is not available when setting loss=['hinge']\",\n \"use GridSearchCV.' % (grid_size, n_iter, grid_size)) assert_warns_message(UserWarning, expected_warning, list, sampler)\",\n \"scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') #\",\n \"v in search_rec.cv_results_.items()}) # Check if score and timing are\",\n \"search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype\",\n \"not want to break without an explicit deprecation cycle and\",\n \"methods with single metric methods\\\"\\\"\\\" assert search_acc.refit == refit if\",\n \"def test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in multiple metric evaluation setting\",\n \"parameter def fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_\",\n \"# this should raise a NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error():\",\n \"assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'):\",\n \"= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y)\",\n \"clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits) cv.fit(X, y)\",\n \"LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1, 1,\",\n \"{}, {'C': [.5]}]) assert len(has_empty) == 4 assert list(has_empty) ==\",\n \"that it gives the correct result assert grid_search.score(X, y) ==\",\n \"in range(2): # tuple + chain transforms {\\\"a\\\": 1, \\\"b\\\":\",\n \"to the cv object rng = np.random.RandomState(0) X, y =\",\n \"\\\"\\\"\\\" return None X, y = make_classification(n_samples=100, n_features=4, random_state=42) clf\",\n \"which # is not the case for the following parameters.\",\n \"Sized from io import StringIO from itertools import chain, product\",\n \"OneTimeSplitter # Neither of the following two estimators inherit from\",\n \"= [.1, 1, 10] for score in ['f1', 'roc_auc']: grid_search\",\n \"sklearn.model_selection._validation import FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree\",\n \"samples # test sampling without replacement in a large grid\",\n \"scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test scorer names\",\n \"k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k,\",\n \"v for k, v in cv_results_acc_rec.items() if not k.endswith('_time')}) def\",\n \"bad cv cv = BrokenKFold(n_splits=3) train_size = 100 ridge =\",\n \"def test_gridsearch_nd(): # Pass X as list in GridSearchCV X_4d\",\n \"# Test exception handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError,\",\n \"assert C == C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200,\",\n \"grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search, 'classes_') #\",\n \"Test that a fit search can be pickled clf =\",\n \"CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform from sklearn.base\",\n \"and \\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\") # Make\",\n \"scorer_keys = search.scorer_.keys() if search.multimetric_ else ['score'] for key in\",\n \"common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True} search =\",\n \"the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test =\",\n \"= GridSearchCV(KernelDensity(), param_grid=dict(bandwidth=[.01, .1, 1]), scoring=custom_scoring) search.fit(X) assert search.best_params_['bandwidth'] ==\",\n \"[1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80)\",\n \"pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): # Test search with multi-output\",\n \"X, y, sample_weight=None, tuple_of_arrays=None, scalar_param=None, callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert\",\n \"works with both dense and sparse matrices X_, y_ =\",\n \"GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that\",\n \"GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) assert\",\n \"support for those without following # proper deprecation cycle. fit_params\",\n \"pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False)\",\n \"refit=False, cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \\\"best_estimator_\\\") and\",\n \"cv=3 ) grid_search.fit(X, y) assert not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\\",\n \"check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params'])\",\n \"upon multimetric scorer assert_raise_message(ValueError, \\\"For evaluating multiple scores, use \\\"\",\n \"to GridSearchCV if n_iter the same as grid_size sampler =\",\n \"for k, v in search_rec.cv_results_.items()}) # Check if score and\",\n \"will not be evaluated as no train/test indices # will\",\n \"the right value for foo_param clf = MockClassifier() grid_search =\",\n \"score def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self, **params):\",\n \"object rng = np.random.RandomState(0) X, y = make_classification(n_samples=15, n_classes=2, random_state=0)\",\n \"for key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of\",\n \"cv=n_splits) cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not\",\n \"[ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0, TypeError, r'Parameter\",\n \"\\\"cv_results_\\\") def test_unsupervised_grid_search(): # test grid-search with unsupervised estimator X,\",\n \"= list( gs.cv_results_['split%d_%s_score' % (s, score_type)][param_i] for s in range(5))\",\n \"make_scorer(accuracy_score) msg = (\\\"fit_grid_point is deprecated in version 0.23 \\\"\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y)\",\n \"= ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i for cv_i in\",\n \"11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:] == (5,\",\n \"got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid\",\n \"assert len(X) == len(Y) self.classes_ = np.unique(Y) return self def\",\n \"estimator. This test make sure _pairwise is delegated to the\",\n \"fail. def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in\",\n \"grid searcher has no classes_ attribute without a refit grid_search\",\n \"alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2]) assert\",\n \"r'Parameter .* is not a dict \\\\(0\\\\)'), ({'foo': 0}, TypeError,\",\n \"gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid\",\n \"# smoketest grid search grid_search_no_score.fit(X, y) # check that best\",\n \"assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking\",\n \"for trivial X and y assert np.all(search.cv_results_[key] >= 0) assert\",\n \"clf = MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search\",\n \"fit(self, X, y): assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True\",\n \"clf = FailingClassifier() # refit=False because we only want to\",\n \"broken estimator; this used to break with # sparse SVMs.\",\n \"0.12's stats dists do not accept seed, hence we use\",\n \"k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit multi-metric search methods\",\n \"def inverse_transform(self, X): return X - self.foo_param predict_proba = predict\",\n \"next two param # settings (C=0.2) are same, the test\",\n \"grid search works when the input features are given in\",\n \"fold and a score of 1/3 on # the other\",\n \"# Test predict_proba when disabled on estimator. X = np.arange(20).reshape(5,\",\n \"# Test that grid search works with both dense and\",\n \"X, y) def test_grid_search_error(): # Test that grid search will\",\n \"specialized_params): # Test the IID parameter TODO: Clearly this test\",\n \"= clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME\",\n \"\\\"cv_results_\\\") def test_y_as_list(): # Pass y as list in GridSearchCV\",\n \"metric evaluation setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function\",\n \"y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): # Test search\",\n \"y) for x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) # Special\",\n \"clf = FailingClassifier() # refit=False because we want to test\",\n \"# to individual folds will be caught and warnings raised\",\n \"test_gridsearch_nd(): # Pass X as list in GridSearchCV X_4d =\",\n \"make_blobs(n_samples=100, n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi,\",\n \"import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats import\",\n \"on classifier that has no score function. clf = LinearSVC(random_state=0)\",\n \"SVC from sklearn.tree import DecisionTreeRegressor from sklearn.tree import DecisionTreeClassifier from\",\n \"def check_series(x): return isinstance(x, TargetType) clf = CheckingClassifier(check_X=check_df, check_y=check_series) grid_search\",\n \"n_iter=3, random_state=0) assert [x for x in sampler] == [x\",\n \"assert ranks[0] <= 2 and ranks[1] <= 2 # Check\",\n \"out of bounds. \\\"\\\"\\\" return out_bound_value X, y = make_classification(n_samples=100,\",\n \"set to a scorer key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy',\",\n \"kernel matrix corresponding to the linear kernel K_train = np.dot(X_[:180],\",\n \"to compete with scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy\",\n \"smoketest grid search grid_search_no_score.fit(X, y) # check that best params\",\n \"cv = KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with\",\n \"be preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim ==\",\n \"as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert\",\n \"is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv =\",\n \"NOTE The precision of time.time in windows is not high\",\n \"# Should raise an error upon multimetric scorer assert_raise_message(ValueError, \\\"For\",\n \"when the precomputed kernel is not array-like # or sparse\",\n \"candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X, y\",\n \"* 3 * 2).reshape(10, 5, 3, 2) y_3d = np.arange(10\",\n \"method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for key in\",\n \"metric\\\" assert (preds_original == preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV,\",\n \"make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf =\",\n \"cv parameter assert isinstance(KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), GeneratorType) gs3 =\",\n \"must match estimator\\\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise',\",\n \"= dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit\",\n \"scoring parameter score_no_scoring = search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y)\",\n \"{'foo_param': [1, 2, 3]}, cv=2, error_score=\\\"raise\\\" ) # The CheckingClassifier\",\n \"and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X)\",\n \"identifying the \\\"best\\\" estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy\",\n \"@pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1,\",\n \"because we want to test the behaviour of the grid\",\n \"param # settings (C=0.2) are same, the test and train\",\n \"= np.arange(20).reshape(5, -1) y = [0, 0, 1, 1, 1]\",\n \"Test the return values of fit_grid_point assert_almost_equal(this_scores, expected_score) assert params\",\n \"where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf =\",\n \"not available as the # loss is 'hinge'. assert not\",\n \"n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not cv_results['param_degree'].mask[i])\",\n \"4 def test_gridsearch_no_predict(): # test grid-search with an estimator without\",\n \"with multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters =\",\n \"in cv_results return cv_results['mean_test_score'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42)\",\n \"returns an out of bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\"\",\n \"the score etc: grid_search.score(X, y) grid_search.predict_proba(X) grid_search.decision_function(X) grid_search.transform(X) # Test\",\n \"[(GridSearchCV, {'alpha': [0.1, 0.01]}), (RandomizedSearchCV, {'alpha': uniform(0.01, 0.1)})] ) def\",\n \"[[mask, ~mask], [~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv,\",\n \"'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring,\",\n \"[0.1, 1]}, scoring='precision', refit=refit_callable_invalid_type) with pytest.raises(TypeError, match='best_index_ returned is not\",\n \"assert len(samples) == 99 hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'], p['b'],\",\n \"* 5 + [1] * 5) clf = CheckingClassifier( check_X=lambda\",\n \"and FMS can find the right number :) assert grid_search.best_params_[\\\"n_clusters\\\"]\",\n \"import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV\",\n \"'rank_test_score') assert (all(cv_results[k] <= 1) for k in score_keys if\",\n \"tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2 assert tuple_of_arrays[1].ndim\",\n \"call score and that it gives the correct result assert\",\n \"sure each score differs clf = DecisionTreeRegressor(random_state=0) X, y =\",\n \"ValueError so this is what we look for. assert_raises(ValueError, gs.fit,\",\n \"cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key\",\n \"preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV,\",\n \"train, test, {'score': scorer}, verbose=True) # FIXME remove test_fit_grid_point_deprecated as\",\n \"import CheckingClassifier, MockDataFrame from scipy.stats import bernoulli, expon, uniform from\",\n \"A dummy function tests when returned 'best_index_' is not integer.\",\n \"score == \\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec)\",\n \"Check if generators are supported as cv and # that\",\n \"BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X, y #\",\n \") def test_scalar_fit_param_compat(SearchCV, param_search): # check support for scalar values\",\n \"it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not hasattr(grid_search,\",\n \"the error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf =\",\n \"sklearn.model_selection import GroupShuffleSplit from sklearn.model_selection import GridSearchCV from sklearn.model_selection import\",\n \"random_state=42) n_splits = 3 n_grid_points = 6 params = [dict(kernel=['rbf',\",\n \"# Check cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys,\",\n \"assert_grid_iter_equals_getitem(grid2) # Special case: empty grid (useful to get default\",\n \"from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from sklearn.impute\",\n \".*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input, error_type, error_message): with\",\n \"can be called only # once if ``cv.split`` is called\",\n \"'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter search = RandomizedSearchCV(SVC(),\",\n \"assert_raise_message(ValueError, \\\"For evaluating multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point,\",\n \"of 'precomputed'. Non-regression test for issue #13920. \\\"\\\"\\\" n_samples =\",\n \"= cv.predict(X) # precompute euclidean metric to validate _pairwise is\",\n \"0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error != 'raise' #\",\n \"MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert\",\n \"assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_allclose from\",\n \"2d-data X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1],\",\n \"ParameterGrid([{'C': [1, 10]}, {}, {'C': [.5]}]) assert len(has_empty) == 4\",\n \"match=\\\"_run_search not implemented.\\\"): # this should raise a NotImplementedError BadSearchCV(SVC()).fit(X,\",\n \"y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE The precision\",\n \"be removed on 0.25 def test_fit_grid_point_deprecated(): X, y = make_classification(random_state=0)\",\n \"random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42),\",\n \"# the other # create \\\"cv\\\" for splits cv =\",\n \"0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y) gs4 =\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make\",\n \"= ParameterGrid(params2) assert len(grid2) == 6 # loop to assert\",\n \"= {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01,\",\n \"{'alpha': uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support\",\n \"grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): # Pass\",\n \"train_test_split( *make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y,\",\n \"{'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C\",\n \"2, 3]}, cv=2).fit(X, y) class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a\",\n \"if n_iter the same as grid_size sampler = ParameterSampler(params, n_iter=8)\",\n \"'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys\",\n \"random_state=0) clf = FailingClassifier() # refit=False because we only want\",\n \"y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\\\"random\\\", n_init=1) #\",\n \"def test_gridsearch_no_predict(): # test grid-search with an estimator without predict.\",\n \"= True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit():\",\n \"parameter (C) need to be a non-empty sequence.\\\", GridSearchCV, clf,\",\n \"that a warning is raised and score reset where appropriate.\",\n \"[grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize(\",\n \"on estimator. X = np.arange(20).reshape(5, -1) y = [0, 0,\",\n \"to\\\" \\\" be a list or numpy array, but got\",\n \"catches the errors when 'best_index_' returns an out of bound\",\n \"random_state=0) est_parameters = {\\\"max_depth\\\": [1, 2, 3, 4]} cv =\",\n \"parameter=None): self.parameter = parameter def fit(self, X, y=None): if self.parameter\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2\",\n \"def test_pickle(): # Test that a fit search can be\",\n \"test_empty_cv_iterator_error(): # Use global X, y # create cv cv\",\n \"`best_index_ != 0` for this dummy clf assert clf.best_index_ !=\",\n \"in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params\",\n \"def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits =\",\n \"score_keys if k != 'rank_test_score') assert (all(cv_results[k] <= 1) for\",\n \"GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') # FailingClassifier\",\n \"= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180])\",\n \"make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score = LinearSVCNoScore(random_state=0)\",\n \"cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data():\",\n \"hasattr(grid_search, \\\"cv_results_\\\") def test_y_as_list(): # Pass y as list in\",\n \"values are close enough to give similar models # which\",\n \"est.fit(X[train], y[train]) expected_score = scorer(est, X[test], y[test]) # Test the\",\n \"{'a': range(10), 'b': range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99,\",\n \"self.has_been_fit_ = True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def\",\n \"np.testing.assert_equal({k: v for k, v in gs.cv_results_.items() if not k.endswith('_time')},\",\n \"\\\\['eggs'\\\\] not seen.\\\" with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg\",\n \"1 # scores are the same as above assert_allclose(test_cv_scores, [1,\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input(): # check cross_val_score doesn't\",\n \"= search_auc.score(X, y) # ensure the test is sane assert\",\n \"assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): # test grid-search with unsupervised\",\n \"`sample_weight`) which # is not the case for the following\",\n \"1]) # Unweighted mean/std is used assert test_mean == pytest.approx(np.mean(test_cv_scores))\",\n \"also checks if the keys # are present assert all((np.all(cv_results_multi[k]\",\n \"@pytest.mark.parametrize('out_bound_value', [-1, 2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\"\",\n \"that parameters that are estimators are cloned before fitting pipe\",\n \"test_scalar_fit_param(SearchCV, param_search): # unofficially sanctioned tolerance for scalar values in\",\n \"y) # ensure the test is sane assert score_auc <\",\n \"y, clone(svc), params, train, test, scorer, verbose=False) est = clone(svc).set_params(**params)\",\n \"1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\" Test refit=callable,\",\n \"non-empty sequence.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": \\\"1,2,3\\\"} clf\",\n \"replacement in a large grid params = {'a': range(10), 'b':\",\n \"assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\",\",\n \"gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True),\",\n \"None) propagates to GridSearchCV # And also check if groups\",\n \"`refit=True` to get a list of keys in # clf.cv_results_.\",\n \"NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which #\",\n \"cv_results structure check_cv_results_array_types(search, param_keys, score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) #\",\n \"= True refit = 'accuracy' if isinstance(scoring, tuple) else refit\",\n \"function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs\",\n \"== cv._pairwise, attr_message def test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of BaseSearchCV\",\n \"3]}, refit=True, n_iter=3, cv=3) random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X),\",\n \"cv and # that the splits are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_))\",\n \"only one point. clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param':\",\n \"# test that correct scores are used n_splits = 3\",\n \"search_no_scoring.score(X, y) score_accuracy = search_accuracy.score(X, y) score_no_score_auc = search_no_score_method_auc.score(X, y)\",\n \"parameter (C) needs to\\\" \\\" be a list or numpy\",\n \"param_grid=param_grid, cv=3) # When the estimator is not fitted, `predict_proba`\",\n \"for p in grid2) assert (points == set((\\\"bar\\\", x, \\\"foo\\\",\",\n \"random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc,\",\n \"doesn't go into infinite loops params_distribution = {'first': bernoulli(.5), 'second':\",\n \"assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype\",\n \"and cv.get_n_splits returned ' 'inconsistent results. Expected \\\\\\\\d+ ' 'splits,\",\n \"estimators = (DecisionTreeRegressor(), DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0, None]} cv\",\n \"assert_array_almost_equal(per_param_scores[2], per_param_scores[3]) def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf,\",\n \"cv = KFold(n_splits=3).split(X) # pop all of it, this should\",\n \"'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure\",\n \"clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], ) grid_search = GridSearchCV(clf,\",\n \"scoring='precision', refit=True) clf.fit(X, y) # Ensure that `best_index_ != 0`\",\n \"of ParameterGrid. params1 = {\\\"foo\\\": [1, 2, 3]} grid1 =\",\n \"a # deep nested comparison of the two cv_results dicts\",\n \"test for issue #13920. \\\"\\\"\\\" n_samples = 50 n_splits =\",\n \"in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v in\",\n \"search.cv_results_[key][1] >= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] <\",\n \"A dummy function tests when returned 'best_index_' is out of\",\n \"'_accuracy', k): v for k, v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall',\",\n \"= GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_\",\n \"and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf')\",\n \"= (\\\"fit_grid_point is deprecated in version 0.23 \\\" \\\"and will\",\n \"grid_searches.append(grid_search) compare_cv_results_multimetric_with_single(*grid_searches) def test_random_search_cv_results_multimetric(): X, y = make_classification(n_samples=50, n_features=4, random_state=42)\",\n \".1 assert search.best_score_ == 42 def test_param_sampler(): # test basic\",\n \"with pytest.raises(ValueError, match='No fits were performed. ' 'Was the CV\",\n \"= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4\",\n \"grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser)\",\n \"enumerate(Cs): clf.set_params(C=C) cv_scores = np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s\",\n \"9 grid_size = 8 expected_warning = ('The total space of\",\n \"on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X, y = make_classification(random_state=0) cv\",\n \"implementation catches the errors when 'best_index_' returns an invalid result.\",\n \"Test with a randomized search for est in estimators: random_search\",\n \"repeated calls yield identical parameters param_distributions = {\\\"C\\\": [0, 1,\",\n \"test_predict_proba_disabled(): # Test predict_proba when disabled on estimator. X =\",\n \"# compute the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T)\",\n \"check that best params are equal assert grid_search_no_score.best_params_ == grid_search.best_params_\",\n \"if k != 'rank_test_score') assert (all(cv_results[k] <= 1) for k\",\n \") # The CheckingClassifier generates an assertion error if #\",\n \"required for those fits # that are expected to fail.\",\n \"= params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that\",\n \"= LinearSVC() cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180])\",\n \"range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i) == 0.0) for cand_i in range(n_candidates) if\",\n \"the train scores, we do not take a weighted mean\",\n \"all(isinstance(cv_results[param], np.ma.MaskedArray) for param in param_keys) assert all(cv_results[key].dtype == object\",\n \"a randomized search for est in estimators: random_search = RandomizedSearchCV(est,\",\n \"assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3]) # Smoke\",\n \"cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert\",\n \"random_state=42) samples = list(sampler) assert len(samples) == 99 hashable_samples =\",\n \"setting loss=['hinge'] # in param_grid param_grid = { 'loss': ['hinge'],\",\n \"sklearn.base import clone from sklearn.exceptions import NotFittedError from sklearn.datasets import\",\n \"return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier():\",\n \"indices are generated for all # the cv splits, for\",\n \"be caught and warnings raised instead. If # refit was\",\n \"= ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x in sampler]\",\n \"caused by fits # to individual folds will be caught\",\n \"as the same train/test indices are generated for all #\",\n \"# are present assert all((np.all(cv_results_multi[k] <= 1) for k in\",\n \"metric to validate _pairwise is working X_precomputed = euclidean_distances(X) clf\",\n \"first two param settings (C=0.1) and the next two param\",\n \"y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] for\",\n \"v in gs2.cv_results_.items() if not k.endswith('_time')}) # Check consistency of\",\n \"{'C': Cs}, scoring=score, cv=n_splits) cv_results = grid_search.fit(X, y).cv_results_ # Test\",\n \"0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True) gs.fit(X, y) # As the first\",\n \"error_message\\\", [(0, TypeError, r'Parameter .* is not a dict or\",\n \"on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter =\",\n \"0.1, 1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0\",\n \"3]}, cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def\",\n \"RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search in\",\n \"is called once per param setting in GridSearchCV.fit # the\",\n \"no train/test indices # will be generated for the 2nd\",\n \"'refitting on the best parameters' % fn_name), getattr(grid_search, fn_name), X)\",\n \"behavior changed: # estimator requires score method to compete with\",\n \"set((\\\"bar\\\", x, \\\"foo\\\", y) for x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"])))\",\n \"import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances\",\n \"in ['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time in\",\n \"GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc, {'C':\",\n \"\\\"foo\\\", y) for x, y in product(params2[\\\"bar\\\"], params2[\\\"foo\\\"]))) assert_grid_iter_equals_getitem(grid2) #\",\n \"test sampling without replacement in a large grid params =\",\n \"not equal\\\" % attr def test__custom_fit_no_run_search(): class NoRunSearchSearchCV(BaseSearchCV): def __init__(self,\",\n \"[0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C =\",\n \"} X = np.arange(24).reshape(6, -1) y = [0, 0, 0,\",\n \"sklearn.model_selection import ParameterGrid from sklearn.model_selection import ParameterSampler from sklearn.model_selection._search import\",\n \"sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from sklearn.neighbors import\",\n \"sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import\",\n \"test_X_as_list(): # Pass X as list in GridSearchCV X =\",\n \"# Test that GSCV can be used for model selection\",\n \"X as list in GridSearchCV X_4d = np.arange(10 * 5\",\n \".1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1],\",\n \"results: if not k.endswith('_time'): # XXX: results['params'] is a list\",\n \"y_pred = cv.predict(K_test) assert np.mean(y_pred == y_test) >= 0 #\",\n \"= GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:])\",\n \"fits were performed. ' 'Was the CV iterator empty\\\\\\\\? '\",\n \"def score(self): raise AttributeError X = np.array([[-1, -1], [-2, -1],\",\n \"'linear') assert all((not cv_results['param_C'].mask[i] and not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for\",\n \"== [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1,\",\n \"properties of param sampler param_distributions = {\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\":\",\n \"1, 0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0)\",\n \"= cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C ==\",\n \"perfect classification on one fold and a score of 1/3\",\n \"ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8 expected_warning =\",\n \"GridSearchCV(clf, {'foo_param': [1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search\",\n \"search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only\",\n \"n_features=4, random_state=42) for method in ('predict', 'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X),\",\n \"def test_grid_search(): # Test that the best estimator contains the\",\n \"X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42 ) class\",\n \"sp import pytest from sklearn.utils.fixes import sp_version from sklearn.utils._testing import\",\n \"search.fit(X, y) for key in ['mean_fit_time', 'std_fit_time']: # NOTE The\",\n \"callable_param=None): super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param)\",\n \"== pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the train\",\n \"# ensure the test is sane assert score_auc < 1.0\",\n \"evaluating multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y,\",\n \"n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores were set\",\n \"n_informative=4, random_state=0) mycv = CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth':\",\n \"'int'>).\\\" \\\" Single values need to be wrapped in a\",\n \"Check that there is a tie in the mean scores\",\n \"isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not hasattr(regressor_results[0], 'coef_') assert\",\n \"# Test that grid search works when the input features\",\n \"refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr(): #\",\n \"# Regression test for bug in refitting # Simulates re-fitting\",\n \"mean scores between # candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0],\",\n \"assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a randomized\",\n \"make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 params = [dict(kernel=['rbf', ],\",\n \").fit(X, y) search_auc = GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check\",\n \"warning if n_iter is bigger than total parameter space params\",\n \"len(Y) self.classes_ = np.unique(Y) return self def predict(self, T): return\",\n \"assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C': [1, 10]}, {},\",\n \"preserved as tuple. assert isinstance(tuple_of_arrays, tuple) assert tuple_of_arrays[0].ndim == 2\",\n \"np.ma.MaskedArray) for key in score_keys) assert all(cv_results[key].dtype == np.float64 for\",\n \"deprecation cycle and API # recommendations for implementing early stopping\",\n \"def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises the\",\n \"1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]})) results = evaluate([{'min_samples_split':\",\n \"# make sure grid search and random search delegate n_features_in\",\n \"with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10),\",\n \"= [0, 0, 1, 1, 1] p = Pipeline([ ('imputer',\",\n \"], degree=[1, 2])] grid_searches = [] for scoring in ({'accuracy':\",\n \"{\\\"random_state\\\": [0, None]} cv = KFold() for est in estimators:\",\n \"= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score='raise') #\",\n \"y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1,\",\n \"ValueError, \\\"Parameter values for parameter (C) need to be a\",\n \"['mean_fit_time', 'std_fit_time']: # NOTE The precision of time.time in windows\",\n \"X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that grid scores\",\n \"sampler) # degenerates to GridSearchCV if n_iter the same as\",\n \"kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test\",\n \"estimator. This test ensures the equivalence of 'precomputed'. Non-regression test\",\n \"checks if the keys # are present assert all((np.all(cv_results_multi[k] <=\",\n \"_run_search(self, evaluate): results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth':\",\n \"disabled when using `refit=callable` assert not hasattr(clf, 'best_score_') def test_gridsearch_nd():\",\n \"test that repeated calls yield identical parameters param_distributions = {\\\"C\\\":\",\n \"== .1 else 0 X, _ = make_blobs(cluster_std=.1, random_state=1, centers=[[0,\",\n \"GeneratorType import re import numpy as np import scipy.sparse as\",\n \"error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure\",\n \"== (7, 11) clf = CheckingClassifier( check_X=check_X, check_y=check_y, methods_to_check=[\\\"fit\\\"], )\",\n \"Ensure `best_score_` is disabled when using `refit=callable` assert not hasattr(clf,\",\n \"predict(self, T): return T.shape[0] def transform(self, X): return X +\",\n \":| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k],\",\n \"grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data,\",\n \"numpy array, but got (<class 'int'>).\\\" \\\" Single values need\",\n \"the best estimator contains the right value for foo_param clf\",\n \"= GridSearchCV(SVC(), param_grid=param_grid, return_train_score=True) random_search = RandomizedSearchCV(SVC(), n_iter=3, param_distributions=param_grid, return_train_score=True)\",\n \"check that errors caused by fits # to individual folds\",\n \"# precompute euclidean metric to validate _pairwise is working X_precomputed\",\n \"return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that has no\",\n \"'groups' parameter should not be None.\\\", gs.fit, X, y) gs.fit(X,\",\n \"param_search): # unofficially sanctioned tolerance for scalar values in fit_params\",\n \"train, test, scorer, verbose=False) est = clone(svc).set_params(**params) est.fit(X[train], y[train]) expected_score\",\n \"0. return score def get_params(self, deep=False): return {'foo_param': self.foo_param} def\",\n \"def get_cand_scores(i): return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_)))\",\n \"4 X, y = make_classification(n_features=n_features) gbdt = HistGradientBoostingClassifier() param_grid =\",\n \"of the parameters in the param_grid. param_grid = { 'loss':\",\n \"self.classes_ = np.unique(Y) return self def predict(self, T): return T.shape[0]\",\n \"X = np.arange(24).reshape(6, -1) y = [0, 0, 0, 1,\",\n \"{'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert hasattr(grid_search,\",\n \"clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator is\",\n \"cv=2).fit(X, y) assert not hasattr(gs, \\\"predict_proba\\\") def test_grid_search_allows_nans(): # Test\",\n \"MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3)\",\n \"Test grid-search on classifier that has no score function. clf\",\n \"properties of ParameterGrid. params1 = {\\\"foo\\\": [1, 2, 3]} grid1\",\n \"large grid params = {'a': range(10), 'b': range(10), 'c': range(10)}\",\n \"(train, test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test],\",\n \"= cv.predict(X_[180:]) C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf =\",\n \"f1_score from sklearn.metrics import recall_score from sklearn.metrics import accuracy_score from\",\n \"[]} clf = SVC() assert_raise_message( ValueError, \\\"Parameter values for parameter\",\n \"getattr(mycv, attr), \\\\ \\\"Attribute %s not equal\\\" % attr def\",\n \"cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check\",\n \"n_features=4, random_state=42) clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound)\",\n \"assert search.best_params_['bandwidth'] == .1 assert search.best_score_ == 42 def test_param_sampler():\",\n \"similar models # which would result in a tie of\",\n \"that this raises an error with pytest.raises(ValueError, match='cv.split and cv.get_n_splits\",\n \"multi-metric search methods with single metric methods\\\"\\\"\\\" assert search_acc.refit ==\",\n \"of # i.i.d. or not assert train_mean == pytest.approx(1) assert\",\n \"have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})\",\n \"for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0 assert\",\n \"dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y = [0, 0,\",\n \"cv=3) gs.fit(X, y) def test_grid_search_cv_splits_consistency(): # Check if a one\",\n \"as above assert_allclose(test_cv_scores, [1, 1. / 3.]) assert_allclose(train_cv_scores, [1, 1])\",\n \"0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X, y) # Give generator as a\",\n \"1, 10] for score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf,\",\n \"for values in ParameterGrid(params): assert values in samples # test\",\n \"= MockClassifier() grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1,\",\n \"param_grid, cv=2) grid_search.fit(X, y) regressor_results = grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression)\",\n \"k.endswith('_time'): # XXX: results['params'] is a list :| results[k] =\",\n \"'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time',\",\n \"['poly'], 'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')\",\n \"ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout\",\n \"cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable, 'scalar_param':\",\n \"spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): #\",\n \"we should tolerate this behavior for # now and be\",\n \"is a non-re-entrant cv where split can be called only\",\n \"def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy function tests when returned 'best_index_'\",\n \"key].dtype == np.int32 def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): # Test\",\n \"expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i for cv_i\",\n \"\\\"\\\"\\\"An LinearSVC classifier that has no score method.\\\"\\\"\\\" @property def\",\n \"predict(self, X): return np.zeros(X.shape[0]) def score(self, X=None, Y=None): return 0.\",\n \"remove test_fit_grid_point_deprecated as # fit_grid_point will be removed on 0.25\",\n \"== [x for x in sampler] def check_cv_results_array_types(search, param_keys, score_keys):\",\n \"# a parameter is missing or has length != len(X).\",\n \"== this_params assert n_test_samples == test.size # Should raise an\",\n \"n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we only\",\n \"**params): self.foo_param = params['foo_param'] return self class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC\",\n \"10]} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=3, random_state=0) assert [x for x\",\n \"score in ['f1', 'roc_auc']: grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score,\",\n \"types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray) for param in\",\n \"= CheckingClassifier( check_X=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], ) cv =\",\n \"* 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True)\",\n \"[{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X,\",\n \"1, 10] clf_no_score = LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs},\",\n \"dummy function tests `refit=callable` interface. Return the index of a\",\n \"RandomizedSearchCV(gbdt, param_grid, n_iter=1) assert not hasattr(gs, 'n_features_in_') assert not hasattr(rs,\",\n \"[LinearRegression(), Ridge()]} grid_search = GridSearchCV(pipe, param_grid, cv=2) grid_search.fit(X, y) regressor_results\",\n \"NotImplementedError BadSearchCV(SVC()).fit(X, y) def test_empty_cv_iterator_error(): # Use global X, y\",\n \"scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C':\",\n \"return np.array(list(gs.cv_results_['split%d_test_score' % s][i] for s in range(gs.n_splits_))) assert all((np.all(get_cand_scores(cand_i)\",\n \"import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.linear_model import Ridge,\",\n \"tuple(\\\"split%d_test_score\\\" % cv_i for cv_i in range(n_splits))) assert all(np.in1d(expected_keys, result_keys))\",\n \"grid search works with both dense and sparse matrices X_,\",\n \"test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the _pairwise property\",\n \"should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1,\",\n \"from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing\",\n \"in fit_params, for instance in LightGBM # that do not\",\n \"'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand = n_search_iter\",\n \"assert not hasattr(clf, \\\"predict_proba\\\") clf.fit(X, y) clf.predict_proba(X) clf.predict_log_proba(X) # Make\",\n \"0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search): #\",\n \"{'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred2 = cv.predict(X_[180:]) C2\",\n \"y = make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2,\",\n \"n_iter=10, random_state=0) samples = [x for x in sampler] assert\",\n \"# GridSearchCV with on_error == 'raise' raises the error X,\",\n \"Clearly this test does something else??? # noise-free simple 2d-data\",\n \"assert_array_equal(y_pred, y_pred3) def test_grid_search_precomputed_kernel(): # Test that grid search works\",\n \"\\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def test_n_features_in(): # make sure grid search\",\n \"refit=callable, which adds flexibility in identifying the \\\"best\\\" estimator. \\\"\\\"\\\"\",\n \"assert that this raises an error with pytest.raises(ValueError, match='cv.split and\",\n \"dict(kernel=['poly', ], degree=[1, 2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel')\",\n \"{\\\"C\\\": []} clf = SVC() assert_raise_message( ValueError, \\\"Parameter values for\",\n \"hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10,\",\n \"train, test, scorer, verbose=False) def test_pickle(): # Test that a\",\n \"assert_raise_message( ValueError, \\\"Parameter values for parameter (C) need to be\",\n \"for cand_i in range(len(res_params)): est.set_params(**res_params[cand_i]) for i, (train, test) in\",\n \"test_mean == pytest.approx(np.mean(test_cv_scores)) assert test_std == pytest.approx(np.std(test_cv_scores)) # For the\",\n \"and be careful not to break support for those without\",\n \"in a large grid params = {'a': range(10), 'b': range(10),\",\n \"n_candidates) # Check masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params'])\",\n \"n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 params =\",\n \"after ' 'refitting on the best parameters' % fn_name), getattr(grid_search,\",\n \"def test_parameter_grid(): # Test basic properties of ParameterGrid. params1 =\",\n \"' + k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k)\",\n \"assert_warns from sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from\",\n \"<= 1) for k in ( 'mean_score_time', 'std_score_time', 'mean_fit_time', 'std_fit_time')))\",\n \"x in sampler] assert len(samples) == 10 for sample in\",\n \"methods\\\"\\\"\\\" assert search_acc.refit == refit if refit: assert search_multi.refit ==\",\n \"Y): assert len(X) == len(Y) self.classes_ = np.unique(Y) return self\",\n \"gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning if n_iter\",\n \"= search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0] for s\",\n \"grid_params, cv=n_splits) cv.fit(X, y) preds_original = cv.predict(X) # precompute euclidean\",\n \"'b', 'c']}, {'third': ['two', 'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter\",\n \"CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_df,\",\n \"def test_grid_search_correct_score_results(): # test that correct scores are used n_splits\",\n \"for instance in LightGBM # that do not exactly respect\",\n \"(points == set((\\\"bar\\\", x, \\\"foo\\\", y) for x, y in\",\n \"= ParameterGrid({}) assert len(empty) == 1 assert list(empty) == [{}]\",\n \"assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def test_grid_search_cv_results_multimetric():\",\n \"train_size = 100 ridge = RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]},\",\n \"'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k) else: assert_allclose(exp_results[k], results[k],\",\n \"fit_params, for instance in LightGBM # that do not exactly\",\n \"= LinearSVC(random_state=0) X, y = make_blobs(random_state=0, centers=2) Cs = [.1,\",\n \"\\\" \\\"and will be removed in version 0.25\\\") params =\",\n \"clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid for parameter (C)\",\n \"def test_fit_grid_point(): X, y = make_classification(random_state=0) cv = StratifiedKFold() svc\",\n \"== grid_search_no_score.score(X, y) # giving no scoring function raises an\",\n \"def test_n_features_in(): # make sure grid search and random search\",\n \"# Check if the search `cv_results`'s array are of correct\",\n \"0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0\",\n \"X) # Test that an invalid refit param raises appropriate\",\n \"unofficially sanctioned tolerance for scalar values in fit_params # non-regression\",\n \") grid_search.fit(X, y) assert not hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search,\",\n \"X, y = make_blobs(n_samples=50, random_state=0) km = KMeans(random_state=0, init=\\\"random\\\", n_init=1)\",\n \"est in estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y)\",\n \"= parameter def fit(self, X, y): assert not hasattr(self, 'has_been_fit_')\",\n \"returns an invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A dummy\",\n \"2 and ranks[1] <= 2 # Check that failed estimator\",\n \"1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert that this raises an\",\n \"this test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',\",\n \"# Ensure that grid scores were set to zero as\",\n \"def __init__(self, estimator, **kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results\",\n \"clf.best_index_ == 0 # Ensure `best_score_` is disabled when using\",\n \"1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) # When the estimator\",\n \"assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that parameters\",\n \"2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def\",\n \"estimator. \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests `refit=callable`\",\n \"to check that errors caused by fits # to individual\",\n \"search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results\",\n \"1, 3]) def test_search_cv_results_none_param(): X, y = [[1], [2], [3],\",\n \"= np.asanyarray(results[k]) if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking '\",\n \"n_cand): # Test the search.cv_results_ contains all the required results\",\n \"= cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke\",\n \"transform(self, X): return X + self.foo_param def inverse_transform(self, X): return\",\n \"2) points = set(tuple(chain(*(sorted(p.items())))) for p in grid2) assert (points\",\n \"KDE def custom_scoring(estimator, X): return 42 if estimator.bandwidth == .1\",\n \"'predict_proba', 'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y))\",\n \"make_classification(n_samples=n_samples, random_state=0) gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=OneTimeSplitter(n_splits=n_splits,\",\n \"_pairwise property must match estimator\\\" for _pairwise_setting in [True, False]:\",\n \"4]} gs = GridSearchCV(gbdt, param_grid) rs = RandomizedSearchCV(gbdt, param_grid, n_iter=1)\",\n \"raises the error X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf\",\n \"dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i]) #\",\n \"y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results,\",\n \"len(empty) == 1 assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda:\",\n \"precomputed # training kernel matrix K_train = np.zeros((10, 20)) y_train\",\n \"no candidates\\\\\\\\?'): ridge.fit(X[:train_size], y[:train_size]) def test_random_search_bad_cv(): # Use global X,\",\n \"\\\"For evaluating multiple scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X,\",\n \"the mean scores between # candidates 1 and 2 alone\",\n \"def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) param_dict =\",\n \"ensures the equivalence of 'precomputed'. Non-regression test for issue #13920.\",\n \"= [{'first': [0, 1], 'second': ['a', 'b', 'c']}, {'third': ['two',\",\n \"(True, False): random_searches = [] for scoring in (('accuracy', 'recall'),\",\n \"gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that\",\n \"groups = rng.randint(0, 3, 15) clf = LinearSVC(random_state=0) grid =\",\n \"property of its estimator. This test make sure _pairwise is\",\n \"integer. \\\"\\\"\\\" return None X, y = make_classification(n_samples=100, n_features=4, random_state=42)\",\n \"sys.stdout = StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param\",\n \"not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV): def __init__(self,\",\n \"cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_ =\",\n \"# Use global X, y # create cv cv =\",\n \"GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y) grid_search_no_score = GridSearchCV(clf_no_score, {'C':\",\n \"a precomputed kernel matrix X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\",\n \"`cv_results`'s array are of correct types cv_results = search.cv_results_ assert\",\n \"10]}])) # Using regressor to make sure each score differs\",\n \"y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search)) assert_array_almost_equal(grid_search.predict(X), grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param':\",\n \"3]}, cv=3, verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip)\",\n \"that has the least `mean_test_score`. \\\"\\\"\\\" # Fit a dummy\",\n \"as required for those fits # that are expected to\",\n \"= GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) grid_search.fit(X, y)\",\n \"= BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise property must match estimator\\\"\",\n \"('params',))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys +\",\n \"= MockClassifier() for scoring in [None, ['accuracy', 'precision']]: grid_search =\",\n \"behavior), and this would cause an # error in this\",\n \"2, 3]}, cv=2, error_score=\\\"raise\\\" ) # The CheckingClassifier generates an\",\n \"'best_index_' returns an invalid result. \\\"\\\"\\\" def refit_callable_invalid_type(cv_results): \\\"\\\"\\\" A\",\n \"Test GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2,\",\n \"verbose=3) grid_search.fit(X, y) X_round_trip = grid_search.inverse_transform(grid_search.transform(X)) assert_array_equal(X, X_round_trip) def test_custom_run_search():\",\n \"def predict(self, X): return np.zeros(shape=(len(X))) model = SearchCV(TestEstimator(), param_search) X,\",\n \"with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] =\",\n \"= np.ones((10, )) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C':\",\n \"i in range(n_candidates) if cv_results['param_kernel'][i] == 'linear') assert all((not cv_results['param_C'].mask[i]\",\n \"uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import clone\",\n \"[1]}, cv=3) grid_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf,\",\n \"make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 #\",\n \"parameter is missing or has length != len(X). err_msg =\",\n \"2]) @pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation\",\n \"fit(self, X, y, r=None): self.r_ = r def predict(self, X):\",\n \"n_samples=80) # split dataset into two folds that are not\",\n \"1.0]}) assert_raises(ValueError, cv.fit, K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that\",\n \")) clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]})\",\n \"search works with both dense and sparse matrices X_, y_\",\n \"for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER) ranks =\",\n \"return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf =\",\n \"3 # Single metric evaluation unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2,\",\n \"check_X = lambda x: x.shape[1:] == (5, 3, 2) check_y\",\n \"1) assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >=\",\n \"the # best estimator n_features = 4 X, y =\",\n \"return # search cannot predict/score without refit X, y =\",\n \"param_grid = {'C': [1, 1.001, 0.001]} grid_search = GridSearchCV(SVC(), param_grid=param_grid,\",\n \"Cs}) grid_search.fit(X, y) assert_array_equal(grid_search.best_estimator_.classes_, grid_search.classes_) # Test that regressors do\",\n \"n_splits = 3 n_grid_points = 6 params = [dict(kernel=['rbf', ],\",\n \"cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize(\",\n \"not hasattr(gs, 'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X,\",\n \"'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy', k): v\",\n \"If True, for multi-metric pass refit='accuracy' if refit: probability =\",\n \"< 1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >=\",\n \"or numpy array, but got (<class 'int'>).\\\" \\\" Single values\",\n \"ParameterGrid(params2) assert len(grid2) == 6 # loop to assert we\",\n \"accepted as a cv parameter. n_samples = 100 n_splits =\",\n \"test) in enumerate(cv.split(X, y)): est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test])\",\n \"} clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3) assert not hasattr(clf, \\\"predict_proba\\\")\",\n \"the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]), # cv.score(X_[:180], y[:180])) # test\",\n \"X, y=None, groups=None, **fit_params): return self # this should not\",\n \"random_state=0).split(X, y), GeneratorType) gs3 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]},\",\n \"refit = 'accuracy' if isinstance(scoring, tuple) else refit else: probability\",\n \"seed, hence we use param grid params = dict(C=np.logspace(-4, 1,\",\n \"in cv_results_multi.items() if not k.endswith('_time')}, {k: v for k, v\",\n \"invalid refit param raises appropriate error messages for refit in\",\n \"not cv_results['param_gamma'].mask[i] and cv_results['param_degree'].mask[i]) for i in range(n_candidates) if cv_results['param_kernel'][i]\",\n \"y) preds_precomputed = cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not identical with\",\n \"case of ties old_stdout = sys.stdout sys.stdout = StringIO() grid_search.fit(X,\",\n \"return 42 if estimator.bandwidth == .1 else 0 X, _\",\n \"[1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def\",\n \"= GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) #\",\n \"we do # not want to break without an explicit\",\n \"__init__(self, parameter=None): self.parameter = parameter def fit(self, X, y): assert\",\n \"= [] for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): #\",\n \"sampler] == [x for x in sampler] def check_cv_results_array_types(search, param_keys,\",\n \"sklearn.utils._testing import assert_warns_message from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import\",\n \"assert search.cv_results_['param_C'][0] == 1 # scores are the same as\",\n \"KFold() estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search\",\n \"infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b', 'c']}\",\n \"raised when the precomputed kernel is not array-like # or\",\n \"implementing early stopping with a user provided # validation set.\",\n \"error_score=0) rs = RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2)\",\n \"partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type, error_message\\\", [(0, TypeError, r'Parameter .*\",\n \"search in (grid_search, random_search): search.fit(X, y) cv_results = search.cv_results_ #\",\n \"'degree': [2, 3]}] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys\",\n \"evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in ['adjusted_rand_score',\",\n \"Test that the best estimator contains the right value for\",\n \"hasattr(grid_search, 'classes_') # Test that the grid searcher has no\",\n \"element.\\\", GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": []} clf =\",\n \"the index of a model that has the least `mean_test_score`.\",\n \"the following two estimators inherit from BaseEstimator, # to test\",\n \"= {\\\"C\\\": \\\"1,2,3\\\"} clf = SVC(gamma='auto') assert_raise_message( ValueError, \\\"Parameter grid\",\n \"results['params'] is a list :| results[k] = np.asanyarray(results[k]) if results[k].dtype.kind\",\n \"= GridSearchCV(clf, grid, cv=cv) # Should not raise an error\",\n \"= [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree':\",\n \"estimators: grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0,\",\n \"cv.fit, X_[:180], y_) def test_grid_search_one_grid_point(): X_, y_ = make_classification(n_samples=200, n_features=100,\",\n \"ParameterSampler from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import FitFailedWarning from\",\n \"# Simulates re-fitting a broken estimator; this used to break\",\n \"GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that grid search\",\n \"y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel\",\n \"assert_array_equal(X, X_round_trip) def test_custom_run_search(): def check_results(results, gscv): exp_results = gscv.cv_results_\",\n \"True def predict(self, X): return np.zeros(X.shape[0]) @ignore_warnings def test_refit(): #\",\n \"4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid():\",\n \"99 hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c']) for p\",\n \"params = {'C': 0.1} train, test = next(StratifiedKFold().split(X, y)) with\",\n \"assert not np.allclose(cv_results['mean_train_score'][1], cv_results['mean_train_score'][2]) # 'min' rank should be assigned\",\n \"with pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \\\"Fit parameter\",\n \"scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params'])\",\n \"np.mean(y_pred == y_pred2) >= .9 assert C == C2 def\",\n \"0] gs = GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs\",\n \"\\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})]\",\n \"grid2) assert (points == set((\\\"bar\\\", x, \\\"foo\\\", y) for x,\",\n \"[RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation catches the\",\n \"y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert np.mean(y_pred == y_pred2)\",\n \"in cv_results return cv_results['mean_test_prec'].argmin() X, y = make_classification(n_samples=100, n_features=4, random_state=42)\",\n \"= RandomizedSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0, n_iter=2) for search\",\n \"# is not the case for the following parameters. But\",\n \"[1, 1]], random_state=0, cluster_std=0.1, shuffle=False, n_samples=80) # split dataset into\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred\",\n \"\\\"The 'groups' parameter should not be None.\\\", gs.fit, X, y)\",\n \"if results[k].dtype.kind == 'O': assert_array_equal(exp_results[k], results[k], err_msg='Checking ' + k)\",\n \"for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs =\",\n \"part gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,\",\n \"property of its estimator. This test ensures the equivalence of\",\n \"search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X, y) search_no_score_method_auc = GridSearchCV(LinearSVCNoScore(), grid,\",\n \"y = make_classification(n_samples=100, n_features=4, random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1,\",\n \"ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert len(samples) == 99\",\n \"function tests `refit=callable` interface. Return the index of a model\",\n \"consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv where\",\n \"= 2) clf = KNeighborsClassifier() cv = GridSearchCV(clf, grid_params, cv=n_splits)\",\n \"def fit(self, X, y, r=None): self.r_ = r def predict(self,\",\n \"{\\\"kernel\\\": [\\\"rbf\\\", \\\"linear\\\"], \\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10,\",\n \") # NOTE: `fit_params` should be data dependent (e.g. `sample_weight`)\",\n \"estimator\\\" for _pairwise_setting in [True, False]: setattr(est, '_pairwise', _pairwise_setting) cv\",\n \"== 1 return self def _fit_param_callable(): pass model = SearchCV(\",\n \"gscv.cv_results_ assert sorted(results.keys()) == sorted(exp_results) for k in results: if\",\n \"# degenerates to GridSearchCV if n_iter the same as grid_size\",\n \"hasattr(grid_search, \\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X,\",\n \"cv_scores[i]) # FIXME remove test_fit_grid_point as the function will be\",\n \"['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler)\",\n \"1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C X_\",\n \"# https://github.com/scikit-learn/scikit-learn/issues/15805 X_train, X_valid, y_train, y_valid = train_test_split( *make_classification(random_state=42), random_state=42\",\n \"y = make_blobs(random_state=0, centers=2) Cs = [.1, 1, 10] clf_no_score\",\n \"\\\"\\\"\\\" A dummy function tests when returned 'best_index_' is not\",\n \"= GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring=scoring, refit=refit_callable) clf.fit(X, y)\",\n \"ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert len(samples) == 7 def\",\n \"assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec = {re.sub('_score$', '_accuracy',\",\n \"(\\\"a\\\", 1, \\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p in\",\n \"k.endswith('_time')}) # Check consistency of folds across the parameters gs\",\n \"[\\\"ham\\\", \\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6\",\n \"== FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',\",\n \"property matches best_estimator_.classes_ X = np.arange(100).reshape(10, 10) y = np.array([0]\",\n \"y) gs.fit(X, y, groups=groups) non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()] for cv\",\n \"grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]}) grid_search.fit(X, y) assert not\",\n \"with a user provided # validation set. non-regression test for:\",\n \") def test_search_default_iid(SearchCV, specialized_params): # Test the IID parameter TODO:\",\n \"DecisionTreeClassifier from sklearn.cluster import KMeans from sklearn.neighbors import KernelDensity from\",\n \"clf.FAILING_PARAMETER def test_grid_search_failing_classifier_raise(): # GridSearchCV with on_error == 'raise' raises\",\n \"0` for this dummy clf assert clf.best_index_ != 0 #\",\n \"gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time', 'mean_score_time',\",\n \"grid search results. np.testing.assert_equal performs a # deep nested comparison\",\n \"import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score\",\n \"n_iter=n_search_iter, cv=n_splits, param_distributions=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ #\",\n \"test. gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False,\",\n \"for implementing early stopping with a user provided # validation\",\n \"value for foo_param clf = MockClassifier() grid_search = None grid_search\",\n \"tied candidates assert_almost_equal(search.cv_results_['rank_test_score'], [1, 1, 3]) def test_search_cv_results_none_param(): X, y\",\n \"this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X, y) class BadSearchCV(BaseSearchCV):\",\n \"< 1.0 assert score_auc != score_accuracy assert_almost_equal(score_accuracy, score_no_scoring) assert_almost_equal(score_auc, score_no_score_auc)\",\n \"an error upon multimetric scorer assert_raise_message(ValueError, \\\"For evaluating multiple scores,\",\n \"grid_searches = [] for scoring in ({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)},\",\n \"@ignore_warnings def test_refit(): # Regression test for bug in refitting\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=3, verbose=3) # make sure\",\n \"% i][cand_i]) # Test with a randomized search for est\",\n \"generated for the 2nd and subsequent cv.split calls. # This\",\n \"= make_classification(n_samples=100, n_features=4, random_state=42) scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'}\",\n \"= 3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),\",\n \"make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter = 30 params\",\n \"if a one time iterable is accepted as a cv\",\n \"test_search_cv__pairwise_property_equivalence_of_precomputed(): \\\"\\\"\\\" Test implementation of BaseSearchCV has the _pairwise property\",\n \") def test_validate_parameter_input(klass, input, error_type, error_message): with pytest.raises(error_type, match=error_message): klass(input)\",\n \"we can iterate over the grid multiple times for i\",\n \"grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\") def test_unsupervised_grid_search(): # test grid-search with\",\n \"k in score_keys if 'time' not in k and k\",\n \"for bug in refitting # Simulates re-fitting a broken estimator;\",\n \"def test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which adds flexibility in identifying\",\n \"value is not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass,\",\n \"assert_almost_equal from sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame\",\n \"unsupervised grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y)\",\n \"* 5) for InputFeatureType, TargetType in types: # X dataframe,\",\n \"= {'max_iter': [3, 4]} gs = GridSearchCV(gbdt, param_grid) rs =\",\n \"_ = make_blobs(cluster_std=.1, random_state=1, centers=[[0, 1], [1, 0], [0, 0]])\",\n \"that has no score method.\\\"\\\"\\\" @property def score(self): raise AttributeError\",\n \"in (('accuracy', 'recall'), 'accuracy', 'recall'): # If True, for multi-metric\",\n \"cv.best_estimator_.C assert_array_equal(y_pred, y_pred2) assert C == C2 # Smoke test\",\n \"to (\\\"a\\\", 1, \\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items())))) for p\",\n \"= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits, shuffle=True), return_train_score=True)\",\n \"\\\"predict_proba\\\") def test_search_train_scores_set_to_false(): X = np.arange(6).reshape(6, -1) y = [0,\",\n \"create cv cv = KFold(n_splits=3).split(X) # pop all of it,\",\n \"\\\"\\\"\\\" Test implementation of BaseSearchCV has the _pairwise property which\",\n \"deprecation cycle. fit_params = { 'tuple_of_arrays': (X_valid, y_valid), 'callable_param': _fit_param_callable,\",\n \"C2 def test_grid_search_sparse_scoring(): X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf\",\n \"{'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X, y) #\",\n \"refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert\",\n \"assert search_acc.refit == refit if refit: assert search_multi.refit == 'accuracy'\",\n \"params = [{'kernel': ['rbf'], 'C': expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'],\",\n \"scorer key\\\", GridSearchCV(clf, {}, refit=refit, scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit,\",\n \"K_train, y_train) class BrokenClassifier(BaseEstimator): \\\"\\\"\\\"Broken classifier that cannot be fit\",\n \"**specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for\",\n \"param setting for score_type in ('train', 'test'): per_param_scores = {}\",\n \"== 'accuracy' else: assert not search_multi.refit return # search cannot\",\n \"parameter in case of ties old_stdout = sys.stdout sys.stdout =\",\n \"y), search_acc.score(X, y)) for key in ('best_index_', 'best_score_', 'best_params_'): assert\",\n \"# pop all of it, this should cause the expected\",\n \"assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)\",\n \"sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf =\",\n \"\\\"no scoring\\\", grid_search_no_score.fit, [[1]]) def test_grid_search_score_method(): X, y = make_classification(n_samples=100,\",\n \"using `refit=callable` assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test\",\n \"GridSearchCV, clf, param_dict) param_dict = {\\\"C\\\": np.ones((3, 2))} clf =\",\n \"error_score=\\\"raise\\\" ) # The CheckingClassifier generates an assertion error if\",\n \"specified # as one of the parameters in the param_grid.\",\n \"# that do not exactly respect the scikit-learn API contract\",\n \"StringIO() grid_search.fit(X, y) sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2\",\n \"== 3 # Single metric evaluation unsupervised grid_search = GridSearchCV(km,\",\n \"cv parameter. n_samples = 100 n_splits = 5 X, y\",\n \"random_state=42) clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]}, scoring='precision', refit=True)\",\n \"scoring={'acc': 'accuracy', 'prec': 'precision'} ).fit, X, y) def test_grid_search_error(): #\",\n \"expon(scale=10), 'gamma': expon(scale=0.1)}, {'kernel': ['poly'], 'degree': [2, 3]}] param_keys =\",\n \"a refit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y)\",\n \"'mean_score_time', 'std_score_time'): cv_results.pop(key) return cv_results # Check if generators are\",\n \"for scoring in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf,\",\n \"expon, uniform from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import\",\n \"fit(self, X, y=None, groups=None, **fit_params): return self # this should\",\n \"sys from types import GeneratorType import re import numpy as\",\n \"estimator, **kwargs): super().__init__(estimator, **kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): #\",\n \"is not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)\",\n \"CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1, 2, 3]},\",\n \"GridSearchCV # And also check if groups is correctly passed\",\n \"search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that correct scores\",\n \"assert list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty =\",\n \"param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3\",\n \"clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf, {'foo_param': [1,\",\n \"performed. ' 'Was the CV iterator empty\\\\\\\\? ' 'Were there\",\n \"test_refit_callable_multi_metric(): \\\"\\\"\\\" Test refit=callable in multiple metric evaluation setting \\\"\\\"\\\"\",\n \"FitFailedWarning from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeRegressor\",\n \"== preds_precomputed).all(), attr_message @pytest.mark.parametrize( \\\"SearchCV, param_search\\\", [(GridSearchCV, {'a': [0.1, 0.01]}),\",\n \"# Make sure the functions predict/transform etc raise meaningful #\",\n \"params = dict(C=np.logspace(-4, 1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for\",\n \"iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def test_validate_parameter_input(klass, input, error_type, error_message):\",\n \"assert all(cv_results['rank_test_score'] >= 1) assert (all(cv_results[k] >= 0) for k\",\n \"2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates =\",\n \"= CheckingClassifier(check_X=check_df, check_y=check_series) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]})\",\n \"[~mask, mask]] common_params = {'estimator': SVC(), 'cv': cv, 'return_train_score': True}\",\n \"key in ('best_index_', 'best_score_', 'best_params_'): assert getattr(search_multi, key) == getattr(search_acc,\",\n \"grid_search = None grid_search = GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3)\",\n \"multi-output estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\":\",\n \"scorer(est, X[test], y[test]) # Test the return values of fit_grid_point\",\n \"dataframe types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series,\",\n \"y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, random_search.cv_results_['split%d_test_score' % i][cand_i])\",\n \"Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental import\",\n \"Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search, 'classes_') def test_trivial_cv_results_attr():\",\n \"second only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y ==\",\n \"TypeError, r'Parameter .* is not a dict \\\\(0\\\\)'), ({'foo': 0},\",\n \"range(n_candidates) if cv_results['param_kernel'][i] == 'rbf') @pytest.mark.parametrize( \\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid':\",\n \"param_keys, score_keys): # Check if the search `cv_results`'s array are\",\n \"ClassifierMixin): def __init__(self, a=None): self.a = a def fit(self, X,\",\n \"GridSearchCV(clf, grid, scoring='roc_auc').fit(X, y) # Check warning only occurs in\",\n \"make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise import euclidean_distances from\",\n \"is accepted as a cv parameter. n_samples = 100 n_splits\",\n \"want to check that errors caused by fits # to\",\n \"= ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0) samples = [x for x in\",\n \"[0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def test_scalar_fit_param(SearchCV, param_search):\",\n \"from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing\",\n \"range(10), 'c': range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples =\",\n \"for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train], y[train]) if\",\n \"of keys in # clf.cv_results_. X, y = make_classification(n_samples=100, n_features=4,\",\n \"caught by grid_search (expected behavior), and this would cause an\",\n \"== 4 assert list(has_empty) == [{'C': 1}, {'C': 10}, {},\",\n \"setting in GridSearchCV.fit # the 2nd and 3rd parameter will\",\n \"param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X,\",\n \"s in range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)):\",\n \"assert len(set(hashable_samples)) == 99 # doesn't go into infinite loops\",\n \"Test implementation catches the errors when 'best_index_' returns an out\",\n \"'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, scoring=scoring, refit=False) grid_search.fit(X, y)\",\n \"+ [1] * 5) clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}],\",\n \"in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores\",\n \"and not # caught by grid_search (expected behavior), and this\",\n \"parameters gs = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.1, 0.2, 0.2]}, cv=KFold(n_splits=n_splits,\",\n \"sklearn.model_selection.tests.common import OneTimeSplitter # Neither of the following two estimators\",\n \"GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C\",\n \"np.array( [search.cv_results_['split%d_test_score' % s][0] for s in range(search.n_splits_)] ) test_mean\",\n \"no score function. clf = LinearSVC(random_state=0) X, y = make_blobs(random_state=0,\",\n \"for fn_name in ('predict', 'predict_proba', 'predict_log_proba', 'transform', 'inverse_transform'): assert_raise_message(NotFittedError, ('refit=False.\",\n \"({'C': 0.1}, {'C': 0.01}, {'C': 0.001}): for train, test in\",\n \"IID parameter TODO: Clearly this test does something else??? #\",\n \"n_test_samples = fit_grid_point( X, y, clone(svc), params, train, test, scorer,\",\n \"messages for refit in [\\\"\\\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError,\",\n \"the ensemble of multiple single metric cv_results from single metric\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}, cv=cv) grid_search.fit(X.tolist(), y).score(X, y) assert\",\n \"{'min_samples_split': 10}]) check_results(results, fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}])) #\",\n \"estimator X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\": [1,\",\n \"self.r_ = r def predict(self, X): return np.zeros(shape=(len(X))) model =\",\n \"search.fit(X, y) cv_results = search.cv_results_ # Check if score and\",\n \"raised and score reset where appropriate. X, y = make_classification(n_samples=20,\",\n \"{'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out\",\n \"'n_features_in_') assert not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert\",\n \"= make_scorer(f1_loss, greater_is_better=False) cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, scoring=F1Loss)\",\n \"[1, 1], [2, 1]]) y = np.array([1, 1, 2, 2])\",\n \"train_mean = search.cv_results_['mean_train_score'][0] train_std = search.cv_results_['std_train_score'][0] assert search.cv_results_['param_C'][0] == 1\",\n \"= RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3) random_search.fit(X,\",\n \"1) for key in ['mean_score_time', 'std_score_time']: assert search.cv_results_[key][1] >= 0\",\n \"missing or has length != len(X). err_msg = r\\\"Expected fit\",\n \"pickled clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1, 2,\",\n \"k) else: assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid):\",\n \"*make_classification(random_state=42), random_state=42 ) class _FitParamClassifier(SGDClassifier): def fit(self, X, y, sample_weight=None,\",\n \"test grid-search with an estimator without predict. # slight duplication\",\n \"[1, 2, 3]}, cv=3, verbose=3) # make sure it selects\",\n \"y=None, groups=None, **fit_params): return self # this should not raise\",\n \"raise warning if n_iter is bigger than total parameter space\",\n \"**kwargs) with pytest.raises(NotImplementedError, match=\\\"_run_search not implemented.\\\"): # this should raise\",\n \">= 0 assert search.cv_results_[key][0] == 0.0 assert np.all(search.cv_results_[key] < 1)\",\n \"y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11) check_X\",\n \"given in the # form of a precomputed kernel matrix\",\n \"[x for x in sampler] if sp_version >= (0, 16):\",\n \"'split0_test_score', 'split1_test_score', 'split2_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time',\",\n \"search for est in estimators: random_search = RandomizedSearchCV(est, est_parameters, cv=cv,\",\n \"hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_, float) assert search.refit_time_ >= 0 def\",\n \"the best parameters' % fn_name), getattr(grid_search, fn_name), X) # Test\",\n \"make_classification(n_samples=200, n_features=100, random_state=0) # compute the training kernel matrix corresponding\",\n \"\\\"\\\"\\\" A dummy function tests `refit=callable` interface. Return the index\",\n \"= {re.sub('_score$', '_accuracy', k): v for k, v in search_acc.cv_results_.items()}\",\n \"gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\"\",\n \"y_train) assert cv.best_score_ >= 0 # compute the test kernel\",\n \"searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search\",\n \"**kwargs): super().__init__(estimator, **kwargs) def _run_search(self, evaluate): results = evaluate([{'max_depth': 1},\",\n \"gs2 = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits), return_train_score=True) gs2.fit(X,\",\n \"C = cv.best_estimator_.C X_ = sp.csr_matrix(X_) clf = LinearSVC() cv\",\n \"99 # doesn't go into infinite loops params_distribution = {'first':\",\n \"right number :) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single metric\",\n \"in range(n_candidates) if gs.cv_results_['param_parameter'][cand_i] == FailingClassifier.FAILING_PARAMETER)) gs = GridSearchCV(clf, [{'parameter':\",\n \"least `mean_test_prec`. \\\"\\\"\\\" assert 'mean_test_prec' in cv_results return cv_results['mean_test_prec'].argmin() X,\",\n \"y)): clf.fit(X[train], y[train]) if score == \\\"f1\\\": correct_score = f1_score(y[test],\",\n \".9 assert C == C2 def test_grid_search_sparse_scoring(): X_, y_ =\",\n \"'refit_time_', 'classes_'}): assert getattr(gscv, attr) == getattr(mycv, attr), \\\\ \\\"Attribute\",\n \"results = evaluate([{'max_depth': 1}, {'max_depth': 2}]) check_results(results, fit_grid({'max_depth': [1, 2]}))\",\n \"results. Expected \\\\\\\\d+ ' 'splits, got \\\\\\\\d+'): ridge.fit(X[:train_size], y[:train_size]) def\",\n \"cv.fit(X_precomputed, y) preds_precomputed = cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not identical\",\n \"assert_raises(ValueError, GridSearchCV, clf, param_dict) def test_grid_search_sparse(): # Test that grid\",\n \"assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise warning if\",\n \"random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time',\",\n \"parameter. n_samples = 100 n_splits = 5 X, y =\",\n \"def score(self, X=None, Y=None): if self.foo_param > 1: score =\",\n \"= ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score',\",\n \"params1 = {\\\"foo\\\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert\",\n \"[(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C': [1, 10]},\",\n \"y) n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates)\",\n \"list\\\" \\\" with one element.\\\", GridSearchCV, clf, param_dict) param_dict =\",\n \"{\\\"foo\\\": [1, 2, 3]} grid1 = ParameterGrid(params1) assert isinstance(grid1, Iterable)\",\n \"a fit search can be pickled clf = MockClassifier() grid_search\",\n \"self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier failed as required\\\") def\",\n \"returned 'best_index_' is not integer. \\\"\\\"\\\" return None X, y\",\n \"[2, 1]]) y = np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid):\",\n \"est.fit(X[train], y[train]) correct_score = est.score(X[test], y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' %\",\n \"1]}, scoring='precision', refit=refit_callable) clf.fit(X, y) assert clf.best_index_ == 0 #\",\n \"'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV):\",\n \"0, 1, 1, 1] clf = LinearSVC(random_state=0) gs = GridSearchCV(clf,\",\n \"look for. assert_raises(ValueError, gs.fit, X, y) def test_parameters_sampler_replacement(): # raise\",\n \"0 def test_grid_search_correct_score_results(): # test that correct scores are used\",\n \"# now and be careful not to break support for\",\n \"list(cv_results.keys()) expected_keys = ((\\\"mean_test_score\\\", \\\"rank_test_score\\\") + tuple(\\\"split%d_test_score\\\" % cv_i for\",\n \"y_test) >= 0 # test error is raised when the\",\n \"scoring, the \\\" \\\"parameter refit must be set to a\",\n \"the correct result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) #\",\n \"s in range(search.n_splits_)] ) test_mean = search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0]\",\n \"# create bad cv cv = BrokenKFold(n_splits=3) train_size = 100\",\n \"= grid_search.cv_results_['param_regressor'] assert isinstance(regressor_results[0], LinearRegression) assert isinstance(regressor_results[1], Ridge) assert not\",\n \"cv.fit(X_[:180], y_[:180]) y_pred3 = cv.predict(X_[180:]) C3 = cv.best_estimator_.C assert C\",\n \"random_state=0) grid_params = {'n_neighbors': [10]} # defaults to euclidean metric\",\n \"[(GridSearchCV, {'a': [0.1, 0.01]}), (RandomizedSearchCV, {'a': uniform(1, 3)})] ) def\",\n \"ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def __init__(self, parameter=None): self.parameter\",\n \"test in cv.split(X, y): this_scores, this_params, n_test_samples = fit_grid_point( X,\",\n \"= {} for param_i in range(4): per_param_scores[param_i] = list( gs.cv_results_['split%d_%s_score'\",\n \"score(self, X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with\",\n \"[x for x in sampler] == [x for x in\",\n \"check_df(x): return isinstance(x, InputFeatureType) def check_series(x): return isinstance(x, TargetType) clf\",\n \"[0]}, 0], TypeError, r'Parameter .* is not a dict \\\\(0\\\\)'),\",\n \"then an exception would be raised on refit and not\",\n \"cv splits, for both param setting for score_type in ('train',\",\n \"[1, 2]}, {'min_samples_split': [5, 10]}])) # Using regressor to make\",\n \"= CustomSearchCV(clf, return_train_score=True).fit(X, y) gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split':\",\n \"splits, for both param setting for score_type in ('train', 'test'):\",\n \"Test implementation of BaseSearchCV has the _pairwise property which matches\",\n \"3 assert_grid_iter_equals_getitem(grid1) params2 = {\\\"foo\\\": [4, 2], \\\"bar\\\": [\\\"ham\\\", \\\"spam\\\",\",\n \"dense and sparse matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)\",\n \"kernel is not array-like # or sparse assert_raises(ValueError, cv.fit, K_train.tolist(),\",\n \"= f1_score(y[test], clf.predict(X[test])) elif score == \\\"roc_auc\\\": dec = clf.decision_function(X[test])\",\n \"not hasattr(rs, 'n_features_in_') gs.fit(X, y) rs.fit(X, y) assert gs.n_features_in_ ==\",\n \"transforms {\\\"a\\\": 1, \\\"b\\\": 2} to (\\\"a\\\", 1, \\\"b\\\", 2)\",\n \"parameter search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param = foo_param def\",\n \"= old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2, 3])\",\n \"cv in group_cvs: gs = GridSearchCV(clf, grid, cv=cv) assert_raise_message(ValueError, \\\"The\",\n \"clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_) def test_grid_search_when_param_grid_includes_range(): # Test that the\",\n \"that an invalid refit param raises appropriate error messages for\",\n \"clf = GridSearchCV(BrokenClassifier(), [{'parameter': [0, 1]}], scoring=\\\"precision\\\", refit=True) clf.fit(X, y)\",\n \"before it's fit grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}) assert not\",\n \"\\\"\\\"\\\" # Fit a dummy clf with `refit=True` to get\",\n \"generators are supported as cv and # that the splits\",\n \"y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0] for s in\",\n \"== C2 # Smoke test the score # np.testing.assert_allclose(f1_score(cv.predict(X_[:180]), y[:180]),\",\n \"# FailingClassifier issues a ValueError so this is what we\",\n \"match=err_msg): searcher.fit(X, y, spam=np.ones(10)) err_msg = \\\"Fit parameter spam has\",\n \"regressor_results[0] is not grid_search.best_estimator_ assert regressor_results[1] is not grid_search.best_estimator_ #\",\n \"API contract but that we do # not want to\",\n \"eggs=np.zeros(10)) @ignore_warnings def test_grid_search_no_score(): # Test grid-search on classifier that\",\n \"pytest.raises(AssertionError, match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10))\",\n \"this should cause the expected ValueError [u for u in\",\n \"in ['adjusted_rand_score', 'fowlkes_mallows_score']: grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring,\",\n \"y) assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Now without a score,\",\n \"make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits, param_grid=params,\",\n \"only from two. mask = np.ones(X.shape[0], dtype=np.bool) mask[np.where(y == 1)[0][::2]]\",\n \"score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]))\",\n \"those fits # that are expected to fail. def get_cand_scores(i):\",\n \"fit_grid_point(X, y, svc, params, train, test, scorer, verbose=False) def test_pickle():\",\n \"super().fit(X, y, sample_weight=sample_weight) assert scalar_param > 0 assert callable(callable_param) #\",\n \"assert all(cv_results[key].dtype == object for key in param_keys) assert not\",\n \"def test_classes__property(): # Test that classes_ property matches best_estimator_.classes_ X\",\n \"def test_parameters_sampler_replacement(): # raise warning if n_iter is bigger than\",\n \"y) random_searches.append(random_search) compare_cv_results_multimetric_with_single(*random_searches) compare_refit_methods_when_refit_with_acc( random_searches[0], random_searches[1], refit) def compare_cv_results_multimetric_with_single( search_multi,\",\n \"y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points =\",\n \"def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches the errors when 'best_index_'\",\n \"are consistent np.testing.assert_equal(_pop_time_keys(gs3.cv_results_), _pop_time_keys(gs4.cv_results_)) # OneTimeSplitter is a non-re-entrant cv\",\n \"key matches those in `cv_results` for key in clf.cv_results_.keys(): assert\",\n \"on_error != 'raise' # Ensures that a warning is raised\",\n \"search = GridSearchCV(SVC(), cv=n_splits, param_grid=params, return_train_score=True) search.fit(X, y) cv_results =\",\n \"gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True, False): random_searches\",\n \"\\\"cv_results_\\\") random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y)\",\n \"grid_search.best_estimator_.foo_param == 2 def test_grid_search_bad_param_grid(): param_dict = {\\\"C\\\": 1} clf\",\n \"gs = GridSearchCV(clf, grid, cv=cv) # Should not raise an\",\n \"if search.multimetric_ else ['score'] for key in scorer_keys: assert cv_results['rank_test_%s'\",\n \"param_distributions = {\\\"C\\\": uniform(0, 1)} sampler = ParameterSampler(param_distributions=param_distributions, n_iter=10, random_state=0)\",\n \"on user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier to test the\",\n \"handling on scoring grid_search.scoring = 'sklearn' assert_raises(ValueError, grid_search.fit, X, y)\",\n \"# Test that grid search returns an error with a\",\n \"\\\"spam\\\", \\\"eggs\\\"]} grid2 = ParameterGrid(params2) assert len(grid2) == 6 #\",\n \"in range(n_splits))) for i, (train, test) in enumerate(cv.split(X, y)): clf.fit(X[train],\",\n \"GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both\",\n \"are given in the # form of a precomputed kernel\",\n \"= {\\\"max_depth\\\": [1, 2, 3, 4]} cv = KFold() estimators\",\n \"default estimator settings) empty = ParameterGrid({}) assert len(empty) == 1\",\n \"that raises a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER = 2 def\",\n \"'sklearn' assert_raises(ValueError, grid_search.fit, X, y) def test_grid_search_pipeline_steps(): # check that\",\n \"* 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher = SearchCV( clf,\",\n \"result assert grid_search.score(X, y) == grid_search_no_score.score(X, y) # giving no\",\n \"[1, 1, 3]) def test_search_cv_results_none_param(): X, y = [[1], [2],\",\n \"from sklearn.model_selection import fit_grid_point from sklearn.model_selection import train_test_split from sklearn.model_selection\",\n \"'best_index_' returns an out of bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results):\",\n \"cv.split calls. # This is a check to make sure\",\n \"SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train) assert\",\n \"param_grid. param_grid = { 'loss': ['log'], } X = np.arange(24).reshape(6,\",\n \"are estimators are cloned before fitting pipe = Pipeline([('regressor', LinearRegression())])\",\n \"%d is smaller ' 'than n_iter=%d. Running %d iterations. For\",\n \"DecisionTreeClassifier(random_state=0)] # Test with grid search cv for est in\",\n \"y[test]) assert_almost_equal( correct_score, grid_search.cv_results_['split%d_test_score' % i][cand_i]) # Test with a\",\n \"clf = SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError,\",\n \"was passed assert not hasattr(param_grid['regressor'][0], 'coef_') assert not hasattr(param_grid['regressor'][1], 'coef_')\",\n \"random_search.fit(X, y) random_search_pickled = pickle.loads(pickle.dumps(random_search)) assert_array_almost_equal(random_search.predict(X), random_search_pickled.predict(X)) def test_grid_search_with_multioutput_data(): #\",\n \"tuple of arrays should be preserved as tuple. assert isinstance(tuple_of_arrays,\",\n \"1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 = cv.predict(X_[180:]) C2 = cv.best_estimator_.C assert\",\n \"data of all 4 blobs, second only from two. mask\",\n \"'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_cand =\",\n \"when returned 'best_index_' is not integer. \\\"\\\"\\\" return None X,\",\n \"sys.stdout = old_stdout assert grid_search.best_estimator_.foo_param == 2 assert_array_equal(grid_search.cv_results_[\\\"param_foo_param\\\"].data, [1, 2,\",\n \"hashable_samples = [\\\"a%db%dc%d\\\" % (p['a'], p['b'], p['c']) for p in\",\n \"a dict \\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.* value is not\",\n \"grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not\",\n \"GridSearchCV with SimpleImputer X = np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :]\",\n \"compute the test kernel matrix K_test = np.dot(X_[180:], X_[:180].T) y_test\",\n \"with both dense and sparse matrices X_, y_ = make_classification(n_samples=200,\",\n \"= FailingClassifier() # refit=False because we want to test the\",\n \"grid_search_no_score.score(X, y) # giving no scoring function raises an error\",\n \"train scores, we do not take a weighted mean irrespective\",\n \"score_keys) check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i]\",\n \"into infinite loops params_distribution = {'first': bernoulli(.5), 'second': ['a', 'b',\",\n \"StratifiedShuffleSplit()] for cv in non_group_cvs: gs = GridSearchCV(clf, grid, cv=cv)\",\n \"= cv.predict(X_precomputed) attr_message = \\\"GridSearchCV not identical with precomputed metric\\\"\",\n \"<= 1 # test that repeated calls yield identical parameters\",\n \"'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples = list(sampler) assert\",\n \"== 2 assert tuple_of_arrays[1].ndim == 1 return self def _fit_param_callable():\",\n \"fit(self, X, y=None): if self.parameter == FailingClassifier.FAILING_PARAMETER: raise ValueError(\\\"Failing classifier\",\n \"matrices X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) clf = LinearSVC()\",\n \"hasattr(grid_search, \\\"best_estimator_\\\") and \\\\ hasattr(grid_search, \\\"best_index_\\\") and \\\\ hasattr(grid_search, \\\"best_params_\\\")\",\n \"= GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y),\",\n \"function will be removed on 0.25 @ignore_warnings(category=FutureWarning) def test_fit_grid_point(): X,\",\n \"'cv': cv, 'return_train_score': True} search = SearchCV(**common_params, **specialized_params) search.fit(X, y)\",\n \"the parameter search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0): self.foo_param = foo_param\",\n \"= RandomizedSearchCV(Ridge(), {'alpha': [1e-3, 1e-2, 1e-1]}, cv=cv, n_jobs=4) # assert\",\n \"match=err_msg): searcher.fit(X, y, spam=np.ones(1), eggs=np.zeros(10)) searcher.fit(X, y, spam=np.ones(10), eggs=np.zeros(10)) @ignore_warnings\",\n \"to the base estimator. Non-regression test for issue #13920. \\\"\\\"\\\"\",\n \"Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error !=\",\n \"[1, 2, 3]}, refit=True, cv=3) grid_search.fit(X, y) grid_search_pickled = pickle.loads(pickle.dumps(grid_search))\",\n \"that correct scores are used n_splits = 3 clf =\",\n \"return score def get_params(self, deep=False): return {'foo_param': self.foo_param} def set_params(self,\",\n \"assert len(samples) == 10 for sample in samples: assert sample[\\\"kernel\\\"]\",\n \"1, 3), gamma=np.logspace(-5, 0, 3, base=0.1)) for refit in (True,\",\n \"1, 2]}], scoring='accuracy', refit=False, error_score=0.0) assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates\",\n \"assert not hasattr(param_grid['regressor'][1], 'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X\",\n \"= np.dot(X_[180:], X_[:180].T) y_test = y_[180:] y_pred = cv.predict(K_test) assert\",\n \"cv = KFold(n_splits=3) grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]},\",\n \"where behavior changed: # estimator requires score method to compete\",\n \"assert grid_search.best_params_[\\\"n_clusters\\\"] == 3 # Single metric evaluation unsupervised grid_search\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(X_[:180].tocoo(), y_[:180]) y_pred2 =\",\n \"warning only occurs in situation where behavior changed: # estimator\",\n \"100 n_splits = 5 X, y = make_classification(n_samples=n_samples, random_state=0) gs\",\n \"to break support for those without following # proper deprecation\",\n \"= GridSearchCV(LinearSVC(random_state=0), {'C': Cs}, refit=False) grid_search.fit(X, y) assert not hasattr(grid_search,\",\n \"== \\\"f1\\\": correct_score = f1_score(y[test], clf.predict(X[test])) elif score == \\\"roc_auc\\\":\",\n \"are of correct types cv_results = search.cv_results_ assert all(isinstance(cv_results[param], np.ma.MaskedArray)\",\n \"random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3, cv=3)\",\n \"3, 2) check_y = lambda x: x.shape[1:] == (7, 11)\",\n \"of arrays should be preserved as tuple. assert isinstance(tuple_of_arrays, tuple)\",\n \"2nd and subsequent cv.split calls. # This is a check\",\n \"space of parameters %d is smaller ' 'than n_iter=%d. Running\",\n \"SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' % s][0]\",\n \"# candidates 1 and 2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1])\",\n \"score reset where appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0)\",\n \"{'C': [1, 10]}, 'n_iter': 2})] ) def test_search_default_iid(SearchCV, specialized_params): #\",\n \"assert not hasattr(clf, 'best_score_') def test_refit_callable_invalid_type(): \\\"\\\"\\\" Test implementation catches\",\n \"# NOTE: `fit_params` should be data dependent (e.g. `sample_weight`) which\",\n \"for score_type in ('train', 'test'): per_param_scores = {} for param_i\",\n \"v in search_acc.cv_results_.items()} cv_results_acc_rec.update({re.sub('_score$', '_recall', k): v for k, v\",\n \"def fit(self, X, Y): assert len(X) == len(Y) self.classes_ =\",\n \"'precision'} ).fit, X, y) def test_grid_search_error(): # Test that grid\",\n \"= cv.best_estimator_.C assert np.mean(y_pred == y_pred2) >= .9 assert C\",\n \"BaseEstimator() attr_message = \\\"BaseSearchCV _pairwise property must match estimator\\\" for\",\n \"the 2nd and 3rd parameter will not be evaluated as\",\n \"out of bound result. \\\"\\\"\\\" def refit_callable_out_bound(cv_results): \\\"\\\"\\\" A dummy\",\n \"y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1, 1]],\",\n \"return self # this should not raise any exceptions NoRunSearchSearchCV(SVC()).fit(X,\",\n \"spam=np.ones(10)) err_msg = \\\"Fit parameter spam has length 1; expected\\\"\",\n \"class LinearSVCNoScore(LinearSVC): \\\"\\\"\\\"An LinearSVC classifier that has no score method.\\\"\\\"\\\"\",\n \"recall_score from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from\",\n \"check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_\",\n \"'values']}] sampler = ParameterSampler(params, n_iter=9) n_iter = 9 grid_size =\",\n \"('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score', 'mean_train_score', 'rank_test_score', 'split0_test_score',\",\n \"= ParameterSampler(params, n_iter=9) n_iter = 9 grid_size = 8 expected_warning\",\n \"score_keys + ('params',))) assert all(cv_results[key].shape == (n_cand,) for key in\",\n \"\\\"Parameter.* value is not iterable .*\\\" r\\\"\\\\(key='foo', value=0\\\\)\\\")] ) def\",\n \"error_message): with pytest.raises(error_type, match=error_message): klass(input) def test_parameter_grid(): # Test basic\",\n \"= 3 n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1,\",\n \"0], [1, 0], [0, 1], [1, 1]], random_state=0, cluster_std=0.1, shuffle=False,\",\n \"search.cv_results_['mean_test_score'][0] test_std = search.cv_results_['std_test_score'][0] train_cv_scores = np.array( [search.cv_results_['split%d_train_score' % s][0]\",\n \"X=None, Y=None): return 0. def test_grid_search_failing_classifier(): # GridSearchCV with on_error\",\n \"= search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_\",\n \"times to be non-zero for trivial X and y assert\",\n \"error upon multimetric scorer assert_raise_message(ValueError, \\\"For evaluating multiple scores, use\",\n \"{\\\"C\\\": np.ones((3, 2))} clf = SVC() assert_raises(ValueError, GridSearchCV, clf, param_dict)\",\n \"sorted(exp_results) for k in results: if not k.endswith('_time'): # XXX:\",\n \"= GridSearchCV(clf, grid, scoring=None).fit(X, y) search_accuracy = GridSearchCV(clf, grid, scoring='accuracy').fit(X,\",\n \"return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results): for key in ('mean_fit_time', 'std_fit_time',\",\n \"estimator without predict. # slight duplication of a test from\",\n \"scores must also be # same as long as the\",\n \"dec) assert_almost_equal(correct_score, cv_scores[i]) # FIXME remove test_fit_grid_point as the function\",\n \"masking cv_results = search.cv_results_ n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and\",\n \"sklearn.utils._testing import assert_allclose from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import\",\n \"make_blobs from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import fit_grid_point from\",\n \"class FailingClassifier(BaseEstimator): \\\"\\\"\\\"Classifier that raises a ValueError on fit()\\\"\\\"\\\" FAILING_PARAMETER\",\n \"scoring=\\\"precision\\\", refit=True) clf.fit(X, y) def test_refit_callable(): \\\"\\\"\\\" Test refit=callable, which\",\n \"attr_message = \\\"BaseSearchCV _pairwise property must match estimator\\\" for _pairwise_setting\",\n \"for k in results: if not k.endswith('_time'): # XXX: results['params']\",\n \"in score_keys if 'time' not in k and k !=\",\n \"because we only want to check that errors caused by\",\n \"regressors do not have a classes_ attribute grid_search = GridSearchCV(Ridge(),\",\n \"n_features_in to the # best estimator n_features = 4 X,\",\n \"# enough for the fit/score times to be non-zero for\",\n \"regressor to make sure each score differs clf = DecisionTreeRegressor(random_state=0)\",\n \"True refit = 'accuracy' if isinstance(scoring, tuple) else refit else:\",\n \"for all # the cv splits, for both param setting\",\n \"uniform(0.01, 0.1)})] ) def test_scalar_fit_param_compat(SearchCV, param_search): # check support for\",\n \"per param # setting. np.testing.assert_equal({k: v for k, v in\",\n \"clf.predict_proba(X) clf.predict_log_proba(X) # Make sure `predict_proba` is not available when\",\n \"grid search cv for est in estimators: grid_search = GridSearchCV(est,\",\n \"assert search.refit_time_ >= 0 def test_grid_search_correct_score_results(): # test that correct\",\n \"grid, cv=cv) assert_raise_message(ValueError, \\\"The 'groups' parameter should not be None.\\\",\",\n \"assert not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X):\",\n \"return T.shape[0] def transform(self, X): return X + self.foo_param def\",\n \"_pairwise is working X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv\",\n \"refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests `refit=callable` interface. Return the\",\n \"grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4]), scoring='fowlkes_mallows_score') grid_search.fit(X, y) assert\",\n \"2])] param_keys = ('param_C', 'param_degree', 'param_gamma', 'param_kernel') score_keys = ('mean_test_score',\",\n \"list(empty) == [{}] assert_grid_iter_equals_getitem(empty) assert_raises(IndexError, lambda: empty[1]) has_empty = ParameterGrid([{'C':\",\n \"== FailingClassifier.FAILING_PARAMETER) ranks = gs.cv_results_['rank_test_score'] # Check that succeeded estimators\",\n \"7 * 11).reshape(10, 7, 11) check_X = lambda x: x.shape[1:]\",\n \"@pytest.mark.parametrize('search_cv', [RandomizedSearchCV, GridSearchCV]) def test_refit_callable_out_bound(out_bound_value, search_cv): \\\"\\\"\\\" Test implementation catches\",\n \"gscv = fit_grid([{'max_depth': [1, 2]}, {'min_samples_split': [5, 10]}]) results =\",\n \"GridSearchCV(clf, {'foo_param': [1, 2, 3]}) grid_search.fit(X_4d, y_3d).score(X, y) assert hasattr(grid_search,\",\n \"`fit_params` should be data dependent (e.g. `sample_weight`) which # is\",\n \"= GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy', refit=False, error_score=float('nan')) assert_warns(FitFailedWarning,\",\n \"This is a check to make sure cv.split is not\",\n \"next(StratifiedKFold().split(X, y)) with pytest.warns(FutureWarning, match=msg): fit_grid_point(X, y, svc, params, train,\",\n \"1], [2, 1]]) y = np.array([1, 1, 2, 2]) def\",\n \"\\\"roc_auc\\\": dec = clf.decision_function(X[test]) correct_score = roc_auc_score(y[test], dec) assert_almost_equal(correct_score, cv_scores[i])\",\n \"2} to (\\\"a\\\", 1, \\\"b\\\", 2) points = set(tuple(chain(*(sorted(p.items())))) for\",\n \"sure cv.split is not called once per param # setting.\",\n \"score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits\",\n \"n_splits = 3 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1,\",\n \"y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_search_iter =\",\n \"'coef_') @pytest.mark.parametrize(\\\"SearchCV\\\", [GridSearchCV, RandomizedSearchCV]) def test_SearchCV_with_fit_params(SearchCV): X = np.arange(100).reshape(10, 10)\",\n \"GridSearchCV(clf, grid, cv=cv) # Should not raise an error gs.fit(X,\",\n \"np.zeros(X.shape[0]) @ignore_warnings def test_refit(): # Regression test for bug in\",\n \"y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None]) @ignore_warnings() def test_search_cv_timing(): svc = LinearSVC(random_state=0)\",\n \"True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric scoring, the \\\" \\\"parameter\",\n \"[5]], [0, 0, 0, 0, 1] estimators = (DecisionTreeRegressor(), DecisionTreeClassifier())\",\n \"when returned 'best_index_' is out of bounds. \\\"\\\"\\\" return out_bound_value\",\n \"y_) clf = SVC(C=1.0, kernel=\\\"rbf\\\", gamma=0.1) clf.fit(X_, y_) assert_array_equal(clf.dual_coef_, cv.best_estimator_.dual_coef_)\",\n \"return_train_score=True) for search in (grid_search, random_search): search.fit(X, y) cv_results =\",\n \"search on user-defined classifiers. class MockClassifier: \\\"\\\"\\\"Dummy classifier to test\",\n \"# test error is raised when the precomputed kernel is\",\n \"in sampler] == [x for x in sampler] def check_cv_results_array_types(search,\",\n \"import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import ParameterGrid\",\n \"refit='accuracy' if refit: probability = True refit = 'accuracy' if\",\n \"# Neither of the following two estimators inherit from BaseEstimator,\",\n \"# defaults to euclidean metric (minkowski p = 2) clf\",\n \"1]}, scoring='precision', refit=refit_callable_out_bound) with pytest.raises(IndexError, match='best_index_ index out of range'):\",\n \"def f1_loss(y_true_, y_pred_): return -f1_score(y_true_, y_pred_) F1Loss = make_scorer(f1_loss, greater_is_better=False)\",\n \"GridSearchCV(clf, {'foo_param': range(1, 4)}, cv=3) grid_search.fit(X, y) assert grid_search.best_estimator_.foo_param ==\",\n \"the required results assert_array_equal(sorted(cv_results.keys()), sorted(param_keys + score_keys + ('params',))) assert\",\n \"= make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3 n_grid_points = 6\",\n \"= SVC(kernel='precomputed') cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) cv.fit(K_train, y_train)\",\n \"assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator(): \\\"\\\"\\\" Test implementation of\",\n \"test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) n_splits = 3\",\n \"for key in param_keys + score_keys) def test_grid_search_cv_results(): X, y\",\n \"import Pipeline from sklearn.linear_model import Ridge, SGDClassifier, LinearRegression from sklearn.experimental\",\n \"test_classes__property(): # Test that classes_ property matches best_estimator_.classes_ X =\",\n \"5) clf = CheckingClassifier( check_y=lambda x: isinstance(x, list), methods_to_check=[\\\"fit\\\"], )\",\n \"def test_transform_inverse_transform_round_trip(): clf = MockClassifier() grid_search = GridSearchCV(clf, {'foo_param': [1,\",\n \"test_parameter_grid(): # Test basic properties of ParameterGrid. params1 = {\\\"foo\\\":\",\n \"# same as long as the same train/test indices are\",\n \"= SearchCV(**common_params, **specialized_params) search.fit(X, y) test_cv_scores = np.array( [search.cv_results_['split%d_test_score' %\",\n \"def test_grid_search_when_param_grid_includes_range(): # Test that the best estimator contains the\",\n \"X dataframe, y series X_df, y_ser = InputFeatureType(X), TargetType(y) def\",\n \"Single values need to be wrapped in a list\\\" \\\"\",\n \"a score, and without y grid_search = GridSearchCV(km, param_grid=dict(n_clusters=[2, 3,\",\n \"0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0), return_train_score=True) gs4.fit(X, y) def _pop_time_keys(cv_results):\",\n \"the fit/score times to be non-zero for trivial X and\",\n \"'split0_train_score', 'split1_train_score', 'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates\",\n \"want to test the behaviour of the grid search part\",\n \"expected_score = scorer(est, X[test], y[test]) # Test the return values\",\n \"False]: setattr(est, '_pairwise', _pairwise_setting) cv = GridSearchCV(est, {'n_neighbors': [10]}) assert\",\n \"n_cand) n_candidates = len(search.cv_results_['params']) assert all((cv_results['param_C'].mask[i] and cv_results['param_gamma'].mask[i] and not\",\n \"'second': ['a', 'b', 'c']} sampler = ParameterSampler(params_distribution, n_iter=7) samples =\",\n \"'split2_train_score', 'std_test_score', 'std_train_score', 'mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time') n_candidates = n_grid_points\",\n \"setting \\\"\\\"\\\" def refit_callable(cv_results): \\\"\\\"\\\" A dummy function tests `refit=callable`\",\n \"X, y = make_blobs(centers=[[0, 0], [1, 0], [0, 1], [1,\",\n \"np.array( list(grid_search.cv_results_['split%d_test_score' % s][candidate_i] for s in range(n_splits))) for i,\",\n \"appropriate. X, y = make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier()\",\n \"cv_results['mean_train_score'][2]) # 'min' rank should be assigned to the tied\",\n \"# Special case: empty grid (useful to get default estimator\",\n \"3, 4]), scoring=scoring, refit=refit) grid_search.fit(X, y) # Both ARI and\",\n \"sparse SVMs. X = np.arange(100).reshape(10, 10) y = np.array([0] *\",\n \"i in range(len(grid))] @pytest.mark.parametrize(\\\"klass\\\", [ParameterGrid, partial(ParameterSampler, n_iter=10)]) @pytest.mark.parametrize( \\\"input, error_type,\",\n \"= ParameterSampler(params, n_iter=8) samples = list(sampler) assert len(samples) == 8\",\n \"make_classification(n_samples=20, n_features=10, random_state=0) clf = FailingClassifier() # refit=False because we\",\n \"[0.1, 0.2, 0.3]}, cv=KFold(n_splits=n_splits, shuffle=True, random_state=0).split(X, y), return_train_score=True) gs3.fit(X, y)\",\n \"n_iter=2) for search in (gs, rs): search.fit(X, y) for key\",\n \"hasattr(regressor_results[0], 'coef_') assert not hasattr(regressor_results[1], 'coef_') assert regressor_results[0] is not\",\n \"= lambda x: x.shape[1:] == (7, 11) clf = CheckingClassifier(\",\n \"raise an error upon multimetric scorer assert_raise_message(ValueError, \\\"For evaluating multiple\",\n \"svc, params, train, test, scorer, verbose=False) def test_pickle(): # Test\",\n \"= GridSearchCV(est, est_parameters, cv=cv) grid_search.fit(X, y) res_params = grid_search.cv_results_['params'] for\",\n \"for k, v in gs.cv_results_.items() if not k.endswith('_time')}, {k: v\",\n \"GroupKFold(n_splits=3), GroupShuffleSplit()] for cv in group_cvs: gs = GridSearchCV(clf, grid,\",\n \"doesn't destroy pandas dataframe types = [(MockDataFrame, MockDataFrame)] try: from\",\n \"grid search grid_search_no_score.fit(X, y) # check that best params are\",\n \"scores, use \\\" \\\"sklearn.model_selection.cross_validate instead.\\\", fit_grid_point, X, y, svc, params,\",\n \"the parameters in the param_grid. param_grid = { 'loss': ['log'],\",\n \"GridSearchCV(km, param_grid=dict(n_clusters=[2, 3, 4])) grid_search.fit(X) assert grid_search.best_params_[\\\"n_clusters\\\"] == 4 def\",\n \"sorted(results.keys()) == sorted(exp_results) for k in results: if not k.endswith('_time'):\",\n \"those in `cv_results` for key in clf.cv_results_.keys(): assert key in\",\n \"InputFeatureType, TargetType in types: # X dataframe, y series X_df,\",\n \"assert_allclose(exp_results[k], results[k], err_msg='Checking ' + k) def fit_grid(param_grid): return GridSearchCV(clf,\",\n \"by grid_search (expected behavior), and this would cause an #\",\n \"from sklearn.metrics import make_scorer from sklearn.metrics import roc_auc_score from sklearn.metrics.pairwise\",\n \"i][cand_i]) # Test with a randomized search for est in\",\n \"in clf.cv_results_.keys(): assert key in cv_results return cv_results['mean_test_score'].argmin() X, y\",\n \"not hasattr(self, 'has_been_fit_') self.has_been_fit_ = True def predict(self, X): return\",\n \"dependent (e.g. `sample_weight`) which # is not the case for\",\n \"[1, 2, 3]}) grid_search.fit(X_df, y_ser).score(X_df, y_ser) grid_search.predict(X_df) assert hasattr(grid_search, \\\"cv_results_\\\")\",\n \"= [0, 0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),\",\n \"param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key in score_keys) assert\",\n \"\\\"SearchCV, specialized_params\\\", [(GridSearchCV, {'param_grid': {'C': [1, 10]}}), (RandomizedSearchCV, {'param_distributions': {'C':\",\n \"mean irrespective of # i.i.d. or not assert train_mean ==\",\n \"0.0 assert np.all(search.cv_results_[key] < 1) assert hasattr(search, \\\"refit_time_\\\") assert isinstance(search.refit_time_,\",\n \"% s][candidate_i] for s in range(n_splits))) for i, (train, test)\",\n \"= GridSearchCV(clf_no_score, {'C': Cs}, scoring='accuracy') # smoketest grid search grid_search_no_score.fit(X,\",\n \"== getattr(search_acc, key) def test_search_cv_results_rank_tie_breaking(): X, y = make_blobs(n_samples=50, random_state=42)\",\n \"refit in [\\\"\\\", 5, True, 'recall', 'accuracy']: assert_raise_message(ValueError, \\\"For multi-metric\",\n \"np.arange(20, dtype=np.float64).reshape(5, -1) X[2, :] = np.nan y = [0,\",\n \"need to be a non-empty sequence.\\\", GridSearchCV, clf, param_dict) param_dict\",\n \"= 6 params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),\",\n \"of the grid search part gs = GridSearchCV(clf, [{'parameter': [0,\",\n \"# The tuple of arrays should be preserved as tuple.\",\n \"{'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'} clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1,\",\n \"not have a classes_ attribute grid_search = GridSearchCV(Ridge(), {'alpha': [1.0,\",\n \"import StratifiedKFold from sklearn.model_selection import StratifiedShuffleSplit from sklearn.model_selection import LeaveOneGroupOut\",\n \"X, y = make_multilabel_classification(return_indicator=True, random_state=0) est_parameters = {\\\"max_depth\\\": [1, 2,\",\n \"X_precomputed = euclidean_distances(X) clf = KNeighborsClassifier(metric='precomputed') cv = GridSearchCV(clf, grid_params,\",\n \"decision_function = predict def score(self, X=None, Y=None): if self.foo_param >\",\n \"works when loss is specified # as one of the\",\n \"cv=cv) grid_search.fit(X, y.tolist()).score(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") @ignore_warnings def test_pandas_input():\",\n \"Multi-metric evaluation unsupervised scoring = ['adjusted_rand_score', 'fowlkes_mallows_score'] for refit in\",\n \"score_keys if 'time' not in k and k != 'rank_test_score')\",\n \"loop to assert we can iterate over the grid multiple\",\n \"+ [1] * 5) clf = CheckingClassifier(expected_fit_params=['spam', 'eggs']) searcher =\",\n \"1] p = Pipeline([ ('imputer', SimpleImputer(strategy='mean', missing_values=np.nan)), ('classifier', MockClassifier()), ])\",\n \"for s in range(search.n_splits_)] ) train_mean = search.cv_results_['mean_train_score'][0] train_std =\",\n \"and random search delegate n_features_in to the # best estimator\",\n \"assert gs.n_features_in_ == n_features assert rs.n_features_in_ == n_features def test_search_cv__pairwise_property_delegated_to_base_estimator():\",\n \"0 mask[np.where(y == 2)[0][::2]] = 0 # this leads to\",\n \"classifier to test the parameter search algorithms\\\"\\\"\\\" def __init__(self, foo_param=0):\",\n \"= make_classification(n_samples=15, n_classes=2, random_state=0) groups = rng.randint(0, 3, 15) clf\",\n \"situation where behavior changed: # estimator requires score method to\",\n \"cv = GridSearchCV(clf, {'C': [0.1, 1.0]}) assert_raises(ValueError, cv.fit, X_[:180], y_)\",\n \"contains the right value for foo_param clf = MockClassifier() grid_search\",\n \"1.0]}, scoring=\\\"f1\\\") cv.fit(X_[:180], y_[:180]) y_pred = cv.predict(X_[180:]) C = cv.best_estimator_.C\",\n \"the search module\\\"\\\"\\\" from collections.abc import Iterable, Sized from io\",\n \"random_state=1, centers=[[0, 1], [1, 0], [0, 0]]) search = GridSearchCV(KernelDensity(),\",\n \"{'foo_param': [0]}, n_iter=1, cv=3) random_search.fit(X, y) assert hasattr(grid_search, \\\"cv_results_\\\") def\",\n \"calls. # This is a check to make sure cv.split\",\n \"\\\" \\\"parameter refit must be set to a scorer key\\\",\",\n \"], C=[1, 10], gamma=[0.1, 1]), dict(kernel=['poly', ], degree=[1, 2])] param_keys\",\n \"in [None, ['accuracy', 'precision']]: grid_search = GridSearchCV( clf, {'foo_param': [1,\",\n \"range(10)} sampler = ParameterSampler(params, n_iter=99, random_state=42) samples = list(sampler) assert\",\n \"3 n_grid_points = 6 params = [dict(kernel=['rbf', ], C=[1, 10],\",\n \"every key matches those in `cv_results` for key in clf.cv_results_.keys():\",\n \"sklearn.utils._testing import ignore_warnings from sklearn.utils._mocking import CheckingClassifier, MockDataFrame from scipy.stats\",\n \"# check that we can call score and that it\",\n \"estimators = [DecisionTreeRegressor(random_state=0), DecisionTreeClassifier(random_state=0)] # Test with grid search cv\",\n \"Non-regression test for issue #13920. \\\"\\\"\\\" n_samples = 50 n_splits\",\n \"grid_search = GridSearchCV(est, est_parameters, cv=cv, ).fit(X, y) assert_array_equal(grid_search.cv_results_['param_random_state'], [0, None])\",\n \"'min' rank should be assigned to the tied candidates assert_almost_equal(search.cv_results_['rank_test_score'],\",\n \"grid_search_pickled.predict(X)) random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True, n_iter=3,\",\n \"from sklearn.metrics import accuracy_score from sklearn.metrics import make_scorer from sklearn.metrics\",\n \"deep nested comparison of the two cv_results dicts np.testing.assert_equal({k: v\",\n \"an exception would be raised on refit and not #\",\n \"for key in param_keys) assert not any(isinstance(cv_results[key], np.ma.MaskedArray) for key\",\n \"compare_cv_results_multimetric_with_single( search_multi, search_acc, search_rec): \\\"\\\"\\\"Compare multi-metric cv_results with the ensemble\",\n \"assert_warns(FitFailedWarning, gs.fit, X, y) n_candidates = len(gs.cv_results_['params']) # Ensure that\",\n \"n_candidates = len(gs.cv_results_['params']) assert all(np.all(np.isnan(get_cand_scores(cand_i))) for cand_i in range(n_candidates) if\",\n \"score_no_score_auc = search_no_score_method_auc.score(X, y) score_auc = search_auc.score(X, y) # ensure\",\n \"2 alone assert_almost_equal(cv_results['mean_test_score'][0], cv_results['mean_test_score'][1]) assert_almost_equal(cv_results['mean_train_score'][0], cv_results['mean_train_score'][1]) assert not np.allclose(cv_results['mean_test_score'][1], cv_results['mean_test_score'][2])\",\n \"precomputed kernel is not array-like # or sparse assert_raises(ValueError, cv.fit,\",\n \"settings) empty = ParameterGrid({}) assert len(empty) == 1 assert list(empty)\",\n \"modify the parameter grid that was passed assert not hasattr(param_grid['regressor'][0],\",\n \"1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) == [grid[i] for\",\n \"X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0) # compute the training\",\n \"if groups is correctly passed to the cv object rng\",\n \"# Check that succeeded estimators have lower ranks assert ranks[0]\",\n \"= np.array([1, 1, 2, 2]) def assert_grid_iter_equals_getitem(grid): assert list(grid) ==\",\n \"'predict_log_proba'): assert_almost_equal(getattr(search_multi, method)(X), getattr(search_acc, method)(X)) assert_almost_equal(search_multi.score(X, y), search_acc.score(X, y)) for\",\n \"[1, 2]}, {'min_samples_split': [5, 10]}]) results = mycv.cv_results_ check_results(results, gscv)\",\n \"assert isinstance(grid1, Iterable) assert isinstance(grid1, Sized) assert len(grid1) == 3\",\n \"'time' not in k and k != 'rank_test_score') # Check\",\n \"are supported as cv and # that the splits are\",\n \"dict \\\\(0\\\\)'), ({'foo': 0}, TypeError, \\\"Parameter.* value is not iterable\",\n \"are close enough to give similar models # which would\",\n \"X[test], y[test]) # Test the return values of fit_grid_point assert_almost_equal(this_scores,\",\n \"(<class 'int'>).\\\" \\\" Single values need to be wrapped in\",\n \"on # the other # create \\\"cv\\\" for splits cv\",\n \"[(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((DataFrame, Series))\",\n \"with single metric methods\\\"\\\"\\\" assert search_acc.refit == refit if refit:\",\n \"({'accuracy': make_scorer(accuracy_score), 'recall': make_scorer(recall_score)}, 'accuracy', 'recall'): grid_search = GridSearchCV(SVC(), cv=n_splits,\",\n \"assert search_multi.multimetric_ assert_array_equal(sorted(search_multi.scorer_), ('accuracy', 'recall')) cv_results_multi = search_multi.cv_results_ cv_results_acc_rec =\",\n \"cv_results_acc_rec.items() if not k.endswith('_time')}) def compare_refit_methods_when_refit_with_acc(search_multi, search_acc, refit): \\\"\\\"\\\"Compare refit\",\n \"# raise warning if n_iter is bigger than total parameter\",\n \"does something else??? # noise-free simple 2d-data X, y =\",\n \"10 for sample in samples: assert sample[\\\"kernel\\\"] in [\\\"rbf\\\", \\\"linear\\\"]\",\n \"in `cv_results` for key in clf.cv_results_.keys(): assert key in cv_results\",\n \"\\\"cv_results_\\\") def test_X_as_list(): # Pass X as list in GridSearchCV\",\n \"= LinearSVCNoScore(random_state=0) grid_search = GridSearchCV(clf, {'C': Cs}, scoring='accuracy') grid_search.fit(X, y)\",\n \"import OneTimeSplitter # Neither of the following two estimators inherit\",\n \"The two C values are close enough to give similar\",\n \"= GridSearchCV(svc, {'C': [0, 1]}, cv=2, error_score=0) rs = RandomizedSearchCV(svc,\",\n \"# assert that this raises an error with pytest.raises(ValueError, match='No\",\n \"assert search_multi.refit == 'accuracy' else: assert not search_multi.refit return #\",\n \"not assert train_mean == pytest.approx(1) assert train_std == pytest.approx(0) def\",\n \"X_[:180].T) y_train = y_[:180] clf = SVC(kernel='precomputed') cv = GridSearchCV(clf,\",\n \"DecisionTreeClassifier()) est_parameters = {\\\"random_state\\\": [0, None]} cv = KFold() for\"\n]","isConversational":false}}},{"rowIdx":190,"cells":{"ngram":{"kind":"list like","value":["Generated by Django 3.2.7 on 2021-09-13 03:52 import uuid from","POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\": [\"zettle_created\"], }, ),","the total amount which is VAT\", max_digits=12, ), ), (","models.DecimalField( decimal_places=2, help_text=\"The part of the payment which goes to","options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[","Zettle account which was used to make this sale.\", max_length=100,","dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations = [ migrations.CreateModel(","this transaction is not a customer payment.\", null=True, ), ),","( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment fee BornHack has to","part of the total amount which is VAT\", max_digits=12, ),","account which was used to make this sale.\", max_length=100, ),","( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"), ), ( \"vat\", models.DecimalField(","( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference for this payment. Can","\"statement_time\", models.DateTimeField( help_text=\"The date and time this movement was added","Zettles systems after this transaction.\", max_digits=12, ), ), ], options={","null=True, ), ), ( \"staff\", models.CharField( help_text=\"The Zettle account which","default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)),","), ), ( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference for this","customer payment.\", null=True, ), ), ( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The","help_text=\"The date and time this movement was added to the","(\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The date and","class Migration(migrations.Migration): dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations =","to BornHack after fees have been substracted.\", max_digits=12, ), ),","max_length=100, ), ), ( \"description\", models.CharField( help_text=\"The description of this","\"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField(","models.DecimalField( decimal_places=2, help_text=\"The payment fee BornHack has to pay to","in Zettles systems after this transaction.\", max_digits=12, ), ), ],","}, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False,","max_length=100), ), ( \"card_issuer\", models.CharField( blank=True, help_text=\"The card issuer. Can","if this was not a card payment.\", max_length=100, null=True, ),","models.DateTimeField( blank=True, help_text=\"The date and time this payment was made.","<reponame>bornhack/bornhack-website # Generated by Django 3.2.7 on 2021-09-13 03:52 import","( \"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=255 ),","( \"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=100 ),","decimal_places=2, help_text=\"The amount of this transaction\", max_digits=12, ), ), (","Django 3.2.7 on 2021-09-13 03:52 import uuid from django.db import","time this receipt was created in Zettles end\" ), ),","\"zettle_created\", models.DateTimeField( help_text=\"The date and time this receipt was created","models class Migration(migrations.Migration): dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations","), ( \"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=255","movement was added to the account statement.\" ), ), (","], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\",","amount of this transaction\", max_digits=12, ), ), ( \"balance\", models.DecimalField(","in Zettles end\" ), ), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt","total amount the customer paid\", max_digits=12, ), ), ( \"fee\",","the customer paid\", max_digits=12, ), ), ( \"fee\", models.DecimalField( decimal_places=2,","to make this sale.\", max_length=100, ), ), ( \"description\", models.CharField(","blank=True, help_text=\"The reference for this payment. Can be empty if","max_digits=12, ), ), ( \"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100), ),","Zettle receipt number.\"), ), ( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The part","\"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100), ), ( \"card_issuer\", models.CharField( blank=True,","models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The date and time","max_digits=12, ), ), ], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], },","this was not a card payment.\", max_length=100, null=True, ), ),","), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True,","payment fee BornHack has to pay to receive this payment\",","fees have been substracted.\", max_digits=12, ), ), ( \"payment_method\", models.CharField(help_text=\"The","\"net\", models.DecimalField( decimal_places=2, help_text=\"The part of the payment which goes","), ( \"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100), ), ( \"card_issuer\",","pay to receive this payment\", max_digits=12, ), ), ( \"net\",","to pay to receive this payment\", max_digits=12, ), ), (","( \"staff\", models.CharField( help_text=\"The Zettle account which was used to","was used to make this sale.\", max_length=100, ), ), (","created in Zettles end\" ), ), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle","by Django 3.2.7 on 2021-09-13 03:52 import uuid from django.db","of this transaction.\", max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)),","description of this transaction.\", max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\",","(\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\": [\"zettle_created\"],","), ], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel(","amount the customer paid\", max_digits=12, ), ), ( \"fee\", models.DecimalField(","\"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ (","03:52 import uuid from django.db import migrations, models class Migration(migrations.Migration):","( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The part of the total amount","), ( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment fee BornHack has","is not a customer payment.\", null=True, ), ), ( \"payment_reference\",","help_text=\"The date and time this payment was made. Can be","primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\",","models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The date and time this movement","blank=True, help_text=\"The date and time this payment was made. Can","a customer payment.\", null=True, ), ), ( \"description\", models.CharField( help_text=\"The","and time this movement was added to the account statement.\"","2021-09-13 03:52 import uuid from django.db import migrations, models class","transaction.\", max_length=100 ), ), ( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount","null=True, ), ), ( \"description\", models.CharField( help_text=\"The description of this","max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={ \"ordering\":","), ), ( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment fee BornHack","payment which goes to BornHack after fees have been substracted.\",","models.DecimalField( decimal_places=2, help_text=\"Our balance in Zettles systems after this transaction.\",","serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField(","help_text=\"The description of this transaction.\", max_length=100 ), ), ( \"amount\",","after fees have been substracted.\", max_digits=12, ), ), ( \"payment_method\",","paid\", max_digits=12, ), ), ( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment","), ), ( \"net\", models.DecimalField( decimal_places=2, help_text=\"The part of the","editable=False, primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), (","help_text=\"The card issuer. Can be empty if this was not","was made. Can be empty if this transaction is not","was created in Zettles end\" ), ), ( \"receipt_number\", models.IntegerField(help_text=\"The","\"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference for this payment. Can be","max_length=100, null=True, ), ), ( \"staff\", models.CharField( help_text=\"The Zettle account","to the account statement.\" ), ), ( \"payment_time\", models.DateTimeField( blank=True,","import migrations, models class Migration(migrations.Migration): dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"),","), ), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"), ), (","migrations, models class Migration(migrations.Migration): dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ]","( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\"created\",","help_text=\"The part of the total amount which is VAT\", max_digits=12,","payment\", max_digits=12, ), ), ( \"net\", models.DecimalField( decimal_places=2, help_text=\"The part","\"card_issuer\", models.CharField( blank=True, help_text=\"The card issuer. Can be empty if","reference for this payment. Can be empty if this transaction","models.DecimalField( decimal_places=2, help_text=\"The amount of this transaction\", max_digits=12, ), ),","decimal_places=2, help_text=\"The part of the payment which goes to BornHack","not a customer payment.\", null=True, ), ), ( \"description\", models.CharField(","and time this payment was made. Can be empty if","max_digits=12, ), ), ( \"total\", models.DecimalField( decimal_places=2, help_text=\"The total amount","card payment.\", max_length=100, null=True, ), ), ( \"staff\", models.CharField( help_text=\"The","\"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"), ), ( \"vat\", models.DecimalField( decimal_places=2,","uuid from django.db import migrations, models class Migration(migrations.Migration): dependencies =","] operations = [ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField(","this receipt was created in Zettles end\" ), ), (","models.CharField(help_text=\"The payment method\", max_length=100), ), ( \"card_issuer\", models.CharField( blank=True, help_text=\"The","models.DecimalField( decimal_places=2, help_text=\"The part of the total amount which is","), ( \"net\", models.DecimalField( decimal_places=2, help_text=\"The part of the payment","transaction\", max_digits=12, ), ), ( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance","decimal_places=2, help_text=\"Our balance in Zettles systems after this transaction.\", max_digits=12,","of this transaction\", max_digits=12, ), ), ( \"balance\", models.DecimalField( decimal_places=2,","), ), ( \"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100), ), (","models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\": [\"zettle_created\"], },","be empty if this transaction is not a customer payment.\",","transaction.\", max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={","( \"total\", models.DecimalField( decimal_places=2, help_text=\"The total amount the customer paid\",","this payment was made. Can be empty if this transaction","( \"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100), ), ( \"card_issuer\", models.CharField(","), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\":","the account statement.\" ), ), ( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The","models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\",","receive this payment\", max_digits=12, ), ), ( \"net\", models.DecimalField( decimal_places=2,","to receive this payment\", max_digits=12, ), ), ( \"net\", models.DecimalField(","), ), ( \"description\", models.CharField( help_text=\"The description of this transaction.\",","3.2.7 on 2021-09-13 03:52 import uuid from django.db import migrations,","(\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The date and","), ), ( \"staff\", models.CharField( help_text=\"The Zettle account which was","payment was made. Can be empty if this transaction is","is VAT\", max_digits=12, ), ), ( \"total\", models.DecimalField( decimal_places=2, help_text=\"The","part of the payment which goes to BornHack after fees","statement.\" ), ), ( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The date and","this transaction.\", max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ],","sale.\", max_length=100, ), ), ( \"description\", models.CharField( help_text=\"The description of","\"vat\", models.DecimalField( decimal_places=2, help_text=\"The part of the total amount which","not a card payment.\", max_length=100, null=True, ), ), ( \"staff\",","a card payment.\", max_length=100, null=True, ), ), ( \"staff\", models.CharField(","decimal_places=2, help_text=\"The payment fee BornHack has to pay to receive","), ), ], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ),","systems after this transaction.\", max_digits=12, ), ), ], options={ \"ordering\":","is not a customer payment.\", null=True, ), ), ( \"description\",","[ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True,","have been substracted.\", max_digits=12, ), ), ( \"payment_method\", models.CharField(help_text=\"The payment","import uuid from django.db import migrations, models class Migration(migrations.Migration): dependencies","blank=True, help_text=\"The card issuer. Can be empty if this was","django.db import migrations, models class Migration(migrations.Migration): dependencies = [ (\"economy\",","after this transaction.\", max_digits=12, ), ), ], options={ \"ordering\": [\"-statement_time\"],","of the total amount which is VAT\", max_digits=12, ), ),","[ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations = [ migrations.CreateModel( name=\"ZettleBalance\", fields=[","), ), ( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance in Zettles","max_digits=12, ), ), ( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance in","which is VAT\", max_digits=12, ), ), ( \"total\", models.DecimalField( decimal_places=2,","serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField(","empty if this transaction is not a customer payment.\", null=True,","issuer. Can be empty if this was not a card","\"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=255 ), ),","help_text=\"The Zettle account which was used to make this sale.\",","[\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4,","this transaction.\", max_digits=12, ), ), ], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\":","time this payment was made. Can be empty if this","help_text=\"The part of the payment which goes to BornHack after","), ( \"staff\", models.CharField( help_text=\"The Zettle account which was used","if this transaction is not a customer payment.\", null=True, ),","( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance in Zettles systems after","help_text=\"The date and time this receipt was created in Zettles","made. Can be empty if this transaction is not a","number.\"), ), ( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The part of the","end\" ), ), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"), ),","max_digits=12, ), ), ( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment fee","help_text=\"The description of this transaction.\", max_length=255 ), ), (\"sold_via\", models.CharField(help_text=\"Always","= [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations = [ migrations.CreateModel( name=\"ZettleBalance\",","payment method\", max_length=100), ), ( \"card_issuer\", models.CharField( blank=True, help_text=\"The card","null=True, ), ), ( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference for","models.DateTimeField( help_text=\"The date and time this receipt was created in","this payment. Can be empty if this transaction is not","has to pay to receive this payment\", max_digits=12, ), ),","), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The date","models.CharField( blank=True, help_text=\"The card issuer. Can be empty if this","VAT\", max_digits=12, ), ), ( \"total\", models.DecimalField( decimal_places=2, help_text=\"The total","which was used to make this sale.\", max_length=100, ), ),","payment.\", max_length=100, null=True, ), ), ( \"staff\", models.CharField( help_text=\"The Zettle","models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The date and time","), ( \"card_issuer\", models.CharField( blank=True, help_text=\"The card issuer. Can be","), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The date","( \"net\", models.DecimalField( decimal_places=2, help_text=\"The part of the payment which","help_text=\"The total amount the customer paid\", max_digits=12, ), ), (","customer paid\", max_digits=12, ), ), ( \"fee\", models.DecimalField( decimal_places=2, help_text=\"The","), ), ( \"total\", models.DecimalField( decimal_places=2, help_text=\"The total amount the","), ( \"total\", models.DecimalField( decimal_places=2, help_text=\"The total amount the customer","models.DecimalField( decimal_places=2, help_text=\"The total amount the customer paid\", max_digits=12, ),","payment. Can be empty if this transaction is not a","for this payment. Can be empty if this transaction is","Migration(migrations.Migration): dependencies = [ (\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations = [","primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\",","\"staff\", models.CharField( help_text=\"The Zettle account which was used to make","make this sale.\", max_length=100, ), ), ( \"description\", models.CharField( help_text=\"The","models.DateTimeField( help_text=\"The date and time this movement was added to","max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\": [\"zettle_created\"], }, ), ]","been substracted.\", max_digits=12, ), ), ( \"payment_method\", models.CharField(help_text=\"The payment method\",","total amount which is VAT\", max_digits=12, ), ), ( \"total\",","balance in Zettles systems after this transaction.\", max_digits=12, ), ),","account statement.\" ), ), ( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The date","models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The date and time this receipt","), ( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference for this payment.","), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The","from django.db import migrations, models class Migration(migrations.Migration): dependencies = [","amount which is VAT\", max_digits=12, ), ), ( \"total\", models.DecimalField(","which goes to BornHack after fees have been substracted.\", max_digits=12,","[\"-statement_time\"], \"get_latest_by\": [\"statement_time\"], }, ), migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\",","card issuer. Can be empty if this was not a","Zettles end\" ), ), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"),","), ( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The date and time this","Can be empty if this was not a card payment.\",","receipt was created in Zettles end\" ), ), ( \"receipt_number\",","models.CharField( help_text=\"The Zettle account which was used to make this","migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False,","), ( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount of this transaction\",","the payment which goes to BornHack after fees have been","models.IntegerField(help_text=\"The Zettle receipt number.\"), ), ( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The","this transaction.\", max_length=100 ), ), ( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The","( \"statement_time\", models.DateTimeField( help_text=\"The date and time this movement was","fee BornHack has to pay to receive this payment\", max_digits=12,","date and time this receipt was created in Zettles end\"","models.IntegerField( blank=True, help_text=\"The reference for this payment. Can be empty","description of this transaction.\", max_length=100 ), ), ( \"amount\", models.DecimalField(","date and time this payment was made. Can be empty","fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ),","substracted.\", max_digits=12, ), ), ( \"payment_method\", models.CharField(help_text=\"The payment method\", max_length=100),","(\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The date and time this","of the payment which goes to BornHack after fees have","\"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=100 ), ),","name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ),","decimal_places=2, help_text=\"The total amount the customer paid\", max_digits=12, ), ),","max_length=100 ), ), ( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount of","payment.\", null=True, ), ), ( \"description\", models.CharField( help_text=\"The description of","time this movement was added to the account statement.\" ),","max_digits=12, ), ), ( \"net\", models.DecimalField( decimal_places=2, help_text=\"The part of","operations = [ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4,","customer payment.\", null=True, ), ), ( \"description\", models.CharField( help_text=\"The description","), ), ( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount of this","this sale.\", max_length=100, ), ), ( \"description\", models.CharField( help_text=\"The description","), ), (\"created\", models.DateTimeField(auto_now_add=True)), (\"updated\", models.DateTimeField(auto_now=True)), ( \"zettle_created\", models.DateTimeField( help_text=\"The","and time this receipt was created in Zettles end\" ),","\"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount of this transaction\", max_digits=12, ),","on 2021-09-13 03:52 import uuid from django.db import migrations, models","help_text=\"The amount of this transaction\", max_digits=12, ), ), ( \"balance\",","this transaction\", max_digits=12, ), ), ( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our","models.CharField( help_text=\"The description of this transaction.\", max_length=255 ), ), (\"sold_via\",","be empty if this was not a card payment.\", max_length=100,","# Generated by Django 3.2.7 on 2021-09-13 03:52 import uuid","\"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\"created\", models.DateTimeField(auto_now_add=True)),","was not a card payment.\", max_length=100, null=True, ), ), (","this payment\", max_digits=12, ), ), ( \"net\", models.DecimalField( decimal_places=2, help_text=\"The","used to make this sale.\", max_length=100, ), ), ( \"description\",","method\", max_length=100), ), ( \"card_issuer\", models.CharField( blank=True, help_text=\"The card issuer.","= [ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False,","BornHack after fees have been substracted.\", max_digits=12, ), ), (","this movement was added to the account statement.\" ), ),","), ), ( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The date and time","help_text=\"The payment fee BornHack has to pay to receive this","( \"amount\", models.DecimalField( decimal_places=2, help_text=\"The amount of this transaction\", max_digits=12,","BornHack has to pay to receive this payment\", max_digits=12, ),","\"payment_time\", models.DateTimeField( blank=True, help_text=\"The date and time this payment was","transaction is not a customer payment.\", null=True, ), ), (","models.CharField( help_text=\"The description of this transaction.\", max_length=100 ), ), (","), ( \"receipt_number\", models.IntegerField(help_text=\"The Zettle receipt number.\"), ), ( \"vat\",","date and time this movement was added to the account","help_text=\"Our balance in Zettles systems after this transaction.\", max_digits=12, ),","transaction.\", max_digits=12, ), ), ], options={ \"ordering\": [\"-statement_time\"], \"get_latest_by\": [\"statement_time\"],","empty if this was not a card payment.\", max_length=100, null=True,","Can be empty if this transaction is not a customer","migrations.CreateModel( name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False,","a customer payment.\", null=True, ), ), ( \"payment_reference\", models.IntegerField( blank=True,","\"total\", models.DecimalField( decimal_places=2, help_text=\"The total amount the customer paid\", max_digits=12,","( \"card_issuer\", models.CharField( blank=True, help_text=\"The card issuer. Can be empty","not a customer payment.\", null=True, ), ), ( \"payment_reference\", models.IntegerField(","\"0026_alter_clearhaussettlement_options\"), ] operations = [ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ( \"uuid\",","\"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance in Zettles systems after this","was added to the account statement.\" ), ), ( \"payment_time\",","(\"updated\", models.DateTimeField(auto_now=True)), ( \"statement_time\", models.DateTimeField( help_text=\"The date and time this","name=\"ZettleReceipt\", fields=[ ( \"uuid\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ),","goes to BornHack after fees have been substracted.\", max_digits=12, ),","help_text=\"The reference for this payment. Can be empty if this","), ), (\"sold_via\", models.CharField(help_text=\"Always POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"],","of this transaction.\", max_length=100 ), ), ( \"amount\", models.DecimalField( decimal_places=2,","payment.\", null=True, ), ), ( \"payment_reference\", models.IntegerField( blank=True, help_text=\"The reference","), ( \"balance\", models.DecimalField( decimal_places=2, help_text=\"Our balance in Zettles systems","decimal_places=2, help_text=\"The part of the total amount which is VAT\",","added to the account statement.\" ), ), ( \"payment_time\", models.DateTimeField(","( \"zettle_created\", models.DateTimeField( help_text=\"The date and time this receipt was","receipt number.\"), ), ( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The part of","), ( \"description\", models.CharField( help_text=\"The description of this transaction.\", max_length=100","( \"payment_time\", models.DateTimeField( blank=True, help_text=\"The date and time this payment","\"fee\", models.DecimalField( decimal_places=2, help_text=\"The payment fee BornHack has to pay","), ( \"vat\", models.DecimalField( decimal_places=2, help_text=\"The part of the total","(\"economy\", \"0026_alter_clearhaussettlement_options\"), ] operations = [ migrations.CreateModel( name=\"ZettleBalance\", fields=[ ("],"string":"[\n \"Generated by Django 3.2.7 on 2021-09-13 03:52 import uuid from\",\n \"POS?\\\", max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"], \\\"get_latest_by\\\": [\\\"zettle_created\\\"], }, ),\",\n \"the total amount which is VAT\\\", max_digits=12, ), ), (\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"The part of the payment which goes to\",\n \"options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[\",\n \"Zettle account which was used to make this sale.\\\", max_length=100,\",\n \"dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [ migrations.CreateModel(\",\n \"this transaction is not a customer payment.\\\", null=True, ), ),\",\n \"( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment fee BornHack has to\",\n \"part of the total amount which is VAT\\\", max_digits=12, ),\",\n \"account which was used to make this sale.\\\", max_length=100, ),\",\n \"( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ), ( \\\"vat\\\", models.DecimalField(\",\n \"( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference for this payment. Can\",\n \"\\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and time this movement was added\",\n \"Zettles systems after this transaction.\\\", max_digits=12, ), ), ], options={\",\n \"null=True, ), ), ( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle account which\",\n \"default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)),\",\n \"), ), ( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference for this\",\n \"customer payment.\\\", null=True, ), ), ( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The\",\n \"help_text=\\\"The date and time this movement was added to the\",\n \"(\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and\",\n \"class Migration(migrations.Migration): dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations =\",\n \"to BornHack after fees have been substracted.\\\", max_digits=12, ), ),\",\n \"max_length=100, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description of this\",\n \"\\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField(\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"The payment fee BornHack has to pay to\",\n \"in Zettles systems after this transaction.\\\", max_digits=12, ), ), ],\",\n \"}, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False,\",\n \"max_length=100), ), ( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card issuer. Can\",\n \"if this was not a card payment.\\\", max_length=100, null=True, ),\",\n \"models.DateTimeField( blank=True, help_text=\\\"The date and time this payment was made.\",\n \"<reponame>bornhack/bornhack-website # Generated by Django 3.2.7 on 2021-09-13 03:52 import\",\n \"( \\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=255 ),\",\n \"( \\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=100 ),\",\n \"decimal_places=2, help_text=\\\"The amount of this transaction\\\", max_digits=12, ), ), (\",\n \"Django 3.2.7 on 2021-09-13 03:52 import uuid from django.db import\",\n \"time this receipt was created in Zettles end\\\" ), ),\",\n \"\\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and time this receipt was created\",\n \"models class Migration(migrations.Migration): dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations\",\n \"), ( \\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=255\",\n \"movement was added to the account statement.\\\" ), ), (\",\n \"], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\",\",\n \"amount of this transaction\\\", max_digits=12, ), ), ( \\\"balance\\\", models.DecimalField(\",\n \"in Zettles end\\\" ), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt\",\n \"total amount the customer paid\\\", max_digits=12, ), ), ( \\\"fee\\\",\",\n \"the customer paid\\\", max_digits=12, ), ), ( \\\"fee\\\", models.DecimalField( decimal_places=2,\",\n \"to make this sale.\\\", max_length=100, ), ), ( \\\"description\\\", models.CharField(\",\n \"blank=True, help_text=\\\"The reference for this payment. Can be empty if\",\n \"max_digits=12, ), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100), ),\",\n \"Zettle receipt number.\\\"), ), ( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part\",\n \"\\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100), ), ( \\\"card_issuer\\\", models.CharField( blank=True,\",\n \"models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and time\",\n \"max_digits=12, ), ), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], },\",\n \"this was not a card payment.\\\", max_length=100, null=True, ), ),\",\n \"), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True,\",\n \"payment fee BornHack has to pay to receive this payment\\\",\",\n \"fees have been substracted.\\\", max_digits=12, ), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The\",\n \"\\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the payment which goes\",\n \"), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100), ), ( \\\"card_issuer\\\",\",\n \"pay to receive this payment\\\", max_digits=12, ), ), ( \\\"net\\\",\",\n \"to pay to receive this payment\\\", max_digits=12, ), ), (\",\n \"( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle account which was used to\",\n \"was used to make this sale.\\\", max_length=100, ), ), (\",\n \"created in Zettles end\\\" ), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle\",\n \"by Django 3.2.7 on 2021-09-13 03:52 import uuid from django.db\",\n \"of this transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)),\",\n \"description of this transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\",\",\n \"(\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"], \\\"get_latest_by\\\": [\\\"zettle_created\\\"],\",\n \"), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel(\",\n \"amount the customer paid\\\", max_digits=12, ), ), ( \\\"fee\\\", models.DecimalField(\",\n \"\\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ (\",\n \"03:52 import uuid from django.db import migrations, models class Migration(migrations.Migration):\",\n \"( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the total amount\",\n \"), ( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment fee BornHack has\",\n \"is not a customer payment.\\\", null=True, ), ), ( \\\"payment_reference\\\",\",\n \"help_text=\\\"The date and time this payment was made. Can be\",\n \"primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\",\",\n \"models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and time this movement\",\n \"blank=True, help_text=\\\"The date and time this payment was made. Can\",\n \"a customer payment.\\\", null=True, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The\",\n \"and time this movement was added to the account statement.\\\"\",\n \"2021-09-13 03:52 import uuid from django.db import migrations, models class\",\n \"transaction.\\\", max_length=100 ), ), ( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount\",\n \"null=True, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description of this\",\n \"max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={ \\\"ordering\\\":\",\n \"), ), ( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment fee BornHack\",\n \"payment which goes to BornHack after fees have been substracted.\\\",\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"Our balance in Zettles systems after this transaction.\\\",\",\n \"serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField(\",\n \"help_text=\\\"The description of this transaction.\\\", max_length=100 ), ), ( \\\"amount\\\",\",\n \"after fees have been substracted.\\\", max_digits=12, ), ), ( \\\"payment_method\\\",\",\n \"paid\\\", max_digits=12, ), ), ( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment\",\n \"), ), ( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the\",\n \"editable=False, primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), (\",\n \"help_text=\\\"The card issuer. Can be empty if this was not\",\n \"was made. Can be empty if this transaction is not\",\n \"was created in Zettles end\\\" ), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The\",\n \"\\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference for this payment. Can be\",\n \"max_length=100, null=True, ), ), ( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle account\",\n \"to the account statement.\\\" ), ), ( \\\"payment_time\\\", models.DateTimeField( blank=True,\",\n \"import migrations, models class Migration(migrations.Migration): dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"),\",\n \"), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ), (\",\n \"migrations, models class Migration(migrations.Migration): dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ]\",\n \"( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\\\"created\\\",\",\n \"help_text=\\\"The part of the total amount which is VAT\\\", max_digits=12,\",\n \"payment\\\", max_digits=12, ), ), ( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part\",\n \"\\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card issuer. Can be empty if\",\n \"reference for this payment. Can be empty if this transaction\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"The amount of this transaction\\\", max_digits=12, ), ),\",\n \"decimal_places=2, help_text=\\\"The part of the payment which goes to BornHack\",\n \"not a customer payment.\\\", null=True, ), ), ( \\\"description\\\", models.CharField(\",\n \"and time this payment was made. Can be empty if\",\n \"max_digits=12, ), ), ( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total amount\",\n \"card payment.\\\", max_length=100, null=True, ), ), ( \\\"staff\\\", models.CharField( help_text=\\\"The\",\n \"\\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ), ( \\\"vat\\\", models.DecimalField( decimal_places=2,\",\n \"uuid from django.db import migrations, models class Migration(migrations.Migration): dependencies =\",\n \"] operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField(\",\n \"this receipt was created in Zettles end\\\" ), ), (\",\n \"models.CharField(help_text=\\\"The payment method\\\", max_length=100), ), ( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"The part of the total amount which is\",\n \"), ( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the payment\",\n \"transaction\\\", max_digits=12, ), ), ( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance\",\n \"decimal_places=2, help_text=\\\"Our balance in Zettles systems after this transaction.\\\", max_digits=12,\",\n \"of this transaction\\\", max_digits=12, ), ), ( \\\"balance\\\", models.DecimalField( decimal_places=2,\",\n \"), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100), ), (\",\n \"models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"], \\\"get_latest_by\\\": [\\\"zettle_created\\\"], },\",\n \"be empty if this transaction is not a customer payment.\\\",\",\n \"transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={\",\n \"( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total amount the customer paid\\\",\",\n \"this payment was made. Can be empty if this transaction\",\n \"( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100), ), ( \\\"card_issuer\\\", models.CharField(\",\n \"), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"], \\\"get_latest_by\\\":\",\n \"the account statement.\\\" ), ), ( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The\",\n \"models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\",\",\n \"receive this payment\\\", max_digits=12, ), ), ( \\\"net\\\", models.DecimalField( decimal_places=2,\",\n \"to receive this payment\\\", max_digits=12, ), ), ( \\\"net\\\", models.DecimalField(\",\n \"), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\",\",\n \"3.2.7 on 2021-09-13 03:52 import uuid from django.db import migrations,\",\n \"(\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and\",\n \"), ), ( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle account which was\",\n \"payment was made. Can be empty if this transaction is\",\n \"is VAT\\\", max_digits=12, ), ), ( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The\",\n \"part of the payment which goes to BornHack after fees\",\n \"statement.\\\" ), ), ( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date and\",\n \"this transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ],\",\n \"sale.\\\", max_length=100, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description of\",\n \"\\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the total amount which\",\n \"not a card payment.\\\", max_length=100, null=True, ), ), ( \\\"staff\\\",\",\n \"a card payment.\\\", max_length=100, null=True, ), ), ( \\\"staff\\\", models.CharField(\",\n \"decimal_places=2, help_text=\\\"The payment fee BornHack has to pay to receive\",\n \"), ), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ),\",\n \"systems after this transaction.\\\", max_digits=12, ), ), ], options={ \\\"ordering\\\":\",\n \"is not a customer payment.\\\", null=True, ), ), ( \\\"description\\\",\",\n \"[ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True,\",\n \"have been substracted.\\\", max_digits=12, ), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment\",\n \"import uuid from django.db import migrations, models class Migration(migrations.Migration): dependencies\",\n \"blank=True, help_text=\\\"The card issuer. Can be empty if this was\",\n \"django.db import migrations, models class Migration(migrations.Migration): dependencies = [ (\\\"economy\\\",\",\n \"after this transaction.\\\", max_digits=12, ), ), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"],\",\n \"of the total amount which is VAT\\\", max_digits=12, ), ),\",\n \"[ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[\",\n \"), ), ( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance in Zettles\",\n \"max_digits=12, ), ), ( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance in\",\n \"which is VAT\\\", max_digits=12, ), ), ( \\\"total\\\", models.DecimalField( decimal_places=2,\",\n \"serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField(\",\n \"empty if this transaction is not a customer payment.\\\", null=True,\",\n \"issuer. Can be empty if this was not a card\",\n \"\\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=255 ), ),\",\n \"help_text=\\\"The Zettle account which was used to make this sale.\\\",\",\n \"[\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4,\",\n \"this transaction.\\\", max_digits=12, ), ), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\":\",\n \"time this payment was made. Can be empty if this\",\n \"help_text=\\\"The part of the payment which goes to BornHack after\",\n \"), ( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle account which was used\",\n \"if this transaction is not a customer payment.\\\", null=True, ),\",\n \"( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance in Zettles systems after\",\n \"help_text=\\\"The date and time this receipt was created in Zettles\",\n \"made. Can be empty if this transaction is not a\",\n \"number.\\\"), ), ( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the\",\n \"end\\\" ), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ),\",\n \"max_digits=12, ), ), ( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment fee\",\n \"help_text=\\\"The description of this transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always\",\n \"= [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\",\",\n \"payment method\\\", max_length=100), ), ( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card\",\n \"null=True, ), ), ( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference for\",\n \"models.DateTimeField( help_text=\\\"The date and time this receipt was created in\",\n \"this payment. Can be empty if this transaction is not\",\n \"has to pay to receive this payment\\\", max_digits=12, ), ),\",\n \"), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date\",\n \"models.CharField( blank=True, help_text=\\\"The card issuer. Can be empty if this\",\n \"VAT\\\", max_digits=12, ), ), ( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total\",\n \"which was used to make this sale.\\\", max_length=100, ), ),\",\n \"payment.\\\", max_length=100, null=True, ), ), ( \\\"staff\\\", models.CharField( help_text=\\\"The Zettle\",\n \"models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and time\",\n \"), ( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card issuer. Can be\",\n \"), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date\",\n \"( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the payment which\",\n \"help_text=\\\"The total amount the customer paid\\\", max_digits=12, ), ), (\",\n \"customer paid\\\", max_digits=12, ), ), ( \\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The\",\n \"), ), ( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total amount the\",\n \"), ( \\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total amount the customer\",\n \"models.DecimalField( decimal_places=2, help_text=\\\"The total amount the customer paid\\\", max_digits=12, ),\",\n \"payment. Can be empty if this transaction is not a\",\n \"for this payment. Can be empty if this transaction is\",\n \"Migration(migrations.Migration): dependencies = [ (\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [\",\n \"primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\",\",\n \"\\\"staff\\\", models.CharField( help_text=\\\"The Zettle account which was used to make\",\n \"make this sale.\\\", max_length=100, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The\",\n \"models.DateTimeField( help_text=\\\"The date and time this movement was added to\",\n \"max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"], \\\"get_latest_by\\\": [\\\"zettle_created\\\"], }, ), ]\",\n \"been substracted.\\\", max_digits=12, ), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\",\",\n \"total amount which is VAT\\\", max_digits=12, ), ), ( \\\"total\\\",\",\n \"balance in Zettles systems after this transaction.\\\", max_digits=12, ), ),\",\n \"account statement.\\\" ), ), ( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date\",\n \"models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and time this receipt\",\n \"), ( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference for this payment.\",\n \"), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The\",\n \"from django.db import migrations, models class Migration(migrations.Migration): dependencies = [\",\n \"amount which is VAT\\\", max_digits=12, ), ), ( \\\"total\\\", models.DecimalField(\",\n \"which goes to BornHack after fees have been substracted.\\\", max_digits=12,\",\n \"[\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"], }, ), migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\",\",\n \"card issuer. Can be empty if this was not a\",\n \"Zettles end\\\" ), ), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"),\",\n \"), ( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date and time this\",\n \"Can be empty if this was not a card payment.\\\",\",\n \"receipt was created in Zettles end\\\" ), ), ( \\\"receipt_number\\\",\",\n \"models.CharField( help_text=\\\"The Zettle account which was used to make this\",\n \"migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False,\",\n \"), ( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount of this transaction\\\",\",\n \"the payment which goes to BornHack after fees have been\",\n \"models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ), ( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The\",\n \"this transaction.\\\", max_length=100 ), ), ( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The\",\n \"( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and time this movement was\",\n \"fee BornHack has to pay to receive this payment\\\", max_digits=12,\",\n \"date and time this receipt was created in Zettles end\\\"\",\n \"models.IntegerField( blank=True, help_text=\\\"The reference for this payment. Can be empty\",\n \"description of this transaction.\\\", max_length=100 ), ), ( \\\"amount\\\", models.DecimalField(\",\n \"date and time this payment was made. Can be empty\",\n \"fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ),\",\n \"substracted.\\\", max_digits=12, ), ), ( \\\"payment_method\\\", models.CharField(help_text=\\\"The payment method\\\", max_length=100),\",\n \"(\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and time this\",\n \"of the payment which goes to BornHack after fees have\",\n \"\\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=100 ), ),\",\n \"name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ),\",\n \"decimal_places=2, help_text=\\\"The total amount the customer paid\\\", max_digits=12, ), ),\",\n \"max_length=100 ), ), ( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount of\",\n \"payment.\\\", null=True, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description of\",\n \"time this movement was added to the account statement.\\\" ),\",\n \"max_digits=12, ), ), ( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of\",\n \"operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4,\",\n \"customer payment.\\\", null=True, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description\",\n \"), ), ( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount of this\",\n \"this sale.\\\", max_length=100, ), ), ( \\\"description\\\", models.CharField( help_text=\\\"The description\",\n \"), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)), (\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The\",\n \"and time this receipt was created in Zettles end\\\" ),\",\n \"\\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount of this transaction\\\", max_digits=12, ),\",\n \"on 2021-09-13 03:52 import uuid from django.db import migrations, models\",\n \"help_text=\\\"The amount of this transaction\\\", max_digits=12, ), ), ( \\\"balance\\\",\",\n \"this transaction\\\", max_digits=12, ), ), ( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our\",\n \"models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=255 ), ), (\\\"sold_via\\\",\",\n \"be empty if this was not a card payment.\\\", max_length=100,\",\n \"# Generated by Django 3.2.7 on 2021-09-13 03:52 import uuid\",\n \"\\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ), ), (\\\"created\\\", models.DateTimeField(auto_now_add=True)),\",\n \"was not a card payment.\\\", max_length=100, null=True, ), ), (\",\n \"this payment\\\", max_digits=12, ), ), ( \\\"net\\\", models.DecimalField( decimal_places=2, help_text=\\\"The\",\n \"used to make this sale.\\\", max_length=100, ), ), ( \\\"description\\\",\",\n \"method\\\", max_length=100), ), ( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card issuer.\",\n \"= [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False,\",\n \"BornHack after fees have been substracted.\\\", max_digits=12, ), ), (\",\n \"this movement was added to the account statement.\\\" ), ),\",\n \"), ), ( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date and time\",\n \"help_text=\\\"The payment fee BornHack has to pay to receive this\",\n \"( \\\"amount\\\", models.DecimalField( decimal_places=2, help_text=\\\"The amount of this transaction\\\", max_digits=12,\",\n \"BornHack has to pay to receive this payment\\\", max_digits=12, ),\",\n \"\\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date and time this payment was\",\n \"transaction is not a customer payment.\\\", null=True, ), ), (\",\n \"models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=100 ), ), (\",\n \"), ( \\\"receipt_number\\\", models.IntegerField(help_text=\\\"The Zettle receipt number.\\\"), ), ( \\\"vat\\\",\",\n \"date and time this movement was added to the account\",\n \"help_text=\\\"Our balance in Zettles systems after this transaction.\\\", max_digits=12, ),\",\n \"transaction.\\\", max_digits=12, ), ), ], options={ \\\"ordering\\\": [\\\"-statement_time\\\"], \\\"get_latest_by\\\": [\\\"statement_time\\\"],\",\n \"empty if this was not a card payment.\\\", max_length=100, null=True,\",\n \"Can be empty if this transaction is not a customer\",\n \"migrations.CreateModel( name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False,\",\n \"a customer payment.\\\", null=True, ), ), ( \\\"payment_reference\\\", models.IntegerField( blank=True,\",\n \"\\\"total\\\", models.DecimalField( decimal_places=2, help_text=\\\"The total amount the customer paid\\\", max_digits=12,\",\n \"( \\\"card_issuer\\\", models.CharField( blank=True, help_text=\\\"The card issuer. Can be empty\",\n \"not a customer payment.\\\", null=True, ), ), ( \\\"payment_reference\\\", models.IntegerField(\",\n \"\\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ ( \\\"uuid\\\",\",\n \"\\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance in Zettles systems after this\",\n \"was added to the account statement.\\\" ), ), ( \\\"payment_time\\\",\",\n \"(\\\"updated\\\", models.DateTimeField(auto_now=True)), ( \\\"statement_time\\\", models.DateTimeField( help_text=\\\"The date and time this\",\n \"name=\\\"ZettleReceipt\\\", fields=[ ( \\\"uuid\\\", models.UUIDField( default=uuid.uuid4, editable=False, primary_key=True, serialize=False, ),\",\n \"goes to BornHack after fees have been substracted.\\\", max_digits=12, ),\",\n \"help_text=\\\"The reference for this payment. Can be empty if this\",\n \"), ), (\\\"sold_via\\\", models.CharField(help_text=\\\"Always POS?\\\", max_length=100)), ], options={ \\\"ordering\\\": [\\\"-zettle_created\\\"],\",\n \"of this transaction.\\\", max_length=100 ), ), ( \\\"amount\\\", models.DecimalField( decimal_places=2,\",\n \"payment.\\\", null=True, ), ), ( \\\"payment_reference\\\", models.IntegerField( blank=True, help_text=\\\"The reference\",\n \"), ( \\\"balance\\\", models.DecimalField( decimal_places=2, help_text=\\\"Our balance in Zettles systems\",\n \"decimal_places=2, help_text=\\\"The part of the total amount which is VAT\\\",\",\n \"added to the account statement.\\\" ), ), ( \\\"payment_time\\\", models.DateTimeField(\",\n \"( \\\"zettle_created\\\", models.DateTimeField( help_text=\\\"The date and time this receipt was\",\n \"receipt number.\\\"), ), ( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of\",\n \"), ( \\\"description\\\", models.CharField( help_text=\\\"The description of this transaction.\\\", max_length=100\",\n \"( \\\"payment_time\\\", models.DateTimeField( blank=True, help_text=\\\"The date and time this payment\",\n \"\\\"fee\\\", models.DecimalField( decimal_places=2, help_text=\\\"The payment fee BornHack has to pay\",\n \"), ( \\\"vat\\\", models.DecimalField( decimal_places=2, help_text=\\\"The part of the total\",\n \"(\\\"economy\\\", \\\"0026_alter_clearhaussettlement_options\\\"), ] operations = [ migrations.CreateModel( name=\\\"ZettleBalance\\\", fields=[ (\"\n]","isConversational":false}}},{"rowIdx":191,"cells":{"ngram":{"kind":"list like","value":["== \"=\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return","* 4 - 2 do skip endwhile else x :=","endif; y := 1\" input_string = line.strip(\"\\n\") lexer = Lexer(input_string)","current char. curr_char = lexer.get_char() while (curr_char != None): while","lexer)) curr_char = new.pop() if curr_char == \"=\": curr_char =","\"if z then while x * 4 - 2 do","inp_str def get_char(self): if self.index < len(self.s): var = self.s[self.index]","= self.s[self.index] self.index += 1 return var input_file = open(str(sys.argv[1]),","sub def symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char","= input_file.read() # \"if z then while x * 4","lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()):","sub new = [] # keeping track of current char.","sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new","+= curr_char curr_char = lexer.get_char() if curr_char == None: break","lexer): sub = \"\" while (curr_char.isalpha() or curr_char.isdigit()): sub +=","z then while x * 4 - 2 do skip","assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char() if next_char","hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\"","curr_char next_char = lexer.get_char() if next_char == \"=\": sub +=","== ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char","lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub = curr_char","keeping track of current char. curr_char = lexer.get_char() while (curr_char","= new.pop() elif curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char =","token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char == \":\": token_check(assignment(curr_char,","sub def longest_sub_string(curr_char, lexer): sub = \"\" while (curr_char.isalpha() or","(curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in \"+-/*();\":","tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\" tokens_list.append(input) else: hashtable[input]","char. curr_char = lexer.get_char() while (curr_char != None): while (curr_char","new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char) sym =","return sym def assignment(curr_char, lexer): sub = curr_char next_char =","break new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char) sym","= new.pop() elif curr_char == \":\": token_check(assignment(curr_char, lexer)) curr_char =","def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif","new = [] # keeping track of current char. curr_char","new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char","1\" input_string = line.strip(\"\\n\") lexer = Lexer(input_string) hashtable = {}","hashtable[input] = \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\"","= \"\" while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char()","or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char","sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def","- 2 do skip endwhile else x := 7 endif;","== None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub","None: break new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char)","while (curr_char == ' ' or curr_char == ''): curr_char",":= 7 endif; y := 1\" input_string = line.strip(\"\\n\") lexer","self.index < len(self.s): var = self.s[self.index] self.index += 1 return","line.strip(\"\\n\") lexer = Lexer(input_string) hashtable = {} tokens_list = []","while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char","# print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return","new.pop() elif curr_char == \":\": token_check(assignment(curr_char, lexer)) curr_char = new.pop()","= open(str(sys.argv[1]), 'r') # Open file for reading line =","break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub = \"\"","\"\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char =","return sub new.append(lexer.get_char()) return sub new = [] # keeping","lexer): sub = curr_char next_char = lexer.get_char() if next_char ==","= lexer.get_char() if next_char == \"=\": sub += next_char new.append(next_char)","class Lexer: def __init__(self, inp_str): self.index = 0 self.s =","self.index = 0 self.s = inp_str def get_char(self): if self.index","sub new.append(lexer.get_char()) return sub new = [] # keeping track","\"ERROR READING\" def digit(curr_char, lexer): sub = \"\" while (curr_char.isdigit()):","' or curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()):","sub = \"\" while (curr_char.isdigit()): sub += curr_char curr_char =","curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub","\"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\" tokens_list.append(input) else:","= lexer.get_char() if curr_char == None: break new.append(curr_char) return sub","< len(self.s): var = self.s[self.index] self.index += 1 return var","== None: break new.append(curr_char) return sub def symbol(curr_char, lexer): #","var input_file = open(str(sys.argv[1]), 'r') # Open file for reading","== \"=\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char()","re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input]","(curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer))","= lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub =","\"\" while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if","self.index += 1 return var input_file = open(str(sys.argv[1]), 'r') #","!= None): while (curr_char == ' ' or curr_char ==","x * 4 - 2 do skip endwhile else x","= curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char,","hashtable[input] = \"SYMBOL\" tokens_list.append(input) else: hashtable[input] = \"ERROR READING\" def","if curr_char == None: break new.append(curr_char) return sub def longest_sub_string(curr_char,","curr_char == None: break new.append(curr_char) return sub def symbol(curr_char, lexer):","new.append(curr_char) return sym def assignment(curr_char, lexer): sub = curr_char next_char","new.append(lexer.get_char()) return sub new = [] # keeping track of","token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char","open(str(sys.argv[1]), 'r') # Open file for reading line = input_file.read()","tokens_list = [] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] =","sub += curr_char curr_char = lexer.get_char() if curr_char == None:","return sub new = [] # keeping track of current","else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable #","= \"\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char","= inp_str def get_char(self): if self.index < len(self.s): var =","input): hashtable[input] = \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] =","input): hashtable[input] = \"SYMBOL\" tokens_list.append(input) else: hashtable[input] = \"ERROR READING\"","\"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif","input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input] =","'r') # Open file for reading line = input_file.read() #","line = input_file.read() # \"if z then while x *","elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in","input): hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] =","= new.pop() if curr_char == \"=\": curr_char = lexer.get_char() else:","track of current char. curr_char = lexer.get_char() while (curr_char !=","if curr_char == None: break new.append(curr_char) return sub def symbol(curr_char,","curr_char == None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer):","if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input):","curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop()","next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new = []","= lexer.get_char() while (curr_char != None): while (curr_char == '","curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens():",":= 1\" input_string = line.strip(\"\\n\") lexer = Lexer(input_string) hashtable =","\":\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char == \"=\":","hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\"","elif curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif","new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub = \"\" while","curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer):","else: hashtable[input] = \"ERROR READING\" def digit(curr_char, lexer): sub =","0 self.s = inp_str def get_char(self): if self.index < len(self.s):","curr_char = lexer.get_char() if curr_char == None: break new.append(curr_char) return","1 return var input_file = open(str(sys.argv[1]), 'r') # Open file","inp_str): self.index = 0 self.s = inp_str def get_char(self): if","re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\" tokens_list.append(input) else: hashtable[input] = \"ERROR","def symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char =","\"=\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def","(curr_char == ' ' or curr_char == ''): curr_char =","digit(curr_char, lexer): sub = \"\" while (curr_char.isdigit()): sub += curr_char","= \"ERROR READING\" def digit(curr_char, lexer): sub = \"\" while","lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char =","__init__(self, inp_str): self.index = 0 self.s = inp_str def get_char(self):","skip endwhile else x := 7 endif; y := 1\"","= \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\" tokens_list.append(input)","y := 1\" input_string = line.strip(\"\\n\") lexer = Lexer(input_string) hashtable","curr_char == \"=\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char =","2 do skip endwhile else x := 7 endif; y","endwhile else x := 7 endif; y := 1\" input_string","= curr_char next_char = lexer.get_char() if next_char == \"=\": sub","in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char ==","lexer): sub = \"\" while (curr_char.isdigit()): sub += curr_char curr_char","lexer = Lexer(input_string) hashtable = {} tokens_list = [] def","return sub def symbol(curr_char, lexer): # print(curr_char) sym = curr_char","input_string = line.strip(\"\\n\") lexer = Lexer(input_string) hashtable = {} tokens_list","elif curr_char == \":\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if","Lexer: def __init__(self, inp_str): self.index = 0 self.s = inp_str","var = self.s[self.index] self.index += 1 return var input_file =","else x := 7 endif; y := 1\" input_string =","new.pop() elif curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char = new.pop()","if next_char == \"=\": sub += next_char new.append(next_char) return sub","{} tokens_list = [] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input]","= lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif","input_file.read() # \"if z then while x * 4 -","= new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif","symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char = lexer.get_char()","7 endif; y := 1\" input_string = line.strip(\"\\n\") lexer =","input_file = open(str(sys.argv[1]), 'r') # Open file for reading line","curr_char == \":\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char","sys class Lexer: def __init__(self, inp_str): self.index = 0 self.s","if self.index < len(self.s): var = self.s[self.index] self.index += 1","= [] # keeping track of current char. curr_char =","[] # keeping track of current char. curr_char = lexer.get_char()","re import sys class Lexer: def __init__(self, inp_str): self.index =","for reading line = input_file.read() # \"if z then while","''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char =","\"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\" tokens_list.append(input) elif","None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub =","curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop()","hashtable[input] = \"ERROR READING\" def digit(curr_char, lexer): sub = \"\"","def __init__(self, inp_str): self.index = 0 self.s = inp_str def","curr_char = new.pop() elif curr_char == \":\": token_check(assignment(curr_char, lexer)) curr_char","tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\",","== \":\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char ==","= lexer.get_char() def tokens(): return hashtable # print(tokens_list) # print(tokens())","def get_char(self): if self.index < len(self.s): var = self.s[self.index] self.index","while (curr_char != None): while (curr_char == ' ' or","<gh_stars>0 import re import sys class Lexer: def __init__(self, inp_str):","x := 7 endif; y := 1\" input_string = line.strip(\"\\n\")","= {} tokens_list = [] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input):","if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char,","tokens_list.append(input) else: hashtable[input] = \"ERROR READING\" def digit(curr_char, lexer): sub","= \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input)","self.s = inp_str def get_char(self): if self.index < len(self.s): var","# \"if z then while x * 4 - 2","next_char = lexer.get_char() if next_char == \"=\": sub += next_char","return var input_file = open(str(sys.argv[1]), 'r') # Open file for","= [] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\"","# Open file for reading line = input_file.read() # \"if","token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in \"+-/*();\": token_check(symbol(curr_char,","file for reading line = input_file.read() # \"if z then","[] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\" tokens_list.append(input)","curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char","or curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char,","re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input]","new.append(next_char) return sub new.append(lexer.get_char()) return sub new = [] #","tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\",","of current char. curr_char = lexer.get_char() while (curr_char != None):","= lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return","hashtable = {} tokens_list = [] def token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\",","elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input] = \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input):","elif re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\" tokens_list.append(input) elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input):","get_char(self): if self.index < len(self.s): var = self.s[self.index] self.index +=","print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return sym","= Lexer(input_string) hashtable = {} tokens_list = [] def token_check(input):","(curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char ==","lexer.get_char() while (curr_char != None): while (curr_char == ' '","curr_char = new.pop() elif curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char","token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable # print(tokens_list)","token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char == \"=\": curr_char","curr_char curr_char = lexer.get_char() if curr_char == None: break new.append(curr_char)","= 0 self.s = inp_str def get_char(self): if self.index <","len(self.s): var = self.s[self.index] self.index += 1 return var input_file","== ' ' or curr_char == ''): curr_char = lexer.get_char()","curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer))","def digit(curr_char, lexer): sub = \"\" while (curr_char.isdigit()): sub +=","if curr_char == \"=\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char","longest_sub_string(curr_char, lexer): sub = \"\" while (curr_char.isalpha() or curr_char.isdigit()): sub","Open file for reading line = input_file.read() # \"if z","lexer.get_char() if next_char == \"=\": sub += next_char new.append(next_char) return","elif re.fullmatch(\"\\+|\\-|\\*|/|\\(|\\)|:=|;\", input): hashtable[input] = \"SYMBOL\" tokens_list.append(input) else: hashtable[input] =","READING\" def digit(curr_char, lexer): sub = \"\" while (curr_char.isdigit()): sub","while x * 4 - 2 do skip endwhile else","def longest_sub_string(curr_char, lexer): sub = \"\" while (curr_char.isalpha() or curr_char.isdigit()):","(curr_char != None): while (curr_char == ' ' or curr_char","sym def assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char()","self.s[self.index] self.index += 1 return var input_file = open(str(sys.argv[1]), 'r')","do skip endwhile else x := 7 endif; y :=","def assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char() if","= \"SYMBOL\" tokens_list.append(input) else: hashtable[input] = \"ERROR READING\" def digit(curr_char,","curr_char = new.pop() if curr_char == \"=\": curr_char = lexer.get_char()","re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\", input): hashtable[input]","curr_char = lexer.get_char() def tokens(): return hashtable # print(tokens_list) #","import re import sys class Lexer: def __init__(self, inp_str): self.index","sub = \"\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char","lexer): # print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char)","+= next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new =","= line.strip(\"\\n\") lexer = Lexer(input_string) hashtable = {} tokens_list =","\"+-/*();\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char == \":\":","lexer.get_char() if curr_char == None: break new.append(curr_char) return sub def","lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable","Lexer(input_string) hashtable = {} tokens_list = [] def token_check(input): if","4 - 2 do skip endwhile else x := 7","curr_char = lexer.get_char() while (curr_char != None): while (curr_char ==","+= 1 return var input_file = open(str(sys.argv[1]), 'r') # Open","(curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if","then while x * 4 - 2 do skip endwhile","new.pop() if curr_char == \"=\": curr_char = lexer.get_char() else: token_check(curr_char)","lexer)) curr_char = new.pop() elif curr_char in \"+-/*();\": token_check(symbol(curr_char, lexer))","sub = curr_char next_char = lexer.get_char() if next_char == \"=\":","reading line = input_file.read() # \"if z then while x","return sub def longest_sub_string(curr_char, lexer): sub = \"\" while (curr_char.isalpha()","next_char == \"=\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char())","lexer)) curr_char = new.pop() elif curr_char == \":\": token_check(assignment(curr_char, lexer))","# keeping track of current char. curr_char = lexer.get_char() while","\"=\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub","None): while (curr_char == ' ' or curr_char == ''):","= \"IDENTIFIER\" tokens_list.append(input) elif re.search(\"[0-9]+\", input): hashtable[input] = \"NUMBER\" tokens_list.append(input)","token_check(input): if re.fullmatch(\"if|then|else|endif|while|do|endwhile|skip\", input): hashtable[input] = \"KEYWORD\" tokens_list.append(input) elif re.search(\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\",","\"SYMBOL\" tokens_list.append(input) else: hashtable[input] = \"ERROR READING\" def digit(curr_char, lexer):","' ' or curr_char == ''): curr_char = lexer.get_char() if","curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char ==","import sys class Lexer: def __init__(self, inp_str): self.index = 0","while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char()"],"string":"[\n \"== \\\"=\\\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return\",\n \"* 4 - 2 do skip endwhile else x :=\",\n \"endif; y := 1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string)\",\n \"current char. curr_char = lexer.get_char() while (curr_char != None): while\",\n \"lexer)) curr_char = new.pop() if curr_char == \\\"=\\\": curr_char =\",\n \"\\\"if z then while x * 4 - 2 do\",\n \"inp_str def get_char(self): if self.index < len(self.s): var = self.s[self.index]\",\n \"= self.s[self.index] self.index += 1 return var input_file = open(str(sys.argv[1]),\",\n \"sub def symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char\",\n \"= input_file.read() # \\\"if z then while x * 4\",\n \"lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()):\",\n \"sub new = [] # keeping track of current char.\",\n \"sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new\",\n \"+= curr_char curr_char = lexer.get_char() if curr_char == None: break\",\n \"lexer): sub = \\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()): sub +=\",\n \"z then while x * 4 - 2 do skip\",\n \"assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char() if next_char\",\n \"hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\"\",\n \"curr_char next_char = lexer.get_char() if next_char == \\\"=\\\": sub +=\",\n \"== ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char\",\n \"lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub = curr_char\",\n \"keeping track of current char. curr_char = lexer.get_char() while (curr_char\",\n \"= new.pop() elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char =\",\n \"token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char == \\\":\\\": token_check(assignment(curr_char,\",\n \"sub def longest_sub_string(curr_char, lexer): sub = \\\"\\\" while (curr_char.isalpha() or\",\n \"(curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in \\\"+-/*();\\\":\",\n \"tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input]\",\n \"char. curr_char = lexer.get_char() while (curr_char != None): while (curr_char\",\n \"new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char) sym =\",\n \"return sym def assignment(curr_char, lexer): sub = curr_char next_char =\",\n \"break new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char) sym\",\n \"= new.pop() elif curr_char == \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char =\",\n \"def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif\",\n \"new = [] # keeping track of current char. curr_char\",\n \"new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char\",\n \"1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable = {}\",\n \"hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\"\",\n \"= \\\"\\\" while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char()\",\n \"or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char\",\n \"sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def\",\n \"- 2 do skip endwhile else x := 7 endif;\",\n \"== None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub\",\n \"None: break new.append(curr_char) return sub def symbol(curr_char, lexer): # print(curr_char)\",\n \"while (curr_char == ' ' or curr_char == ''): curr_char\",\n \":= 7 endif; y := 1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer\",\n \"self.index < len(self.s): var = self.s[self.index] self.index += 1 return\",\n \"line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable = {} tokens_list = []\",\n \"while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char\",\n \"# print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return\",\n \"new.pop() elif curr_char == \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char = new.pop()\",\n \"= open(str(sys.argv[1]), 'r') # Open file for reading line =\",\n \"break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub = \\\"\\\"\",\n \"\\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char =\",\n \"return sub new.append(lexer.get_char()) return sub new = [] # keeping\",\n \"lexer): sub = curr_char next_char = lexer.get_char() if next_char ==\",\n \"= lexer.get_char() if next_char == \\\"=\\\": sub += next_char new.append(next_char)\",\n \"class Lexer: def __init__(self, inp_str): self.index = 0 self.s =\",\n \"self.index = 0 self.s = inp_str def get_char(self): if self.index\",\n \"sub new.append(lexer.get_char()) return sub new = [] # keeping track\",\n \"\\\"ERROR READING\\\" def digit(curr_char, lexer): sub = \\\"\\\" while (curr_char.isdigit()):\",\n \"' or curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()):\",\n \"sub = \\\"\\\" while (curr_char.isdigit()): sub += curr_char curr_char =\",\n \"curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub\",\n \"\\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else:\",\n \"= lexer.get_char() if curr_char == None: break new.append(curr_char) return sub\",\n \"< len(self.s): var = self.s[self.index] self.index += 1 return var\",\n \"== None: break new.append(curr_char) return sub def symbol(curr_char, lexer): #\",\n \"var input_file = open(str(sys.argv[1]), 'r') # Open file for reading\",\n \"== \\\"=\\\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char()\",\n \"re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input]\",\n \"(curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer))\",\n \"= lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer): sub =\",\n \"\\\"\\\" while (curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if\",\n \"self.index += 1 return var input_file = open(str(sys.argv[1]), 'r') #\",\n \"!= None): while (curr_char == ' ' or curr_char ==\",\n \"x * 4 - 2 do skip endwhile else x\",\n \"= curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char,\",\n \"hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] = \\\"ERROR READING\\\" def\",\n \"if curr_char == None: break new.append(curr_char) return sub def longest_sub_string(curr_char,\",\n \"curr_char == None: break new.append(curr_char) return sub def symbol(curr_char, lexer):\",\n \"new.append(curr_char) return sym def assignment(curr_char, lexer): sub = curr_char next_char\",\n \"new.append(lexer.get_char()) return sub new = [] # keeping track of\",\n \"token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char\",\n \"open(str(sys.argv[1]), 'r') # Open file for reading line = input_file.read()\",\n \"tokens_list = [] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] =\",\n \"sub += curr_char curr_char = lexer.get_char() if curr_char == None:\",\n \"return sub new = [] # keeping track of current\",\n \"else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable #\",\n \"= \\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char\",\n \"= inp_str def get_char(self): if self.index < len(self.s): var =\",\n \"input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] =\",\n \"input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] = \\\"ERROR READING\\\"\",\n \"\\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif\",\n \"input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input] =\",\n \"'r') # Open file for reading line = input_file.read() #\",\n \"line = input_file.read() # \\\"if z then while x *\",\n \"elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in\",\n \"input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] =\",\n \"= new.pop() if curr_char == \\\"=\\\": curr_char = lexer.get_char() else:\",\n \"track of current char. curr_char = lexer.get_char() while (curr_char !=\",\n \"if curr_char == None: break new.append(curr_char) return sub def symbol(curr_char,\",\n \"curr_char == None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer):\",\n \"if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input):\",\n \"curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop()\",\n \"next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new = []\",\n \"= lexer.get_char() while (curr_char != None): while (curr_char == '\",\n \"curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens():\",\n \":= 1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable =\",\n \"\\\":\\\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char == \\\"=\\\":\",\n \"hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\"\",\n \"elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif\",\n \"new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub = \\\"\\\" while\",\n \"curr_char curr_char = lexer.get_char() new.append(curr_char) return sym def assignment(curr_char, lexer):\",\n \"else: hashtable[input] = \\\"ERROR READING\\\" def digit(curr_char, lexer): sub =\",\n \"0 self.s = inp_str def get_char(self): if self.index < len(self.s):\",\n \"curr_char = lexer.get_char() if curr_char == None: break new.append(curr_char) return\",\n \"1 return var input_file = open(str(sys.argv[1]), 'r') # Open file\",\n \"inp_str): self.index = 0 self.s = inp_str def get_char(self): if\",\n \"re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] = \\\"ERROR\",\n \"def symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char =\",\n \"\\\"=\\\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def\",\n \"(curr_char == ' ' or curr_char == ''): curr_char =\",\n \"digit(curr_char, lexer): sub = \\\"\\\" while (curr_char.isdigit()): sub += curr_char\",\n \"= \\\"ERROR READING\\\" def digit(curr_char, lexer): sub = \\\"\\\" while\",\n \"lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char =\",\n \"__init__(self, inp_str): self.index = 0 self.s = inp_str def get_char(self):\",\n \"skip endwhile else x := 7 endif; y := 1\\\"\",\n \"= \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input)\",\n \"y := 1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable\",\n \"curr_char == \\\"=\\\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char =\",\n \"2 do skip endwhile else x := 7 endif; y\",\n \"endwhile else x := 7 endif; y := 1\\\" input_string\",\n \"= curr_char next_char = lexer.get_char() if next_char == \\\"=\\\": sub\",\n \"in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char ==\",\n \"lexer): sub = \\\"\\\" while (curr_char.isdigit()): sub += curr_char curr_char\",\n \"lexer = Lexer(input_string) hashtable = {} tokens_list = [] def\",\n \"return sub def symbol(curr_char, lexer): # print(curr_char) sym = curr_char\",\n \"input_string = line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable = {} tokens_list\",\n \"elif curr_char == \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if\",\n \"Lexer: def __init__(self, inp_str): self.index = 0 self.s = inp_str\",\n \"var = self.s[self.index] self.index += 1 return var input_file =\",\n \"else x := 7 endif; y := 1\\\" input_string =\",\n \"new.pop() elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char = new.pop()\",\n \"if next_char == \\\"=\\\": sub += next_char new.append(next_char) return sub\",\n \"{} tokens_list = [] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input]\",\n \"= lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif\",\n \"input_file.read() # \\\"if z then while x * 4 -\",\n \"= new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif\",\n \"symbol(curr_char, lexer): # print(curr_char) sym = curr_char curr_char = lexer.get_char()\",\n \"7 endif; y := 1\\\" input_string = line.strip(\\\"\\\\n\\\") lexer =\",\n \"input_file = open(str(sys.argv[1]), 'r') # Open file for reading line\",\n \"curr_char == \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char\",\n \"sys class Lexer: def __init__(self, inp_str): self.index = 0 self.s\",\n \"if self.index < len(self.s): var = self.s[self.index] self.index += 1\",\n \"= [] # keeping track of current char. curr_char =\",\n \"[] # keeping track of current char. curr_char = lexer.get_char()\",\n \"re import sys class Lexer: def __init__(self, inp_str): self.index =\",\n \"for reading line = input_file.read() # \\\"if z then while\",\n \"''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char =\",\n \"\\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif\",\n \"None: break new.append(curr_char) return sub def longest_sub_string(curr_char, lexer): sub =\",\n \"curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop()\",\n \"hashtable[input] = \\\"ERROR READING\\\" def digit(curr_char, lexer): sub = \\\"\\\"\",\n \"def __init__(self, inp_str): self.index = 0 self.s = inp_str def\",\n \"curr_char = new.pop() elif curr_char == \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char\",\n \"tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\",\",\n \"== \\\":\\\": token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char ==\",\n \"= lexer.get_char() def tokens(): return hashtable # print(tokens_list) # print(tokens())\",\n \"def get_char(self): if self.index < len(self.s): var = self.s[self.index] self.index\",\n \"while (curr_char != None): while (curr_char == ' ' or\",\n \"<gh_stars>0 import re import sys class Lexer: def __init__(self, inp_str):\",\n \"x := 7 endif; y := 1\\\" input_string = line.strip(\\\"\\\\n\\\")\",\n \"= {} tokens_list = [] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input):\",\n \"if (curr_char.isdigit()): token_check(digit(curr_char, lexer)) curr_char = new.pop() elif (curr_char.isalpha()): token_check(longest_sub_string(curr_char,\",\n \"tokens_list.append(input) else: hashtable[input] = \\\"ERROR READING\\\" def digit(curr_char, lexer): sub\",\n \"= \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input)\",\n \"self.s = inp_str def get_char(self): if self.index < len(self.s): var\",\n \"# \\\"if z then while x * 4 - 2\",\n \"next_char = lexer.get_char() if next_char == \\\"=\\\": sub += next_char\",\n \"return var input_file = open(str(sys.argv[1]), 'r') # Open file for\",\n \"= [] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\"\",\n \"# Open file for reading line = input_file.read() # \\\"if\",\n \"token_check(longest_sub_string(curr_char, lexer)) curr_char = new.pop() elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char,\",\n \"file for reading line = input_file.read() # \\\"if z then\",\n \"[] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input)\",\n \"curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char\",\n \"or curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char,\",\n \"re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input]\",\n \"new.append(next_char) return sub new.append(lexer.get_char()) return sub new = [] #\",\n \"tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\",\",\n \"of current char. curr_char = lexer.get_char() while (curr_char != None):\",\n \"= lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return\",\n \"hashtable = {} tokens_list = [] def token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\",\",\n \"elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input] = \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input):\",\n \"elif re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input) elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input):\",\n \"get_char(self): if self.index < len(self.s): var = self.s[self.index] self.index +=\",\n \"print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char) return sym\",\n \"= Lexer(input_string) hashtable = {} tokens_list = [] def token_check(input):\",\n \"(curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char ==\",\n \"lexer.get_char() while (curr_char != None): while (curr_char == ' '\",\n \"curr_char = new.pop() elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char\",\n \"token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable # print(tokens_list)\",\n \"token_check(assignment(curr_char, lexer)) curr_char = new.pop() if curr_char == \\\"=\\\": curr_char\",\n \"curr_char curr_char = lexer.get_char() if curr_char == None: break new.append(curr_char)\",\n \"= 0 self.s = inp_str def get_char(self): if self.index <\",\n \"len(self.s): var = self.s[self.index] self.index += 1 return var input_file\",\n \"== ' ' or curr_char == ''): curr_char = lexer.get_char()\",\n \"curr_char == ''): curr_char = lexer.get_char() if (curr_char.isdigit()): token_check(digit(curr_char, lexer))\",\n \"def digit(curr_char, lexer): sub = \\\"\\\" while (curr_char.isdigit()): sub +=\",\n \"if curr_char == \\\"=\\\": curr_char = lexer.get_char() else: token_check(curr_char) curr_char\",\n \"longest_sub_string(curr_char, lexer): sub = \\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()): sub\",\n \"Open file for reading line = input_file.read() # \\\"if z\",\n \"lexer.get_char() if next_char == \\\"=\\\": sub += next_char new.append(next_char) return\",\n \"elif re.fullmatch(\\\"\\\\+|\\\\-|\\\\*|/|\\\\(|\\\\)|:=|;\\\", input): hashtable[input] = \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] =\",\n \"READING\\\" def digit(curr_char, lexer): sub = \\\"\\\" while (curr_char.isdigit()): sub\",\n \"while x * 4 - 2 do skip endwhile else\",\n \"def longest_sub_string(curr_char, lexer): sub = \\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()):\",\n \"(curr_char != None): while (curr_char == ' ' or curr_char\",\n \"sym def assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char()\",\n \"self.s[self.index] self.index += 1 return var input_file = open(str(sys.argv[1]), 'r')\",\n \"do skip endwhile else x := 7 endif; y :=\",\n \"def assignment(curr_char, lexer): sub = curr_char next_char = lexer.get_char() if\",\n \"= \\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] = \\\"ERROR READING\\\" def digit(curr_char,\",\n \"curr_char = new.pop() if curr_char == \\\"=\\\": curr_char = lexer.get_char()\",\n \"re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\", input): hashtable[input]\",\n \"curr_char = lexer.get_char() def tokens(): return hashtable # print(tokens_list) #\",\n \"import re import sys class Lexer: def __init__(self, inp_str): self.index\",\n \"sub = \\\"\\\" while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char\",\n \"lexer): # print(curr_char) sym = curr_char curr_char = lexer.get_char() new.append(curr_char)\",\n \"+= next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub new =\",\n \"= line.strip(\\\"\\\\n\\\") lexer = Lexer(input_string) hashtable = {} tokens_list =\",\n \"\\\"+-/*();\\\": token_check(symbol(curr_char, lexer)) curr_char = new.pop() elif curr_char == \\\":\\\":\",\n \"lexer.get_char() if curr_char == None: break new.append(curr_char) return sub def\",\n \"lexer.get_char() else: token_check(curr_char) curr_char = lexer.get_char() def tokens(): return hashtable\",\n \"Lexer(input_string) hashtable = {} tokens_list = [] def token_check(input): if\",\n \"4 - 2 do skip endwhile else x := 7\",\n \"curr_char = lexer.get_char() while (curr_char != None): while (curr_char ==\",\n \"+= 1 return var input_file = open(str(sys.argv[1]), 'r') # Open\",\n \"(curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if\",\n \"then while x * 4 - 2 do skip endwhile\",\n \"new.pop() if curr_char == \\\"=\\\": curr_char = lexer.get_char() else: token_check(curr_char)\",\n \"lexer)) curr_char = new.pop() elif curr_char in \\\"+-/*();\\\": token_check(symbol(curr_char, lexer))\",\n \"sub = curr_char next_char = lexer.get_char() if next_char == \\\"=\\\":\",\n \"reading line = input_file.read() # \\\"if z then while x\",\n \"return sub def longest_sub_string(curr_char, lexer): sub = \\\"\\\" while (curr_char.isalpha()\",\n \"next_char == \\\"=\\\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char())\",\n \"lexer)) curr_char = new.pop() elif curr_char == \\\":\\\": token_check(assignment(curr_char, lexer))\",\n \"# keeping track of current char. curr_char = lexer.get_char() while\",\n \"\\\"=\\\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return sub\",\n \"None): while (curr_char == ' ' or curr_char == ''):\",\n \"= \\\"IDENTIFIER\\\" tokens_list.append(input) elif re.search(\\\"[0-9]+\\\", input): hashtable[input] = \\\"NUMBER\\\" tokens_list.append(input)\",\n \"token_check(input): if re.fullmatch(\\\"if|then|else|endif|while|do|endwhile|skip\\\", input): hashtable[input] = \\\"KEYWORD\\\" tokens_list.append(input) elif re.search(\\\"([a-z]|[A-Z])([a-z]|[A-Z]|[0-9])*\\\",\",\n \"\\\"SYMBOL\\\" tokens_list.append(input) else: hashtable[input] = \\\"ERROR READING\\\" def digit(curr_char, lexer):\",\n \"' ' or curr_char == ''): curr_char = lexer.get_char() if\",\n \"curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char() if curr_char ==\",\n \"import sys class Lexer: def __init__(self, inp_str): self.index = 0\",\n \"while (curr_char.isalpha() or curr_char.isdigit()): sub += curr_char curr_char = lexer.get_char()\"\n]","isConversational":false}}},{"rowIdx":192,"cells":{"ngram":{"kind":"list like","value":["sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv)","sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv) ==","if len(sys.argv) == 1: raise Exception('Please provide the WARC file","import sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if","from warc_dedup import deduplicate def main(): if len(sys.argv) == 1:","WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ == '__main__': main()","== 1: raise Exception('Please provide the WARC file as argument.')","len(sys.argv) == 1: raise Exception('Please provide the WARC file as","import deduplicate def main(): if len(sys.argv) == 1: raise Exception('Please","raise Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if","the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ == '__main__':","Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__","<filename>deduplicate.py import sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main():","def main(): if len(sys.argv) == 1: raise Exception('Please provide the","main(): if len(sys.argv) == 1: raise Exception('Please provide the WARC","deduplicate def main(): if len(sys.argv) == 1: raise Exception('Please provide","1: raise Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate()","warc_dedup import deduplicate def main(): if len(sys.argv) == 1: raise","provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ =="],"string":"[\n \"sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv)\",\n \"sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv) ==\",\n \"if len(sys.argv) == 1: raise Exception('Please provide the WARC file\",\n \"import sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if\",\n \"from warc_dedup import deduplicate def main(): if len(sys.argv) == 1:\",\n \"WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ == '__main__': main()\",\n \"== 1: raise Exception('Please provide the WARC file as argument.')\",\n \"len(sys.argv) == 1: raise Exception('Please provide the WARC file as\",\n \"import deduplicate def main(): if len(sys.argv) == 1: raise Exception('Please\",\n \"raise Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if\",\n \"the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ == '__main__':\",\n \"Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__\",\n \"<filename>deduplicate.py import sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main():\",\n \"def main(): if len(sys.argv) == 1: raise Exception('Please provide the\",\n \"main(): if len(sys.argv) == 1: raise Exception('Please provide the WARC\",\n \"deduplicate def main(): if len(sys.argv) == 1: raise Exception('Please provide\",\n \"1: raise Exception('Please provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate()\",\n \"warc_dedup import deduplicate def main(): if len(sys.argv) == 1: raise\",\n \"provide the WARC file as argument.') deduplicate.Warc(*sys.argv[1:]).deduplicate() if __name__ ==\"\n]","isConversational":false}}},{"rowIdx":193,"cells":{"ngram":{"kind":"list like","value":["url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL =","xmltodict.parse(curve.content) # This is based around retrieving the n last","f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None,","result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?'","<reponame>johnjdailey/FinMesh import os import requests import xmltodict import csv import","# # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url):","# # # # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/'","f'&file_type={file_type}' if date: url += f'&date={date}' if start_date: url +=","requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type:","# # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ##","GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data","= GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if","f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order: url +=","url = append_fred_token(url) result = requests.get(url) return result.text # #","url += f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return","url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units:","observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time","Organized in Python dictionary format by bond length. # Formatting","the requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if","+ f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start: url","url += f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order:","FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token =","the whole yield curve so there is no need to","XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content)",": float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), } return","content = entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield","url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit:","+= f'&date={date}' if start_date: url += f'&start_date={start_date}' url = append_fred_token(url)","= 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL","f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method: url +=","url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL =","url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset:","requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type:","if file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result =","aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if","float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']),","# # # # # # # # # #","url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end:","limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None):","government treasury bond yields. Organized in Python dictionary format by","start_date=None): ## Returns the historical, geographically organized time series data","# # # # # # GOVERNMENT YIELD CURVE DATA","= len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains the","append_fred_token(url) result = requests.get(url) return result.text # # # #","of n days. feed = parse_curve['feed'] entry = feed['entry'] last_entry","# # FRED DATA BELOW # # # # #","parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties']","result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None,","# # # # # GOVERNMENT YIELD CURVE DATA #","format by bond length. # Formatting of XML to Python","feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that","treasury bond yields. Organized in Python dictionary format by bond","url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type:","requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based around retrieving","import os import requests import xmltodict import csv import json","fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None,","length. # Formatting of XML to Python Dict curve =","url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}'","+= f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units: url","+= f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order: url","= GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url","date=None, start_date=None): ## Returns the historical, geographically organized time series","limit: url += f'&limit={limit}' if offset: url += f'&offset={offset}' if","# GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury","# # # # # # # FRED DATA BELOW","there is no need to bring in each rate. yield_curve_values","# # GOVERNMENT YIELD CURVE DATA # # # #","= append_fred_token(url) result = requests.get(url) return result.text # # #","# # # GOVERNMENT YIELD CURVE DATA # # #","# # # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def","# # # # # # # # FRED_BASE_URL =","import json # # # # # # # #","series historical data for the requested FRED data. url =","'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month'","if observation_start: url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}'","bring in each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'],","# GOVERNMENT YIELD CURVE DATA # # # # #","yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month'","+= f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method: url","YIELD CURVE DATA # # # # # # #","average of n days. feed = parse_curve['feed'] entry = feed['entry']","+= f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end: url","vintage_dates=None): ## Returns time series historical data for the requested",": float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' :","GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None):","if observation_end: url += f'&observation_end={observation_end}' if units: url += f'&units={units}'","data for the requested FRED data. url = GEOFRED_SERIES_META_URL +","rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']),","# # # # # # # # GOVERNMENT YIELD","+= f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start: url","import csv import json # # # # # #","for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}'","Python dictionary format by bond length. # Formatting of XML","xmltodict import csv import json # # # # #","DATA # # # # # # # # #","is no need to bring in each rate. yield_curve_values =","+= f'&file_type={file_type}' if date: url += f'&date={date}' if start_date: url","import requests import xmltodict import csv import json # #","entry = feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] #","to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) #","+ f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date: url","'1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month'","= requests.get(url) return result.text # # # # # #","f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates: url +=","FRED DATA BELOW # # # # # # #","import xmltodict import csv import json # # # #","FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None,","Returns meta data for the requested FRED data. url =","file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url)","GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns","last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains","# Formatting of XML to Python Dict curve = requests.get(GOV_YIELD_URL)","frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical data","def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL","offset: url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if","+= f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url","FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None,","feed = parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content","the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if","# # # # # # # # # FRED","if units: url += f'&units={units}' if frequency: url += f'&frequency={frequency}'","bond length. # Formatting of XML to Python Dict curve","GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url =","'10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), }","requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id,","len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains the whole","f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url +=","geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical, geographically organized",": float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' :","frequency: url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if","Returns time series historical data for the requested FRED data.","if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}'","return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None,","series data for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL","file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None,","output_type=None, vintage_dates=None): ## Returns time series historical data for the","if file_type: url += f'&file_type={file_type}' if date: url += f'&date={date}'","curve so there is no need to bring in each","'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None,","FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url","# # # # # # # GOVERNMENT YIELD CURVE","realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if","'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL =","need to bring in each rate. yield_curve_values = { 'date'","'2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year'","float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']),","units: url += f'&units={units}' if frequency: url += f'&frequency={frequency}' if","dates or average of n days. feed = parse_curve['feed'] entry","float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']),","so there is no need to bring in each rate.","in each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month'","f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL","CURVE DATA # # # # # # # #","requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id,","file_type: url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if","'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return","if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}'","# # # # # # # # GOV_YIELD_URL =","= 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN')","os import requests import xmltodict import csv import json #","f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start: url +=","Formatting of XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve","f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end: url +=","= FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None,","= requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def","organized time series data for the requested FRED data. url","units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical","## Returns the historical, geographically organized time series data for","realtime_end: url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if","'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data for the","# This is based around retrieving the n last dates","meta data for the requested FRED data. url = GEOFRED_SERIES_META_URL","yields. Organized in Python dictionary format by bond length. #","= requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def","+= f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return result.text","f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset: url +=","url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url =","GOVERNMENT YIELD CURVE DATA # # # # # #","for the requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}'","f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency: url +=","+ 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data for","result.text # # # # # # # # #","'5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year'",": float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' :","'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical,","data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url +=","yield curve so there is no need to bring in","get_yield(): ## Returns government treasury bond yields. Organized in Python",": float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' :",": float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' :","date: url += f'&date={date}' if start_date: url += f'&start_date={start_date}' url","def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None,","+ 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the","realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None,","f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url +=","# # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns","observation_start: url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if",": entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' :","GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns","no need to bring in each rate. yield_curve_values = {","start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url)","+= f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset: url","# # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield():","Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This","f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url +=","requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type:","url += f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method:","f'&date={date}' if start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result","DATA BELOW # # # # # # # #","url += f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return","f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date: url +=","aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical data for","Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is","f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result","= parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content =","of XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve =","Returns the historical, geographically organized time series data for the","= xmltodict.parse(curve.content) # This is based around retrieving the n","# # # # FRED DATA BELOW # # #","url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end:","# FRED DATA BELOW # # # # # #","'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond yields. Organized","# # # FRED DATA BELOW # # # #","{ 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']),","observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series","historical, geographically organized time series data for the requested FRED","# # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government","file_type=None): ## Returns meta data for the requested FRED data.","# # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def","result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?'","= append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL","the requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if","Dict that contains the whole yield curve so there is","around retrieving the n last dates or average of n","= GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta","token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?'","url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return","url += f'&date={date}' if start_date: url += f'&start_date={start_date}' url =","'2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year'","# # # # # # # # # GOV_YIELD_URL","+ f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url = append_fred_token(url)","entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']),","= FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}' if","retrieving the n last dates or average of n days.","output_type: url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url","+= f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates: url","by bond length. # Formatting of XML to Python Dict","url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}'","'1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year'","requests import xmltodict import csv import json # # #","dictionary format by bond length. # Formatting of XML to","+= f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url)","append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL +","requests.get(url) return result.text # # # # # # #",": float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' :","is based around retrieving the n last dates or average","f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result","# # # # GOVERNMENT YIELD CURVE DATA # #","time series historical data for the requested FRED data. url","the n last dates or average of n days. feed","return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None,","if sort_order: url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}'","float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']),","This is based around retrieving the n last dates or","GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}'","or average of n days. feed = parse_curve['feed'] entry =","f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return result.text #","= { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' :","csv import json # # # # # # #","url += f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency:","entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield curve so","to bring in each rate. yield_curve_values = { 'date' :","vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url)","data for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL +","+ 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None,","data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url +=","return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None):",": float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' :","GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond","n days. feed = parse_curve['feed'] entry = feed['entry'] last_entry =","url += f'&file_type={file_type}' if date: url += f'&date={date}' if start_date:","# # # # # # # # # FRED_BASE_URL","= GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ##","float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), } return yield_curve_values","= feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict","float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']),","the historical, geographically organized time series data for the requested","# # # # # FRED DATA BELOW # #","based around retrieving the n last dates or average of","if limit: url += f'&limit={limit}' if offset: url += f'&offset={offset}'","observation_end: url += f'&observation_end={observation_end}' if units: url += f'&units={units}' if","+= f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency: url","data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url +=","geographically organized time series data for the requested FRED data.","# # # # # # # # # GOVERNMENT","if file_type: url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}'","that contains the whole yield curve so there is no","whole yield curve so there is no need to bring","if date: url += f'&date={date}' if start_date: url += f'&start_date={start_date}'","last dates or average of n days. feed = parse_curve['feed']","return result.text # # # # # # # #","if offset: url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}'","float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']),","f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL","# # # # # # # # FRED DATA","url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start:","+= f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return result.text","# # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL =","data for the requested FRED data. url = FRED_SERIES_OBS_URL +","+= f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return result.text","result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ##","GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date:","+= f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url","= os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def","BELOW # # # # # # # # #","'6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year'","realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None,","float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']),","days. feed = parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1","'3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year'","= 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond yields.","url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}'","result = requests.get(url) return result.text # # # # #","def get_yield(): ## Returns government treasury bond yields. Organized in","## Returns meta data for the requested FRED data. url","contains the whole yield curve so there is no need","file_type=None, date=None, start_date=None): ## Returns the historical, geographically organized time","float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']),","if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}'","historical data for the requested FRED data. url = FRED_SERIES_OBS_URL","f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units: url +=",": float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' :","sort_order: url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if","n last dates or average of n days. feed =","if start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result =","url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates:","def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical, geographically","+= f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url","= append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL","# FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token","offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ##","os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series,","sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns","if frequency: url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}'","time series data for the requested FRED data. url =","geofred_series_meta(series_id, file_type=None): ## Returns meta data for the requested FRED","file_type: url += f'&file_type={file_type}' if date: url += f'&date={date}' if","for the requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}'","FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start:","in Python dictionary format by bond length. # Formatting of","curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based","if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result =","f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start: url +=","'3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year'","if output_type: url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}'","append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL +","json # # # # # # # # #","# # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/'","parse_curve = xmltodict.parse(curve.content) # This is based around retrieving the","= requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based around","bond yields. Organized in Python dictionary format by bond length.","# # # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL","FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url","FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url","## Returns time series historical data for the requested FRED","each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' :","append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL +","Returns government treasury bond yields. Organized in Python dictionary format","# # # # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019'","= entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield curve","def geofred_series_meta(series_id, file_type=None): ## Returns meta data for the requested","# # # # # # FRED DATA BELOW #","## Returns government treasury bond yields. Organized in Python dictionary","# Dict that contains the whole yield curve so there"],"string":"[\n \"url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL =\",\n \"xmltodict.parse(curve.content) # This is based around retrieving the n last\",\n \"f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None,\",\n \"result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?'\",\n \"<reponame>johnjdailey/FinMesh import os import requests import xmltodict import csv import\",\n \"# # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url):\",\n \"# # # # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/'\",\n \"f'&file_type={file_type}' if date: url += f'&date={date}' if start_date: url +=\",\n \"requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type:\",\n \"# # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ##\",\n \"GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data\",\n \"= GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if\",\n \"f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order: url +=\",\n \"url = append_fred_token(url) result = requests.get(url) return result.text # #\",\n \"url += f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return\",\n \"url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units:\",\n \"observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time\",\n \"Organized in Python dictionary format by bond length. # Formatting\",\n \"the requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if\",\n \"+ f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start: url\",\n \"url += f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order:\",\n \"FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token =\",\n \"the whole yield curve so there is no need to\",\n \"XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content)\",\n \": float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), } return\",\n \"content = entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield\",\n \"url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit:\",\n \"+= f'&date={date}' if start_date: url += f'&start_date={start_date}' url = append_fred_token(url)\",\n \"= 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL\",\n \"f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method: url +=\",\n \"url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL =\",\n \"url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset:\",\n \"requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type:\",\n \"if file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result =\",\n \"aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if\",\n \"float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']),\",\n \"# # # # # # # # # #\",\n \"url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end:\",\n \"limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None):\",\n \"government treasury bond yields. Organized in Python dictionary format by\",\n \"start_date=None): ## Returns the historical, geographically organized time series data\",\n \"# # # # # # GOVERNMENT YIELD CURVE DATA\",\n \"= len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains the\",\n \"append_fred_token(url) result = requests.get(url) return result.text # # # #\",\n \"of n days. feed = parse_curve['feed'] entry = feed['entry'] last_entry\",\n \"# # FRED DATA BELOW # # # # #\",\n \"parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties']\",\n \"result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None,\",\n \"# # # # # GOVERNMENT YIELD CURVE DATA #\",\n \"format by bond length. # Formatting of XML to Python\",\n \"feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that\",\n \"treasury bond yields. Organized in Python dictionary format by bond\",\n \"url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type:\",\n \"requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based around retrieving\",\n \"import os import requests import xmltodict import csv import json\",\n \"fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None,\",\n \"length. # Formatting of XML to Python Dict curve =\",\n \"url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}'\",\n \"+= f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units: url\",\n \"+= f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order: url\",\n \"= GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url\",\n \"date=None, start_date=None): ## Returns the historical, geographically organized time series\",\n \"limit: url += f'&limit={limit}' if offset: url += f'&offset={offset}' if\",\n \"# GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury\",\n \"# # # # # # # FRED DATA BELOW\",\n \"there is no need to bring in each rate. yield_curve_values\",\n \"# # GOVERNMENT YIELD CURVE DATA # # # #\",\n \"= append_fred_token(url) result = requests.get(url) return result.text # # #\",\n \"# # # GOVERNMENT YIELD CURVE DATA # # #\",\n \"# # # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def\",\n \"# # # # # # # # FRED_BASE_URL =\",\n \"import json # # # # # # # #\",\n \"series historical data for the requested FRED data. url =\",\n \"'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month'\",\n \"if observation_start: url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}'\",\n \"bring in each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'],\",\n \"# GOVERNMENT YIELD CURVE DATA # # # # #\",\n \"yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month'\",\n \"+= f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method: url\",\n \"YIELD CURVE DATA # # # # # # #\",\n \"average of n days. feed = parse_curve['feed'] entry = feed['entry']\",\n \"+= f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end: url\",\n \"vintage_dates=None): ## Returns time series historical data for the requested\",\n \": float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' :\",\n \"GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None):\",\n \"if observation_end: url += f'&observation_end={observation_end}' if units: url += f'&units={units}'\",\n \"data for the requested FRED data. url = GEOFRED_SERIES_META_URL +\",\n \"rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']),\",\n \"# # # # # # # # GOVERNMENT YIELD\",\n \"+= f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start: url\",\n \"import csv import json # # # # # #\",\n \"for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}'\",\n \"Python dictionary format by bond length. # Formatting of XML\",\n \"xmltodict import csv import json # # # # #\",\n \"DATA # # # # # # # # #\",\n \"is no need to bring in each rate. yield_curve_values =\",\n \"+= f'&file_type={file_type}' if date: url += f'&date={date}' if start_date: url\",\n \"import requests import xmltodict import csv import json # #\",\n \"entry = feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] #\",\n \"to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) #\",\n \"+ f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date: url\",\n \"'1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month'\",\n \"= requests.get(url) return result.text # # # # # #\",\n \"f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates: url +=\",\n \"FRED DATA BELOW # # # # # # #\",\n \"import xmltodict import csv import json # # # #\",\n \"FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None,\",\n \"Returns meta data for the requested FRED data. url =\",\n \"file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url)\",\n \"GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns\",\n \"last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains\",\n \"# Formatting of XML to Python Dict curve = requests.get(GOV_YIELD_URL)\",\n \"frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical data\",\n \"def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL\",\n \"offset: url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if\",\n \"+= f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url\",\n \"FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None,\",\n \"feed = parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content\",\n \"the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if\",\n \"# # # # # # # # # FRED\",\n \"if units: url += f'&units={units}' if frequency: url += f'&frequency={frequency}'\",\n \"bond length. # Formatting of XML to Python Dict curve\",\n \"GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url =\",\n \"'10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), }\",\n \"requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id,\",\n \"len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains the whole\",\n \"f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url +=\",\n \"geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical, geographically organized\",\n \": float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' :\",\n \"frequency: url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if\",\n \"Returns time series historical data for the requested FRED data.\",\n \"if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}'\",\n \"return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None,\",\n \"series data for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL\",\n \"file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None,\",\n \"output_type=None, vintage_dates=None): ## Returns time series historical data for the\",\n \"if file_type: url += f'&file_type={file_type}' if date: url += f'&date={date}'\",\n \"curve so there is no need to bring in each\",\n \"'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None,\",\n \"FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url\",\n \"# # # # # # # GOVERNMENT YIELD CURVE\",\n \"realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if\",\n \"'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL =\",\n \"need to bring in each rate. yield_curve_values = { 'date'\",\n \"'2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year'\",\n \"float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']),\",\n \"units: url += f'&units={units}' if frequency: url += f'&frequency={frequency}' if\",\n \"dates or average of n days. feed = parse_curve['feed'] entry\",\n \"float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']),\",\n \"so there is no need to bring in each rate.\",\n \"in each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month'\",\n \"f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL\",\n \"CURVE DATA # # # # # # # #\",\n \"requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id,\",\n \"file_type: url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if\",\n \"'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return\",\n \"if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}'\",\n \"# # # # # # # # GOV_YIELD_URL =\",\n \"= 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN')\",\n \"os import requests import xmltodict import csv import json #\",\n \"f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start: url +=\",\n \"Formatting of XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve\",\n \"f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end: url +=\",\n \"= FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None,\",\n \"= requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def\",\n \"organized time series data for the requested FRED data. url\",\n \"units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical\",\n \"## Returns the historical, geographically organized time series data for\",\n \"realtime_end: url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if\",\n \"'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data for the\",\n \"# This is based around retrieving the n last dates\",\n \"meta data for the requested FRED data. url = GEOFRED_SERIES_META_URL\",\n \"yields. Organized in Python dictionary format by bond length. #\",\n \"= requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def\",\n \"+= f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return result.text\",\n \"f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset: url +=\",\n \"url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url =\",\n \"GOVERNMENT YIELD CURVE DATA # # # # # #\",\n \"for the requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}'\",\n \"f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency: url +=\",\n \"+ 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data for\",\n \"result.text # # # # # # # # #\",\n \"'5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year'\",\n \": float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' :\",\n \"'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical,\",\n \"data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url +=\",\n \"yield curve so there is no need to bring in\",\n \"get_yield(): ## Returns government treasury bond yields. Organized in Python\",\n \": float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' :\",\n \": float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' :\",\n \"date: url += f'&date={date}' if start_date: url += f'&start_date={start_date}' url\",\n \"def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None,\",\n \"+ 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the\",\n \"realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None,\",\n \"f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url +=\",\n \"# # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns\",\n \"observation_start: url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if\",\n \": entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' :\",\n \"GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns\",\n \"no need to bring in each rate. yield_curve_values = {\",\n \"start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url)\",\n \"+= f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset: url\",\n \"# # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield():\",\n \"Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This\",\n \"f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url +=\",\n \"requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type:\",\n \"url += f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method:\",\n \"f'&date={date}' if start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result\",\n \"DATA BELOW # # # # # # # #\",\n \"url += f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return\",\n \"f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date: url +=\",\n \"aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical data for\",\n \"Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is\",\n \"f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result\",\n \"= parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content =\",\n \"of XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve =\",\n \"Returns the historical, geographically organized time series data for the\",\n \"= xmltodict.parse(curve.content) # This is based around retrieving the n\",\n \"# # # # FRED DATA BELOW # # #\",\n \"url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end:\",\n \"# FRED DATA BELOW # # # # # #\",\n \"'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond yields. Organized\",\n \"# # # FRED DATA BELOW # # # #\",\n \"{ 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']),\",\n \"observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series\",\n \"historical, geographically organized time series data for the requested FRED\",\n \"# # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government\",\n \"file_type=None): ## Returns meta data for the requested FRED data.\",\n \"# # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def\",\n \"result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?'\",\n \"= append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL\",\n \"the requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if\",\n \"Dict that contains the whole yield curve so there is\",\n \"around retrieving the n last dates or average of n\",\n \"= GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta\",\n \"token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?'\",\n \"url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return\",\n \"url += f'&date={date}' if start_date: url += f'&start_date={start_date}' url =\",\n \"'2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year'\",\n \"# # # # # # # # # GOV_YIELD_URL\",\n \"+ f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url = append_fred_token(url)\",\n \"entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']),\",\n \"= FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}' if\",\n \"retrieving the n last dates or average of n days.\",\n \"output_type: url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url\",\n \"+= f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates: url\",\n \"by bond length. # Formatting of XML to Python Dict\",\n \"url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}'\",\n \"'1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year'\",\n \"requests import xmltodict import csv import json # # #\",\n \"dictionary format by bond length. # Formatting of XML to\",\n \"+= f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url)\",\n \"append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL +\",\n \"requests.get(url) return result.text # # # # # # #\",\n \": float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' :\",\n \"is based around retrieving the n last dates or average\",\n \"f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result\",\n \"# # # # GOVERNMENT YIELD CURVE DATA # #\",\n \"time series historical data for the requested FRED data. url\",\n \"the n last dates or average of n days. feed\",\n \"return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None,\",\n \"if sort_order: url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}'\",\n \"float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']),\",\n \"This is based around retrieving the n last dates or\",\n \"GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}'\",\n \"or average of n days. feed = parse_curve['feed'] entry =\",\n \"f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return result.text #\",\n \"= { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' :\",\n \"csv import json # # # # # # #\",\n \"url += f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency:\",\n \"entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield curve so\",\n \"to bring in each rate. yield_curve_values = { 'date' :\",\n \"vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url)\",\n \"data for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL +\",\n \"+ 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None,\",\n \"data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url +=\",\n \"return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None):\",\n \": float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' :\",\n \"GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond\",\n \"n days. feed = parse_curve['feed'] entry = feed['entry'] last_entry =\",\n \"url += f'&file_type={file_type}' if date: url += f'&date={date}' if start_date:\",\n \"# # # # # # # # # FRED_BASE_URL\",\n \"= GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ##\",\n \"float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), } return yield_curve_values\",\n \"= feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict\",\n \"float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']),\",\n \"the historical, geographically organized time series data for the requested\",\n \"# # # # # FRED DATA BELOW # #\",\n \"based around retrieving the n last dates or average of\",\n \"if limit: url += f'&limit={limit}' if offset: url += f'&offset={offset}'\",\n \"observation_end: url += f'&observation_end={observation_end}' if units: url += f'&units={units}' if\",\n \"+= f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency: url\",\n \"data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url +=\",\n \"geographically organized time series data for the requested FRED data.\",\n \"# # # # # # # # # GOVERNMENT\",\n \"if file_type: url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}'\",\n \"that contains the whole yield curve so there is no\",\n \"whole yield curve so there is no need to bring\",\n \"if date: url += f'&date={date}' if start_date: url += f'&start_date={start_date}'\",\n \"last dates or average of n days. feed = parse_curve['feed']\",\n \"return result.text # # # # # # # #\",\n \"if offset: url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}'\",\n \"float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']),\",\n \"f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL\",\n \"# # # # # # # # FRED DATA\",\n \"url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start:\",\n \"+= f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return result.text\",\n \"# # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL =\",\n \"data for the requested FRED data. url = FRED_SERIES_OBS_URL +\",\n \"+= f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return result.text\",\n \"result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ##\",\n \"GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date:\",\n \"+= f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url\",\n \"= os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def\",\n \"BELOW # # # # # # # # #\",\n \"'6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year'\",\n \"realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None,\",\n \"float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']),\",\n \"days. feed = parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1\",\n \"'3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year'\",\n \"= 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond yields.\",\n \"url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}'\",\n \"result = requests.get(url) return result.text # # # # #\",\n \"def get_yield(): ## Returns government treasury bond yields. Organized in\",\n \"## Returns meta data for the requested FRED data. url\",\n \"contains the whole yield curve so there is no need\",\n \"file_type=None, date=None, start_date=None): ## Returns the historical, geographically organized time\",\n \"float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']),\",\n \"if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}'\",\n \"historical data for the requested FRED data. url = FRED_SERIES_OBS_URL\",\n \"f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units: url +=\",\n \": float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' :\",\n \"sort_order: url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if\",\n \"n last dates or average of n days. feed =\",\n \"if start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result =\",\n \"url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates:\",\n \"def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical, geographically\",\n \"+= f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url\",\n \"= append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL\",\n \"# FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token\",\n \"offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ##\",\n \"os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series,\",\n \"sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns\",\n \"if frequency: url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}'\",\n \"time series data for the requested FRED data. url =\",\n \"geofred_series_meta(series_id, file_type=None): ## Returns meta data for the requested FRED\",\n \"file_type: url += f'&file_type={file_type}' if date: url += f'&date={date}' if\",\n \"for the requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}'\",\n \"FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start:\",\n \"in Python dictionary format by bond length. # Formatting of\",\n \"curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based\",\n \"if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result =\",\n \"f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start: url +=\",\n \"'3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year'\",\n \"if output_type: url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}'\",\n \"append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL +\",\n \"json # # # # # # # # #\",\n \"# # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/'\",\n \"parse_curve = xmltodict.parse(curve.content) # This is based around retrieving the\",\n \"= requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based around\",\n \"bond yields. Organized in Python dictionary format by bond length.\",\n \"# # # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL\",\n \"FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url\",\n \"FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url\",\n \"## Returns time series historical data for the requested FRED\",\n \"each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' :\",\n \"append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL +\",\n \"Returns government treasury bond yields. Organized in Python dictionary format\",\n \"# # # # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019'\",\n \"= entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield curve\",\n \"def geofred_series_meta(series_id, file_type=None): ## Returns meta data for the requested\",\n \"# # # # # # FRED DATA BELOW #\",\n \"## Returns government treasury bond yields. Organized in Python dictionary\",\n \"# Dict that contains the whole yield curve so there\"\n]","isConversational":false}}},{"rowIdx":194,"cells":{"ngram":{"kind":"list like","value":["webhook verify token can be set here, if set as","change various variables here to customize and set up the","Mattermost server '''----------------------------Flask Configuration----------------------------''' #set external IP for the Flask","as a String mm_channel = None #Set a custom Username","custom Profile Image for the Client mm_profileimage = 'https://i.imgur.com/7Wg3Tgs.png' #Telekom","be enabled in the settings.json of the Mattermost server '''----------------------------Flask","for the Flask Server to create a Webhook for ALM","the settings.json of the Mattermost server '''----------------------------Flask Configuration----------------------------''' #set external","it will be autogenerated & changed on each startup. wh_token","'''----------------------------Flask Configuration----------------------------''' #set external IP for the Flask Server to","#The latter two need to be enabled in the settings.json","available external adress) external_ip = False #default: 5000 port =","go to `Main Menu -> Integrations -> Incoming Webhooks` and","can change various variables here to customize and set up","settings.json of the Mattermost server '''----------------------------Flask Configuration----------------------------''' #set external IP","to only available external adress) external_ip = False #default: 5000","#Telekom T Image #The latter two need to be enabled","channel name as a String mm_channel = None #Set a","for the Mattermost-Octane Bridge. 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mmcv.Config.fromfile(config) self.pth","torch import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import","index)) return image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs","open(save, 'w') as f: f.write(str_results) if __name__ == '__main__': config_file","def __init__(self, config, pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config)","fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image filename for","os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index =","height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128:","box[:4]))) str_results += ' '.join([img, cls, bbox_str]) + '\\n' with","return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes =",":, :] pmin = np.min(img) pmax = np.max(img) img =","[] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model {}","i + 1) plt.axis('off') img = x[0, i, :, :]","self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading","plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf()","result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in result] result","to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name,","= '' for i, img in enumerate(self.img_detected): result = self.results[i]","import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import init_detector,","= [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result)","return result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for","add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1]","-1] > det_thr] for det, det_thr in zip(result, det_thrs)] if","self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def","module of neural network \"\"\" # os.makedirs(work_dir, exist_ok=True) module_name =","det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result","labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox","= add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[...,","__name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector","list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2] <","result = self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] *","vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels =","> vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts =","_, ids = nms(result, nms_th) total_num = 0 nms_result =","plt.axis('off') img = x[0, i, :, :] pmin = np.min(img)","index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:,","map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) #","def save_results(self, save): str_results = '' for i, img in","/ (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img =","prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs,","result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls","0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img =","img in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for","* len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file =","gfjiang import os.path as osp import mmcv import numpy as","plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i","[det[det[..., -1] > det_thr] for det, det_thr in zip(result, det_thrs)]","for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func =","module: module of neural network \"\"\" # os.makedirs(work_dir, exist_ok=True) module_name","height if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32))","img = x[0, i, :, :] pmin = np.min(img) pmax","for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root)","object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num","= inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs, float):","work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate","enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img,","det_cls in result] result = np.vstack(result) _, ids = nms(result,","right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height *","result = self.results[i] img = osp.basename(img) for cls_index, dets in",":] pmin = np.min(img) pmax = np.max(img) img = ((img","for box in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results","i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds =","dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids < num))[0]]","dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels)","= [det_thrs] * len(result) if vis: to_file = osp.join(save_root, osp.basename(img))","self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if","mmdet.ops import nms from mmdet.apis import init_detector, inference_detector def draw_features(module,","= cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def","\"\"\" Generate image filename for hook function Parameters: ----------- module:","import nms from mmdet.apis import init_detector, inference_detector def draw_features(module, input,","prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result =","neural network \"\"\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name","save_results(self, save): str_results = '' for i, img in enumerate(self.img_detected):","= height if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32,","from functools import partial import torch import math from cvtools.utils.path","bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0],","vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img,","from mmdet.apis import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'):","np.vstack(result) _, ids = nms(result, nms_th) total_num = 0 nms_result","f: f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file","= self.results[i] img = osp.basename(img) for cls_index, dets in enumerate(result):","img) # result = self.nms(result) if isinstance(det_thrs, float): det_thrs =","osp import mmcv import numpy as np import cvtools import","nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self, img, bbox_result,","result] result = np.vstack(result) _, ids = nms(result, nms_th) total_num","for det, det_thr in zip(result, det_thrs)] if len(result) == 0:","in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon',","self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str, map(int, box[:4])))","osp.basename(img) for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for","np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds]","draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results","work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels))","cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:, :, ::-1]","# 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename,","pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8","= list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2]","< num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self,","for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box","bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls,","math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from","# '.' is surely exist, to make first loop condition","np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels","(base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self, config, pth):","= int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128: return","in range(height * width): plt.subplot(height, width, i + 1) plt.axis('off')","import cv2.cv2 as cv from functools import partial import torch","{} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self,","bbox_str]) + '\\n' with open(save, 'w') as f: f.write(str_results) if","first loop condition True while osp.exists(image_name): index += 1 image_name","= [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)] img","self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr]","= bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for","as cv from functools import partial import torch import math","np.min(img) pmax = np.max(img) img = ((img - pmin) /","in enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets: bbox_str","os.path as osp import mmcv import numpy as np import","np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds]","= [len(det_cls) for det_cls in result] result = np.vstack(result) _,","+ 1) plt.axis('off') img = x[0, i, :, :] pmin","cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self,","vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result =","[len(det_cls) for det_cls in result] result = np.vstack(result) _, ids","cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import","= module_name.split('(')[0] index = 0 image_name = '.' # '.'","wspace=0.05, hspace=0.05) for i in range(height * width): plt.subplot(height, width,","vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''):","module_name = str(module) base_name = module_name.split('(')[0] index = 0 image_name","det, det_thr in zip(result, det_thrs)] if len(result) == 0: print('detect:","img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot):","suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] >","if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path","= np.max(img) img = ((img - pmin) / (pmax -","img = osp.basename(img) for cls_index, dets in enumerate(result): cls =","bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1],","h, w = self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if","vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1],","else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img","save_root=''): result = inference_detector(self.model, img) # result = self.nms(result) if","vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs","import mmcv import numpy as np import cvtools import matplotlib.pyplot","str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar =","[ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ]","to make first loop condition True while osp.exists(image_name): index +=","image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = []","num in dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids","width, i + 1) plt.axis('off') img = x[0, i, :,","for i, img in enumerate(self.img_detected): result = self.results[i] img =","if vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb')","exist, to make first loop condition True while osp.exists(image_name): index","'.' is surely exist, to make first loop condition True","as f: f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py'","in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self,","out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if","isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if vis: to_file","self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in","self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5,","pmax = np.max(img) img = ((img - pmin) / (pmax","class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = [] self.cfg","'w') as f: f.write(str_results) if __name__ == '__main__': config_file =","# result = self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs]","-*- # @Time : 2021/1/3 15:15 # @Author : gfjiang","det_thrs)] if len(result) == 0: print('detect: image {} has no","import cvtools import matplotlib.pyplot as plt import cv2.cv2 as cv","mmdet.apis import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x","for num in dets_num: ids_cls = ids[np.where((total_num <= ids) &","float): det_thrs = [det_thrs] * len(result) if vis: to_file =","img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [","((img - pmin) / (pmax - pmin + 0.000001))*255 #","= partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results =","pmin = np.min(img) pmax = np.max(img) img = ((img -","x = output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width","plt.subplot(height, width, i + 1) plt.axis('off') img = x[0, i,","vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr] for det,","= str(module) base_name = module_name.split('(')[0] index = 0 image_name =","nms_th) total_num = 0 nms_result = [] for num in","result = inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs,","cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for","= init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected = []","cls, bbox_str]) + '\\n' with open(save, 'w') as f: f.write(str_results)","image_name = '.' # '.' is surely exist, to make","= mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis,","fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image","if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if vis:","','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str]) +","image filename for hook function Parameters: ----------- module: module of","self.pth = pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg,","as osp import mmcv import numpy as np import cvtools","str(module) base_name = module_name.split('(')[0] index = 0 image_name = '.'","enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets: bbox_str =","= [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False,","det_thrs = [det_thrs] * len(result) if vis: to_file = osp.join(save_root,","= 0 nms_result = [] for num in dets_num: ids_cls","] labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0]","function Parameters: ----------- module: module of neural network \"\"\" #","width = height if list(output.shape)[2] < 128: return fig =","# 生成heat map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的","pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True, save_root='work_dirs/attention_vis/')","'.join([img, cls, bbox_str]) + '\\n' with open(save, 'w') as f:",":, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module,","= self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot):","nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result)","img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename =","# self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''):","in zip(result, det_thrs)] if len(result) == 0: print('detect: image {}","== 0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result)","config, pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth =","cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _,","self.results[i] img = osp.basename(img) for cls_index, dets in enumerate(result): cls","image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index)) return","-1] > vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts","x[0, i, :, :] pmin = np.min(img) pmax = np.max(img)","det_thr in zip(result, det_thrs)] if len(result) == 0: print('detect: image","# 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img","image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def","= ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str])","hook function Parameters: ----------- module: module of neural network \"\"\"","ids = nms(result, nms_th) total_num = 0 nms_result = []","vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module in","0 image_name = '.' # '.' is surely exist, to","np import cvtools import matplotlib.pyplot as plt import cv2.cv2 as","model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results =","import torch import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops","img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:,","nms(result, nms_th) total_num = 0 nms_result = [] for num","int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128: return fig","cv from functools import partial import torch import math from","isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar","= self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result)","+ '\\n' with open(save, 'w') as f: f.write(str_results) if __name__","result = [det[det[..., -1] > det_thr] for det, det_thr in","name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial(","# @Time : 2021/1/3 15:15 # @Author : gfjiang import","= get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'):","detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img)","0 nms_result = [] for num in dets_num: ids_cls =","= [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model","+= ' '.join([img, cls, bbox_str]) + '\\n' with open(save, 'w')","for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds","nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in result]","[] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5,","= np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes =","'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True,","output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height =","in dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids <","index += 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' %","dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets:","make first loop condition True while osp.exists(image_name): index += 1","result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr] for","bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h,","img = ((img - pmin) / (pmax - pmin +","while osp.exists(image_name): index += 1 image_name = osp.join( work_dir, 'feats',","i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels =","(ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def","= labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in","filename for hook function Parameters: ----------- module: module of neural","'feats', '%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object): def","ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num +=","num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self, img,","savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module,","osp.exists(image_name): index += 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png'","= img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat","128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95,","line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale","for hook function Parameters: ----------- module: module of neural network","modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save):","# @Author : gfjiang import os.path as osp import mmcv","i, img in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img)","= plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for","self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs))","in result] result = np.vstack(result) _, ids = nms(result, nms_th)","plt import cv2.cv2 as cv from functools import partial import","self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth))","== '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector =","bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img,","det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False,","module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for i, img","nms_th=0.3): dets_num = [len(det_cls) for det_cls in result] result =","'%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self,","= osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index)) return image_name","as np import cvtools import matplotlib.pyplot as plt import cv2.cv2","mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth)) self.model =","def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model,","det_thr] for det, det_thr in zip(result, det_thrs)] if len(result) ==","mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr,","import partial import torch import math from cvtools.utils.path import add_prefix_filename_suffix","get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\"","+= 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name,","cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box in","of neural network \"\"\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module)","img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) #","save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result","to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32)","@Time : 2021/1/3 15:15 # @Author : gfjiang import os.path","if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05,","= mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth)) self.model","in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += '","bboxes = bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2))","functools import partial import torch import math from cvtools.utils.path import","[] for num in dets_num: ids_cls = ids[np.where((total_num <= ids)","img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def","= osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr,","plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image filename for hook","cv.COLORMAP_JET) # 生成heat map img = img[:, :, ::-1] #","1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index))","= img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename","__call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs","base_name = module_name.split('(')[0] index = 0 image_name = '.' #","in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir)","[self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)] img =","modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module in self.model.named_modules():","top=0.95, wspace=0.05, hspace=0.05) for i in range(height * width): plt.subplot(height,","enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for cls_index, dets","# os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index","+ 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img","Parameters: ----------- module: module of neural network \"\"\" # os.makedirs(work_dir,","self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func","::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir)","[det_thrs] * len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file","add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import init_detector, inference_detector","- pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) #","device='cuda:0') self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d))","= [det[det[..., -1] > det_thr] for det, det_thr in zip(result,","= np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i,","ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return","pmin) / (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img","fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)","total_num += num return nms_result def vis(self, img, bbox_result, vis_thr=0.5,","# print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close()","ids_cls = ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls])","def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image filename for hook function","& (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result","15:15 # @Author : gfjiang import os.path as osp import","pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0')","self.pth, device='cuda:0') self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d,","vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i,","bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height * width):","= output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width =","loop condition True while osp.exists(image_name): index += 1 image_name =","is surely exist, to make first loop condition True while","dets_num = [len(det_cls) for det_cls in result] result = np.vstack(result)","img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) #","1) plt.axis('off') img = x[0, i, :, :] pmin =","img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file)","= [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self,","vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5,","= ((img - pmin) / (pmax - pmin + 0.000001))*255",": 2021/1/3 15:15 # @Author : gfjiang import os.path as","init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected = [] #","labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes","转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img =","module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features,","Generate image filename for hook function Parameters: ----------- module: module","self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5,","osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file)","nms_result = [] for num in dets_num: ids_cls = ids[np.where((total_num","draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for","f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file =","partial import torch import math from cvtools.utils.path import add_prefix_filename_suffix from","self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num =","input, output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height","to_file=to_file) result = [det[det[..., -1] > det_thr] for det, det_thr","= pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth,","range(height * width): plt.subplot(height, width, i + 1) plt.axis('off') img","= 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True, save_root='work_dirs/attention_vis/') detector.save_results('work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/detect_result.txt')","work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for i,","float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET)","osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result","% (base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self, config,","= nms(result, nms_th) total_num = 0 nms_result = [] for","dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img,","in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for cls_index,","= [] for num in dets_num: ids_cls = ids[np.where((total_num <=","import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms","----------- module: module of neural network \"\"\" # os.makedirs(work_dir, exist_ok=True)","for i in range(height * width): plt.subplot(height, width, i +","condition True while osp.exists(image_name): index += 1 image_name = osp.join(","np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox","from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis","+= cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for","0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return","= np.vstack(result) _, ids = nms(result, nms_th) total_num = 0","np.max(img) img = ((img - pmin) / (pmax - pmin","fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height","str_results += ' '.join([img, cls, bbox_str]) + '\\n' with open(save,","- pmin) / (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255","pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth","exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index = 0","list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95,","'__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file,","bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index,","print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results","inference_detector def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels","torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path,","len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file,","[] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path,","i, :, :] pmin = np.min(img) pmax = np.max(img) img","def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels =","vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else:","<= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num","def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels","img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map","True while osp.exists(image_name): index += 1 image_name = osp.join( work_dir,","= cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:, :,","partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = ''","print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result","network \"\"\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name =","box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w =","'.' # '.' is surely exist, to make first loop","num return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes","bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for","work_dir, 'feats', '%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object):","cv2.cv2 as cv from functools import partial import torch import","import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x =","mmcv import numpy as np import cvtools import matplotlib.pyplot as","2021/1/3 15:15 # @Author : gfjiang import os.path as osp","from mmdet.ops import nms from mmdet.apis import init_detector, inference_detector def","no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3):","zip(result, det_thrs)] if len(result) == 0: print('detect: image {} has","self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls)","self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected =","as plt import cv2.cv2 as cv from functools import partial","return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05,","-*- encoding:utf-8 -*- # @Time : 2021/1/3 15:15 # @Author","self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func)","zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2)","= 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png',","AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = [] self.cfg =","生成heat map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img)","import os.path as osp import mmcv import numpy as np",": gfjiang import os.path as osp import mmcv import numpy","cvtools import matplotlib.pyplot as plt import cv2.cv2 as cv from","* width): plt.subplot(height, width, i + 1) plt.axis('off') img =","> det_thr] for det, det_thr in zip(result, det_thrs)] if len(result)","'\\n' with open(save, 'w') as f: f.write(str_results) if __name__ ==","= self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str, map(int,","i in range(height * width): plt.subplot(height, width, i + 1)","# float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img,","= osp.basename(img) for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index]","total_num = 0 nms_result = [] for num in dets_num:","cls = self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str,","isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self,","module_name.split('(')[0] index = 0 image_name = '.' # '.' is","bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds = np.where(bboxes[:,","imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img,","def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str):","enumerate(bbox_result) ] labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] >","det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path)","= '.' # '.' is surely exist, to make first","save): str_results = '' for i, img in enumerate(self.img_detected): result","2)) for index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts(","save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs +=","< 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05,","32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in",":-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w","str_results = '' for i, img in enumerate(self.img_detected): result =","import numpy as np import cvtools import matplotlib.pyplot as plt","def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in","if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth'","labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in","self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if","+= imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs):","result = np.vstack(result) _, ids = nms(result, nms_th) total_num =","w = self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module,","img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height))","surely exist, to make first loop condition True while osp.exists(image_name):","map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str]) + '\\n'","box in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results +=","inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels","output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height","\"\"\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0]","index = 0 image_name = '.' # '.' is surely","texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)]","with open(save, 'w') as f: f.write(str_results) if __name__ == '__main__':","if len(result) == 0: print('detect: image {} has no object.'.format(img))","(pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8)","if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def","= np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels =","len(result) == 0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img)","config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file)","labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels,","hspace=0.05) for i in range(height * width): plt.subplot(height, width, i","注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100)","print(\"{}/{}\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def","for index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img,","dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image filename","init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy()","@Author : gfjiang import os.path as osp import mmcv import","has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result,","to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result =","work_dir='./'): \"\"\" Generate image filename for hook function Parameters: -----------","draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1]","imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs +=","_, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update()","' '.join([img, cls, bbox_str]) + '\\n' with open(save, 'w') as","'' for i, img in enumerate(self.img_detected): result = self.results[i] img","matplotlib.pyplot as plt import cv2.cv2 as cv from functools import","return image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs =","numpy as np import cvtools import matplotlib.pyplot as plt import","width): plt.subplot(height, width, i + 1) plt.axis('off') img = x[0,","inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs, float): det_thrs","= x[0, i, :, :] pmin = np.min(img) pmax =","...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected","nms from mmdet.apis import init_detector, inference_detector def draw_features(module, input, output,","get_image_name_for_hook(module, work_dir='./'): \"\"\" Generate image filename for hook function Parameters:","= ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num","to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result,","vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result = self.nms(result)","def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module","+= num return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'):","{} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = []"],"string":"[\n \"import matplotlib.pyplot as plt import cv2.cv2 as cv from functools\",\n \"= np.min(img) pmax = np.max(img) img = ((img - pmin)\",\n \"# -*- encoding:utf-8 -*- # @Time : 2021/1/3 15:15 #\",\n \"for det_cls in result] result = np.vstack(result) _, ids =\",\n \"= 0 image_name = '.' # '.' is surely exist,\",\n \"encoding:utf-8 -*- # @Time : 2021/1/3 15:15 # @Author :\",\n \"osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index)) return image_name class\",\n \"in enumerate(bbox_result) ] labels = np.concatenate(labels) inds = np.where(bboxes[:, -1]\",\n \"__init__(self, config, pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth\",\n \"torch import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import\",\n \"index)) return image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs\",\n \"open(save, 'w') as f: f.write(str_results) if __name__ == '__main__': config_file\",\n \"def __init__(self, config, pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config)\",\n \"fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image filename for\",\n \"os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index =\",\n \"height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128:\",\n \"box[:4]))) str_results += ' '.join([img, cls, bbox_str]) + '\\\\n' with\",\n \"return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes =\",\n \":, :] pmin = np.min(img) pmax = np.max(img) img =\",\n \"[] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model {}\",\n \"i + 1) plt.axis('off') img = x[0, i, :, :]\",\n \"self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading\",\n \"plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf()\",\n \"result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in result] result\",\n \"to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name,\",\n \"= '' for i, img in enumerate(self.img_detected): result = self.results[i]\",\n \"import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import init_detector,\",\n \"= [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result)\",\n \"return result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for\",\n \"add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1]\",\n \"-1] > det_thr] for det, det_thr in zip(result, det_thrs)] if\",\n \"self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def\",\n \"module of neural network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name =\",\n \"det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result\",\n \"labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox\",\n \"= add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[...,\",\n \"__name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector\",\n \"list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2] <\",\n \"result = self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] *\",\n \"vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels =\",\n \"> vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts =\",\n \"_, ids = nms(result, nms_th) total_num = 0 nms_result =\",\n \"plt.axis('off') img = x[0, i, :, :] pmin = np.min(img)\",\n \"index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:,\",\n \"map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) #\",\n \"def save_results(self, save): str_results = '' for i, img in\",\n \"/ (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img =\",\n \"prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs,\",\n \"result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls\",\n \"0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img =\",\n \"img in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for\",\n \"* len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file =\",\n \"gfjiang import os.path as osp import mmcv import numpy as\",\n \"plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i\",\n \"[det[det[..., -1] > det_thr] for det, det_thr in zip(result, det_thrs)]\",\n \"for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func =\",\n \"module: module of neural network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name\",\n \"height if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32))\",\n \"img = x[0, i, :, :] pmin = np.min(img) pmax\",\n \"for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root)\",\n \"object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num\",\n \"= inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs, float):\",\n \"work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate\",\n \"enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img,\",\n \"det_cls in result] result = np.vstack(result) _, ids = nms(result,\",\n \"right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height *\",\n \"result = self.results[i] img = osp.basename(img) for cls_index, dets in\",\n \":] pmin = np.min(img) pmax = np.max(img) img = ((img\",\n \"for box in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results\",\n \"i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds =\",\n \"dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids < num))[0]]\",\n \"dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels)\",\n \"= [det_thrs] * len(result) if vis: to_file = osp.join(save_root, osp.basename(img))\",\n \"self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if\",\n \"mmdet.ops import nms from mmdet.apis import init_detector, inference_detector def draw_features(module,\",\n \"= cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def\",\n \"\\\"\\\"\\\" Generate image filename for hook function Parameters: ----------- module:\",\n \"import nms from mmdet.apis import init_detector, inference_detector def draw_features(module, input,\",\n \"prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result =\",\n \"neural network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name\",\n \"save_results(self, save): str_results = '' for i, img in enumerate(self.img_detected):\",\n \"= height if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32,\",\n \"from functools import partial import torch import math from cvtools.utils.path\",\n \"bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0],\",\n \"vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img,\",\n \"from mmdet.apis import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'):\",\n \"np.vstack(result) _, ids = nms(result, nms_th) total_num = 0 nms_result\",\n \"f: f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file\",\n \"= self.results[i] img = osp.basename(img) for cls_index, dets in enumerate(result):\",\n \"img) # result = self.nms(result) if isinstance(det_thrs, float): det_thrs =\",\n \"osp import mmcv import numpy as np import cvtools import\",\n \"nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self, img, bbox_result,\",\n \"result] result = np.vstack(result) _, ids = nms(result, nms_th) total_num\",\n \"for det, det_thr in zip(result, det_thrs)] if len(result) == 0:\",\n \"in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon',\",\n \"self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str, map(int, box[:4])))\",\n \"osp.basename(img) for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for\",\n \"np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds]\",\n \"draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results\",\n \"work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels))\",\n \"cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:, :, ::-1]\",\n \"# 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename,\",\n \"pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8\",\n \"= list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if list(output.shape)[2]\",\n \"< num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self,\",\n \"for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box\",\n \"bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls,\",\n \"math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from\",\n \"# '.' is surely exist, to make first loop condition\",\n \"np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels\",\n \"(base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self, config, pth):\",\n \"= int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128: return\",\n \"in range(height * width): plt.subplot(height, width, i + 1) plt.axis('off')\",\n \"import cv2.cv2 as cv from functools import partial import torch\",\n \"{} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self,\",\n \"bbox_str]) + '\\\\n' with open(save, 'w') as f: f.write(str_results) if\",\n \"first loop condition True while osp.exists(image_name): index += 1 image_name\",\n \"= [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)] img\",\n \"self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr]\",\n \"= bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for\",\n \"as cv from functools import partial import torch import math\",\n \"np.min(img) pmax = np.max(img) img = ((img - pmin) /\",\n \"in enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets: bbox_str\",\n \"os.path as osp import mmcv import numpy as np import\",\n \"np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds]\",\n \"= [len(det_cls) for det_cls in result] result = np.vstack(result) _,\",\n \"+ 1) plt.axis('off') img = x[0, i, :, :] pmin\",\n \"cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self,\",\n \"vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result =\",\n \"[len(det_cls) for det_cls in result] result = np.vstack(result) _, ids\",\n \"cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import\",\n \"= module_name.split('(')[0] index = 0 image_name = '.' # '.'\",\n \"wspace=0.05, hspace=0.05) for i in range(height * width): plt.subplot(height, width,\",\n \"vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''):\",\n \"module_name = str(module) base_name = module_name.split('(')[0] index = 0 image_name\",\n \"det, det_thr in zip(result, det_thrs)] if len(result) == 0: print('detect:\",\n \"img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot):\",\n \"suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] >\",\n \"if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path\",\n \"= np.max(img) img = ((img - pmin) / (pmax -\",\n \"img = osp.basename(img) for cls_index, dets in enumerate(result): cls =\",\n \"bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1],\",\n \"h, w = self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if\",\n \"vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1],\",\n \"else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img\",\n \"save_root=''): result = inference_detector(self.model, img) # result = self.nms(result) if\",\n \"vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs\",\n \"import mmcv import numpy as np import cvtools import matplotlib.pyplot\",\n \"str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar =\",\n \"[ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ]\",\n \"to make first loop condition True while osp.exists(image_name): index +=\",\n \"image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = []\",\n \"num in dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids\",\n \"width, i + 1) plt.axis('off') img = x[0, i, :,\",\n \"for i, img in enumerate(self.img_detected): result = self.results[i] img =\",\n \"if vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb')\",\n \"exist, to make first loop condition True while osp.exists(image_name): index\",\n \"'.' is surely exist, to make first loop condition True\",\n \"as f: f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py'\",\n \"in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self,\",\n \"out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height if\",\n \"isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if vis: to_file\",\n \"self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in\",\n \"self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5,\",\n \"pmax = np.max(img) img = ((img - pmin) / (pmax\",\n \"class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = [] self.cfg\",\n \"'w') as f: f.write(str_results) if __name__ == '__main__': config_file =\",\n \"# result = self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs]\",\n \"-*- # @Time : 2021/1/3 15:15 # @Author : gfjiang\",\n \"det_thrs)] if len(result) == 0: print('detect: image {} has no\",\n \"import cvtools import matplotlib.pyplot as plt import cv2.cv2 as cv\",\n \"mmdet.apis import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x\",\n \"for num in dets_num: ids_cls = ids[np.where((total_num <= ids) &\",\n \"float): det_thrs = [det_thrs] * len(result) if vis: to_file =\",\n \"img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [\",\n \"((img - pmin) / (pmax - pmin + 0.000001))*255 #\",\n \"= partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results =\",\n \"pmin = np.min(img) pmax = np.max(img) img = ((img -\",\n \"x = output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width\",\n \"plt.subplot(height, width, i + 1) plt.axis('off') img = x[0, i,\",\n \"vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr] for det,\",\n \"= str(module) base_name = module_name.split('(')[0] index = 0 image_name =\",\n \"nms_th) total_num = 0 nms_result = [] for num in\",\n \"result = inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs,\",\n \"cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for\",\n \"= init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected = []\",\n \"cls, bbox_str]) + '\\\\n' with open(save, 'w') as f: f.write(str_results)\",\n \"image_name = '.' # '.' is surely exist, to make\",\n \"= mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis,\",\n \"fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image\",\n \"if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if vis:\",\n \"','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str]) +\",\n \"image filename for hook function Parameters: ----------- module: module of\",\n \"self.pth = pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg,\",\n \"as osp import mmcv import numpy as np import cvtools\",\n \"str(module) base_name = module_name.split('(')[0] index = 0 image_name = '.'\",\n \"enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets: bbox_str =\",\n \"= [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False,\",\n \"det_thrs = [det_thrs] * len(result) if vis: to_file = osp.join(save_root,\",\n \"= 0 nms_result = [] for num in dets_num: ids_cls\",\n \"] labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0]\",\n \"function Parameters: ----------- module: module of neural network \\\"\\\"\\\" #\",\n \"width = height if list(output.shape)[2] < 128: return fig =\",\n \"# 生成heat map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的\",\n \"pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True, save_root='work_dirs/attention_vis/')\",\n \"'.join([img, cls, bbox_str]) + '\\\\n' with open(save, 'w') as f:\",\n \":, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module,\",\n \"= self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot):\",\n \"nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result)\",\n \"img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename =\",\n \"# self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''):\",\n \"in zip(result, det_thrs)] if len(result) == 0: print('detect: image {}\",\n \"== 0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result)\",\n \"config, pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth =\",\n \"cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _,\",\n \"self.results[i] img = osp.basename(img) for cls_index, dets in enumerate(result): cls\",\n \"image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index)) return\",\n \"-1] > vis_thr)[0] bboxes = bboxes[inds] labels = labels[inds] texts\",\n \"x[0, i, :, :] pmin = np.min(img) pmax = np.max(img)\",\n \"det_thr in zip(result, det_thrs)] if len(result) == 0: print('detect: image\",\n \"# 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img\",\n \"image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def\",\n \"= ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str])\",\n \"hook function Parameters: ----------- module: module of neural network \\\"\\\"\\\"\",\n \"ids = nms(result, nms_th) total_num = 0 nms_result = []\",\n \"vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module in\",\n \"0 image_name = '.' # '.' is surely exist, to\",\n \"np import cvtools import matplotlib.pyplot as plt import cv2.cv2 as\",\n \"model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results =\",\n \"import torch import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops\",\n \"img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:,\",\n \"nms(result, nms_th) total_num = 0 nms_result = [] for num\",\n \"int(math.sqrt(out_channels)) width = height if list(output.shape)[2] < 128: return fig\",\n \"cv from functools import partial import torch import math from\",\n \"isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar\",\n \"= self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result)\",\n \"+ '\\\\n' with open(save, 'w') as f: f.write(str_results) if __name__\",\n \"result = [det[det[..., -1] > det_thr] for det, det_thr in\",\n \"name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial(\",\n \"# @Time : 2021/1/3 15:15 # @Author : gfjiang import\",\n \"= get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'):\",\n \"detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img)\",\n \"0 nms_result = [] for num in dets_num: ids_cls =\",\n \"= [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model\",\n \"+= ' '.join([img, cls, bbox_str]) + '\\\\n' with open(save, 'w')\",\n \"for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds\",\n \"nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in result]\",\n \"[] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5,\",\n \"= np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes =\",\n \"'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True,\",\n \"output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height =\",\n \"in dets_num: ids_cls = ids[np.where((total_num <= ids) & (ids <\",\n \"index += 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' %\",\n \"dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box in dets:\",\n \"make first loop condition True while osp.exists(image_name): index += 1\",\n \"result, vis_thr=vis_thr, to_file=to_file) result = [det[det[..., -1] > det_thr] for\",\n \"bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h,\",\n \"img = ((img - pmin) / (pmax - pmin +\",\n \"while osp.exists(image_name): index += 1 image_name = osp.join( work_dir, 'feats',\",\n \"i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels =\",\n \"(ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def\",\n \"= labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in\",\n \"filename for hook function Parameters: ----------- module: module of neural\",\n \"'feats', '%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object): def\",\n \"ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num +=\",\n \"num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result def vis(self, img,\",\n \"savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module,\",\n \"osp.exists(image_name): index += 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png'\",\n \"= img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat\",\n \"128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95,\",\n \"line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale\",\n \"for hook function Parameters: ----------- module: module of neural network\",\n \"modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save):\",\n \"# @Author : gfjiang import os.path as osp import mmcv\",\n \"i, img in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img)\",\n \"= plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for\",\n \"self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs))\",\n \"in result] result = np.vstack(result) _, ids = nms(result, nms_th)\",\n \"plt import cv2.cv2 as cv from functools import partial import\",\n \"self.cfg = mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth))\",\n \"== '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector =\",\n \"bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img,\",\n \"det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False,\",\n \"module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for i, img\",\n \"nms_th=0.3): dets_num = [len(det_cls) for det_cls in result] result =\",\n \"'%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self,\",\n \"= osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index)) return image_name\",\n \"as np import cvtools import matplotlib.pyplot as plt import cv2.cv2\",\n \"mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth)) self.model =\",\n \"def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model,\",\n \"det_thr] for det, det_thr in zip(result, det_thrs)] if len(result) ==\",\n \"mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr,\",\n \"import partial import torch import math from cvtools.utils.path import add_prefix_filename_suffix\",\n \"get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\"\",\n \"+= 1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name,\",\n \"cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index] for box in\",\n \"of neural network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module)\",\n \"img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) #\",\n \"save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result\",\n \"to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32)\",\n \"@Time : 2021/1/3 15:15 # @Author : gfjiang import os.path\",\n \"if list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05,\",\n \"= mmcv.Config.fromfile(config) self.pth = pth print('loading model {} ...'.format(pth)) self.model\",\n \"in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += '\",\n \"bboxes = bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2))\",\n \"functools import partial import torch import math from cvtools.utils.path import\",\n \"[] for num in dets_num: ids_cls = ids[np.where((total_num <= ids)\",\n \"img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def\",\n \"= osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr,\",\n \"plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image filename for hook\",\n \"cv.COLORMAP_JET) # 生成heat map img = img[:, :, ::-1] #\",\n \"1 image_name = osp.join( work_dir, 'feats', '%s_%d.png' % (base_name, index))\",\n \"= img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename\",\n \"__call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs\",\n \"base_name = module_name.split('(')[0] index = 0 image_name = '.' #\",\n \"in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir)\",\n \"[self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)] img =\",\n \"modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module in self.model.named_modules():\",\n \"top=0.95, wspace=0.05, hspace=0.05) for i in range(height * width): plt.subplot(height,\",\n \"enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for cls_index, dets\",\n \"# os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index\",\n \"+ 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img\",\n \"Parameters: ----------- module: module of neural network \\\"\\\"\\\" # os.makedirs(work_dir,\",\n \"self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func\",\n \"::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir)\",\n \"[det_thrs] * len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file\",\n \"add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis import init_detector, inference_detector\",\n \"- pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) #\",\n \"device='cuda:0') self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d))\",\n \"= [det[det[..., -1] > det_thr] for det, det_thr in zip(result,\",\n \"= np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i,\",\n \"ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return\",\n \"pmin) / (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img\",\n \"fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)\",\n \"total_num += num return nms_result def vis(self, img, bbox_result, vis_thr=0.5,\",\n \"# print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close()\",\n \"ids_cls = ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls])\",\n \"def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image filename for hook function\",\n \"& (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num return nms_result\",\n \"15:15 # @Author : gfjiang import os.path as osp import\",\n \"pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0')\",\n \"self.pth, device='cuda:0') self.results = [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d,\",\n \"vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i,\",\n \"bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height * width):\",\n \"= output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width =\",\n \"loop condition True while osp.exists(image_name): index += 1 image_name =\",\n \"is surely exist, to make first loop condition True while\",\n \"dets_num = [len(det_cls) for det_cls in result] result = np.vstack(result)\",\n \"img, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) #\",\n \"1) plt.axis('off') img = x[0, i, :, :] pmin =\",\n \"img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file)\",\n \"= [] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self,\",\n \"vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5, vis=False, vis_thr=0.5,\",\n \"= ((img - pmin) / (pmax - pmin + 0.000001))*255\",\n \": 2021/1/3 15:15 # @Author : gfjiang import os.path as\",\n \"init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected = [] #\",\n \"labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes\",\n \"转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img =\",\n \"module in self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features,\",\n \"Generate image filename for hook function Parameters: ----------- module: module\",\n \"self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update() def detect(self, img, det_thrs=0.5,\",\n \"osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file)\",\n \"nms_result = [] for num in dets_num: ids_cls = ids[np.where((total_num\",\n \"draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for\",\n \"f.write(str_results) if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file =\",\n \"partial import torch import math from cvtools.utils.path import add_prefix_filename_suffix from\",\n \"self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num =\",\n \"input, output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1] height\",\n \"to_file=to_file) result = [det[det[..., -1] > det_thr] for det, det_thr\",\n \"= pth print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth,\",\n \"range(height * width): plt.subplot(height, width, i + 1) plt.axis('off') img\",\n \"= 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png', vis=True, save_root='work_dirs/attention_vis/') detector.save_results('work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/detect_result.txt')\",\n \"work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = '' for i,\",\n \"float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET)\",\n \"osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result\",\n \"% (base_name, index)) return image_name class AerialDetectionOBB(object): def __init__(self, config,\",\n \"= nms(result, nms_th) total_num = 0 nms_result = [] for\",\n \"dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results += ' '.join([img,\",\n \"in enumerate(self.img_detected): result = self.results[i] img = osp.basename(img) for cls_index,\",\n \"= [] for num in dets_num: ids_cls = ids[np.where((total_num <=\",\n \"import math from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms\",\n \"----------- module: module of neural network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True)\",\n \"for i in range(height * width): plt.subplot(height, width, i +\",\n \"condition True while osp.exists(image_name): index += 1 image_name = osp.join(\",\n \"np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox\",\n \"from cvtools.utils.path import add_prefix_filename_suffix from mmdet.ops import nms from mmdet.apis\",\n \"+= cvtools.get_files_list(imgs_or_path) else: self.imgs += imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for\",\n \"0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return\",\n \"= np.vstack(result) _, ids = nms(result, nms_th) total_num = 0\",\n \"np.max(img) img = ((img - pmin) / (pmax - pmin\",\n \"fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in range(height\",\n \"str_results += ' '.join([img, cls, bbox_str]) + '\\\\n' with open(save,\",\n \"- pmin) / (pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255\",\n \"pth): self.imgs = [] self.cfg = mmcv.Config.fromfile(config) self.pth = pth\",\n \"exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0] index = 0\",\n \"list(output.shape)[2] < 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95,\",\n \"'__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file,\",\n \"bboxes[inds] labels = labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index,\",\n \"print('loading model {} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results\",\n \"inference_detector def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels\",\n \"torch.nn.MaxPool2d)) def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path,\",\n \"len(result) if vis: to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file,\",\n \"[] self.img_detected = [] # self.vis_feats((torch.nn.Conv2d, torch.nn.MaxPool2d)) def __call__(self, imgs_or_path,\",\n \"i, :, :] pmin = np.min(img) pmax = np.max(img) img\",\n \"def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels =\",\n \"vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else:\",\n \"<= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num += num\",\n \"def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes = np.vstack(bbox_result) labels\",\n \"img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map\",\n \"True while osp.exists(image_name): index += 1 image_name = osp.join( work_dir,\",\n \"= cv.applyColorMap(img, cv.COLORMAP_JET) # 生成heat map img = img[:, :,\",\n \"partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self, save): str_results = ''\",\n \"print('detect: image {} has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result\",\n \"network \\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name =\",\n \"box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w =\",\n \"'.' # '.' is surely exist, to make first loop\",\n \"num return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'): bboxes\",\n \"bboxes = np.vstack(bbox_result) labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for\",\n \"work_dir, 'feats', '%s_%d.png' % (base_name, index)) return image_name class AerialDetectionOBB(object):\",\n \"cv2.cv2 as cv from functools import partial import torch import\",\n \"import init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x =\",\n \"mmcv import numpy as np import cvtools import matplotlib.pyplot as\",\n \"2021/1/3 15:15 # @Author : gfjiang import os.path as osp\",\n \"from mmdet.ops import nms from mmdet.apis import init_detector, inference_detector def\",\n \"no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result, nms_th=0.3):\",\n \"zip(result, det_thrs)] if len(result) == 0: print('detect: image {} has\",\n \"self.results.append(result) return result def nms(self, result, nms_th=0.3): dets_num = [len(det_cls)\",\n \"self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected =\",\n \"as plt import cv2.cv2 as cv from functools import partial\",\n \"return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05,\",\n \"-*- encoding:utf-8 -*- # @Time : 2021/1/3 15:15 # @Author\",\n \"self.model.named_modules(): if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func)\",\n \"zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img, bboxes[:, :-1], box_format='polygon', line_width=2)\",\n \"= 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file) detector('/media/data/DOTA/crop/P2701_2926_1597_3949_2620.png',\",\n \"AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs = [] self.cfg =\",\n \"生成heat map img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img)\",\n \"import os.path as osp import mmcv import numpy as np\",\n \": gfjiang import os.path as osp import mmcv import numpy\",\n \"cvtools import matplotlib.pyplot as plt import cv2.cv2 as cv from\",\n \"* width): plt.subplot(height, width, i + 1) plt.axis('off') img =\",\n \"> det_thr] for det, det_thr in zip(result, det_thrs)] if len(result)\",\n \"'\\\\n' with open(save, 'w') as f: f.write(str_results) if __name__ ==\",\n \"= self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str, map(int,\",\n \"i in range(height * width): plt.subplot(height, width, i + 1)\",\n \"# float在[0，1]之间，转换成0-255 img = img.astype(np.uint8) # 转成unit8 img = cv.applyColorMap(img,\",\n \"= osp.basename(img) for cls_index, dets in enumerate(result): cls = self.model.CLASSES[cls_index]\",\n \"total_num = 0 nms_result = [] for num in dets_num:\",\n \"cls = self.model.CLASSES[cls_index] for box in dets: bbox_str = ','.join(map(str,\",\n \"isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def save_results(self,\",\n \"module_name.split('(')[0] index = 0 image_name = '.' # '.' is\",\n \"bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) inds = np.where(bboxes[:,\",\n \"imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs): self.detect(img,\",\n \"def __call__(self, imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str):\",\n \"enumerate(bbox_result) ] labels = np.concatenate(labels) inds = np.where(bboxes[:, -1] >\",\n \"det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path)\",\n \"= '.' # '.' is surely exist, to make first\",\n \"save): str_results = '' for i, img in enumerate(self.img_detected): result\",\n \"2)) for index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts(\",\n \"save_root=''): if isinstance(imgs_or_path, str): self.imgs += cvtools.get_files_list(imgs_or_path) else: self.imgs +=\",\n \"< 128: return fig = plt.figure(figsize=(32, 32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05,\",\n \"32)) fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) for i in\",\n \":-1], box_format='polygon', line_width=2) cvtools.imwrite(img, to_file) def vis_feats(self, modules_for_plot): h, w\",\n \"str_results = '' for i, img in enumerate(self.img_detected): result =\",\n \"import numpy as np import cvtools import matplotlib.pyplot as plt\",\n \"def nms(self, result, nms_th=0.3): dets_num = [len(det_cls) for det_cls in\",\n \"if __name__ == '__main__': config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth'\",\n \"labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in\",\n \"self.nms(result) if isinstance(det_thrs, float): det_thrs = [det_thrs] * len(result) if\",\n \"+= imgs_or_path prog_bar = mmcv.ProgressBar(len(self.imgs)) for _, img in enumerate(self.imgs):\",\n \"result = np.vstack(result) _, ids = nms(result, nms_th) total_num =\",\n \"w = self.cfg.data.train.img_scale for name, module in self.model.named_modules(): if isinstance(module,\",\n \"img = img[:, :, ::-1] # 注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height))\",\n \"surely exist, to make first loop condition True while osp.exists(image_name):\",\n \"map(int, box[:4]))) str_results += ' '.join([img, cls, bbox_str]) + '\\\\n'\",\n \"box in dets: bbox_str = ','.join(map(str, map(int, box[:4]))) str_results +=\",\n \"inds = np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels\",\n \"output.cpu().numpy() out_channels = list(output.shape)[1] height = int(math.sqrt(out_channels)) width = height\",\n \"\\\"\\\"\\\" # os.makedirs(work_dir, exist_ok=True) module_name = str(module) base_name = module_name.split('(')[0]\",\n \"index = 0 image_name = '.' # '.' is surely\",\n \"texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels, bboxes)]\",\n \"with open(save, 'w') as f: f.write(str_results) if __name__ == '__main__':\",\n \"if len(result) == 0: print('detect: image {} has no object.'.format(img))\",\n \"(pmax - pmin + 0.000001))*255 # float在[0，1]之间，转换成0-255 img = img.astype(np.uint8)\",\n \"if isinstance(module, modules_for_plot): draw_features_func = partial( draw_features, work_dir=self.cfg.work_dir) module.register_forward_hook(draw_features_func) def\",\n \"= np.where(bboxes[:, -1] > vis_thr)[0] bboxes = bboxes[inds] labels =\",\n \"len(result) == 0: print('detect: image {} has no object.'.format(img)) self.img_detected.append(img)\",\n \"config_file = 'configs/DOTA/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2.py' pth_file = 'work_dirs/faster_rcnn_RoITrans_r50_fpn_1x_dota_1gpus_mdanet2/epoch_12.pth' detector = AerialDetectionOBB(config_file, pth_file)\",\n \"labels[inds] texts = [self.model.CLASSES[index]+'|'+str(round(bbox[-1], 2)) for index, bbox in zip(labels,\",\n \"hspace=0.05) for i in range(height * width): plt.subplot(height, width, i\",\n \"注意cv2（BGR）和matplotlib(RGB)通道是相反的 plt.imshow(img) # print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100)\",\n \"print(\\\"{}/{}\\\".format(i,width*height)) savename = get_image_name_for_hook(module, work_dir) fig.savefig(savename, dpi=100) fig.clf() plt.close() def\",\n \"for index, bbox in zip(labels, bboxes)] img = cvtools.draw_boxes_texts( img,\",\n \"dpi=100) fig.clf() plt.close() def get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image filename\",\n \"init_detector, inference_detector def draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy()\",\n \"@Author : gfjiang import os.path as osp import mmcv import\",\n \"has no object.'.format(img)) self.img_detected.append(img) self.results.append(result) return result def nms(self, result,\",\n \"to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result, vis_thr=vis_thr, to_file=to_file) result =\",\n \"work_dir='./'): \\\"\\\"\\\" Generate image filename for hook function Parameters: -----------\",\n \"draw_features(module, input, output, work_dir='./'): x = output.cpu().numpy() out_channels = list(output.shape)[1]\",\n \"imgs_or_path, det_thrs=0.5, vis=False, vis_thr=0.5, save_root=''): if isinstance(imgs_or_path, str): self.imgs +=\",\n \"_, img in enumerate(self.imgs): self.detect(img, det_thrs=det_thrs, vis=vis, vis_thr=vis_thr, save_root=save_root) prog_bar.update()\",\n \"' '.join([img, cls, bbox_str]) + '\\\\n' with open(save, 'w') as\",\n \"'' for i, img in enumerate(self.img_detected): result = self.results[i] img\",\n \"matplotlib.pyplot as plt import cv2.cv2 as cv from functools import\",\n \"return image_name class AerialDetectionOBB(object): def __init__(self, config, pth): self.imgs =\",\n \"numpy as np import cvtools import matplotlib.pyplot as plt import\",\n \"width): plt.subplot(height, width, i + 1) plt.axis('off') img = x[0,\",\n \"inference_detector(self.model, img) # result = self.nms(result) if isinstance(det_thrs, float): det_thrs\",\n \"= x[0, i, :, :] pmin = np.min(img) pmax =\",\n \"...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = [] self.img_detected\",\n \"nms from mmdet.apis import init_detector, inference_detector def draw_features(module, input, output,\",\n \"get_image_name_for_hook(module, work_dir='./'): \\\"\\\"\\\" Generate image filename for hook function Parameters:\",\n \"= ids[np.where((total_num <= ids) & (ids < num))[0]] nms_result.append(result[ids_cls]) total_num\",\n \"to_file = osp.join(save_root, osp.basename(img)) to_file = add_prefix_filename_suffix(to_file, suffix='_obb') self.vis(img, result,\",\n \"vis_thr=0.5, save_root=''): result = inference_detector(self.model, img) # result = self.nms(result)\",\n \"def vis_feats(self, modules_for_plot): h, w = self.cfg.data.train.img_scale for name, module\",\n \"+= num return nms_result def vis(self, img, bbox_result, vis_thr=0.5, to_file='vis.jpg'):\",\n \"{} ...'.format(pth)) self.model = init_detector(self.cfg, self.pth, device='cuda:0') self.results = []\"\n]","isConversational":false}}},{"rowIdx":197,"cells":{"ngram":{"kind":"list like","value":["\"\"\"get the margin of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def","dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None):","True elif title == \"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif","~~~~~~~~~~~~~~ This module provides the low level functions with the","movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed window\") def confirm(self):","{ 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try:","low level functions with the service. \"\"\" import time import","fullname: mark_closed_list = [x for x in product.select( \"div.quantity-list-input-wrapper\") if","check_counter=None): \"\"\"search for a res\"\"\" logger.debug(\"searching result\") result = self.get_result(0)","product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product =","mode self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath( path['limit-gain-%s'","= 'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin","= margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return","the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if widg:","name = name.lower() string = string.lower() if counter is None:","self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product","if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0)","position via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message","return num(val) except KeyError as e: raise exceptions.BaseExc(e) def get_price(self,","max_roof: logger.debug(\"success: price corresponding\") # bind mov self.mov = x","mode, value): \"\"\"set limit in movement window\"\"\" self._check_open() if (mode","\"\"\"get unit value of stock based on margin, memoized\"\"\" #","= logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class for stocks\"\"\" def __init__(self,","quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity set\") def get_price(self,","with the service. \"\"\" import time import re from datetime","logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product)","return num(msg_text) except Exception: if msg_text.lower().find(\"higher\") != -1: value +=","launch(self): \"\"\"launch browser and virtual display, first of all to","import BeautifulSoup from splinter import Browser from .glob import Glob","collection\") pip = get_pip(mov=self) quant = 1 / pip if","MovementWindow(object): \"\"\"add movement window\"\"\" def __init__(self, api, product): self.api =","try: logger.debug(f\"visiting %s\" % url) self.browser.visit(url) logger.debug(f\"connected to %s\" %","if it's a weekend if mode == \"demo\" and datetime.now().isoweekday()","the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant: {quant} - pip:","/ quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def","Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except Exception: raise exceptions.BrowserException( self.brow_name, \"failed","level api to interface with the service\"\"\" def __init__(self, brow=\"firefox\"):","Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value found in the collection\") return","margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val","brow self.positions = [] self.movements = [] self.stocks = []","if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res,","'Market Closed'): logger.error(\"market closed, position can't be closed\") raise exceptions.MarketClosed()","closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\") except","self.set_quantity(quant) # update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant:","if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value","= [x for x in self.api.movements if x.product == self.product","mode not in [\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click()","Closed'): logger.error(\"market closed, position can't be closed\") raise exceptions.MarketClosed() raise","else: return False class MovementWindow(object): \"\"\"add movement window\"\"\" def __init__(self,","quant def set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity =","closed, position can't be closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message","limit\") def decode(self, message): \"\"\"decode text pop-up\"\"\" title = self.api.css1(\"div.title\",","get_research_name(self, res): \"\"\"return result name\"\"\" if res is None: return","while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\"failed to","based on margin, memoized\"\"\" # find in the collection try:","if time.time() > timeout: raise TimeoutError(\"failed to close pos %s\"","self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if","result name\"\"\" if res is None: return None return self.api.css1(\"span.instrument-name\",","datetime import datetime from pyvirtualdisplay import Display from bs4 import","== \"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self,","return self.records class Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self, product, quantity,","= int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self, quant):","if all(x for x in ['quantity', 'mode'] if hasattr(self, x)):","timeout = time.time() + 10 while not self.elCss(path['alert-box']): if time.time()","self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set mode (buy or","css_path, dom=None): \"\"\"css find function abbreviation\"\"\" if dom is None:","x in self.api.movements if x.product == self.product and x.quantity ==","self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state = 'open' def _check_open(self):","Glob from .links import path from .utils import num, expect,","logger.debug(f\"connected to %s\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\")","# init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\"virtual display","return value else: self.decode(message) return None def get_mov_margin(self): \"\"\"get the","Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \"\"\"factory method pattern\"\"\"","break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\") except Exception","= [] def new_rec(self, rec): \"\"\"add a record\"\"\" self.records.append(rec) return","from .glob import Glob from .links import path from .utils","in string: return True else: return False counter = counter.lower()","info_label = accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\" % info_label).text return","self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \"\"\"check if","be closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait","== \"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum","exceptions.WindowException() def close(self): \"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state =","= num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return gain def bind_mov(self): \"\"\"bind","as {username}\") # check if it's a weekend if mode","counter is None: if name in string: return True else:","' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low level","if mode not in ['buy', 'sell']: raise ValueError() self._check_open() price","float(sell_price) else: return False class MovementWindow(object): \"\"\"add movement window\"\"\" def","[x for x in product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") !=","= 1 / pip if hasattr(self, 'quantity'): old_quant == self.quantity","self.positions = [] self.movements = [] self.stocks = [] #","self.api.css1(\"div.text\", message).text if title == \"Insufficient Funds\": self.insfu = True","return pos_id @property def close_tag(self): \"\"\"obtain close tag\"\"\" return f\"#{self.id}","element is present by css\"\"\" if dom is None: dom","is None: dom = self.browser def _css1(path, domm): \"\"\"virtual local","current price\"\"\" if mode not in ['buy', 'sell']: raise ValueError()","\"failed to launch\") return True def css(self, css_path, dom=None): \"\"\"css","\"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if","if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data =","given data\"\"\" pos_id = self.soup_data['id'] self.id = pos_id return pos_id","\"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list: sell_price =","\"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price = price return price def","except KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name): soup","closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until","new_rec(self, rec): \"\"\"add a record\"\"\" self.records.append(rec) return self.records class Movement(object):","corresponding\") # bind mov self.mov = x return x else:","\"\"\"low level api to interface with the service\"\"\" def __init__(self,","False def open(self, name_counter=None): \"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click()","open(self, name_counter=None): \"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click()","path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in","dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def","unit_value[self.product] logger.debug(\"unit_value found in the collection\") return unit_value_res except KeyError:","- '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low level api","def update(self, soup): \"\"\"update the soup\"\"\" self.soup_data = soup return","div.tradebox-price-%s\" % mode).text) self.price = price return price def get_unit_value(self):","# logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base","self.get_mov_margin() logger.debug(f\"quant: {quant} - pip: {pip} - margin: {margin}\") if","= 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id =","close(self): \"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed","stop_limit\") if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif","position %d\" % (x + 1)) return result, name def","= logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for x in self.api.movements if","product self.state = 'initialized' self.insfu = False def open(self, name_counter=None):","e: logger.critical(\"login failed\") raise exceptions.BaseExc(e) return True def logout(self): \"\"\"logout","hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s movement appended","self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state","__init__(self, brow=\"firefox\"): self.brow_name = brow self.positions = [] self.movements =","pip: {pip} - margin: {margin}\") if 'old_quant' in locals(): self.set_quantity(old_quant)","= x return x else: logger.debug(\"fail: price %f not corresponding","close(self): \"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException:","confirm(self): \"\"\"confirm the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\")","1}]\" try: res = self.api.xpath(evalxpath)[0] return res except Exception: return","in the collection\") return unit_value_res except KeyError: logger.debug(\"unit_value not found","present by css\"\"\" if dom is None: dom = self.browser","Browser from .glob import Glob from .links import path from","= \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" % url) self.browser.visit(url) logger.debug(f\"connected to","tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level functions with","xpath, dom=None): \"\"\"check if element is present by css\"\"\" if","of all to be launched\"\"\" try: # init virtual Display","value): \"\"\"set limit in movement window\"\"\" self._check_open() if (mode not","time.time() + 30 while not self.elCss(path['logo']): if time.time() > timeout:","self.mode = 'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text)","= quant return quant def set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open()","= self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \"\"\"check","display, first of all to be launched\"\"\" try: # init","return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\"demo\"): \"\"\"login function\"\"\"","\"\"\"get pos result, where 0 is first\"\"\" evalxpath = path['res']","BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity","product at position %d\" % (x + 1)) return result,","for x in mov_list: # find approximate price max_roof =","is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self,","if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}}","def _check_open(self): if self.state == 'open': return True else: raise","import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class for","sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else: return False class","self.price * 0.01 min_roof = self.price - self.price * 0.01","def elCss(self, css_path, dom=None): \"\"\"check if element is present by","= self.get_mov_margin() logger.debug(f\"quant: {quant} - pip: {pip} - margin: {margin}\")","return False class MovementWindow(object): \"\"\"add movement window\"\"\" def __init__(self, api,","raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self, message, value, mult=0.1): \"\"\"decode","name in string and counter not in string: return True","name, dom=None): \"\"\"name find function abbreviation\"\"\" if dom is None:","info_label).text return num(val) except KeyError as e: raise exceptions.BaseExc(e) def","self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state = 'open' def _check_open(self): if","quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant","* 0.01 min_roof = self.price - self.price * 0.01 if","logger.critical(\"login failed\") raise exceptions.BaseExc(e) return True def logout(self): \"\"\"logout func","exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for","self.soup_data = soup return soup def find_id(self): \"\"\"find pos ID","exceptions.BaseExc(e) return True def logout(self): \"\"\"logout func (quit browser)\"\"\" try:","limit in movement window\"\"\" self._check_open() if (mode not in [\"unit\",","self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s movement appended to the","unit value of stock based on margin, memoized\"\"\" # find","title == \"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def","expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \"\"\"xpath find function abbreviation\"\"\"","args=[css_path]) def css1(self, css_path, dom=None): \"\"\"return the first value of","launched\") except Exception: raise exceptions.BrowserException( self.brow_name, \"failed to launch\") return","self.market = True self.records = [] def new_rec(self, rec): \"\"\"add","time import re from datetime import datetime from pyvirtualdisplay import","datetime from pyvirtualdisplay import Display from bs4 import BeautifulSoup from","if name in string and counter in string: logger.debug(\"check_name: counter","= num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def update(self, soup): \"\"\"update the","time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\"failed to close pos","in string\") return False elif name in string and counter","splinter import Browser from .glob import Glob from .links import","raise exceptions.WindowException() def close(self): \"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state","get_unit_value(self): \"\"\"get unit value of stock based on margin, memoized\"\"\"","self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def update(self, soup): \"\"\"update","= mode self.price = price def __repr__(self): return ' -","= 0 while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x))","Exception as e: logger.critical(\"login failed\") raise exceptions.BaseExc(e) return True def","self.vbro.stop() logger.info(\"logged out\") return True def get_bottom_info(self, info): accepted_values =","mode not in ['buy', 'sell']: raise ValueError() self._check_open() price =","%f\" % (self.price, x.price)) continue # if nothing, return None","'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath(","unit_value_res except KeyError: logger.debug(\"unit_value not found in the collection\") pip","= brow self.positions = [] self.movements = [] self.stocks =","mode == \"demo\" and datetime.now().isoweekday() in range(5, 8): timeout =","None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath,","timeout for logging in timeout = time.time() + 30 while","the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set mode","in as {username}\") # check if it's a weekend if","[] def new_rec(self, rec): \"\"\"add a record\"\"\" self.records.append(rec) return self.records","try: self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except Exception: raise","= self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name)","path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] =","logger.debug(\"closed pos %s\" % self.id) def get_gain(self): \"\"\"get current profit\"\"\"","= api self.product = product self.state = 'initialized' self.insfu =","* 0.01 if min_roof < x.price < max_roof: logger.debug(\"success: price","self.quantity = quant return quant def set_quantity(self, quant): \"\"\"set quantity\"\"\"","can't be closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) #","self.movements = [] self.stocks = [] # init globals Glob()","'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s","string and counter in string: logger.debug(\"check_name: counter found in string\")","is None: return None return self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos):","'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id()","movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set mode (buy","+ f\"[{pos + 1}]\" try: res = self.api.xpath(evalxpath)[0] return res","fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x","expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \"\"\"return the first value","unit_val = margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val})","self.state = 'initialized' self.insfu = False def open(self, name_counter=None): \"\"\"open","class Stock(object): \"\"\"base class for stocks\"\"\" def __init__(self, product): self.product","it's a weekend if mode == \"demo\" and datetime.now().isoweekday() in","def __init__(self, brow=\"firefox\"): self.brow_name = brow self.positions = [] self.movements","method pattern\"\"\" return self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position object\"\"\" def","'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" %","['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\"","# update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant: {quant}","return x else: logger.debug(\"fail: price %f not corresponding to %f\"","price max_roof = self.price + self.price * 0.01 min_roof =","if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s movement","accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\" % info_label).text return num(val) except","\"\"\" import time import re from datetime import datetime from","self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \"\"\"return the","raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\")","\"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed window\")","get_pip(mov=self) quant = 1 / pip if hasattr(self, 'quantity'): old_quant","False elif name in string and counter not in string:","in string: logger.debug(\"check_name: counter found in string\") return False elif","self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a timeout for logging in","'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info]","self.mode = mode logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte current quantity\"\"\"","sell)\"\"\" self._check_open() if mode not in [\"buy\", \"sell\"]: raise ValueError()","mode, price): self.product = product self.quantity = quantity self.mode =","as e: logger.critical(\"login failed\") raise exceptions.BaseExc(e) return True def logout(self):","True def css(self, css_path, dom=None): \"\"\"css find function abbreviation\"\"\" if","value\"\"\" try: msg_text = self.api.css1(\"div.text\", message).text return num(msg_text) except Exception:","= 'closed' logger.debug(\"closed window\") def confirm(self): \"\"\"confirm the movement\"\"\" self._check_open()","= self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product","num(val) except KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name):","int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self, quant): \"\"\"set","logger.info(\"logged out\") return True def get_bottom_info(self, info): accepted_values = {","self.css\"\"\" if dom is None: dom = self.browser def _css1(path,","x.product == self.product and x.quantity == self.quantity and x.mode ==","= num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def update(self,","try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value found in","'open': return True else: raise exceptions.WindowException() def close(self): \"\"\"close a","self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def","self.api = api self.product = product self.state = 'initialized' self.insfu","for logging in timeout = time.time() + 30 while not","if name in string: return True else: return False counter","the service. \"\"\" import time import re from datetime import","domm): \"\"\"virtual local func\"\"\" return self.css(path, domm)[0] return expect(_css1, args=[css_path,","return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \"\"\"check if element","api self.product = product self.state = 'initialized' self.insfu = False","price corresponding\") # bind mov self.mov = x return x","try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a timeout for logging","self.price)) logger.debug(\"%s movement appended to the list\" % self.product) self.state","Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self, message, value, mult=0.1):","if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the","name.lower() in fullname: mark_closed_list = [x for x in product.select(","not found\") return None else: logger.debug(\"success: found movement\") for x","self.vbro.start() logger.debug(\"virtual display launched\") except Exception: raise exceptions.VBroException() try: self.browser","bind_mov(self): \"\"\"bind the corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list =","return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \"\"\"return the first","True self.records = [] def new_rec(self, rec): \"\"\"add a record\"\"\"","window\"\"\" def __init__(self, api, product): self.api = api self.product =","is None: if name in string: return True else: return","< max_roof: logger.debug(\"success: price corresponding\") # bind mov self.mov =","value of self.css\"\"\" if dom is None: dom = self.browser","\"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return","pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update","\"\"\"virtual local func\"\"\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom])","\"\"\"base class for stocks\"\"\" def __init__(self, product): self.product = product","url) self.browser.visit(url) logger.debug(f\"connected to %s\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection","self.get_result(x) x += 1 logger.debug(\"found product at position %d\" %","\"\"\"css find function abbreviation\"\"\" if dom is None: dom =","if self.state == 'open': return True else: raise exceptions.WindowException() def","a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed window\") def","url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" % url) self.browser.visit(url) logger.debug(f\"connected","\"\"\"name find function abbreviation\"\"\" if dom is None: dom =","= True elif title == \"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text))","def __init__(self, api, html_div): \"\"\"initialized from div\"\"\" self.api = api","self.state == 'open': return True else: raise exceptions.WindowException() def close(self):","css_path, dom=None): \"\"\"return the first value of self.css\"\"\" if dom","cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value","- self.price * 0.01 if min_roof < x.price < max_roof:","in product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list:","pos %s\" % self.id) logger.debug(\"closed pos %s\" % self.id) def","if mode == \"demo\" and datetime.now().isoweekday() in range(5, 8): timeout","unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \"\"\"factory method","self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat","position\"\"\" def __init__(self, product, quantity, mode, price): self.product = product","def search_res(self, res, check_counter=None): \"\"\"search for a res\"\"\" logger.debug(\"searching result\")","exceptions from tradingAPI import exceptions import selenium.common.exceptions # logging import","func\"\"\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self,","import selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level') class","timed out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a","abbreviation\"\"\" if dom is None: dom = self.browser return expect(dom.find_by_css,","record\"\"\" self.records.append(rec) return self.records class Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self,","self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\", message).text if title == \"Insufficient","api, html_div): \"\"\"initialized from div\"\"\" self.api = api if isinstance(html_div,","tradingAPI import exceptions import selenium.common.exceptions # logging import logging logger","self.price + self.price * 0.01 min_roof = self.price - self.price","in [\"gain\", \"loss\", \"both\"]): raise ValueError() if not hasattr(self, 'stop_limit'):","= mode logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open()","try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \"not started\") return False","= self.css1(\"div#%s span.equity-item-value\" % info_label).text return num(val) except KeyError as","self.product = product self.quantity = quantity self.mode = mode self.price","return True else: return False def get_research_name(self, res): \"\"\"return result","failed\") raise exceptions.BaseExc(e) return True def logout(self): \"\"\"logout func (quit","html_div): \"\"\"factory method pattern\"\"\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos)","'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\" %","self.product) self.state = 'conclused' logger.debug(\"confirmed movement\") def search_res(self, res, check_counter=None):","if title == \"Insufficient Funds\": self.insfu = True elif title","= self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \"\"\"check","margin: {margin}\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin","= Display() self.vbro.start() logger.debug(\"virtual display launched\") except Exception: raise exceptions.VBroException()","define a timeout for logging in timeout = time.time() +","self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode =","message\") def decode_update(self, message, value, mult=0.1): \"\"\"decode and update the","set_limit(self, category, mode, value): \"\"\"set limit in movement window\"\"\" self._check_open()","== 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value))","= {'gain': {}, 'loss': {}} logger.debug(\"initialized stop_limit\") if category ==","nothing, return None return None def new_pos(self, html_div): \"\"\"factory method","div.close-icon\" def close(self): \"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click()","is None: dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self,","class Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self, product, quantity, mode, price):","update(self, soup): \"\"\"update the soup\"\"\" self.soup_data = soup return soup","exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been closed #","dom is None: dom = self.browser def _css1(path, domm): \"\"\"virtual","abbreviation\"\"\" if dom is None: dom = self.browser return expect(dom.find_by_xpath,","display launched\") except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name)",".utils import num, expect, get_pip # exceptions from tradingAPI import","where 0 is first\"\"\" evalxpath = path['res'] + f\"[{pos +","name, string, counter=None): \"\"\"if both in string return False\"\"\" name","return self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position object\"\"\" def __init__(self, api,","'closed' logger.debug(\"closed window\") def confirm(self): \"\"\"confirm the movement\"\"\" self._check_open() self.get_price()","ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\"mode set\") def","functions with the service. \"\"\" import time import re from","a res\"\"\" logger.debug(\"searching result\") result = self.get_result(0) name = self.get_research_name(result)","self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin = self.get_mov_margin()","'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value","if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else: self.mode =","1 / pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant)","movement\") def search_res(self, res, check_counter=None): \"\"\"search for a res\"\"\" logger.debug(\"searching","in fullname: mark_closed_list = [x for x in product.select( \"div.quantity-list-input-wrapper\")","api to interface with the service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name","pip = get_pip(mov=self) quant = 1 / pip if hasattr(self,","closed # set a timeout timeout = time.time() + 10","class LowLevelAPI(object): \"\"\"low level api to interface with the service\"\"\"","\"not started\") return False self.vbro.stop() logger.info(\"logged out\") return True def","logger.debug(\"set limit\") def decode(self, message): \"\"\"decode text pop-up\"\"\" title =","def decode_update(self, message, value, mult=0.1): \"\"\"decode and update the value\"\"\"","self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed window\") def confirm(self): \"\"\"confirm","'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\"set limit\") def","and counter not in string: return True else: return False","dom=None): \"\"\"check if element is present by css\"\"\" if dom","return self.get_result(x) x += 1 logger.debug(\"found product at position %d\"","name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in soup.select(\"div.tradebox\"):","self.product = product self.market = True self.records = [] def","% info_label).text return num(val) except KeyError as e: raise exceptions.BaseExc(e)","not in [\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode","name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name,","from splinter import Browser from .glob import Glob from .links","raise TimeoutError(\"failed to close pos %s\" % self.id) logger.debug(\"closed pos","gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return gain def bind_mov(self):","True else: raise exceptions.WindowException() def close(self): \"\"\"close a movement\"\"\" self._check_open()","= unit_value[self.product] logger.debug(\"unit_value found in the collection\") return unit_value_res except","quant = 1 / pip if hasattr(self, 'quantity'): old_quant ==","on margin, memoized\"\"\" # find in the collection try: unit_value","% mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value))","pop-up\"\"\" title = self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\", message).text if","logger.debug(\"fail: price %f not corresponding to %f\" % (self.price, x.price))","x in product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if","= False def open(self, name_counter=None): \"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible:","soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname: mark_closed_list =","_css1(path, domm): \"\"\"virtual local func\"\"\" return self.css(path, domm)[0] return expect(_css1,","= 'open' def _check_open(self): if self.state == 'open': return True","None: if name in string: return True else: return False","css_path, dom=None): \"\"\"check if element is present by css\"\"\" if","BeautifulSoup from splinter import Browser from .glob import Glob from","the collection\") return unit_value_res except KeyError: logger.debug(\"unit_value not found in","0 while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if","class PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self, product, quantity, mode, price):","string = string.lower() if counter is None: if name in","mode (buy or sell)\"\"\" self._check_open() if mode not in [\"buy\",","= mode self.stop_limit[cat]['value'] = value logger.debug(\"set limit\") def decode(self, message):","if dom is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path])","self.api.css1('.widget_message div.text').text) # wait until it's been closed # set","logger.debug(\"searching result\") result = self.get_result(0) name = self.get_research_name(result) x =","'loss': {}} logger.debug(\"initialized stop_limit\") if category == 'gain': self.api.xpath( path['limit-gain-%s'","= unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \"\"\"factory","self.id) def get_gain(self): \"\"\"get current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain","def get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity","False counter = counter.lower() if name in string and counter","logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class","launched\") except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\"browser","trading alert-box closed\") except Exception as e: logger.critical(\"login failed\") raise","num(msg_text) except Exception: if msg_text.lower().find(\"higher\") != -1: value += value","\"\"\"get current price\"\"\" if mode not in ['buy', 'sell']: raise","None else: logger.debug(\"success: found movement\") for x in mov_list: #","new_mov(self, name): \"\"\"factory method pattern\"\"\" return self.MovementWindow(self, name) class Position(PurePosition):","close pos %s\" % self.id) logger.debug(\"closed pos %s\" % self.id)","< x.price < max_roof: logger.debug(\"success: price corresponding\") # bind mov","value of stock based on margin, memoized\"\"\" # find in","x in mov_list: # find approximate price max_roof = self.price","corresponding to %f\" % (self.price, x.price)) continue # if nothing,","from bs4 import BeautifulSoup from splinter import Browser from .glob","api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data","appended to the list\" % self.product) self.state = 'conclused' logger.debug(\"confirmed","name in string: return True else: return False counter =","found in the collection\") return unit_value_res except KeyError: logger.debug(\"unit_value not","quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self,","expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \"\"\"check if element is","self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def","dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None):","logger.debug(\"confirmed movement\") def search_res(self, res, check_counter=None): \"\"\"search for a res\"\"\"","1 logger.debug(\"found product at position %d\" % (x + 1))","counter in string: logger.debug(\"check_name: counter found in string\") return False","pos result, where 0 is first\"\"\" evalxpath = path['res'] +","== \"Insufficient Funds\": self.insfu = True elif title == \"Maximum","self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x)","import path from .utils import num, expect, get_pip # exceptions","and x.quantity == self.quantity and x.mode == self.mode] if not","logger.debug(\"success: price corresponding\") # bind mov self.mov = x return","exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode'] if hasattr(self,","self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity set\") def get_price(self, mode='buy'):","launch\") return True def css(self, css_path, dom=None): \"\"\"css find function","path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' %","\"\"\"factory method pattern\"\"\" return self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position object\"\"\"","product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else: return False class MovementWindow(object): \"\"\"add","result = self.get_result(0) name = self.get_research_name(result) x = 0 while","not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\"weekend trading alert-box not","import datetime from pyvirtualdisplay import Display from bs4 import BeautifulSoup","raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except","self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg)","class MovementWindow(object): \"\"\"add movement window\"\"\" def __init__(self, api, product): self.api","\"\"\"check if element is present by css\"\"\" if dom is","in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode,","quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity set\")","logger.debug(\"initialized stop_limit\") if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value))","not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is","[\"gain\", \"loss\", \"both\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit","movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for x in","return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \"\"\"name find","!= 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category","product self.quantity = quantity self.mode = mode self.price = price","mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if","res except Exception: return None def set_limit(self, category, mode, value):","\"\"\"decode and update the value\"\"\" try: msg_text = self.api.css1(\"div.text\", message).text","except Exception: if msg_text.lower().find(\"higher\") != -1: value += value *","res, check_counter=None): \"\"\"search for a res\"\"\" logger.debug(\"searching result\") result =","def css1(self, css_path, dom=None): \"\"\"return the first value of self.css\"\"\"","gain def bind_mov(self): \"\"\"bind the corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\")","% mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain',","selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() #","x.mode == self.mode] if not mov_list: logger.debug(\"fail: mov not found\")","logger.debug(\"check_name: counter found in string\") return False elif name in","via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text","= self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\", message).text if title ==","* mult return value else: self.decode(message) return None def get_mov_margin(self):","KeyError: logger.debug(\"unit_value not found in the collection\") pip = get_pip(mov=self)",".text return float(sell_price) else: return False class MovementWindow(object): \"\"\"add movement","path from .utils import num, expect, get_pip # exceptions from","movement\"\"\" def __init__(self, product, quantity, mode, price): self.product = product","(mode not in [\"unit\", \"value\"] or category not in [\"gain\",","def _css1(path, domm): \"\"\"virtual local func\"\"\" return self.css(path, domm)[0] return","in ['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \"div.orderdialog","PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self, product, quantity, mode, price): self.product","ValueError() self._check_open() price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price","in string and counter not in string: return True else:","price def get_unit_value(self): \"\"\"get unit value of stock based on","'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val =","close tag\"\"\" return f\"#{self.id} div.close-icon\" def close(self): \"\"\"close position via","def __init__(self, product, quantity, mode, price): self.product = product self.quantity","\"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity set\") def","= time.time() + 10 while not self.elCss(path['alert-box']): if time.time() >","raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's","expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \"\"\"name find function","def xpath(self, xpath, dom=None): \"\"\"xpath find function abbreviation\"\"\" if dom","num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def update(self, soup):","text = self.api.css1(\"div.text\", message).text if title == \"Insufficient Funds\": self.insfu","return None else: logger.debug(\"success: found movement\") for x in mov_list:","self.api.xpath(evalxpath)[0] return res except Exception: return None def set_limit(self, category,","{'gain': {}, 'loss': {}} logger.debug(\"initialized stop_limit\") if category == 'gain':","self._check_open() price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price =","+ 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise","value logger.debug(\"set limit\") def decode(self, message): \"\"\"decode text pop-up\"\"\" title","# check if it's a weekend if mode == \"demo\"","for a res\"\"\" logger.debug(\"searching result\") result = self.get_result(0) name =","price\"\"\" if mode not in ['buy', 'sell']: raise ValueError() self._check_open()","if mode not in [\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode +","self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position object\"\"\" def __init__(self, api, html_div):","= pos_id return pos_id @property def close_tag(self): \"\"\"obtain close tag\"\"\"","if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category","\"\"\"initialized from div\"\"\" self.api = api if isinstance(html_div, type('')): self.soup_data","to %s\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise","logger.debug(\"%s movement appended to the list\" % self.product) self.state =","if dom is None: dom = self.browser def _css1(path, domm):","a timeout for logging in timeout = time.time() + 30","in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product]","def get_mov_margin(self): \"\"\"get the margin of the movement\"\"\" self._check_open() return","self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s movement appended to the list\"","% (x + 1)) return result, name def check_name(self, name,","bind mov self.mov = x return x else: logger.debug(\"fail: price","'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin =","not in ['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1(","-*- coding: utf-8 -*- \"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides","\"\"\"return result name\"\"\" if res is None: return None return","dom is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def","time.time() > timeout: logger.warning(\"weekend trading alert-box not closed\") break if","or category not in [\"gain\", \"loss\", \"both\"]): raise ValueError() if","found movement\") for x in mov_list: # find approximate price","to launch\") return True def css(self, css_path, dom=None): \"\"\"css find","\"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level functions","range(5, 8): timeout = time.time() + 10 while not self.elCss(path['alert-box']):","import Browser from .glob import Glob from .links import path","num(self.soup_data.select(\"td.margin\")[0].text) self.id = self.find_id() def update(self, soup): \"\"\"update the soup\"\"\"","== 'open': return True else: raise exceptions.WindowException() def close(self): \"\"\"close","self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product,","found in the collection\") pip = get_pip(mov=self) quant = 1","logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class for stocks\"\"\" def __init__(self, product):","def set_limit(self, category, mode, value): \"\"\"set limit in movement window\"\"\"","self.product = self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']):","the corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for","mode='buy'): \"\"\"get current price\"\"\" if mode not in ['buy', 'sell']:","= 'initialized' self.insfu = False def open(self, name_counter=None): \"\"\"open the","val = self.css1(\"div#%s span.equity-item-value\" % info_label).text return num(val) except KeyError","+ self.price * 0.01 min_roof = self.price - self.price *","name def check_name(self, name, string, counter=None): \"\"\"if both in string","set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity","in timeout = time.time() + 30 while not self.elCss(path['logo']): if","with with given data\"\"\" pos_id = self.soup_data['id'] self.id = pos_id","value elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath(","logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1","self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\"failed to close","if res is None: return None return self.api.css1(\"span.instrument-name\", res).text def","= BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\"td.name\")[0].text","def __init__(self, product): self.product = product self.market = True self.records","= price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode),","self.product = product self.state = 'initialized' self.insfu = False def","function\"\"\" url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" % url) self.browser.visit(url)","'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for","\"\"\"launch browser and virtual display, first of all to be","= self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode","# define a timeout for logging in timeout = time.time()","= counter.lower() if name in string and counter in string:","x = 0 while not self.check_name(res, name, counter=check_counter): name =","object\"\"\" def __init__(self, api, html_div): \"\"\"initialized from div\"\"\" self.api =","return True else: raise exceptions.WindowException() def close(self): \"\"\"close a movement\"\"\"","in mov_list: # find approximate price max_roof = self.price +","x.price < max_roof: logger.debug(\"success: price corresponding\") # bind mov self.mov","if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state = 'open' def","self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\"closed window\") def confirm(self): \"\"\"confirm the","as e: raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup(","css1(self, css_path, dom=None): \"\"\"return the first value of self.css\"\"\" if","args=[css_path, dom]) def search_name(self, name, dom=None): \"\"\"name find function abbreviation\"\"\"","import re from datetime import datetime from pyvirtualdisplay import Display","-1] if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else:","'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label","if x.product == self.product and x.quantity == self.quantity and x.mode","def find_id(self): \"\"\"find pos ID with with given data\"\"\" pos_id","in [\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode =","service. \"\"\" import time import re from datetime import datetime","window\") def confirm(self): \"\"\"confirm the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg","until it's been closed # set a timeout timeout =","the low level functions with the service. \"\"\" import time","def search_name(self, name, dom=None): \"\"\"name find function abbreviation\"\"\" if dom","self.mode] if not mov_list: logger.debug(\"fail: mov not found\") return None","the value\"\"\" try: msg_text = self.api.css1(\"div.text\", message).text return num(msg_text) except","= self.api.xpath(evalxpath)[0] return res except Exception: return None def set_limit(self,","self.records class Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self, product, quantity, mode,","(self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\"market closed, position can't be","in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell' self.price","self.price - self.price * 0.01 if min_roof < x.price <","exceptions.BrowserException(self.brow_name, \"not started\") return False self.vbro.stop() logger.info(\"logged out\") return True","and datetime.now().isoweekday() in range(5, 8): timeout = time.time() + 10","level functions with the service. \"\"\" import time import re","f\"[{pos + 1}]\" try: res = self.api.xpath(evalxpath)[0] return res except","quant return quant def set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant)))","mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] =","try: # init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\"virtual","self.set_quantity(old_quant) unit_val = margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product:","string: return True else: return False counter = counter.lower() if","soup def find_id(self): \"\"\"find pos ID with with given data\"\"\"","== self.quantity and x.mode == self.mode] if not mov_list: logger.debug(\"fail:","def confirm(self): \"\"\"confirm the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg =","logger.debug(f\"quant: {quant} - pip: {pip} - margin: {margin}\") if 'old_quant'","time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant: {quant} - pip: {pip} -","except Exception as e: logger.critical(\"login failed\") raise exceptions.BaseExc(e) return True","category, mode, value): \"\"\"set limit in movement window\"\"\" self._check_open() if","- margin: {margin}\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val =","get_gain(self): \"\"\"get current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain","domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \"\"\"name","if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in","def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class","not in string: return True else: return False def get_research_name(self,","\"\"\"decode text pop-up\"\"\" title = self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\",","unit_val}) return unit_val def new_mov(self, name): \"\"\"factory method pattern\"\"\" return","product self.state = 'open' def _check_open(self): if self.state == 'open':","self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode']","product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list: sell_price","x.price)) continue # if nothing, return None return None def","get_result(self, pos): \"\"\"get pos result, where 0 is first\"\"\" evalxpath","def get_unit_value(self): \"\"\"get unit value of stock based on margin,","None: dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath,","= gain return gain def bind_mov(self): \"\"\"bind the corresponding movement\"\"\"","0.01 if min_roof < x.price < max_roof: logger.debug(\"success: price corresponding\")","elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s'","ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss':","close_tag(self): \"\"\"obtain close tag\"\"\" return f\"#{self.id} div.close-icon\" def close(self): \"\"\"close","username, password, mode=\"demo\"): \"\"\"login function\"\"\" url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting","string: return True else: return False def get_research_name(self, res): \"\"\"return","self.brow_name = brow self.positions = [] self.movements = [] self.stocks","for x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product,","dom=None): \"\"\"xpath find function abbreviation\"\"\" if dom is None: dom","raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x","\"\"\"add a record\"\"\" self.records.append(rec) return self.records class Movement(object): \"\"\"class-storing movement\"\"\"","num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else: self.mode","from .links import path from .utils import num, expect, get_pip","'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the site time.sleep(0.5)","\"\"\"gte current quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant","is None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self,","# wait until it's been closed # set a timeout","selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object):","self.price = price class PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self, product,","self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if","dom = self.browser def _css1(path, domm): \"\"\"virtual local func\"\"\" return","exceptions import selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level')","get_price(self, mode='buy'): \"\"\"get current price\"\"\" if mode not in ['buy',","mult return value else: self.decode(message) return None def get_mov_margin(self): \"\"\"get","# exceptions from tradingAPI import exceptions import selenium.common.exceptions # logging","res is None: return None return self.api.css1(\"span.instrument-name\", res).text def get_result(self,","unit_value_res = unit_value[self.product] logger.debug(\"unit_value found in the collection\") return unit_value_res","- pip: {pip} - margin: {margin}\") if 'old_quant' in locals():","pos_id @property def close_tag(self): \"\"\"obtain close tag\"\"\" return f\"#{self.id} div.close-icon\"","else: self.decode(message) return None def get_mov_margin(self): \"\"\"get the margin of","result, where 0 is first\"\"\" evalxpath = path['res'] + f\"[{pos","def set_mode(self, mode): \"\"\"set mode (buy or sell)\"\"\" self._check_open() if","soup): \"\"\"update the soup\"\"\" self.soup_data = soup return soup def","-*- \"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level","self.soup_data['id'] self.id = pos_id return pos_id @property def close_tag(self): \"\"\"obtain","in range(5, 8): timeout = time.time() + 10 while not","xpath(self, xpath, dom=None): \"\"\"xpath find function abbreviation\"\"\" if dom is","self.quantity and x.mode == self.mode] if not mov_list: logger.debug(\"fail: mov","\"\"\"search for a res\"\"\" logger.debug(\"searching result\") result = self.get_result(0) name","raise exceptions.BrowserException( self.brow_name, \"failed to launch\") return True def css(self,","raise exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text))","logger.debug(\"found product at position %d\" % (x + 1)) return","'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\"initialized stop_limit\") if","logger.debug(\"success: found movement\") for x in mov_list: # find approximate","if (mode not in [\"unit\", \"value\"] or category not in","return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \"\"\"xpath find function","position can't be closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text)","set\") def get_price(self, mode='buy'): \"\"\"get current price\"\"\" if mode not","= quantity self.mode = mode self.price = price class PurePosition(object):","'open' def _check_open(self): if self.state == 'open': return True else:","(x + 1)) return result, name def check_name(self, name, string,","if dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath])","{}} logger.debug(\"initialized stop_limit\") if category == 'gain': self.api.xpath( path['limit-gain-%s' %","\"\"\"get current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return","if counter is None: if name in string: return True","to interface with the service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name =","self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\"mode set\") def get_quantity(self):","tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text ==","div.text').text) # wait until it's been closed # set a","res): \"\"\"return result name\"\"\" if res is None: return None","counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise","+ 1)) return result, name def check_name(self, name, string, counter=None):","self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] =","while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\"login failed\") raise","Stock(object): \"\"\"base class for stocks\"\"\" def __init__(self, product): self.product =","result\") result = self.get_result(0) name = self.get_research_name(result) x = 0","elif title == \"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\")","= BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in soup.select(\"div.tradebox\"): fullname =","'conclused' logger.debug(\"confirmed movement\") def search_res(self, res, check_counter=None): \"\"\"search for a","[] # init globals Glob() def launch(self): \"\"\"launch browser and","= product self.state = 'open' def _check_open(self): if self.state ==","old_quant == self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin","corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for x","\"\"\"login function\"\"\" url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" % url)","dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password,","[] self.movements = [] self.stocks = [] # init globals","self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name):","not found in the collection\") pip = get_pip(mov=self) quant =","import Glob from .links import path from .utils import num,","window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if","product self.market = True self.records = [] def new_rec(self, rec):","if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else: return","not in [\"gain\", \"loss\", \"both\"]): raise ValueError() if not hasattr(self,","def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total',","dom is None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def","init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\"virtual display launched\")","text pop-up\"\"\" title = self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\", message).text","def launch(self): \"\"\"launch browser and virtual display, first of all","check_name(self, name, string, counter=None): \"\"\"if both in string return False\"\"\"","mov not found\") return None else: logger.debug(\"success: found movement\") for","res).text def get_result(self, pos): \"\"\"get pos result, where 0 is","logger.debug(\"weekend trading alert-box closed\") except Exception as e: logger.critical(\"login failed\")","product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x for x","self.records.append(rec) return self.records class Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self, product,","args=[xpath]) def elCss(self, css_path, dom=None): \"\"\"check if element is present","def open(self, name_counter=None): \"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else:","= get_pip(mov=self) quant = 1 / pip if hasattr(self, 'quantity'):","is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter)","mov_list: logger.debug(\"fail: mov not found\") return None else: logger.debug(\"success: found","= self.browser def _css1(path, domm): \"\"\"virtual local func\"\"\" return self.css(path,","be launched\"\"\" try: # init virtual Display self.vbro = Display()","a weekend if mode == \"demo\" and datetime.now().isoweekday() in range(5,","self.stocks = [] # init globals Glob() def launch(self): \"\"\"launch","set a timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag):","\"\"\"xpath find function abbreviation\"\"\" if dom is None: dom =","/ pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) #","name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click()","else: logger.debug(\"success: found movement\") for x in mov_list: # find","None def get_mov_margin(self): \"\"\"get the margin of the movement\"\"\" self._check_open()","self.price = price return price def get_unit_value(self): \"\"\"get unit value","%s\" % url) self.browser.visit(url) logger.debug(f\"connected to %s\" % url) except","self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if","name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res)","def bind_mov(self): \"\"\"bind the corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list","return None def set_limit(self, category, mode, value): \"\"\"set limit in","soup return soup def find_id(self): \"\"\"find pos ID with with","div.title').text == 'Market Closed'): logger.error(\"market closed, position can't be closed\")","= quantity self.mode = mode self.price = price def __repr__(self):","pos ID with with given data\"\"\" pos_id = self.soup_data['id'] self.id","Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\")","self.id = pos_id return pos_id @property def close_tag(self): \"\"\"obtain close","and update the value\"\"\" try: msg_text = self.api.css1(\"div.text\", message).text return","timeout: raise TimeoutError(\"failed to close pos %s\" % self.id) logger.debug(\"closed","self.price = price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity),","self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None):","%s\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise try:","div\"\"\" self.api = api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div,","= num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else:","browser)\"\"\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \"not started\") return","self.soup_data = html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if","= product self.quantity = quantity self.mode = mode self.price =","utf-8 -*- \"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low","raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"):","self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \"\"\"check if","msg_text = self.api.css1(\"div.text\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\"higher\")","{pip} - margin: {margin}\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val","else: return False counter = counter.lower() if name in string","profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return gain def","module provides the low level functions with the service. \"\"\"","% (self.price, x.price)) continue # if nothing, return None return","self.get_result(0) name = self.get_research_name(result) x = 0 while not self.check_name(res,","is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self,","class Position(PurePosition): \"\"\"position object\"\"\" def __init__(self, api, html_div): \"\"\"initialized from","\"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" % url) self.browser.visit(url) logger.debug(f\"connected to %s\"","def logout(self): \"\"\"logout func (quit browser)\"\"\" try: self.browser.quit() except Exception:","return price def get_unit_value(self): \"\"\"get unit value of stock based","else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click()","'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity =","and virtual display, first of all to be launched\"\"\" try:","import num, expect, get_pip # exceptions from tradingAPI import exceptions","elif title == \"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title","mode self.price = price class PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self,","and x.mode == self.mode] if not mov_list: logger.debug(\"fail: mov not","return False elif name in string and counter not in","self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath( path['limit-gain-%s' %","else: raise exceptions.WindowException() def close(self): \"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click()","\"\"\"class-storing movement\"\"\" def __init__(self, product, quantity, mode, price): self.product =","unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value found in the","url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password)","if min_roof < x.price < max_roof: logger.debug(\"success: price corresponding\") #","dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None):","exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\"))","movement window\"\"\" self._check_open() if (mode not in [\"unit\", \"value\"] or","quant logger.debug(\"quantity set\") def get_price(self, mode='buy'): \"\"\"get current price\"\"\" if","x return x else: logger.debug(\"fail: price %f not corresponding to","not closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\")","== self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin =","self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if","x += 1 logger.debug(\"found product at position %d\" % (x","\"Insufficient Funds\": self.insfu = True elif title == \"Maximum Quantity","time.time() > timeout: logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in","update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant: {quant} -","# -*- coding: utf-8 -*- \"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module","result, name def check_name(self, name, string, counter=None): \"\"\"if both in","args=[css_path]) def elXpath(self, xpath, dom=None): \"\"\"check if element is present","elif name in string and counter not in string: return","name in string and counter in string: logger.debug(\"check_name: counter found","widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in ['quantity',","dom is None: dom = self.browser return expect(dom.find_by_name, args=[name]) def","window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result,","of stock based on margin, memoized\"\"\" # find in the","self.get_research_name(result) x = 0 while not self.check_name(res, name, counter=check_counter): name","self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\"demo\"): \"\"\"login","= string.lower() if counter is None: if name in string:","== 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss':","if not mov_list: logger.debug(\"fail: mov not found\") return None else:","max_roof = self.price + self.price * 0.01 min_roof = self.price","logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class for stocks\"\"\" def","mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else: return False","+= 1 logger.debug(\"found product at position %d\" % (x +","\"sell\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\"mode","self.stop_limit[cat]['value'] = value logger.debug(\"set limit\") def decode(self, message): \"\"\"decode text","brow=\"firefox\"): self.brow_name = brow self.positions = [] self.movements = []","'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\"","= 'conclused' logger.debug(\"confirmed movement\") def search_res(self, res, check_counter=None): \"\"\"search for","product, quantity, mode, price): self.product = product self.quantity = quantity","\"\"\"bind the corresponding movement\"\"\" logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x","category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif","exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self, message, value, mult=0.1): \"\"\"decode and","return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low","name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state =","self.gain = gain return gain def bind_mov(self): \"\"\"bind the corresponding","{margin}\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin /","Funds\": self.insfu = True elif title == \"Maximum Quantity Limit\":","self.api.movements if x.product == self.product and x.quantity == self.quantity and","import time import re from datetime import datetime from pyvirtualdisplay","{username}\") # check if it's a weekend if mode ==","!= -1: value += value * mult return value else:","title == \"Insufficient Funds\": self.insfu = True elif title ==","rec): \"\"\"add a record\"\"\" self.records.append(rec) return self.records class Movement(object): \"\"\"class-storing","product): self.api = api self.product = product self.state = 'initialized'","%f not corresponding to %f\" % (self.price, x.price)) continue #","'.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low level api to","True else: return False def get_research_name(self, res): \"\"\"return result name\"\"\"","\"\"\"set limit in movement window\"\"\" self._check_open() if (mode not in","logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open() quant =","name): \"\"\"factory method pattern\"\"\" return self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position","out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a timeout","> timeout: logger.warning(\"weekend trading alert-box not closed\") break if self.elCss(path['alert-box']):","globals Glob() def launch(self): \"\"\"launch browser and virtual display, first","\"\"\"if both in string return False\"\"\" name = name.lower() string","= self.api.css1(\"div.text\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\"higher\") !=","expect, get_pip # exceptions from tradingAPI import exceptions import selenium.common.exceptions","current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return gain","for product in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in","self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\"set limit\") def decode(self,","not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\"initialized","weekend if mode == \"demo\" and datetime.now().isoweekday() in range(5, 8):","= price class PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self, product, quantity,","msg_text.lower().find(\"higher\") != -1: value += value * mult return value","a timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1)","virtual display, first of all to be launched\"\"\" try: #","price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price = price","get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result':","get_pip # exceptions from tradingAPI import exceptions import selenium.common.exceptions #","css(self, css_path, dom=None): \"\"\"css find function abbreviation\"\"\" if dom is","return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name,","interface with the service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name = brow","= self.get_research_name(result) x = 0 while not self.check_name(res, name, counter=check_counter):","= Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value found in the collection\")","CredentialsException(username) time.sleep(1) logger.info(f\"logged in as {username}\") # check if it's","to the list\" % self.product) self.state = 'conclused' logger.debug(\"confirmed movement\")","message, value, mult=0.1): \"\"\"decode and update the value\"\"\" try: msg_text","string return False\"\"\" name = name.lower() string = string.lower() if","found in string\") return False elif name in string and","selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\"market closed, position","name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\"found product at","def get_gain(self): \"\"\"get current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text) self.gain =","pos %s\" % self.id) def get_gain(self): \"\"\"get current profit\"\"\" gain","None def set_limit(self, category, mode, value): \"\"\"set limit in movement","self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s'","__repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object):","\"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum Quantity","= product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price) else: return False class MovementWindow(object):","= self.get_result(0) name = self.get_research_name(result) x = 0 while not","= time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() >","widg = self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x","raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been closed","value * mult return value else: self.decode(message) return None def","time.time() > timeout: raise TimeoutError(\"failed to close pos %s\" %","the list\" % self.product) self.state = 'conclused' logger.debug(\"confirmed movement\") def",".glob import Glob from .links import path from .utils import","local func\"\"\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def","return quant def set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity","html_div): \"\"\"initialized from div\"\"\" self.api = api if isinstance(html_div, type('')):","\"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self, message,","= accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\" % info_label).text return num(val)","= self.api.css1(\"div.text\", message).text if title == \"Insufficient Funds\": self.insfu =","timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if","if dom is None: dom = self.browser return expect(dom.find_by_css, args=[css_path])","to %f\" % (self.price, x.price)) continue # if nothing, return","first of all to be launched\"\"\" try: # init virtual","approximate price max_roof = self.price + self.price * 0.01 min_roof","'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label =","collection\") return unit_value_res except KeyError: logger.debug(\"unit_value not found in the","string: logger.debug(\"check_name: counter found in string\") return False elif name","self.api.css1(\"div.text\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\"higher\") != -1:","def new_pos(self, html_div): \"\"\"factory method pattern\"\"\" pos = self.Position(self, html_div)","\"\"\"return the first value of self.css\"\"\" if dom is None:","= time.time() + 30 while not self.elCss(path['logo']): if time.time() >","= quant logger.debug(\"quantity set\") def get_price(self, mode='buy'): \"\"\"get current price\"\"\"","mode logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open() quant","return False\"\"\" name = name.lower() string = string.lower() if counter","\"\"\"logout func (quit browser)\"\"\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name,","string, counter=None): \"\"\"if both in string return False\"\"\" name =","= [x for x in product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\")","return soup def find_id(self): \"\"\"find pos ID with with given","in [\"unit\", \"value\"] or category not in [\"gain\", \"loss\", \"both\"]):","{}, 'loss': {}} logger.debug(\"initialized stop_limit\") if category == 'gain': self.api.xpath(","raise exceptions.BaseExc(e) return True def logout(self): \"\"\"logout func (quit browser)\"\"\"","= self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \"\"\"return","logger.error(\"market closed, position can't be closed\") raise exceptions.MarketClosed() raise exceptions.WidgetException(","and counter in string: logger.debug(\"check_name: counter found in string\") return","price return price def get_unit_value(self): \"\"\"get unit value of stock","self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None:","This module provides the low level functions with the service.","= [] self.stocks = [] # init globals Glob() def","return True def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free',","Position(PurePosition): \"\"\"position object\"\"\" def __init__(self, api, html_div): \"\"\"initialized from div\"\"\"","return result, name def check_name(self, name, string, counter=None): \"\"\"if both","accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin':","if self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\"found","= mode self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath(","result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product","%s\" % self.id) def get_gain(self): \"\"\"get current profit\"\"\" gain =","unit_val def new_mov(self, name): \"\"\"factory method pattern\"\"\" return self.MovementWindow(self, name)","mode self.stop_limit[cat]['value'] = value logger.debug(\"set limit\") def decode(self, message): \"\"\"decode","isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div","failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in as {username}\") # check","with given data\"\"\" pos_id = self.soup_data['id'] self.id = pos_id return","from tradingAPI import exceptions import selenium.common.exceptions # logging import logging","soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in soup.select(\"div.tradebox\"): fullname","(\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell'","while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\"weekend trading alert-box","self.price * 0.01 if min_roof < x.price < max_roof: logger.debug(\"success:","update the value\"\"\" try: msg_text = self.api.css1(\"div.text\", message).text return num(msg_text)","logger.debug(f\"browser {self.brow_name} launched\") except Exception: raise exceptions.BrowserException( self.brow_name, \"failed to","None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path,","all(x for x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement(","if msg_text.lower().find(\"higher\") != -1: value += value * mult return","\"\"\"add movement window\"\"\" def __init__(self, api, product): self.api = api","TimeoutError(\"failed to close pos %s\" % self.id) logger.debug(\"closed pos %s\"","= [] # init globals Glob() def launch(self): \"\"\"launch browser","str(self.price)]) class LowLevelAPI(object): \"\"\"low level api to interface with the","return unit_val def new_mov(self, name): \"\"\"factory method pattern\"\"\" return self.MovementWindow(self,","in movement window\"\"\" self._check_open() if (mode not in [\"unit\", \"value\"]","logger.debug(f\"visiting %s\" % url) self.browser.visit(url) logger.debug(f\"connected to %s\" % url)","raise exceptions.BrowserException(self.brow_name, \"not started\") return False self.vbro.stop() logger.info(\"logged out\") return","value += value * mult return value else: self.decode(message) return","not mov_list: logger.debug(\"fail: mov not found\") return None else: logger.debug(\"success:","category not in [\"gain\", \"loss\", \"both\"]): raise ValueError() if not","{self.brow_name} launched\") except Exception: raise exceptions.BrowserException( self.brow_name, \"failed to launch\")","price %f not corresponding to %f\" % (self.price, x.price)) continue","check if it's a weekend if mode == \"demo\" and","bs4 import BeautifulSoup from splinter import Browser from .glob import","logger.critical(\"connection timed out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define","movement\") for x in mov_list: # find approximate price max_roof","= self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for","self.find_id() def update(self, soup): \"\"\"update the soup\"\"\" self.soup_data = soup","== \"demo\" and datetime.now().isoweekday() in range(5, 8): timeout = time.time()","str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low level api to interface","elXpath(self, xpath, dom=None): \"\"\"check if element is present by css\"\"\"","css\"\"\" if dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath,","self.state = 'conclused' logger.debug(\"confirmed movement\") def search_res(self, res, check_counter=None): \"\"\"search","name\"\"\" if res is None: return None return self.api.css1(\"span.instrument-name\", res).text","self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos): \"\"\"get pos result, where 0","self.mode = mode self.price = price def __repr__(self): return '","Exception: if msg_text.lower().find(\"higher\") != -1: value += value * mult","pos): \"\"\"get pos result, where 0 is first\"\"\" evalxpath =","except Exception: return None def set_limit(self, category, mode, value): \"\"\"set","__init__(self, api, product): self.api = api self.product = product self.state","self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\") except Exception as e:","first\"\"\" evalxpath = path['res'] + f\"[{pos + 1}]\" try: res","raise exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode'] if","method pattern\"\"\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos) return pos","self.mode, self.price)) logger.debug(\"%s movement appended to the list\" % self.product)","out\") return True def get_bottom_info(self, info): accepted_values = { 'free_funds':","0.01 min_roof = self.price - self.price * 0.01 if min_roof","quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self,","self._check_open() if mode not in [\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode","get_mov_margin(self): \"\"\"get the margin of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text)","self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market","mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value']","mode=\"demo\"): \"\"\"login function\"\"\" url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\" %","mult=0.1): \"\"\"decode and update the value\"\"\" try: msg_text = self.api.css1(\"div.text\",","margin of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode):","is first\"\"\" evalxpath = path['res'] + f\"[{pos + 1}]\" try:","virtual Display self.vbro = Display() self.vbro.start() logger.debug(\"virtual display launched\") except","logout(self): \"\"\"logout func (quit browser)\"\"\" try: self.browser.quit() except Exception: raise","self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \"\"\"xpath find","return gain def bind_mov(self): \"\"\"bind the corresponding movement\"\"\" logger =","= soup return soup def find_id(self): \"\"\"find pos ID with","exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been","x else: logger.debug(\"fail: price %f not corresponding to %f\" %","self.brow_name, \"failed to launch\") return True def css(self, css_path, dom=None):","from .utils import num, expect, get_pip # exceptions from tradingAPI","return False counter = counter.lower() if name in string and","% self.id) def get_gain(self): \"\"\"get current profit\"\"\" gain = num(self.soup_data.select(\"td.ppl\")[0].text)","None: return None return self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos): \"\"\"get","value else: self.decode(message) return None def get_mov_margin(self): \"\"\"get the margin","price): self.product = product self.quantity = quantity self.mode = mode","self.product = product self.state = 'open' def _check_open(self): if self.state","login(self, username, password, mode=\"demo\"): \"\"\"login function\"\"\" url = \"https://trading212.com/it/login\" try:","self.state = 'open' def _check_open(self): if self.state == 'open': return","== self.mode] if not mov_list: logger.debug(\"fail: mov not found\") return","return float(sell_price) else: return False class MovementWindow(object): \"\"\"add movement window\"\"\"","timeout: logger.warning(\"weekend trading alert-box not closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click()","except Exception: raise exceptions.BrowserException( self.brow_name, \"failed to launch\") return True","\"html.parser\") for product in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower()","hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\"initialized stop_limit\")","margin, memoized\"\"\" # find in the collection try: unit_value =","try: msg_text = self.api.css1(\"div.text\", message).text return num(msg_text) except Exception: if","logger.debug(\"fail: mov not found\") return None else: logger.debug(\"success: found movement\")","0 is first\"\"\" evalxpath = path['res'] + f\"[{pos + 1}]\"","to close pos %s\" % self.id) logger.debug(\"closed pos %s\" %","BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower()","soup\"\"\" self.soup_data = soup return soup def find_id(self): \"\"\"find pos","def close(self): \"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except","def get_price(self, mode='buy'): \"\"\"get current price\"\"\" if mode not in","value, mult=0.1): \"\"\"decode and update the value\"\"\" try: msg_text =","decode_update(self, message, value, mult=0.1): \"\"\"decode and update the value\"\"\" try:","logger.debug(\"unit_value not found in the collection\") pip = get_pip(mov=self) quant","counter = counter.lower() if name in string and counter in","def css(self, css_path, dom=None): \"\"\"css find function abbreviation\"\"\" if dom","+ 10 while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\"weekend","launched\"\"\" try: # init virtual Display self.vbro = Display() self.vbro.start()","self.mode = mode self.price = price class PurePosition(object): \"\"\"class-storing position\"\"\"","mov_list: # find approximate price max_roof = self.price + self.price","with the service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name = brow self.positions","= self.price + self.price * 0.01 min_roof = self.price -","[x for x in self.api.movements if x.product == self.product and","current quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return","+ '-btn']).click() self.mode = mode logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte","dom]) def search_name(self, name, dom=None): \"\"\"name find function abbreviation\"\"\" if","> timeout: raise TimeoutError(\"failed to close pos %s\" % self.id)","return True else: return False counter = counter.lower() if name","exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x +=","% url) self.browser.visit(url) logger.debug(f\"connected to %s\" % url) except selenium.common.exceptions.WebDriverException:","= value logger.debug(\"set limit\") def decode(self, message): \"\"\"decode text pop-up\"\"\"","None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username,","abbreviation\"\"\" if dom is None: dom = self.browser return expect(dom.find_by_name,","the first value of self.css\"\"\" if dom is None: dom","name) class Position(PurePosition): \"\"\"position object\"\"\" def __init__(self, api, html_div): \"\"\"initialized","if dom is None: dom = self.browser return expect(dom.find_by_name, args=[name])","counter found in string\") return False elif name in string","counter not in string: return True else: return False def","if time.time() > timeout: logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged","return None return None def new_pos(self, html_div): \"\"\"factory method pattern\"\"\"","self.records = [] def new_rec(self, rec): \"\"\"add a record\"\"\" self.records.append(rec)","return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \"\"\"check if element","def close(self): \"\"\"close a movement\"\"\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed'","'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category ==","type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product","self.browser.visit(url) logger.debug(f\"connected to %s\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed","= api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else:","self.quantity = quantity self.mode = mode self.price = price class","self.api = api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser')","for x in self.api.movements if x.product == self.product and x.quantity","movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if widg: self.decode(widg[0])","= mode self.price = price class PurePosition(object): \"\"\"class-storing position\"\"\" def","from pyvirtualdisplay import Display from bs4 import BeautifulSoup from splinter","== 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both':","except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click()","the collection\") pip = get_pip(mov=self) quant = 1 / pip","self.quantity = quant logger.debug(\"quantity set\") def get_price(self, mode='buy'): \"\"\"get current","path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode","num(self.soup_data.select(\"td.ppl\")[0].text) self.gain = gain return gain def bind_mov(self): \"\"\"bind the","self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise","= self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \"\"\"xpath","self.product and x.quantity == self.quantity and x.mode == self.mode] if","set\") def get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value))","(self.price, x.price)) continue # if nothing, return None return None","string\") return False elif name in string and counter not","at position %d\" % (x + 1)) return result, name","if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin / quant","def login(self, username, password, mode=\"demo\"): \"\"\"login function\"\"\" url = \"https://trading212.com/it/login\"","except KeyError: logger.debug(\"unit_value not found in the collection\") pip =","self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None:","\"\"\"find pos ID with with given data\"\"\" pos_id = self.soup_data['id']","dom is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def","self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode']","for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] =","trading alert-box not closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading","not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\"login failed\") raise CredentialsException(username)","x.quantity == self.quantity and x.mode == self.mode] if not mov_list:","self.css1(path['log']).click() # define a timeout for logging in timeout =","in self.api.movements if x.product == self.product and x.quantity == self.quantity","self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\"quantity set\") def get_price(self, mode='buy'): \"\"\"get","span.equity-item-value\" % info_label).text return num(val) except KeyError as e: raise","exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except Exception:","if element is present by css\"\"\" if dom is None:","+ 1}]\" try: res = self.api.xpath(evalxpath)[0] return res except Exception:","is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter):","of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set","self.quantity, self.mode, self.price)) logger.debug(\"%s movement appended to the list\" %","self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy'","self.soup_data.select(\"td.direction\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell' self.price =","mov self.mov = x return x else: logger.debug(\"fail: price %f","-1: value += value * mult return value else: self.decode(message)","'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\"div#%s","service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name = brow self.positions = []","time.time() + 10 while not self.elCss(path['alert-box']): if time.time() > timeout:","first value of self.css\"\"\" if dom is None: dom =","in string return False\"\"\" name = name.lower() string = string.lower()","10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\"failed","'initialized' self.insfu = False def open(self, name_counter=None): \"\"\"open the window\"\"\"","in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname: mark_closed_list","Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum Quantity Limit\":","xpath, dom=None): \"\"\"xpath find function abbreviation\"\"\" if dom is None:","logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \"\"\"base class for stocks\"\"\"","find approximate price max_roof = self.price + self.price * 0.01","\"\"\"class-storing position\"\"\" def __init__(self, product, quantity, mode, price): self.product =","% mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode","Glob() def launch(self): \"\"\"launch browser and virtual display, first of","return unit_value_res except KeyError: logger.debug(\"unit_value not found in the collection\")","logger.warning(\"weekend trading alert-box not closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend","price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)])","x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text","html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\" in","%d\" % (x + 1)) return result, name def check_name(self,","try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'):","def get_research_name(self, res): \"\"\"return result name\"\"\" if res is None:","timeout: logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in as {username}\")","result.click() if self.api.elCss(\"div.widget_message\"): self.decode(self.api.css1(\"div.widget_message\")) self.product = product self.state = 'open'","string.lower() if counter is None: if name in string: return","# init globals Glob() def launch(self): \"\"\"launch browser and virtual","LowLevelAPI(object): \"\"\"low level api to interface with the service\"\"\" def","(buy or sell)\"\"\" self._check_open() if mode not in [\"buy\", \"sell\"]:","= html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text) if (\"direction-label-buy\"","started\") return False self.vbro.stop() logger.info(\"logged out\") return True def get_bottom_info(self,","the soup\"\"\" self.soup_data = soup return soup def find_id(self): \"\"\"find","both in string return False\"\"\" name = name.lower() string =","def decode(self, message): \"\"\"decode text pop-up\"\"\" title = self.api.css1(\"div.title\", message).text","price class PurePosition(object): \"\"\"class-storing position\"\"\" def __init__(self, product, quantity, mode,","False def get_research_name(self, res): \"\"\"return result name\"\"\" if res is","res = self.api.xpath(evalxpath)[0] return res except Exception: return None def","is present by css\"\"\" if dom is None: dom =","self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\"found product","% self.product) self.state = 'conclused' logger.debug(\"confirmed movement\") def search_res(self, res,","\"\"\"position object\"\"\" def __init__(self, api, html_div): \"\"\"initialized from div\"\"\" self.api","self.css1(\"div#%s span.equity-item-value\" % info_label).text return num(val) except KeyError as e:","expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\"demo\"): \"\"\"login function\"\"\" url","raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in as {username}\") # check if","def new_mov(self, name): \"\"\"factory method pattern\"\"\" return self.MovementWindow(self, name) class","raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\"mode set\")","self.decode(message) return None def get_mov_margin(self): \"\"\"get the margin of the","Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum Quantity Limit\": raise","_check_open(self): if self.state == 'open': return True else: raise exceptions.WindowException()","str(self.mode), str(self.price)]) class LowLevelAPI(object): \"\"\"low level api to interface with","if nothing, return None return None def new_pos(self, html_div): \"\"\"factory","'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val","while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name","from div\"\"\" self.api = api if isinstance(html_div, type('')): self.soup_data =","self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product =","\"\"\"confirm the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\"div.widget_message\") if","num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price = price return price","category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category !=","# bind mov self.mov = x return x else: logger.debug(\"fail:","+= value * mult return value else: self.decode(message) return None","% mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value']","site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\"quant: {quant} - pip: {pip}","30 while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\"login failed\")","self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except Exception: raise exceptions.BrowserException(","it's been closed # set a timeout timeout = time.time()","Exception: return None def set_limit(self, category, mode, value): \"\"\"set limit","1)) return result, name def check_name(self, name, string, counter=None): \"\"\"if","func (quit browser)\"\"\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \"not","exceptions.MaxQuantLimit(num(text)) elif title == \"Minimum Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded","% url) except selenium.common.exceptions.WebDriverException: logger.critical(\"connection timed out\") raise try: self.search_name(\"login[username]\").fill(username)","else: self.soup_data = html_div self.product = self.soup_data.select(\"td.name\")[0].text self.quantity = num(self.soup_data.select(\"td.quantity\")[0].text)","'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode']","logger.debug(\"quantity set\") def get_price(self, mode='buy'): \"\"\"get current price\"\"\" if mode","return True def logout(self): \"\"\"logout func (quit browser)\"\"\" try: self.browser.quit()","logger = logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for x in self.api.movements","password, mode=\"demo\"): \"\"\"login function\"\"\" url = \"https://trading212.com/it/login\" try: logger.debug(f\"visiting %s\"","\"\"\"factory method pattern\"\"\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos) return","counter.lower() if name in string and counter in string: logger.debug(\"check_name:","mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']:","to be launched\"\"\" try: # init virtual Display self.vbro =","or sell)\"\"\" self._check_open() if mode not in [\"buy\", \"sell\"]: raise","\"value\"] or category not in [\"gain\", \"loss\", \"both\"]): raise ValueError()","css\"\"\" if dom is None: dom = self.browser return expect(dom.is_element_present_by_css,","self.id = self.find_id() def update(self, soup): \"\"\"update the soup\"\"\" self.soup_data","else: self.mode = 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text)","window\"\"\" self._check_open() if (mode not in [\"unit\", \"value\"] or category","been closed # set a timeout timeout = time.time() +","time.sleep(1) logger.info(f\"logged in as {username}\") # check if it's a","self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\") except Exception as e: logger.critical(\"login","continue # if nothing, return None return None def new_pos(self,","# if nothing, return None return None def new_pos(self, html_div):","self._check_open() if (mode not in [\"unit\", \"value\"] or category not","except Exception: raise exceptions.BrowserException(self.brow_name, \"not started\") return False self.vbro.stop() logger.info(\"logged","['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\"set limit\")","self.quantity = quantity self.mode = mode self.price = price def","wait until it's been closed # set a timeout timeout","logger.debug(\"unit_value found in the collection\") return unit_value_res except KeyError: logger.debug(\"unit_value","the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product)","info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl',","if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product =","if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1] if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\","elCss(self, css_path, dom=None): \"\"\"check if element is present by css\"\"\"","+ 30 while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\"login","\"both\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain':","found\") return None else: logger.debug(\"success: found movement\") for x in","= path['res'] + f\"[{pos + 1}]\" try: res = self.api.xpath(evalxpath)[0]","set_mode(self, mode): \"\"\"set mode (buy or sell)\"\"\" self._check_open() if mode","= price return price def get_unit_value(self): \"\"\"get unit value of","10 while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\"weekend trading","list\" % self.product) self.state = 'conclused' logger.debug(\"confirmed movement\") def search_res(self,","message).text text = self.api.css1(\"div.text\", message).text if title == \"Insufficient Funds\":","self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is","return True def css(self, css_path, dom=None): \"\"\"css find function abbreviation\"\"\"","find function abbreviation\"\"\" if dom is None: dom = self.browser","@property def close_tag(self): \"\"\"obtain close tag\"\"\" return f\"#{self.id} div.close-icon\" def","mov_list = [x for x in self.api.movements if x.product ==","closed\") except Exception as e: logger.critical(\"login failed\") raise exceptions.BaseExc(e) return","self.insfu = False def open(self, name_counter=None): \"\"\"open the window\"\"\" if","all to be launched\"\"\" try: # init virtual Display self.vbro","self.browser def _css1(path, domm): \"\"\"virtual local func\"\"\" return self.css(path, domm)[0]","def check_name(self, name, string, counter=None): \"\"\"if both in string return","Movement(object): \"\"\"class-storing movement\"\"\" def __init__(self, product, quantity, mode, price): self.product","= value elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value))","is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self,","class for stocks\"\"\" def __init__(self, product): self.product = product self.market","else: logger.debug(\"fail: price %f not corresponding to %f\" % (self.price,","= product self.market = True self.records = [] def new_rec(self,","quantity self.mode = mode self.price = price def __repr__(self): return","res\"\"\" logger.debug(\"searching result\") result = self.get_result(0) name = self.get_research_name(result) x","name = self.get_research_name(result) x = 0 while not self.check_name(res, name,","the margin of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self,","the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value","{quant} - pip: {pip} - margin: {margin}\") if 'old_quant' in","return None def new_pos(self, html_div): \"\"\"factory method pattern\"\"\" pos =","False self.vbro.stop() logger.info(\"logged out\") return True def get_bottom_info(self, info): accepted_values","stocks\"\"\" def __init__(self, product): self.product = product self.market = True","None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click()","[\"buy\", \"sell\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode","!= -1] if mark_closed_list: sell_price = product.select(\"div.tradebox-price-sell\")[0]\\ .text return float(sell_price)","return res except Exception: return None def set_limit(self, category, mode,","quantity self.mode = mode self.price = price class PurePosition(object): \"\"\"class-storing","in string and counter in string: logger.debug(\"check_name: counter found in","else: return False def get_research_name(self, res): \"\"\"return result name\"\"\" if","message).text if title == \"Insufficient Funds\": self.insfu = True elif","== 'Market Closed'): logger.error(\"market closed, position can't be closed\") raise","except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\"browser {self.brow_name}","in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\"set","if time.time() > timeout: logger.warning(\"weekend trading alert-box not closed\") break","movement window\"\"\" def __init__(self, api, product): self.api = api self.product","movement appended to the list\" % self.product) self.state = 'conclused'","return None return self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos): \"\"\"get pos","expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \"\"\"check if element is","8): timeout = time.time() + 10 while not self.elCss(path['alert-box']): if","product in soup.select(\"div.tradebox\"): fullname = product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname:","not corresponding to %f\" % (self.price, x.price)) continue # if","by css\"\"\" if dom is None: dom = self.browser return","logger.debug(\"virtual display launched\") except Exception: raise exceptions.VBroException() try: self.browser =","counter=check_counter): return self.get_result(x) x += 1 logger.debug(\"found product at position","in locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value =","timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time()","dom=None): \"\"\"name find function abbreviation\"\"\" if dom is None: dom","True def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value':","[\"unit\", \"value\"] or category not in [\"gain\", \"loss\", \"both\"]): raise","args=[name]) def xpath(self, xpath, dom=None): \"\"\"xpath find function abbreviation\"\"\" if","None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return","self.id) logger.debug(\"closed pos %s\" % self.id) def get_gain(self): \"\"\"get current","string and counter not in string: return True else: return","self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a timeout for logging in timeout","from datetime import datetime from pyvirtualdisplay import Display from bs4","self.insfu = True elif title == \"Maximum Quantity Limit\": raise","logger.debug(\"decoded message\") def decode_update(self, message, value, mult=0.1): \"\"\"decode and update","pos_id = self.soup_data['id'] self.id = pos_id return pos_id @property def","time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout:","% mode).text) self.price = price return price def get_unit_value(self): \"\"\"get","= [] self.movements = [] self.stocks = [] # init","gain return gain def bind_mov(self): \"\"\"bind the corresponding movement\"\"\" logger","browser and virtual display, first of all to be launched\"\"\"","args=[xpath]) def login(self, username, password, mode=\"demo\"): \"\"\"login function\"\"\" url =","logger.info(f\"logged in as {username}\") # check if it's a weekend","return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set mode (buy or sell)\"\"\"","== self.product and x.quantity == self.quantity and x.mode == self.mode]","category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' %","path['res'] + f\"[{pos + 1}]\" try: res = self.api.xpath(evalxpath)[0] return","for stocks\"\"\" def __init__(self, product): self.product = product self.market =","min_roof = self.price - self.price * 0.01 if min_roof <","= self.price - self.price * 0.01 if min_roof < x.price","def set_quantity(self, quant): \"\"\"set quantity\"\"\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant","if dom is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath])","Display self.vbro = Display() self.vbro.start() logger.debug(\"virtual display launched\") except Exception:","\"demo\" and datetime.now().isoweekday() in range(5, 8): timeout = time.time() +","elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category","Display from bs4 import BeautifulSoup from splinter import Browser from","function abbreviation\"\"\" if dom is None: dom = self.browser return","if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\"market closed, position can't",".links import path from .utils import num, expect, get_pip #","message): \"\"\"decode text pop-up\"\"\" title = self.api.css1(\"div.title\", message).text text =","title == \"Maximum Quantity Limit\": raise exceptions.MaxQuantLimit(num(text)) elif title ==","of self.css\"\"\" if dom is None: dom = self.browser def","raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {},","try: res = self.api.xpath(evalxpath)[0] return res except Exception: return None","coding: utf-8 -*- \"\"\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the","try: info_label = accepted_values[info] val = self.css1(\"div#%s span.equity-item-value\" % info_label).text","pattern\"\"\" return self.MovementWindow(self, name) class Position(PurePosition): \"\"\"position object\"\"\" def __init__(self,","True else: return False counter = counter.lower() if name in","provides the low level functions with the service. \"\"\" import","alert-box closed\") except Exception as e: logger.critical(\"login failed\") raise exceptions.BaseExc(e)","def get_result(self, pos): \"\"\"get pos result, where 0 is first\"\"\"","quantity, mode, price): self.product = product self.quantity = quantity self.mode","raise try: self.search_name(\"login[username]\").fill(username) self.search_name(\"login[password]\").fill(password) self.css1(path['log']).click() # define a timeout for","raise ValueError() self._check_open() price = num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text)","self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category == 'both':","def new_rec(self, rec): \"\"\"add a record\"\"\" self.records.append(rec) return self.records class","dom=None): \"\"\"css find function abbreviation\"\"\" if dom is None: dom","self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \"not started\") return False self.vbro.stop()","return False def get_research_name(self, res): \"\"\"return result name\"\"\" if res","\"loss\", \"both\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit =","import Display from bs4 import BeautifulSoup from splinter import Browser","logger.debug(\"closed window\") def confirm(self): \"\"\"confirm the movement\"\"\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click()","# set a timeout timeout = time.time() + 10 while","logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in as {username}\") #","re from datetime import datetime from pyvirtualdisplay import Display from","return False self.vbro.stop() logger.info(\"logged out\") return True def get_bottom_info(self, info):","memoized\"\"\" # find in the collection try: unit_value = Glob().theCollector.collection['unit_value']","data\"\"\" pos_id = self.soup_data['id'] self.id = pos_id return pos_id @property","counter=None): \"\"\"if both in string return False\"\"\" name = name.lower()","find in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res =","\"\"\"update the soup\"\"\" self.soup_data = soup return soup def find_id(self):","num, expect, get_pip # exceptions from tradingAPI import exceptions import","dom=None): \"\"\"return the first value of self.css\"\"\" if dom is","collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\"unit_value found","Display() self.vbro.start() logger.debug(\"virtual display launched\") except Exception: raise exceptions.VBroException() try:","= True self.records = [] def new_rec(self, rec): \"\"\"add a","KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name): soup =","return self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos): \"\"\"get pos result, where","init globals Glob() def launch(self): \"\"\"launch browser and virtual display,","not in [\"unit\", \"value\"] or category not in [\"gain\", \"loss\",","stock based on margin, memoized\"\"\" # find in the collection","def __init__(self, api, product): self.api = api self.product = product","self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise","= Browser(self.brow_name) logger.debug(f\"browser {self.brow_name} launched\") except Exception: raise exceptions.BrowserException( self.brow_name,","self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\"initialized stop_limit\") if category","min_roof < x.price < max_roof: logger.debug(\"success: price corresponding\") # bind","__init__(self, api, html_div): \"\"\"initialized from div\"\"\" self.api = api if","%s\" % self.id) logger.debug(\"closed pos %s\" % self.id) def get_gain(self):","return f\"#{self.id} div.close-icon\" def close(self): \"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click()","in the collection\") pip = get_pip(mov=self) quant = 1 /","['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price))","\"\"\"obtain close tag\"\"\" return f\"#{self.id} div.close-icon\" def close(self): \"\"\"close position","search_res(self, res, check_counter=None): \"\"\"search for a res\"\"\" logger.debug(\"searching result\") result","> timeout: logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1) logger.info(f\"logged in as","if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box closed\") except Exception as","api, product): self.api = api self.product = product self.state =","# find approximate price max_roof = self.price + self.price *","self.mov = x return x else: logger.debug(\"fail: price %f not","decode(self, message): \"\"\"decode text pop-up\"\"\" title = self.api.css1(\"div.title\", message).text text","alert-box not closed\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\"weekend trading alert-box","new_pos(self, html_div): \"\"\"factory method pattern\"\"\" pos = self.Position(self, html_div) pos.bind_mov()","import exceptions import selenium.common.exceptions # logging import logging logger =","Exception: raise exceptions.BrowserException( self.brow_name, \"failed to launch\") return True def","# find in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res","product): self.product = product self.market = True self.records = []","e: raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html,","self.elCss(path['logo']): if time.time() > timeout: logger.critical(\"login failed\") raise CredentialsException(username) time.sleep(1)","name_counter=None): \"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened","'-btn']).click() self.mode = mode logger.debug(\"mode set\") def get_quantity(self): \"\"\"gte current","f\"#{self.id} div.close-icon\" def close(self): \"\"\"close position via tag\"\"\" self.api.css1(self.close_tag).click() try:","return None def get_mov_margin(self): \"\"\"get the margin of the movement\"\"\"","def get_price(self, name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product","self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\"weekend trading alert-box not closed\")","self.vbro = Display() self.vbro.start() logger.debug(\"virtual display launched\") except Exception: raise","title = self.api.css1(\"div.title\", message).text text = self.api.css1(\"div.text\", message).text if title","None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path,","False\"\"\" name = name.lower() string = string.lower() if counter is","= { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'}","name.lower() string = string.lower() if counter is None: if name","in string: return True else: return False def get_research_name(self, res):","num(self.api.css1(\"span.cfd-order-info-item-value\").text) def set_mode(self, mode): \"\"\"set mode (buy or sell)\"\"\" self._check_open()","= self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\"demo\"):","margin = self.get_mov_margin() logger.debug(f\"quant: {quant} - pip: {pip} - margin:","[] self.stocks = [] # init globals Glob() def launch(self):","get_price(self, name): soup = BeautifulSoup( self.css1(\"div.scrollable-area-content\").html, \"html.parser\") for product in","None return self.api.css1(\"span.instrument-name\", res).text def get_result(self, pos): \"\"\"get pos result,","= self.find_id() def update(self, soup): \"\"\"update the soup\"\"\" self.soup_data =","None: dom = self.browser def _css1(path, domm): \"\"\"virtual local func\"\"\"","timeout = time.time() + 30 while not self.elCss(path['logo']): if time.time()","a record\"\"\" self.records.append(rec) return self.records class Movement(object): \"\"\"class-storing movement\"\"\" def","message).text return num(msg_text) except Exception: if msg_text.lower().find(\"higher\") != -1: value","True def logout(self): \"\"\"logout func (quit browser)\"\"\" try: self.browser.quit() except","mode).text) self.price = price return price def get_unit_value(self): \"\"\"get unit","if name.lower() in fullname: mark_closed_list = [x for x in","tag\"\"\" return f\"#{self.id} div.close-icon\" def close(self): \"\"\"close position via tag\"\"\"","logging in timeout = time.time() + 30 while not self.elCss(path['logo']):","find_id(self): \"\"\"find pos ID with with given data\"\"\" pos_id =","pyvirtualdisplay import Display from bs4 import BeautifulSoup from splinter import","__init__(self, product, quantity, mode, price): self.product = product self.quantity =","evalxpath = path['res'] + f\"[{pos + 1}]\" try: res =","def elXpath(self, xpath, dom=None): \"\"\"check if element is present by","self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\"market","category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category ==","def close_tag(self): \"\"\"obtain close tag\"\"\" return f\"#{self.id} div.close-icon\" def close(self):","the service\"\"\" def __init__(self, brow=\"firefox\"): self.brow_name = brow self.positions =","logging.getLogger(\"tradingAPI.low_level.bind_mov\") mov_list = [x for x in self.api.movements if x.product","None return None def new_pos(self, html_div): \"\"\"factory method pattern\"\"\" pos","= product.select(\"span.instrument-name\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x for","None def new_pos(self, html_div): \"\"\"factory method pattern\"\"\" pos = self.Position(self,","exceptions.BrowserException( self.brow_name, \"failed to launch\") return True def css(self, css_path,","= name.lower() string = string.lower() if counter is None: if","datetime.now().isoweekday() in range(5, 8): timeout = time.time() + 10 while","__init__(self, product): self.product = product self.market = True self.records =","mode): \"\"\"set mode (buy or sell)\"\"\" self._check_open() if mode not","= self.soup_data['id'] self.id = pos_id return pos_id @property def close_tag(self):","for x in product.select( \"div.quantity-list-input-wrapper\") if x.select( \"div.placeholder\")[0].text.lower().find(\"close\") != -1]","hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the site","Quantity Limit\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\"decoded message\") def decode_update(self, message, value,","Exception: raise exceptions.BrowserException(self.brow_name, \"not started\") return False self.vbro.stop() logger.info(\"logged out\")","x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity,","self.state = 'closed' logger.debug(\"closed window\") def confirm(self): \"\"\"confirm the movement\"\"\"","pos_id return pos_id @property def close_tag(self): \"\"\"obtain close tag\"\"\" return","ID with with given data\"\"\" pos_id = self.soup_data['id'] self.id =","get_quantity(self): \"\"\"gte current quantity\"\"\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity =","dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None):","\"\"\"set mode (buy or sell)\"\"\" self._check_open() if mode not in","search_name(self, name, dom=None): \"\"\"name find function abbreviation\"\"\" if dom is","locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value = unit_val","% self.id) logger.debug(\"closed pos %s\" % self.id) def get_gain(self): \"\"\"get","except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\"market closed,","\"\"\"open the window\"\"\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\"opened window\")","mode self.price = price def __repr__(self): return ' - '.join([str(self.product),","(quit browser)\"\"\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \"not started\")","x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\"%s movement appended to","mark_closed_list = [x for x in product.select( \"div.quantity-list-input-wrapper\") if x.select(","= num(self.api.css1( \"div.orderdialog div.tradebox-price-%s\" % mode).text) self.price = price return","self.mode = 'sell' self.price = num(self.soup_data.select(\"td.averagePrice\")[0].text) self.margin = num(self.soup_data.select(\"td.margin\")[0].text) self.id","= product self.state = 'initialized' self.insfu = False def open(self,","False class MovementWindow(object): \"\"\"add movement window\"\"\" def __init__(self, api, product):","self.api.css(\"div.widget_message\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x"],"string":"[\n \"\\\"\\\"\\\"get the margin of the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def\",\n \"dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None):\",\n \"True elif title == \\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif\",\n \"~~~~~~~~~~~~~~ This module provides the low level functions with the\",\n \"movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed window\\\") def confirm(self):\",\n \"{ 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try:\",\n \"low level functions with the service. \\\"\\\"\\\" import time import\",\n \"fullname: mark_closed_list = [x for x in product.select( \\\"div.quantity-list-input-wrapper\\\") if\",\n \"check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result = self.get_result(0)\",\n \"product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product =\",\n \"mode self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath( path['limit-gain-%s'\",\n \"= 'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin\",\n \"= margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return\",\n \"the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if widg:\",\n \"name = name.lower() string = string.lower() if counter is None:\",\n \"self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product\",\n \"if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0)\",\n \"position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message\",\n \"return num(val) except KeyError as e: raise exceptions.BaseExc(e) def get_price(self,\",\n \"max_roof: logger.debug(\\\"success: price corresponding\\\") # bind mov self.mov = x\",\n \"mode, value): \\\"\\\"\\\"set limit in movement window\\\"\\\"\\\" self._check_open() if (mode\",\n \"\\\"\\\"\\\"get unit value of stock based on margin, memoized\\\"\\\"\\\" #\",\n \"= logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def __init__(self,\",\n \"quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity set\\\") def get_price(self,\",\n \"with the service. \\\"\\\"\\\" import time import re from datetime\",\n \"logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product)\",\n \"return num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\") != -1: value +=\",\n \"launch(self): \\\"\\\"\\\"launch browser and virtual display, first of all to\",\n \"import BeautifulSoup from splinter import Browser from .glob import Glob\",\n \"collection\\\") pip = get_pip(mov=self) quant = 1 / pip if\",\n \"MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self, api, product): self.api =\",\n \"try: logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" %\",\n \"if it's a weekend if mode == \\\"demo\\\" and datetime.now().isoweekday()\",\n \"the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip:\",\n \"/ quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def\",\n \"Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed\",\n \"level api to interface with the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"):\",\n \"Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found in the collection\\\") return\",\n \"margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val\",\n \"brow self.positions = [] self.movements = [] self.stocks = []\",\n \"if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res,\",\n \"'Market Closed'): logger.error(\\\"market closed, position can't be closed\\\") raise exceptions.MarketClosed()\",\n \"closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\") except\",\n \"self.set_quantity(quant) # update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant:\",\n \"if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value\",\n \"= [x for x in self.api.movements if x.product == self.product\",\n \"mode not in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click()\",\n \"Closed'): logger.error(\\\"market closed, position can't be closed\\\") raise exceptions.MarketClosed() raise\",\n \"else: return False class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self,\",\n \"quant def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity =\",\n \"closed, position can't be closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message\",\n \"limit\\\") def decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\",\",\n \"get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if res is None: return\",\n \"while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\\\"failed to\",\n \"based on margin, memoized\\\"\\\"\\\" # find in the collection try:\",\n \"if time.time() > timeout: raise TimeoutError(\\\"failed to close pos %s\\\"\",\n \"self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if\",\n \"result name\\\"\\\"\\\" if res is None: return None return self.api.css1(\\\"span.instrument-name\\\",\",\n \"datetime import datetime from pyvirtualdisplay import Display from bs4 import\",\n \"== \\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self,\",\n \"return self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product, quantity,\",\n \"= int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self, quant):\",\n \"if all(x for x in ['quantity', 'mode'] if hasattr(self, x)):\",\n \"timeout = time.time() + 10 while not self.elCss(path['alert-box']): if time.time()\",\n \"self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set mode (buy or\",\n \"css_path, dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if dom is None:\",\n \"x in self.api.movements if x.product == self.product and x.quantity ==\",\n \"self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state = 'open' def _check_open(self):\",\n \"Glob from .links import path from .utils import num, expect,\",\n \"logger.debug(f\\\"connected to %s\\\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\")\",\n \"# init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual display\",\n \"return value else: self.decode(message) return None def get_mov_margin(self): \\\"\\\"\\\"get the\",\n \"Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\"\",\n \"break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\") except Exception\",\n \"= [] def new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return\",\n \"from .glob import Glob from .links import path from .utils\",\n \"in string: return True else: return False counter = counter.lower()\",\n \"info_label = accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return\",\n \"self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if\",\n \"be closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait\",\n \"== \\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum\",\n \"exceptions.WindowException() def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state =\",\n \"= num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return gain def bind_mov(self): \\\"\\\"\\\"bind\",\n \"as {username}\\\") # check if it's a weekend if mode\",\n \"counter is None: if name in string: return True else:\",\n \"' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level\",\n \"if mode not in ['buy', 'sell']: raise ValueError() self._check_open() price\",\n \"float(sell_price) else: return False class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def\",\n \"[x for x in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") !=\",\n \"= 1 / pip if hasattr(self, 'quantity'): old_quant == self.quantity\",\n \"self.positions = [] self.movements = [] self.stocks = [] #\",\n \"self.api.css1(\\\"div.text\\\", message).text if title == \\\"Insufficient Funds\\\": self.insfu = True\",\n \"return pos_id @property def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id}\",\n \"element is present by css\\\"\\\"\\\" if dom is None: dom\",\n \"is None: dom = self.browser def _css1(path, domm): \\\"\\\"\\\"virtual local\",\n \"current price\\\"\\\"\\\" if mode not in ['buy', 'sell']: raise ValueError()\",\n \"\\\"failed to launch\\\") return True def css(self, css_path, dom=None): \\\"\\\"\\\"css\",\n \"\\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if\",\n \"if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data =\",\n \"given data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id = pos_id return pos_id\",\n \"\\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list: sell_price =\",\n \"\\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price = price return price def\",\n \"except KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name): soup\",\n \"closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until\",\n \"new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return self.records class Movement(object):\",\n \"corresponding\\\") # bind mov self.mov = x return x else:\",\n \"\\\"\\\"\\\"low level api to interface with the service\\\"\\\"\\\" def __init__(self,\",\n \"False def open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click()\",\n \"open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click()\",\n \"path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in\",\n \"dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def\",\n \"unit_value[self.product] logger.debug(\\\"unit_value found in the collection\\\") return unit_value_res except KeyError:\",\n \"- '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level api\",\n \"def update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data = soup return\",\n \"div.tradebox-price-%s\\\" % mode).text) self.price = price return price def get_unit_value(self):\",\n \"# logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base\",\n \"self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip: {pip} - margin: {margin}\\\") if\",\n \"= 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id =\",\n \"close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed\",\n \"stop_limit\\\") if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif\",\n \"position %d\\\" % (x + 1)) return result, name def\",\n \"= logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for x in self.api.movements if\",\n \"product self.state = 'initialized' self.insfu = False def open(self, name_counter=None):\",\n \"e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return True def logout(self): \\\"\\\"\\\"logout\",\n \"hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement appended\",\n \"self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state\",\n \"__init__(self, brow=\\\"firefox\\\"): self.brow_name = brow self.positions = [] self.movements =\",\n \"pip: {pip} - margin: {margin}\\\") if 'old_quant' in locals(): self.set_quantity(old_quant)\",\n \"= x return x else: logger.debug(\\\"fail: price %f not corresponding\",\n \"close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException:\",\n \"confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\")\",\n \"1}]\\\" try: res = self.api.xpath(evalxpath)[0] return res except Exception: return\",\n \"in the collection\\\") return unit_value_res except KeyError: logger.debug(\\\"unit_value not found\",\n \"present by css\\\"\\\"\\\" if dom is None: dom = self.browser\",\n \"Browser from .glob import Glob from .links import path from\",\n \"= \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected to\",\n \"tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level functions with\",\n \"xpath, dom=None): \\\"\\\"\\\"check if element is present by css\\\"\\\"\\\" if\",\n \"of all to be launched\\\"\\\"\\\" try: # init virtual Display\",\n \"value): \\\"\\\"\\\"set limit in movement window\\\"\\\"\\\" self._check_open() if (mode not\",\n \"time.time() + 30 while not self.elCss(path['logo']): if time.time() > timeout:\",\n \"self.mode = 'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text)\",\n \"= quant return quant def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open()\",\n \"= self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \\\"\\\"\\\"check\",\n \"display, first of all to be launched\\\"\\\"\\\" try: # init\",\n \"return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"get pos result, where 0 is first\\\"\\\"\\\" evalxpath = path['res']\",\n \"BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity\",\n \"product at position %d\\\" % (x + 1)) return result,\",\n \"for x in mov_list: # find approximate price max_roof =\",\n \"is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self,\",\n \"if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}}\",\n \"def _check_open(self): if self.state == 'open': return True else: raise\",\n \"import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class for\",\n \"sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else: return False class\",\n \"self.price * 0.01 min_roof = self.price - self.price * 0.01\",\n \"def elCss(self, css_path, dom=None): \\\"\\\"\\\"check if element is present by\",\n \"= self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip: {pip} - margin: {margin}\\\")\",\n \"return False class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self, api,\",\n \"raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode\",\n \"name in string and counter not in string: return True\",\n \"name, dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if dom is None:\",\n \"info_label).text return num(val) except KeyError as e: raise exceptions.BaseExc(e) def\",\n \"self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state = 'open' def _check_open(self): if\",\n \"quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant\",\n \"* 0.01 min_roof = self.price - self.price * 0.01 if\",\n \"logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return True def logout(self): \\\"\\\"\\\"logout func\",\n \"exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for\",\n \"self.soup_data = soup return soup def find_id(self): \\\"\\\"\\\"find pos ID\",\n \"exceptions.BaseExc(e) return True def logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try:\",\n \"limit in movement window\\\"\\\"\\\" self._check_open() if (mode not in [\\\"unit\\\",\",\n \"self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement appended to the\",\n \"unit value of stock based on margin, memoized\\\"\\\"\\\" # find\",\n \"title == \\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def\",\n \"expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\"\",\n \"args=[css_path]) def css1(self, css_path, dom=None): \\\"\\\"\\\"return the first value of\",\n \"launched\\\") except Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\") return\",\n \"self.market = True self.records = [] def new_rec(self, rec): \\\"\\\"\\\"add\",\n \"time import re from datetime import datetime from pyvirtualdisplay import\",\n \"datetime from pyvirtualdisplay import Display from bs4 import BeautifulSoup from\",\n \"if name in string and counter in string: logger.debug(\\\"check_name: counter\",\n \"= num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def update(self, soup): \\\"\\\"\\\"update the\",\n \"time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\\\"failed to close pos\",\n \"in string\\\") return False elif name in string and counter\",\n \"splinter import Browser from .glob import Glob from .links import\",\n \"raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state\",\n \"get_unit_value(self): \\\"\\\"\\\"get unit value of stock based on margin, memoized\\\"\\\"\\\"\",\n \"self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def update(self, soup): \\\"\\\"\\\"update\",\n \"= mode self.price = price def __repr__(self): return ' -\",\n \"= 0 while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x))\",\n \"Exception as e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return True def\",\n \"self.vbro.stop() logger.info(\\\"logged out\\\") return True def get_bottom_info(self, info): accepted_values =\",\n \"mode not in ['buy', 'sell']: raise ValueError() self._check_open() price =\",\n \"%f\\\" % (self.price, x.price)) continue # if nothing, return None\",\n \"'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath(\",\n \"unit_value_res except KeyError: logger.debug(\\\"unit_value not found in the collection\\\") pip\",\n \"= brow self.positions = [] self.movements = [] self.stocks =\",\n \"mode == \\\"demo\\\" and datetime.now().isoweekday() in range(5, 8): timeout =\",\n \"None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath,\",\n \"timeout for logging in timeout = time.time() + 30 while\",\n \"the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set mode\",\n \"in as {username}\\\") # check if it's a weekend if\",\n \"[] def new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return self.records\",\n \"try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception: raise\",\n \"= self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name)\",\n \"path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] =\",\n \"logger.debug(\\\"closed pos %s\\\" % self.id) def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\"\",\n \"= api self.product = product self.state = 'initialized' self.insfu =\",\n \"* 0.01 if min_roof < x.price < max_roof: logger.debug(\\\"success: price\",\n \"self.quantity = quant return quant def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\"\",\n \"can't be closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) #\",\n \"self.movements = [] self.stocks = [] # init globals Glob()\",\n \"'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s\",\n \"string and counter in string: logger.debug(\\\"check_name: counter found in string\\\")\",\n \"is None: return None return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos):\",\n \"'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id()\",\n \"movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set mode (buy\",\n \"+ f\\\"[{pos + 1}]\\\" try: res = self.api.xpath(evalxpath)[0] return res\",\n \"fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x\",\n \"expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \\\"\\\"\\\"return the first value\",\n \"unit_val = margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val})\",\n \"self.state = 'initialized' self.insfu = False def open(self, name_counter=None): \\\"\\\"\\\"open\",\n \"class Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def __init__(self, product): self.product\",\n \"it's a weekend if mode == \\\"demo\\\" and datetime.now().isoweekday() in\",\n \"def __init__(self, brow=\\\"firefox\\\"): self.brow_name = brow self.positions = [] self.movements\",\n \"method pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def\",\n \"'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" %\",\n \"['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\"\",\n \"# update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant}\",\n \"return x else: logger.debug(\\\"fail: price %f not corresponding to %f\\\"\",\n \"price max_roof = self.price + self.price * 0.01 min_roof =\",\n \"if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement\",\n \"accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return num(val) except\",\n \"\\\"\\\"\\\" import time import re from datetime import datetime from\",\n \"self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \\\"\\\"\\\"return the\",\n \"raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\")\",\n \"\\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed window\\\")\",\n \"get_pip(mov=self) quant = 1 / pip if hasattr(self, 'quantity'): old_quant\",\n \"False elif name in string and counter not in string:\",\n \"in string: logger.debug(\\\"check_name: counter found in string\\\") return False elif\",\n \"self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a timeout for logging in\",\n \"'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info]\",\n \"self.mode = mode logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\"\",\n \"sell)\\\"\\\"\\\" self._check_open() if mode not in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError()\",\n \"mode, price): self.product = product self.quantity = quantity self.mode =\",\n \"as e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return True def logout(self):\",\n \"True def css(self, css_path, dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if\",\n \"value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except Exception:\",\n \"= 'closed' logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open()\",\n \"= self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product\",\n \"num(val) except KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name):\",\n \"int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self, quant): \\\"\\\"\\\"set\",\n \"logger.info(\\\"logged out\\\") return True def get_bottom_info(self, info): accepted_values = {\",\n \"self.css\\\"\\\"\\\" if dom is None: dom = self.browser def _css1(path,\",\n \"x.product == self.product and x.quantity == self.quantity and x.mode ==\",\n \"= num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def update(self,\",\n \"try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found in\",\n \"'open': return True else: raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close a\",\n \"self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def\",\n \"self.api = api self.product = product self.state = 'initialized' self.insfu\",\n \"for logging in timeout = time.time() + 30 while not\",\n \"if name in string: return True else: return False counter\",\n \"the service. \\\"\\\"\\\" import time import re from datetime import\",\n \"domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path, domm)[0] return expect(_css1, args=[css_path,\",\n \"return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if element\",\n \"api self.product = product self.state = 'initialized' self.insfu = False\",\n \"price corresponding\\\") # bind mov self.mov = x return x\",\n \"try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a timeout for logging\",\n \"self.price)) logger.debug(\\\"%s movement appended to the list\\\" % self.product) self.state\",\n \"Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self, message, value, mult=0.1):\",\n \"if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the\",\n \"name.lower() in fullname: mark_closed_list = [x for x in product.select(\",\n \"not found\\\") return None else: logger.debug(\\\"success: found movement\\\") for x\",\n \"self.vbro.start() logger.debug(\\\"virtual display launched\\\") except Exception: raise exceptions.VBroException() try: self.browser\",\n \"bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list =\",\n \"return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \\\"\\\"\\\"return the first\",\n \"True self.records = [] def new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\"\",\n \"window\\\"\\\"\\\" def __init__(self, api, product): self.api = api self.product =\",\n \"is None: if name in string: return True else: return\",\n \"< max_roof: logger.debug(\\\"success: price corresponding\\\") # bind mov self.mov =\",\n \"value of self.css\\\"\\\"\\\" if dom is None: dom = self.browser\",\n \"\\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return\",\n \"pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update\",\n \"\\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom])\",\n \"\\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def __init__(self, product): self.product = product\",\n \"url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection\",\n \"self.get_result(x) x += 1 logger.debug(\\\"found product at position %d\\\" %\",\n \"\\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if dom is None: dom =\",\n \"if self.state == 'open': return True else: raise exceptions.WindowException() def\",\n \"a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed window\\\") def\",\n \"url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected\",\n \"\\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if dom is None: dom =\",\n \"= True elif title == \\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text))\",\n \"def __init__(self, api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api = api\",\n \"self.state == 'open': return True else: raise exceptions.WindowException() def close(self):\",\n \"css_path, dom=None): \\\"\\\"\\\"return the first value of self.css\\\"\\\"\\\" if dom\",\n \"cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value\",\n \"- self.price * 0.01 if min_roof < x.price < max_roof:\",\n \"in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list:\",\n \"pos %s\\\" % self.id) logger.debug(\\\"closed pos %s\\\" % self.id) def\",\n \"if mode == \\\"demo\\\" and datetime.now().isoweekday() in range(5, 8): timeout\",\n \"unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \\\"\\\"\\\"factory method\",\n \"self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat\",\n \"position\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product = product\",\n \"def search_res(self, res, check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\")\",\n \"exceptions from tradingAPI import exceptions import selenium.common.exceptions # logging import\",\n \"func\\\"\\\"\\\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self,\",\n \"import selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level') class\",\n \"timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a\",\n \"abbreviation\\\"\\\"\\\" if dom is None: dom = self.browser return expect(dom.find_by_css,\",\n \"record\\\"\\\"\\\" self.records.append(rec) return self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self,\",\n \"self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\", message).text if title == \\\"Insufficient\",\n \"api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api = api if isinstance(html_div,\",\n \"tradingAPI import exceptions import selenium.common.exceptions # logging import logging logger\",\n \"self.price + self.price * 0.01 min_roof = self.price - self.price\",\n \"in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError() if not hasattr(self, 'stop_limit'):\",\n \"= mode logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open()\",\n \"try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False\",\n \"= self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return num(val) except KeyError as\",\n \"self.product = product self.quantity = quantity self.mode = mode self.price\",\n \"return True else: return False def get_research_name(self, res): \\\"\\\"\\\"return result\",\n \"failed\\\") raise exceptions.BaseExc(e) return True def logout(self): \\\"\\\"\\\"logout func (quit\",\n \"html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos)\",\n \"'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\" %\",\n \"self.product) self.state = 'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self, res, check_counter=None):\",\n \"if title == \\\"Insufficient Funds\\\": self.insfu = True elif title\",\n \"= self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check\",\n \"margin: {margin}\\\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin\",\n \"= Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\") except Exception: raise exceptions.VBroException()\",\n \"define a timeout for logging in timeout = time.time() +\",\n \"self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode =\",\n \"message\\\") def decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode and update the\",\n \"set_limit(self, category, mode, value): \\\"\\\"\\\"set limit in movement window\\\"\\\"\\\" self._check_open()\",\n \"== 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value))\",\n \"= {'gain': {}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if category ==\",\n \"nothing, return None return None def new_pos(self, html_div): \\\"\\\"\\\"factory method\",\n \"div.close-icon\\\" def close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click()\",\n \"is None: dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self,\",\n \"class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product, quantity, mode, price):\",\n \"update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data = soup return soup\",\n \"exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been closed #\",\n \"dom is None: dom = self.browser def _css1(path, domm): \\\"\\\"\\\"virtual\",\n \"abbreviation\\\"\\\"\\\" if dom is None: dom = self.browser return expect(dom.find_by_xpath,\",\n \"display launched\\\") except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name)\",\n \".utils import num, expect, get_pip # exceptions from tradingAPI import\",\n \"where 0 is first\\\"\\\"\\\" evalxpath = path['res'] + f\\\"[{pos +\",\n \"name, string, counter=None): \\\"\\\"\\\"if both in string return False\\\"\\\"\\\" name\",\n \"return self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api,\",\n \"'closed' logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price()\",\n \"ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\\\"mode set\\\") def\",\n \"functions with the service. \\\"\\\"\\\" import time import re from\",\n \"a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result = self.get_result(0) name = self.get_research_name(result)\",\n \"self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin = self.get_mov_margin()\",\n \"'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value\",\n \"if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else: self.mode =\",\n \"1 / pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant)\",\n \"movement\\\") def search_res(self, res, check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching\",\n \"in fullname: mark_closed_list = [x for x in product.select( \\\"div.quantity-list-input-wrapper\\\")\",\n \"api to interface with the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name\",\n \"pip = get_pip(mov=self) quant = 1 / pip if hasattr(self,\",\n \"closed # set a timeout timeout = time.time() + 10\",\n \"class LowLevelAPI(object): \\\"\\\"\\\"low level api to interface with the service\\\"\\\"\\\"\",\n \"\\\"not started\\\") return False self.vbro.stop() logger.info(\\\"logged out\\\") return True def\",\n \"logger.debug(\\\"set limit\\\") def decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title =\",\n \"def decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode and update the value\\\"\\\"\\\"\",\n \"self._check_open() self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm\",\n \"'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\") def\",\n \"and counter not in string: return True else: return False\",\n \"dom=None): \\\"\\\"\\\"check if element is present by css\\\"\\\"\\\" if dom\",\n \"return self.get_result(x) x += 1 logger.debug(\\\"found product at position %d\\\"\",\n \"name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"):\",\n \"self.product = product self.market = True self.records = [] def\",\n \"% info_label).text return num(val) except KeyError as e: raise exceptions.BaseExc(e)\",\n \"not in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode\",\n \"name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name,\",\n \"from splinter import Browser from .glob import Glob from .links\",\n \"raise TimeoutError(\\\"failed to close pos %s\\\" % self.id) logger.debug(\\\"closed pos\",\n \"gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return gain def bind_mov(self):\",\n \"True else: raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open()\",\n \"= unit_value[self.product] logger.debug(\\\"unit_value found in the collection\\\") return unit_value_res except\",\n \"quant = 1 / pip if hasattr(self, 'quantity'): old_quant ==\",\n \"on margin, memoized\\\"\\\"\\\" # find in the collection try: unit_value\",\n \"% mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value))\",\n \"pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\", message).text if\",\n \"logger.debug(\\\"fail: price %f not corresponding to %f\\\" % (self.price, x.price))\",\n \"x in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if\",\n \"= False def open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible:\",\n \"soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname: mark_closed_list =\",\n \"_css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path, domm)[0] return expect(_css1,\",\n \"= 'open' def _check_open(self): if self.state == 'open': return True\",\n \"None: if name in string: return True else: return False\",\n \"css_path, dom=None): \\\"\\\"\\\"check if element is present by css\\\"\\\"\\\" if\",\n \"BeautifulSoup from splinter import Browser from .glob import Glob from\",\n \"the collection\\\") return unit_value_res except KeyError: logger.debug(\\\"unit_value not found in\",\n \"0 while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if\",\n \"class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product, quantity, mode, price):\",\n \"string = string.lower() if counter is None: if name in\",\n \"mode (buy or sell)\\\"\\\"\\\" self._check_open() if mode not in [\\\"buy\\\",\",\n \"= mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\") def decode(self, message):\",\n \"if dom is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path])\",\n \"self.api.css1('.widget_message div.text').text) # wait until it's been closed # set\",\n \"logger.debug(\\\"searching result\\\") result = self.get_result(0) name = self.get_research_name(result) x =\",\n \"'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if category == 'gain': self.api.xpath( path['limit-gain-%s'\",\n \"= unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name): \\\"\\\"\\\"factory\",\n \"self.id) def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain\",\n \"def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity\",\n \"False counter = counter.lower() if name in string and counter\",\n \"logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class\",\n \"launched\\\") except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser\",\n \"trading alert-box closed\\\") except Exception as e: logger.critical(\\\"login failed\\\") raise\",\n \"num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\") != -1: value += value\",\n \"\\\"\\\"\\\"get current price\\\"\\\"\\\" if mode not in ['buy', 'sell']: raise\",\n \"None else: logger.debug(\\\"success: found movement\\\") for x in mov_list: #\",\n \"new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class Position(PurePosition):\",\n \"close pos %s\\\" % self.id) logger.debug(\\\"closed pos %s\\\" % self.id)\",\n \"< x.price < max_roof: logger.debug(\\\"success: price corresponding\\\") # bind mov\",\n \"value of stock based on margin, memoized\\\"\\\"\\\" # find in\",\n \"x in mov_list: # find approximate price max_roof = self.price\",\n \"corresponding to %f\\\" % (self.price, x.price)) continue # if nothing,\",\n \"from bs4 import BeautifulSoup from splinter import Browser from .glob\",\n \"api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data\",\n \"appended to the list\\\" % self.product) self.state = 'conclused' logger.debug(\\\"confirmed\",\n \"name in string: return True else: return False counter =\",\n \"found in the collection\\\") return unit_value_res except KeyError: logger.debug(\\\"unit_value not\",\n \"quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return quant def set_quantity(self,\",\n \"expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \\\"\\\"\\\"check if element is\",\n \"self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def\",\n \"dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None):\",\n \"logger.debug(\\\"confirmed movement\\\") def search_res(self, res, check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\"\",\n \"1 logger.debug(\\\"found product at position %d\\\" % (x + 1))\",\n \"counter in string: logger.debug(\\\"check_name: counter found in string\\\") return False\",\n \"pos result, where 0 is first\\\"\\\"\\\" evalxpath = path['res'] +\",\n \"== \\\"Insufficient Funds\\\": self.insfu = True elif title == \\\"Maximum\",\n \"self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x)\",\n \"import path from .utils import num, expect, get_pip # exceptions\",\n \"and x.quantity == self.quantity and x.mode == self.mode] if not\",\n \"logger.debug(\\\"success: price corresponding\\\") # bind mov self.mov = x return\",\n \"exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode'] if hasattr(self,\",\n \"self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'):\",\n \"launch\\\") return True def css(self, css_path, dom=None): \\\"\\\"\\\"css find function\",\n \"path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' %\",\n \"\\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\"\",\n \"product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else: return False class MovementWindow(object): \\\"\\\"\\\"add\",\n \"result = self.get_result(0) name = self.get_research_name(result) x = 0 while\",\n \"not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\\\"weekend trading alert-box not\",\n \"import datetime from pyvirtualdisplay import Display from bs4 import BeautifulSoup\",\n \"raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except\",\n \"self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg)\",\n \"class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self, api, product): self.api\",\n \"\\\"\\\"\\\"check if element is present by css\\\"\\\"\\\" if dom is\",\n \"in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode,\",\n \"quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity set\\\")\",\n \"logger.debug(\\\"initialized stop_limit\\\") if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value))\",\n \"not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is\",\n \"[\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit\",\n \"movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for x in\",\n \"return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \\\"\\\"\\\"name find\",\n \"!= 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category\",\n \"product self.quantity = quantity self.mode = mode self.price = price\",\n \"mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if\",\n \"res except Exception: return None def set_limit(self, category, mode, value):\",\n \"\\\"\\\"\\\"decode and update the value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\", message).text\",\n \"except Exception: if msg_text.lower().find(\\\"higher\\\") != -1: value += value *\",\n \"res, check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result =\",\n \"def css1(self, css_path, dom=None): \\\"\\\"\\\"return the first value of self.css\\\"\\\"\\\"\",\n \"gain def bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\")\",\n \"% mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain',\",\n \"selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() #\",\n \"x.mode == self.mode] if not mov_list: logger.debug(\\\"fail: mov not found\\\")\",\n \"logger.debug(\\\"check_name: counter found in string\\\") return False elif name in\",\n \"via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text\",\n \"= self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\", message).text if title ==\",\n \"* mult return value else: self.decode(message) return None def get_mov_margin(self):\",\n \"KeyError: logger.debug(\\\"unit_value not found in the collection\\\") pip = get_pip(mov=self)\",\n \".text return float(sell_price) else: return False class MovementWindow(object): \\\"\\\"\\\"add movement\",\n \"path from .utils import num, expect, get_pip # exceptions from\",\n \"movement\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product = product\",\n \"(mode not in [\\\"unit\\\", \\\"value\\\"] or category not in [\\\"gain\\\",\",\n \"def _css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path, domm)[0] return\",\n \"in ['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1( \\\"div.orderdialog\",\n \"PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product\",\n \"ValueError() self._check_open() price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price\",\n \"in string and counter not in string: return True else:\",\n \"price def get_unit_value(self): \\\"\\\"\\\"get unit value of stock based on\",\n \"'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val =\",\n \"close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close position via\",\n \"def __init__(self, product, quantity, mode, price): self.product = product self.quantity\",\n \"\\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity set\\\") def\",\n \"= time.time() + 10 while not self.elCss(path['alert-box']): if time.time() >\",\n \"raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's\",\n \"expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \\\"\\\"\\\"name find function\",\n \"def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if dom\",\n \"num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def update(self, soup):\",\n \"text = self.api.css1(\\\"div.text\\\", message).text if title == \\\"Insufficient Funds\\\": self.insfu\",\n \"return None else: logger.debug(\\\"success: found movement\\\") for x in mov_list:\",\n \"self.api.xpath(evalxpath)[0] return res except Exception: return None def set_limit(self, category,\",\n \"{'gain': {}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if category == 'gain':\",\n \"self._check_open() price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price =\",\n \"+ 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise\",\n \"value logger.debug(\\\"set limit\\\") def decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title\",\n \"# check if it's a weekend if mode == \\\"demo\\\"\",\n \"for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result = self.get_result(0) name =\",\n \"price\\\"\\\"\\\" if mode not in ['buy', 'sell']: raise ValueError() self._check_open()\",\n \"if mode not in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode +\",\n \"self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api, html_div):\",\n \"= pos_id return pos_id @property def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\"\",\n \"if category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category\",\n \"\\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api = api if isinstance(html_div, type('')): self.soup_data\",\n \"to %s\\\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise\",\n \"logger.debug(\\\"%s movement appended to the list\\\" % self.product) self.state =\",\n \"if dom is None: dom = self.browser def _css1(path, domm):\",\n \"a timeout for logging in timeout = time.time() + 30\",\n \"in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product]\",\n \"def get_mov_margin(self): \\\"\\\"\\\"get the margin of the movement\\\"\\\"\\\" self._check_open() return\",\n \"self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement appended to the list\\\"\",\n \"% (x + 1)) return result, name def check_name(self, name,\",\n \"bind mov self.mov = x return x else: logger.debug(\\\"fail: price\",\n \"'buy' else: self.mode = 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin =\",\n \"not in ['buy', 'sell']: raise ValueError() self._check_open() price = num(self.api.css1(\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides\",\n \"\\\"\\\"\\\"return result name\\\"\\\"\\\" if res is None: return None return\",\n \"dom is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def\",\n \"time.time() > timeout: logger.warning(\\\"weekend trading alert-box not closed\\\") break if\",\n \"or category not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError() if\",\n \"found movement\\\") for x in mov_list: # find approximate price\",\n \"to launch\\\") return True def css(self, css_path, dom=None): \\\"\\\"\\\"css find\",\n \"\\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level functions\",\n \"range(5, 8): timeout = time.time() + 10 while not self.elCss(path['alert-box']):\",\n \"import Browser from .glob import Glob from .links import path\",\n \"num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id = self.find_id() def update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\"\",\n \"== 'open': return True else: raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close\",\n \"self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product,\",\n \"found in the collection\\\") pip = get_pip(mov=self) quant = 1\",\n \"logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def __init__(self, product):\",\n \"def set_limit(self, category, mode, value): \\\"\\\"\\\"set limit in movement window\\\"\\\"\\\"\",\n \"self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']):\",\n \"the corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for\",\n \"mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if mode not in ['buy', 'sell']:\",\n \"= 'initialized' self.insfu = False def open(self, name_counter=None): \\\"\\\"\\\"open the\",\n \"val = self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return num(val) except KeyError\",\n \"+ self.price * 0.01 min_roof = self.price - self.price *\",\n \"name def check_name(self, name, string, counter=None): \\\"\\\"\\\"if both in string\",\n \"set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity\",\n \"in timeout = time.time() + 30 while not self.elCss(path['logo']): if\",\n \"with with given data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id = pos_id\",\n \"value elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath(\",\n \"logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1\",\n \"self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\\\"failed to close\",\n \"if res is None: return None return self.api.css1(\\\"span.instrument-name\\\", res).text def\",\n \"= BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text\",\n \"def __init__(self, product): self.product = product self.market = True self.records\",\n \"= price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode),\",\n \"self.product = product self.state = 'initialized' self.insfu = False def\",\n \"function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url)\",\n \"'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for\",\n \"\\\"\\\"\\\"launch browser and virtual display, first of all to be\",\n \"= self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode\",\n \"# define a timeout for logging in timeout = time.time()\",\n \"= counter.lower() if name in string and counter in string:\",\n \"x = 0 while not self.check_name(res, name, counter=check_counter): name =\",\n \"object\\\"\\\"\\\" def __init__(self, api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api =\",\n \"return True else: raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\"\",\n \"in mov_list: # find approximate price max_roof = self.price +\",\n \"x.price < max_roof: logger.debug(\\\"success: price corresponding\\\") # bind mov self.mov\",\n \"if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state = 'open' def\",\n \"self.api.css1(path['close']).click() self.state = 'closed' logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm the\",\n \"as e: raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup(\",\n \"css1(self, css_path, dom=None): \\\"\\\"\\\"return the first value of self.css\\\"\\\"\\\" if\",\n \"args=[css_path, dom]) def search_name(self, name, dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\"\",\n \"import re from datetime import datetime from pyvirtualdisplay import Display\",\n \"-1] if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else:\",\n \"'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label\",\n \"if x.product == self.product and x.quantity == self.quantity and x.mode\",\n \"def find_id(self): \\\"\\\"\\\"find pos ID with with given data\\\"\\\"\\\" pos_id\",\n \"in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode =\",\n \"service. \\\"\\\"\\\" import time import re from datetime import datetime\",\n \"window\\\") def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg\",\n \"until it's been closed # set a timeout timeout =\",\n \"the low level functions with the service. \\\"\\\"\\\" import time\",\n \"def search_name(self, name, dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if dom\",\n \"self.mode] if not mov_list: logger.debug(\\\"fail: mov not found\\\") return None\",\n \"the value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except\",\n \"= self.api.xpath(evalxpath)[0] return res except Exception: return None def set_limit(self,\",\n \"self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product, quantity, mode,\",\n \"(self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\\\"market closed, position can't be\",\n \"in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell' self.price\",\n \"self.price - self.price * 0.01 if min_roof < x.price <\",\n \"exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False self.vbro.stop() logger.info(\\\"logged out\\\") return True\",\n \"and datetime.now().isoweekday() in range(5, 8): timeout = time.time() + 10\",\n \"level functions with the service. \\\"\\\"\\\" import time import re\",\n \"f\\\"[{pos + 1}]\\\" try: res = self.api.xpath(evalxpath)[0] return res except\",\n \"quant return quant def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant)))\",\n \"mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] =\",\n \"try: # init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual\",\n \"self.set_quantity(old_quant) unit_val = margin / quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product:\",\n \"string: return True else: return False counter = counter.lower() if\",\n \"soup def find_id(self): \\\"\\\"\\\"find pos ID with with given data\\\"\\\"\\\"\",\n \"== self.quantity and x.mode == self.mode] if not mov_list: logger.debug(\\\"fail:\",\n \"def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg =\",\n \"logger.debug(f\\\"quant: {quant} - pip: {pip} - margin: {margin}\\\") if 'old_quant'\",\n \"time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip: {pip} -\",\n \"except Exception as e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return True\",\n \"category, mode, value): \\\"\\\"\\\"set limit in movement window\\\"\\\"\\\" self._check_open() if\",\n \"- margin: {margin}\\\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val =\",\n \"get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain\",\n \"domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None): \\\"\\\"\\\"name\",\n \"if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in\",\n \"def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class\",\n \"not in string: return True else: return False def get_research_name(self,\",\n \"\\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\",\",\n \"unit_val}) return unit_val def new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return\",\n \"product self.state = 'open' def _check_open(self): if self.state == 'open':\",\n \"self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode']\",\n \"product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list: sell_price\",\n \"x.price)) continue # if nothing, return None return None def\",\n \"get_result(self, pos): \\\"\\\"\\\"get pos result, where 0 is first\\\"\\\"\\\" evalxpath\",\n \"def get_unit_value(self): \\\"\\\"\\\"get unit value of stock based on margin,\",\n \"None: dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath,\",\n \"= gain return gain def bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\"\",\n \"0.01 if min_roof < x.price < max_roof: logger.debug(\\\"success: price corresponding\\\")\",\n \"elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s'\",\n \"ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss':\",\n \"close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close\",\n \"username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting\",\n \"string: return True else: return False def get_research_name(self, res): \\\"\\\"\\\"return\",\n \"self.brow_name = brow self.positions = [] self.movements = [] self.stocks\",\n \"for x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product,\",\n \"dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if dom is None: dom\",\n \"raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x\",\n \"\\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\"\",\n \"num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else: self.mode\",\n \"from .links import path from .utils import num, expect, get_pip\",\n \"'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the site time.sleep(0.5)\",\n \"\\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant\",\n \"is None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self,\",\n \"# wait until it's been closed # set a timeout\",\n \"selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object):\",\n \"self.price = price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product,\",\n \"self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if\",\n \"dom = self.browser def _css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return\",\n \"exceptions import selenium.common.exceptions # logging import logging logger = logging.getLogger('tradingAPI.low_level')\",\n \"get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if mode not in ['buy',\",\n \"mult return value else: self.decode(message) return None def get_mov_margin(self): \\\"\\\"\\\"get\",\n \"# exceptions from tradingAPI import exceptions import selenium.common.exceptions # logging\",\n \"res is None: return None return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self,\",\n \"unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found in the collection\\\") return unit_value_res\",\n \"- pip: {pip} - margin: {margin}\\\") if 'old_quant' in locals():\",\n \"pos_id @property def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\"\",\n \"else: self.decode(message) return None def get_mov_margin(self): \\\"\\\"\\\"get the margin of\",\n \"result, where 0 is first\\\"\\\"\\\" evalxpath = path['res'] + f\\\"[{pos\",\n \"def set_mode(self, mode): \\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\" self._check_open() if\",\n \"soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data = soup return soup def\",\n \"-*- \\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low level\",\n \"self.soup_data['id'] self.id = pos_id return pos_id @property def close_tag(self): \\\"\\\"\\\"obtain\",\n \"in range(5, 8): timeout = time.time() + 10 while not\",\n \"xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if dom is\",\n \"self.quantity and x.mode == self.mode] if not mov_list: logger.debug(\\\"fail: mov\",\n \"\\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result = self.get_result(0) name\",\n \"raise exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\") return True def css(self,\",\n \"raise exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text))\",\n \"logger.debug(\\\"found product at position %d\\\" % (x + 1)) return\",\n \"'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if\",\n \"logger.debug(\\\"success: found movement\\\") for x in mov_list: # find approximate\",\n \"if (mode not in [\\\"unit\\\", \\\"value\\\"] or category not in\",\n \"return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find function\",\n \"position can't be closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text)\",\n \"set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if mode not\",\n \"= quantity self.mode = mode self.price = price class PurePosition(object):\",\n \"'open' def _check_open(self): if self.state == 'open': return True else:\",\n \"(x + 1)) return result, name def check_name(self, name, string,\",\n \"if dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath])\",\n \"{}} logger.debug(\\\"initialized stop_limit\\\") if category == 'gain': self.api.xpath( path['limit-gain-%s' %\",\n \"\\\"\\\"\\\"get current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return\",\n \"if counter is None: if name in string: return True\",\n \"to interface with the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name =\",\n \"self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\\\"mode set\\\") def get_quantity(self):\",\n \"tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text ==\",\n \"div.text').text) # wait until it's been closed # set a\",\n \"res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if res is None: return None\",\n \"counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise\",\n \"+ 1)) return result, name def check_name(self, name, string, counter=None):\",\n \"self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] =\",\n \"while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\\\"login failed\\\") raise\",\n \"Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def __init__(self, product): self.product =\",\n \"result\\\") result = self.get_result(0) name = self.get_research_name(result) x = 0\",\n \"elif title == \\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\")\",\n \"= BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"): fullname =\",\n \"'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self, res, check_counter=None): \\\"\\\"\\\"search for a\",\n \"[] # init globals Glob() def launch(self): \\\"\\\"\\\"launch browser and\",\n \"= product self.state = 'open' def _check_open(self): if self.state ==\",\n \"old_quant == self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin\",\n \"corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for x\",\n \"\\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" % url)\",\n \"dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password,\",\n \"[] self.movements = [] self.stocks = [] # init globals\",\n \"self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self, name):\",\n \"not found in the collection\\\") pip = get_pip(mov=self) quant =\",\n \"import Glob from .links import path from .utils import num,\",\n \"window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if\",\n \"product self.market = True self.records = [] def new_rec(self, rec):\",\n \"if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else: return\",\n \"not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError() if not hasattr(self,\",\n \"def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total',\",\n \"dom is None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def\",\n \"init virtual Display self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\")\",\n \"text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\", message).text\",\n \"def launch(self): \\\"\\\"\\\"launch browser and virtual display, first of all\",\n \"check_name(self, name, string, counter=None): \\\"\\\"\\\"if both in string return False\\\"\\\"\\\"\",\n \"mov not found\\\") return None else: logger.debug(\\\"success: found movement\\\") for\",\n \"res).text def get_result(self, pos): \\\"\\\"\\\"get pos result, where 0 is\",\n \"logger.debug(\\\"weekend trading alert-box closed\\\") except Exception as e: logger.critical(\\\"login failed\\\")\",\n \"product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x for x\",\n \"self.records.append(rec) return self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product,\",\n \"args=[xpath]) def elCss(self, css_path, dom=None): \\\"\\\"\\\"check if element is present\",\n \"def open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else:\",\n \"= get_pip(mov=self) quant = 1 / pip if hasattr(self, 'quantity'):\",\n \"is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter)\",\n \"mov_list: logger.debug(\\\"fail: mov not found\\\") return None else: logger.debug(\\\"success: found\",\n \"= self.browser def _css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path,\",\n \"be launched\\\"\\\"\\\" try: # init virtual Display self.vbro = Display()\",\n \"a weekend if mode == \\\"demo\\\" and datetime.now().isoweekday() in range(5,\",\n \"self.stocks = [] # init globals Glob() def launch(self): \\\"\\\"\\\"launch\",\n \"set a timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag):\",\n \"\\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if dom is None: dom =\",\n \"/ pip if hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) #\",\n \"name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click()\",\n \"else: logger.debug(\\\"success: found movement\\\") for x in mov_list: # find\",\n \"None def get_mov_margin(self): \\\"\\\"\\\"get the margin of the movement\\\"\\\"\\\" self._check_open()\",\n \"self.price = price return price def get_unit_value(self): \\\"\\\"\\\"get unit value\",\n \"%s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" % url) except\",\n \"self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if\",\n \"name = self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res)\",\n \"def bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list\",\n \"return None def set_limit(self, category, mode, value): \\\"\\\"\\\"set limit in\",\n \"soup return soup def find_id(self): \\\"\\\"\\\"find pos ID with with\",\n \"div.title').text == 'Market Closed'): logger.error(\\\"market closed, position can't be closed\\\")\",\n \"= quantity self.mode = mode self.price = price def __repr__(self):\",\n \"pos ID with with given data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id\",\n \"Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\")\",\n \"self.id = pos_id return pos_id @property def close_tag(self): \\\"\\\"\\\"obtain close\",\n \"and update the value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\", message).text return\",\n \"timeout: raise TimeoutError(\\\"failed to close pos %s\\\" % self.id) logger.debug(\\\"closed\",\n \"self.price = price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity),\",\n \"self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name, dom=None):\",\n \"%s\\\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise try:\",\n \"div\\\"\\\"\\\" self.api = api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div,\",\n \"= num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else:\",\n \"browser)\\\"\\\"\\\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return\",\n \"self.soup_data = html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if\",\n \"= product self.quantity = quantity self.mode = mode self.price =\",\n \"utf-8 -*- \\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the low\",\n \"raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"):\",\n \"self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \\\"\\\"\\\"check if\",\n \"msg_text = self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\")\",\n \"{pip} - margin: {margin}\\\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val\",\n \"else: return False counter = counter.lower() if name in string\",\n \"profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return gain def\",\n \"module provides the low level functions with the service. \\\"\\\"\\\"\",\n \"% (self.price, x.price)) continue # if nothing, return None return\",\n \"self.get_result(0) name = self.get_research_name(result) x = 0 while not self.check_name(res,\",\n \"is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self,\",\n \"class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api, html_div): \\\"\\\"\\\"initialized from\",\n \"\\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\"\",\n \"def logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try: self.browser.quit() except Exception:\",\n \"return price def get_unit_value(self): \\\"\\\"\\\"get unit value of stock based\",\n \"else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click()\",\n \"'html.parser') else: self.soup_data = html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity =\",\n \"and virtual display, first of all to be launched\\\"\\\"\\\" try:\",\n \"import num, expect, get_pip # exceptions from tradingAPI import exceptions\",\n \"elif title == \\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title\",\n \"mode self.price = price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self,\",\n \"and x.mode == self.mode] if not mov_list: logger.debug(\\\"fail: mov not\",\n \"return False elif name in string and counter not in\",\n \"self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath( path['limit-gain-%s' %\",\n \"else: raise exceptions.WindowException() def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click()\",\n \"\\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product =\",\n \"unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found in the\",\n \"url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password)\",\n \"if min_roof < x.price < max_roof: logger.debug(\\\"success: price corresponding\\\") #\",\n \"dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None):\",\n \"exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\"))\",\n \"movement window\\\"\\\"\\\" self._check_open() if (mode not in [\\\"unit\\\", \\\"value\\\"] or\",\n \"quant logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if\",\n \"x return x else: logger.debug(\\\"fail: price %f not corresponding to\",\n \"not closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\")\",\n \"== self.quantity self.set_quantity(quant) # update the site time.sleep(0.5) margin =\",\n \"self.get_research_name(self.get_result(x)) if name is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if\",\n \"x += 1 logger.debug(\\\"found product at position %d\\\" % (x\",\n \"\\\"Insufficient Funds\\\": self.insfu = True elif title == \\\"Maximum Quantity\",\n \"time.time() > timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in\",\n \"update the site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant} -\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module\",\n \"result, name def check_name(self, name, string, counter=None): \\\"\\\"\\\"if both in\",\n \"args=[css_path]) def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if element is present\",\n \"elif name in string and counter not in string: return\",\n \"name in string and counter in string: logger.debug(\\\"check_name: counter found\",\n \"widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x in ['quantity',\",\n \"dom is None: dom = self.browser return expect(dom.find_by_name, args=[name]) def\",\n \"window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result,\",\n \"of stock based on margin, memoized\\\"\\\"\\\" # find in the\",\n \"self.get_research_name(result) x = 0 while not self.check_name(res, name, counter=check_counter): name\",\n \"self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login\",\n \"= string.lower() if counter is None: if name in string:\",\n \"== 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss':\",\n \"if not mov_list: logger.debug(\\\"fail: mov not found\\\") return None else:\",\n \"max_roof = self.price + self.price * 0.01 min_roof = self.price\",\n \"logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\" def\",\n \"mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else: return False\",\n \"+= 1 logger.debug(\\\"found product at position %d\\\" % (x +\",\n \"\\\"sell\\\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\\\"mode\",\n \"self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\") def decode(self, message): \\\"\\\"\\\"decode text\",\n \"brow=\\\"firefox\\\"): self.brow_name = brow self.positions = [] self.movements = []\",\n \"'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\"\",\n \"= 'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self, res, check_counter=None): \\\"\\\"\\\"search for\",\n \"product, quantity, mode, price): self.product = product self.quantity = quantity\",\n \"\\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x\",\n \"category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif\",\n \"exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode and\",\n \"return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low\",\n \"name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state =\",\n \"self.gain = gain return gain def bind_mov(self): \\\"\\\"\\\"bind the corresponding\",\n \"{margin}\\\") if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin /\",\n \"Funds\\\": self.insfu = True elif title == \\\"Maximum Quantity Limit\\\":\",\n \"self.api.movements if x.product == self.product and x.quantity == self.quantity and\",\n \"import time import re from datetime import datetime from pyvirtualdisplay\",\n \"{username}\\\") # check if it's a weekend if mode ==\",\n \"!= -1: value += value * mult return value else:\",\n \"title == \\\"Insufficient Funds\\\": self.insfu = True elif title ==\",\n \"rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return self.records class Movement(object): \\\"\\\"\\\"class-storing\",\n \"product): self.api = api self.product = product self.state = 'initialized'\",\n \"%f not corresponding to %f\\\" % (self.price, x.price)) continue #\",\n \"'.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level api to\",\n \"True else: return False def get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\"\",\n \"\\\"\\\"\\\"set limit in movement window\\\"\\\"\\\" self._check_open() if (mode not in\",\n \"logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant =\",\n \"name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position\",\n \"out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a timeout\",\n \"> timeout: logger.warning(\\\"weekend trading alert-box not closed\\\") break if self.elCss(path['alert-box']):\",\n \"globals Glob() def launch(self): \\\"\\\"\\\"launch browser and virtual display, first\",\n \"\\\"\\\"\\\"if both in string return False\\\"\\\"\\\" name = name.lower() string\",\n \"= self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\") !=\",\n \"expect, get_pip # exceptions from tradingAPI import exceptions import selenium.common.exceptions\",\n \"current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return gain\",\n \"for product in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in\",\n \"self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\") def decode(self,\",\n \"not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\\\"initialized\",\n \"weekend if mode == \\\"demo\\\" and datetime.now().isoweekday() in range(5, 8):\",\n \"= price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product, quantity,\",\n \"msg_text.lower().find(\\\"higher\\\") != -1: value += value * mult return value\",\n \"a timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1)\",\n \"virtual display, first of all to be launched\\\"\\\"\\\" try: #\",\n \"price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price = price\",\n \"get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result':\",\n \"get_pip # exceptions from tradingAPI import exceptions import selenium.common.exceptions #\",\n \"css(self, css_path, dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if dom is\",\n \"return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def search_name(self, name,\",\n \"interface with the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name = brow\",\n \"= self.get_research_name(result) x = 0 while not self.check_name(res, name, counter=check_counter):\",\n \"= Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found in the collection\\\")\",\n \"CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as {username}\\\") # check if it's\",\n \"to the list\\\" % self.product) self.state = 'conclused' logger.debug(\\\"confirmed movement\\\")\",\n \"message, value, mult=0.1): \\\"\\\"\\\"decode and update the value\\\"\\\"\\\" try: msg_text\",\n \"string return False\\\"\\\"\\\" name = name.lower() string = string.lower() if\",\n \"found in string\\\") return False elif name in string and\",\n \"selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\\\"market closed, position\",\n \"name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\\\"found product at\",\n \"def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain =\",\n \"pos %s\\\" % self.id) def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain\",\n \"None def set_limit(self, category, mode, value): \\\"\\\"\\\"set limit in movement\",\n \"self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category == 'loss': self.api.xpath( path['limit-loss-%s'\",\n \"__repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object):\",\n \"\\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum Quantity\",\n \"= product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price) else: return False class MovementWindow(object):\",\n \"= self.get_result(0) name = self.get_research_name(result) x = 0 while not\",\n \"= time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() >\",\n \"widg = self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x\",\n \"raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been closed\",\n \"value * mult return value else: self.decode(message) return None def\",\n \"time.time() > timeout: raise TimeoutError(\\\"failed to close pos %s\\\" %\",\n \"the list\\\" % self.product) self.state = 'conclused' logger.debug(\\\"confirmed movement\\\") def\",\n \".glob import Glob from .links import path from .utils import\",\n \"local func\\\"\\\"\\\" return self.css(path, domm)[0] return expect(_css1, args=[css_path, dom]) def\",\n \"return quant def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity\",\n \"html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api = api if isinstance(html_div, type('')):\",\n \"\\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self, message,\",\n \"= accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return num(val)\",\n \"= self.api.css1(\\\"div.text\\\", message).text if title == \\\"Insufficient Funds\\\": self.insfu =\",\n \"timeout timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if\",\n \"if dom is None: dom = self.browser return expect(dom.find_by_css, args=[css_path])\",\n \"to %f\\\" % (self.price, x.price)) continue # if nothing, return\",\n \"first of all to be launched\\\"\\\"\\\" try: # init virtual\",\n \"approximate price max_roof = self.price + self.price * 0.01 min_roof\",\n \"'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label =\",\n \"collection\\\") return unit_value_res except KeyError: logger.debug(\\\"unit_value not found in the\",\n \"string: logger.debug(\\\"check_name: counter found in string\\\") return False elif name\",\n \"self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\") != -1:\",\n \"def new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos = self.Position(self, html_div)\",\n \"\\\"\\\"\\\"return the first value of self.css\\\"\\\"\\\" if dom is None:\",\n \"= time.time() + 30 while not self.elCss(path['logo']): if time.time() >\",\n \"= quant logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\"\",\n \"mode logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant\",\n \"return False\\\"\\\"\\\" name = name.lower() string = string.lower() if counter\",\n \"\\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name,\",\n \"string, counter=None): \\\"\\\"\\\"if both in string return False\\\"\\\"\\\" name =\",\n \"= [x for x in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\")\",\n \"return soup def find_id(self): \\\"\\\"\\\"find pos ID with with given\",\n \"in [\\\"unit\\\", \\\"value\\\"] or category not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]):\",\n \"{}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if category == 'gain': self.api.xpath(\",\n \"raise exceptions.BaseExc(e) return True def logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\"\",\n \"= self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path, dom=None): \\\"\\\"\\\"return\",\n \"logger.error(\\\"market closed, position can't be closed\\\") raise exceptions.MarketClosed() raise exceptions.WidgetException(\",\n \"and counter in string: logger.debug(\\\"check_name: counter found in string\\\") return\",\n \"price return price def get_unit_value(self): \\\"\\\"\\\"get unit value of stock\",\n \"self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None:\",\n \"This module provides the low level functions with the service.\",\n \"= [] self.stocks = [] # init globals Glob() def\",\n \"return True def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free',\",\n \"Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\"\",\n \"return result, name def check_name(self, name, string, counter=None): \\\"\\\"\\\"if both\",\n \"accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin':\",\n \"if self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\\\"found\",\n \"= mode self.stop_limit[category]['value'] = value elif category == 'both': self.api.xpath(\",\n \"result, product = self.search_res(self.product, name_counter) result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product\",\n \"%s\\\" % self.id) def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain =\",\n \"unit_val def new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self, name)\",\n \"mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\") def decode(self, message): \\\"\\\"\\\"decode\",\n \"isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div\",\n \"failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as {username}\\\") # check\",\n \"with given data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id = pos_id return\",\n \"from tradingAPI import exceptions import selenium.common.exceptions # logging import logging\",\n \"soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"): fullname\",\n \"(\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell'\",\n \"while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\\\"weekend trading alert-box\",\n \"self.price * 0.01 if min_roof < x.price < max_roof: logger.debug(\\\"success:\",\n \"update the value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\", message).text return num(msg_text)\",\n \"logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed to\",\n \"None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path,\",\n \"all(x for x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement(\",\n \"if msg_text.lower().find(\\\"higher\\\") != -1: value += value * mult return\",\n \"\\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self, api, product): self.api = api\",\n \"TimeoutError(\\\"failed to close pos %s\\\" % self.id) logger.debug(\\\"closed pos %s\\\"\",\n \"= [] # init globals Glob() def launch(self): \\\"\\\"\\\"launch browser\",\n \"str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level api to interface with the\",\n \"return unit_val def new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self,\",\n \"in movement window\\\"\\\"\\\" self._check_open() if (mode not in [\\\"unit\\\", \\\"value\\\"]\",\n \"logger.debug(f\\\"visiting %s\\\" % url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" % url)\",\n \"raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False self.vbro.stop() logger.info(\\\"logged out\\\") return\",\n \"value += value * mult return value else: self.decode(message) return\",\n \"not mov_list: logger.debug(\\\"fail: mov not found\\\") return None else: logger.debug(\\\"success:\",\n \"category not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError() if not\",\n \"{self.brow_name} launched\\\") except Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\")\",\n \"price %f not corresponding to %f\\\" % (self.price, x.price)) continue\",\n \"check if it's a weekend if mode == \\\"demo\\\" and\",\n \"bs4 import BeautifulSoup from splinter import Browser from .glob import\",\n \"logger.critical(\\\"connection timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define\",\n \"movement\\\") for x in mov_list: # find approximate price max_roof\",\n \"= self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for\",\n \"self.find_id() def update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data = soup\",\n \"== \\\"demo\\\" and datetime.now().isoweekday() in range(5, 8): timeout = time.time()\",\n \"str(self.quantity), str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level api to interface\",\n \"elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if element is present by css\\\"\\\"\\\"\",\n \"css\\\"\\\"\\\" if dom is None: dom = self.browser return expect(dom.is_element_present_by_xpath,\",\n \"self.state = 'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self, res, check_counter=None): \\\"\\\"\\\"search\",\n \"name\\\"\\\"\\\" if res is None: return None return self.api.css1(\\\"span.instrument-name\\\", res).text\",\n \"self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos): \\\"\\\"\\\"get pos result, where 0\",\n \"self.mode = mode self.price = price def __repr__(self): return '\",\n \"Exception: if msg_text.lower().find(\\\"higher\\\") != -1: value += value * mult\",\n \"pos): \\\"\\\"\\\"get pos result, where 0 is first\\\"\\\"\\\" evalxpath =\",\n \"except Exception: return None def set_limit(self, category, mode, value): \\\"\\\"\\\"set\",\n \"__init__(self, api, product): self.api = api self.product = product self.state\",\n \"self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\") except Exception as e:\",\n \"first\\\"\\\"\\\" evalxpath = path['res'] + f\\\"[{pos + 1}]\\\" try: res\",\n \"raise exceptions.WidgetException(widg) if all(x for x in ['quantity', 'mode'] if\",\n \"method pattern\\\"\\\"\\\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos) return pos\",\n \"self.mode, self.price)) logger.debug(\\\"%s movement appended to the list\\\" % self.product)\",\n \"out\\\") return True def get_bottom_info(self, info): accepted_values = { 'free_funds':\",\n \"0.01 min_roof = self.price - self.price * 0.01 if min_roof\",\n \"quant self.unit_value = unit_val Glob().unit_valueHandler.add_val({self.product: unit_val}) return unit_val def new_mov(self,\",\n \"self._check_open() if mode not in [\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode\",\n \"get_mov_margin(self): \\\"\\\"\\\"get the margin of the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text)\",\n \"self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market\",\n \"mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value']\",\n \"mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\" %\",\n \"mult=0.1): \\\"\\\"\\\"decode and update the value\\\"\\\"\\\" try: msg_text = self.api.css1(\\\"div.text\\\",\",\n \"margin of the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode):\",\n \"is first\\\"\\\"\\\" evalxpath = path['res'] + f\\\"[{pos + 1}]\\\" try:\",\n \"virtual Display self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\") except\",\n \"logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try: self.browser.quit() except Exception: raise\",\n \"self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find\",\n \"return gain def bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger =\",\n \"= soup return soup def find_id(self): \\\"\\\"\\\"find pos ID with\",\n \"exceptions.MarketClosed() raise exceptions.WidgetException( self.api.css1('.widget_message div.text').text) # wait until it's been\",\n \"x else: logger.debug(\\\"fail: price %f not corresponding to %f\\\" %\",\n \"self.brow_name, \\\"failed to launch\\\") return True def css(self, css_path, dom=None):\",\n \"from .utils import num, expect, get_pip # exceptions from tradingAPI\",\n \"return False counter = counter.lower() if name in string and\",\n \"% self.id) def get_gain(self): \\\"\\\"\\\"get current profit\\\"\\\"\\\" gain = num(self.soup_data.select(\\\"td.ppl\\\")[0].text)\",\n \"None: return None return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos): \\\"\\\"\\\"get\",\n \"value else: self.decode(message) return None def get_mov_margin(self): \\\"\\\"\\\"get the margin\",\n \"price): self.product = product self.quantity = quantity self.mode = mode\",\n \"self.product = product self.state = 'open' def _check_open(self): if self.state\",\n \"login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try:\",\n \"self.state = 'open' def _check_open(self): if self.state == 'open': return\",\n \"== self.mode] if not mov_list: logger.debug(\\\"fail: mov not found\\\") return\",\n \"return float(sell_price) else: return False class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\"\",\n \"timeout: logger.warning(\\\"weekend trading alert-box not closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click()\",\n \"except Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\") return True\",\n \"\\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower()\",\n \"hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\")\",\n \"margin, memoized\\\"\\\"\\\" # find in the collection try: unit_value =\",\n \"try: msg_text = self.api.css1(\\\"div.text\\\", message).text return num(msg_text) except Exception: if\",\n \"logger.debug(\\\"fail: mov not found\\\") return None else: logger.debug(\\\"success: found movement\\\")\",\n \"0 is first\\\"\\\"\\\" evalxpath = path['res'] + f\\\"[{pos + 1}]\\\"\",\n \"to close pos %s\\\" % self.id) logger.debug(\\\"closed pos %s\\\" %\",\n \"BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower()\",\n \"soup\\\"\\\"\\\" self.soup_data = soup return soup def find_id(self): \\\"\\\"\\\"find pos\",\n \"def close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try: self.api.xpath(path['ok_but'])[0].click() except\",\n \"def get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if mode not in\",\n \"value, mult=0.1): \\\"\\\"\\\"decode and update the value\\\"\\\"\\\" try: msg_text =\",\n \"decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode and update the value\\\"\\\"\\\" try:\",\n \"logger.debug(\\\"unit_value not found in the collection\\\") pip = get_pip(mov=self) quant\",\n \"counter = counter.lower() if name in string and counter in\",\n \"def css(self, css_path, dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if dom\",\n \"+ 10 while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\\\"weekend\",\n \"launched\\\"\\\"\\\" try: # init virtual Display self.vbro = Display() self.vbro.start()\",\n \"self.mode = mode self.price = price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\"\",\n \"mov_list: # find approximate price max_roof = self.price + self.price\",\n \"with the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name = brow self.positions\",\n \"= self.price + self.price * 0.01 min_roof = self.price -\",\n \"[x for x in self.api.movements if x.product == self.product and\",\n \"current quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity = quant return\",\n \"+ '-btn']).click() self.mode = mode logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte\",\n \"dom]) def search_name(self, name, dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if\",\n \"> timeout: raise TimeoutError(\\\"failed to close pos %s\\\" % self.id)\",\n \"return True else: return False counter = counter.lower() if name\",\n \"exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return self.get_result(x) x +=\",\n \"% url) self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" % url) except selenium.common.exceptions.WebDriverException:\",\n \"= value logger.debug(\\\"set limit\\\") def decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\"\",\n \"None: dom = self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username,\",\n \"abbreviation\\\"\\\"\\\" if dom is None: dom = self.browser return expect(dom.find_by_name,\",\n \"the first value of self.css\\\"\\\"\\\" if dom is None: dom\",\n \"name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api, html_div): \\\"\\\"\\\"initialized\",\n \"if dom is None: dom = self.browser return expect(dom.find_by_name, args=[name])\",\n \"counter found in string\\\") return False elif name in string\",\n \"counter not in string: return True else: return False def\",\n \"if time.time() > timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged\",\n \"return None return None def new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\"\",\n \"self.records = [] def new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec)\",\n \"return expect(dom.find_by_xpath, args=[xpath]) def elCss(self, css_path, dom=None): \\\"\\\"\\\"check if element\",\n \"def close(self): \\\"\\\"\\\"close a movement\\\"\\\"\\\" self._check_open() self.api.css1(path['close']).click() self.state = 'closed'\",\n \"'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category ==\",\n \"type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product\",\n \"self.browser.visit(url) logger.debug(f\\\"connected to %s\\\" % url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed\",\n \"= api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser') else:\",\n \"self.quantity = quantity self.mode = mode self.price = price class\",\n \"self.api = api if isinstance(html_div, type('')): self.soup_data = BeautifulSoup(html_div, 'html.parser')\",\n \"for x in self.api.movements if x.product == self.product and x.quantity\",\n \"movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0])\",\n \"= mode self.price = price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def\",\n \"from pyvirtualdisplay import Display from bs4 import BeautifulSoup from splinter\",\n \"== 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both':\",\n \"except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click()\",\n \"the collection\\\") pip = get_pip(mov=self) quant = 1 / pip\",\n \"self.quantity = quant logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get current\",\n \"path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode\",\n \"num(self.soup_data.select(\\\"td.ppl\\\")[0].text) self.gain = gain return gain def bind_mov(self): \\\"\\\"\\\"bind the\",\n \"self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise\",\n \"= self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath\",\n \"self.product and x.quantity == self.quantity and x.mode == self.mode] if\",\n \"set\\\") def get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value))\",\n \"(self.price, x.price)) continue # if nothing, return None return None\",\n \"string\\\") return False elif name in string and counter not\",\n \"at position %d\\\" % (x + 1)) return result, name\",\n \"if 'old_quant' in locals(): self.set_quantity(old_quant) unit_val = margin / quant\",\n \"def login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\"\",\n \"except KeyError: logger.debug(\\\"unit_value not found in the collection\\\") pip =\",\n \"self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name is None:\",\n \"\\\"\\\"\\\"find pos ID with with given data\\\"\\\"\\\" pos_id = self.soup_data['id']\",\n \"dom is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def\",\n \"self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode']\",\n \"for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] =\",\n \"trading alert-box not closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading\",\n \"not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username)\",\n \"x.quantity == self.quantity and x.mode == self.mode] if not mov_list:\",\n \"self.css1(path['log']).click() # define a timeout for logging in timeout =\",\n \"in self.api.movements if x.product == self.product and x.quantity == self.quantity\",\n \"self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get\",\n \"span.equity-item-value\\\" % info_label).text return num(val) except KeyError as e: raise\",\n \"exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception:\",\n \"if element is present by css\\\"\\\"\\\" if dom is None:\",\n \"+ 1}]\\\" try: res = self.api.xpath(evalxpath)[0] return res except Exception:\",\n \"is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter):\",\n \"of the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set\",\n \"self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement appended to the list\\\" %\",\n \"self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy'\",\n \"self.soup_data.select(\\\"td.direction\\\")[0].span['class']): self.mode = 'buy' else: self.mode = 'sell' self.price =\",\n \"mov self.mov = x return x else: logger.debug(\\\"fail: price %f\",\n \"-1: value += value * mult return value else: self.decode(message)\",\n \"'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val = self.css1(\\\"div#%s\",\n \"service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name = brow self.positions = []\",\n \"time.time() + 10 while not self.elCss(path['alert-box']): if time.time() > timeout:\",\n \"first value of self.css\\\"\\\"\\\" if dom is None: dom =\",\n \"in string return False\\\"\\\"\\\" name = name.lower() string = string.lower()\",\n \"10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout: raise TimeoutError(\\\"failed\",\n \"'initialized' self.insfu = False def open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\"\",\n \"in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname: mark_closed_list\",\n \"Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum Quantity Limit\\\":\",\n \"xpath, dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if dom is None:\",\n \"logging logger = logging.getLogger('tradingAPI.low_level') class Stock(object): \\\"\\\"\\\"base class for stocks\\\"\\\"\\\"\",\n \"find approximate price max_roof = self.price + self.price * 0.01\",\n \"\\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product =\",\n \"% mode])[0].fill(str(value)) for cat in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode\",\n \"Glob() def launch(self): \\\"\\\"\\\"launch browser and virtual display, first of\",\n \"return unit_value_res except KeyError: logger.debug(\\\"unit_value not found in the collection\\\")\",\n \"logger.warning(\\\"weekend trading alert-box not closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend\",\n \"price def __repr__(self): return ' - '.join([str(self.product), str(self.quantity), str(self.mode), str(self.price)])\",\n \"x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text\",\n \"html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\" in\",\n \"%d\\\" % (x + 1)) return result, name def check_name(self,\",\n \"try: self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'):\",\n \"def get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if res is None:\",\n \"timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as {username}\\\")\",\n \"result.click() if self.api.elCss(\\\"div.widget_message\\\"): self.decode(self.api.css1(\\\"div.widget_message\\\")) self.product = product self.state = 'open'\",\n \"string.lower() if counter is None: if name in string: return\",\n \"# init globals Glob() def launch(self): \\\"\\\"\\\"launch browser and virtual\",\n \"LowLevelAPI(object): \\\"\\\"\\\"low level api to interface with the service\\\"\\\"\\\" def\",\n \"(buy or sell)\\\"\\\"\\\" self._check_open() if mode not in [\\\"buy\\\", \\\"sell\\\"]:\",\n \"= html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text) if (\\\"direction-label-buy\\\"\",\n \"started\\\") return False self.vbro.stop() logger.info(\\\"logged out\\\") return True def get_bottom_info(self,\",\n \"the soup\\\"\\\"\\\" self.soup_data = soup return soup def find_id(self): \\\"\\\"\\\"find\",\n \"both in string return False\\\"\\\"\\\" name = name.lower() string =\",\n \"def decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text\",\n \"price class PurePosition(object): \\\"\\\"\\\"class-storing position\\\"\\\"\\\" def __init__(self, product, quantity, mode,\",\n \"False def get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if res is\",\n \"res = self.api.xpath(evalxpath)[0] return res except Exception: return None def\",\n \"is present by css\\\"\\\"\\\" if dom is None: dom =\",\n \"self.check_name(res, name, counter=check_counter): return self.get_result(x) x += 1 logger.debug(\\\"found product\",\n \"% self.product) self.state = 'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self, res,\",\n \"\\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self, api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api\",\n \"self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text return num(val) except KeyError as e:\",\n \"expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url\",\n \"raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as {username}\\\") # check if\",\n \"def new_mov(self, name): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class\",\n \"raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode logger.debug(\\\"mode set\\\")\",\n \"self.decode(message) return None def get_mov_margin(self): \\\"\\\"\\\"get the margin of the\",\n \"Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum Quantity Limit\\\": raise\",\n \"_check_open(self): if self.state == 'open': return True else: raise exceptions.WindowException()\",\n \"str(self.mode), str(self.price)]) class LowLevelAPI(object): \\\"\\\"\\\"low level api to interface with\",\n \"if nothing, return None return None def new_pos(self, html_div): \\\"\\\"\\\"factory\",\n \"'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'} try: info_label = accepted_values[info] val\",\n \"while not self.check_name(res, name, counter=check_counter): name = self.get_research_name(self.get_result(x)) if name\",\n \"from div\\\"\\\"\\\" self.api = api if isinstance(html_div, type('')): self.soup_data =\",\n \"self.soup_data = BeautifulSoup(html_div, 'html.parser') else: self.soup_data = html_div self.product =\",\n \"\\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click() widg = self.api.css(\\\"div.widget_message\\\") if\",\n \"num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price = price return price\",\n \"category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category !=\",\n \"# bind mov self.mov = x return x else: logger.debug(\\\"fail:\",\n \"+= value * mult return value else: self.decode(message) return None\",\n \"% mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value']\",\n \"site time.sleep(0.5) margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip: {pip}\",\n \"30 while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\\\"login failed\\\")\",\n \"self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception: raise exceptions.BrowserException(\",\n \"it's been closed # set a timeout timeout = time.time()\",\n \"Exception: return None def set_limit(self, category, mode, value): \\\"\\\"\\\"set limit\",\n \"1)) return result, name def check_name(self, name, string, counter=None): \\\"\\\"\\\"if\",\n \"func (quit browser)\\\"\\\"\\\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not\",\n \"exceptions.MaxQuantLimit(num(text)) elif title == \\\"Minimum Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded\",\n \"% url) except selenium.common.exceptions.WebDriverException: logger.critical(\\\"connection timed out\\\") raise try: self.search_name(\\\"login[username]\\\").fill(username)\",\n \"else: self.soup_data = html_div self.product = self.soup_data.select(\\\"td.name\\\")[0].text self.quantity = num(self.soup_data.select(\\\"td.quantity\\\")[0].text)\",\n \"'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category != 'both': self.stop_limit[category]['mode']\",\n \"logger.debug(\\\"quantity set\\\") def get_price(self, mode='buy'): \\\"\\\"\\\"get current price\\\"\\\"\\\" if mode\",\n \"return True def logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try: self.browser.quit()\",\n \"logger = logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for x in self.api.movements\",\n \"password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url = \\\"https://trading212.com/it/login\\\" try: logger.debug(f\\\"visiting %s\\\"\",\n \"\\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos = self.Position(self, html_div) pos.bind_mov() self.positions.append(pos) return\",\n \"counter.lower() if name in string and counter in string: logger.debug(\\\"check_name:\",\n \"mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) for cat in ['gain', 'loss']:\",\n \"to be launched\\\"\\\"\\\" try: # init virtual Display self.vbro =\",\n \"or sell)\\\"\\\"\\\" self._check_open() if mode not in [\\\"buy\\\", \\\"sell\\\"]: raise\",\n \"\\\"value\\\"] or category not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise ValueError()\",\n \"css\\\"\\\"\\\" if dom is None: dom = self.browser return expect(dom.is_element_present_by_css,\",\n \"self.id = self.find_id() def update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data\",\n \"else: self.mode = 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text)\",\n \"window\\\"\\\"\\\" self._check_open() if (mode not in [\\\"unit\\\", \\\"value\\\"] or category\",\n \"been closed # set a timeout timeout = time.time() +\",\n \"time.sleep(1) logger.info(f\\\"logged in as {username}\\\") # check if it's a\",\n \"self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\") except Exception as e: logger.critical(\\\"login\",\n \"continue # if nothing, return None return None def new_pos(self,\",\n \"# if nothing, return None return None def new_pos(self, html_div):\",\n \"self._check_open() if (mode not in [\\\"unit\\\", \\\"value\\\"] or category not\",\n \"except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False self.vbro.stop() logger.info(\\\"logged\",\n \"['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set limit\\\")\",\n \"self.quantity = quantity self.mode = mode self.price = price def\",\n \"wait until it's been closed # set a timeout timeout\",\n \"logger.debug(\\\"unit_value found in the collection\\\") return unit_value_res except KeyError: logger.debug(\\\"unit_value\",\n \"the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product)\",\n \"info): accepted_values = { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl',\",\n \"if self.get_result(0) is None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product =\",\n \"if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1] if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\\",\n \"elCss(self, css_path, dom=None): \\\"\\\"\\\"check if element is present by css\\\"\\\"\\\"\",\n \"+ 30 while not self.elCss(path['logo']): if time.time() > timeout: logger.critical(\\\"login\",\n \"\\\"both\\\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain':\",\n \"found\\\") return None else: logger.debug(\\\"success: found movement\\\") for x in\",\n \"= path['res'] + f\\\"[{pos + 1}]\\\" try: res = self.api.xpath(evalxpath)[0]\",\n \"set_mode(self, mode): \\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\" self._check_open() if mode\",\n \"= price return price def get_unit_value(self): \\\"\\\"\\\"get unit value of\",\n \"10 while not self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\\\"weekend trading\",\n \"list\\\" % self.product) self.state = 'conclused' logger.debug(\\\"confirmed movement\\\") def search_res(self,\",\n \"message).text text = self.api.css1(\\\"div.text\\\", message).text if title == \\\"Insufficient Funds\\\":\",\n \"self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is\",\n \"return True def css(self, css_path, dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\"\",\n \"find function abbreviation\\\"\\\"\\\" if dom is None: dom = self.browser\",\n \"@property def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def\",\n \"mov_list = [x for x in self.api.movements if x.product ==\",\n \"closed\\\") except Exception as e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e) return\",\n \"self.insfu = False def open(self, name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if\",\n \"all to be launched\\\"\\\"\\\" try: # init virtual Display self.vbro\",\n \"self.browser def _css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\" return self.css(path, domm)[0]\",\n \"def check_name(self, name, string, counter=None): \\\"\\\"\\\"if both in string return\",\n \"Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def __init__(self, product, quantity, mode, price): self.product\",\n \"= value elif category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value))\",\n \"is None: dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self,\",\n \"class for stocks\\\"\\\"\\\" def __init__(self, product): self.product = product self.market\",\n \"else: logger.debug(\\\"fail: price %f not corresponding to %f\\\" % (self.price,\",\n \"= product self.market = True self.records = [] def new_rec(self,\",\n \"quantity self.mode = mode self.price = price def __repr__(self): return\",\n \"res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result = self.get_result(0) name = self.get_research_name(result) x\",\n \"name = self.get_research_name(result) x = 0 while not self.check_name(res, name,\",\n \"the margin of the movement\\\"\\\"\\\" self._check_open() return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self,\",\n \"the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value\",\n \"{quant} - pip: {pip} - margin: {margin}\\\") if 'old_quant' in\",\n \"return None def new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos =\",\n \"False self.vbro.stop() logger.info(\\\"logged out\\\") return True def get_bottom_info(self, info): accepted_values\",\n \"stocks\\\"\\\"\\\" def __init__(self, product): self.product = product self.market = True\",\n \"None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(self.product) result, product = self.search_res(self.product, name_counter) result.click()\",\n \"[\\\"buy\\\", \\\"sell\\\"]: raise ValueError() self.api.css1(path[mode + '-btn']).click() self.mode = mode\",\n \"!= -1] if mark_closed_list: sell_price = product.select(\\\"div.tradebox-price-sell\\\")[0]\\\\ .text return float(sell_price)\",\n \"return res except Exception: return None def set_limit(self, category, mode,\",\n \"quantity self.mode = mode self.price = price class PurePosition(object): \\\"\\\"\\\"class-storing\",\n \"in string and counter in string: logger.debug(\\\"check_name: counter found in\",\n \"else: return False def get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if\",\n \"message).text if title == \\\"Insufficient Funds\\\": self.insfu = True elif\",\n \"== 'Market Closed'): logger.error(\\\"market closed, position can't be closed\\\") raise\",\n \"except Exception: raise exceptions.VBroException() try: self.browser = Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name}\",\n \"in ['gain', 'loss']: self.stop_limit[cat]['mode'] = mode self.stop_limit[cat]['value'] = value logger.debug(\\\"set\",\n \"if time.time() > timeout: logger.warning(\\\"weekend trading alert-box not closed\\\") break\",\n \"movement window\\\"\\\"\\\" def __init__(self, api, product): self.api = api self.product\",\n \"movement appended to the list\\\" % self.product) self.state = 'conclused'\",\n \"return None return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos): \\\"\\\"\\\"get pos\",\n \"expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if element is\",\n \"8): timeout = time.time() + 10 while not self.elCss(path['alert-box']): if\",\n \"product in soup.select(\\\"div.tradebox\\\"): fullname = product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname:\",\n \"not corresponding to %f\\\" % (self.price, x.price)) continue # if\",\n \"by css\\\"\\\"\\\" if dom is None: dom = self.browser return\",\n \"logger.debug(\\\"virtual display launched\\\") except Exception: raise exceptions.VBroException() try: self.browser =\",\n \"counter=check_counter): return self.get_result(x) x += 1 logger.debug(\\\"found product at position\",\n \"in locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value =\",\n \"timeout = time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time()\",\n \"dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if dom is None: dom\",\n \"True def get_bottom_info(self, info): accepted_values = { 'free_funds': 'equity-free', 'account_value':\",\n \"[\\\"unit\\\", \\\"value\\\"] or category not in [\\\"gain\\\", \\\"loss\\\", \\\"both\\\"]): raise\",\n \"args=[name]) def xpath(self, xpath, dom=None): \\\"\\\"\\\"xpath find function abbreviation\\\"\\\"\\\" if\",\n \"None: self.api.css1(path['close']).click() raise exceptions.ProductNotFound(res) logger.debug(name) if self.check_name(res, name, counter=check_counter): return\",\n \"self.id) logger.debug(\\\"closed pos %s\\\" % self.id) def get_gain(self): \\\"\\\"\\\"get current\",\n \"string and counter not in string: return True else: return\",\n \"self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a timeout for logging in timeout\",\n \"from datetime import datetime from pyvirtualdisplay import Display from bs4\",\n \"self.insfu = True elif title == \\\"Maximum Quantity Limit\\\": raise\",\n \"logger.debug(\\\"decoded message\\\") def decode_update(self, message, value, mult=0.1): \\\"\\\"\\\"decode and update\",\n \"pos_id = self.soup_data['id'] self.id = pos_id return pos_id @property def\",\n \"time.time() + 10 while self.api.elCss(self.close_tag): time.sleep(0.1) if time.time() > timeout:\",\n \"% mode).text) self.price = price return price def get_unit_value(self): \\\"\\\"\\\"get\",\n \"= [] self.movements = [] self.stocks = [] # init\",\n \"gain return gain def bind_mov(self): \\\"\\\"\\\"bind the corresponding movement\\\"\\\"\\\" logger\",\n \"browser and virtual display, first of all to be launched\\\"\\\"\\\"\",\n \"args=[xpath]) def login(self, username, password, mode=\\\"demo\\\"): \\\"\\\"\\\"login function\\\"\\\"\\\" url =\",\n \"logger.info(f\\\"logged in as {username}\\\") # check if it's a weekend\",\n \"return num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\"\",\n \"== self.product and x.quantity == self.quantity and x.mode == self.mode]\",\n \"category == 'both': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) self.api.xpath( path['limit-loss-%s' %\",\n \"path['res'] + f\\\"[{pos + 1}]\\\" try: res = self.api.xpath(evalxpath)[0] return\",\n \"for stocks\\\"\\\"\\\" def __init__(self, product): self.product = product self.market =\",\n \"min_roof = self.price - self.price * 0.01 if min_roof <\",\n \"= self.price - self.price * 0.01 if min_roof < x.price\",\n \"def set_quantity(self, quant): \\\"\\\"\\\"set quantity\\\"\\\"\\\" self._check_open() self.api.css1(path['quantity']).fill(str(int(quant))) self.quantity = quant\",\n \"if dom is None: dom = self.browser return expect(dom.find_by_xpath, args=[xpath])\",\n \"Display self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\") except Exception:\",\n \"\\\"demo\\\" and datetime.now().isoweekday() in range(5, 8): timeout = time.time() +\",\n \"elif category == 'loss': self.api.xpath( path['limit-loss-%s' % mode])[0].fill(str(value)) if category\",\n \"Display from bs4 import BeautifulSoup from splinter import Browser from\",\n \"function abbreviation\\\"\\\"\\\" if dom is None: dom = self.browser return\",\n \"if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\\\"market closed, position can't\",\n \".links import path from .utils import num, expect, get_pip #\",\n \"message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text text =\",\n \"title == \\\"Maximum Quantity Limit\\\": raise exceptions.MaxQuantLimit(num(text)) elif title ==\",\n \"of self.css\\\"\\\"\\\" if dom is None: dom = self.browser def\",\n \"raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit = {'gain': {},\",\n \"try: res = self.api.xpath(evalxpath)[0] return res except Exception: return None\",\n \"coding: utf-8 -*- \\\"\\\"\\\" tradingAPI.low_level ~~~~~~~~~~~~~~ This module provides the\",\n \"try: info_label = accepted_values[info] val = self.css1(\\\"div#%s span.equity-item-value\\\" % info_label).text\",\n \"pattern\\\"\\\"\\\" return self.MovementWindow(self, name) class Position(PurePosition): \\\"\\\"\\\"position object\\\"\\\"\\\" def __init__(self,\",\n \"True else: return False counter = counter.lower() if name in\",\n \"provides the low level functions with the service. \\\"\\\"\\\" import\",\n \"alert-box closed\\\") except Exception as e: logger.critical(\\\"login failed\\\") raise exceptions.BaseExc(e)\",\n \"def get_result(self, pos): \\\"\\\"\\\"get pos result, where 0 is first\\\"\\\"\\\"\",\n \"quantity, mode, price): self.product = product self.quantity = quantity self.mode\",\n \"raise try: self.search_name(\\\"login[username]\\\").fill(username) self.search_name(\\\"login[password]\\\").fill(password) self.css1(path['log']).click() # define a timeout for\",\n \"raise ValueError() self._check_open() price = num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text)\",\n \"self.stop_limit[category]['mode'] = mode self.stop_limit[category]['value'] = value elif category == 'both':\",\n \"def new_rec(self, rec): \\\"\\\"\\\"add a record\\\"\\\"\\\" self.records.append(rec) return self.records class\",\n \"dom=None): \\\"\\\"\\\"css find function abbreviation\\\"\\\"\\\" if dom is None: dom\",\n \"self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False self.vbro.stop()\",\n \"return False def get_research_name(self, res): \\\"\\\"\\\"return result name\\\"\\\"\\\" if res\",\n \"\\\"loss\\\", \\\"both\\\"]): raise ValueError() if not hasattr(self, 'stop_limit'): self.stop_limit =\",\n \"import Display from bs4 import BeautifulSoup from splinter import Browser\",\n \"logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\" self._check_open() self.get_price() self.api.css1(path['confirm-btn']).click()\",\n \"# set a timeout timeout = time.time() + 10 while\",\n \"logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as {username}\\\") #\",\n \"re from datetime import datetime from pyvirtualdisplay import Display from\",\n \"return False self.vbro.stop() logger.info(\\\"logged out\\\") return True def get_bottom_info(self, info):\",\n \"memoized\\\"\\\"\\\" # find in the collection try: unit_value = Glob().theCollector.collection['unit_value']\",\n \"data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id = pos_id return pos_id @property\",\n \"counter=None): \\\"\\\"\\\"if both in string return False\\\"\\\"\\\" name = name.lower()\",\n \"find in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res =\",\n \"\\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data = soup return soup def find_id(self):\",\n \"num, expect, get_pip # exceptions from tradingAPI import exceptions import\",\n \"dom=None): \\\"\\\"\\\"return the first value of self.css\\\"\\\"\\\" if dom is\",\n \"collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res = unit_value[self.product] logger.debug(\\\"unit_value found\",\n \"Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\") except Exception: raise exceptions.VBroException() try:\",\n \"= True self.records = [] def new_rec(self, rec): \\\"\\\"\\\"add a\",\n \"KeyError as e: raise exceptions.BaseExc(e) def get_price(self, name): soup =\",\n \"return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos): \\\"\\\"\\\"get pos result, where\",\n \"init globals Glob() def launch(self): \\\"\\\"\\\"launch browser and virtual display,\",\n \"not in [\\\"unit\\\", \\\"value\\\"] or category not in [\\\"gain\\\", \\\"loss\\\",\",\n \"stock based on margin, memoized\\\"\\\"\\\" # find in the collection\",\n \"def __init__(self, api, product): self.api = api self.product = product\",\n \"self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\") self.api.css1(path['search-box']).fill(self.product) if self.get_result(0) is None: self.api.css1(path['close']).click() raise\",\n \"= Browser(self.brow_name) logger.debug(f\\\"browser {self.brow_name} launched\\\") except Exception: raise exceptions.BrowserException( self.brow_name,\",\n \"self.stop_limit = {'gain': {}, 'loss': {}} logger.debug(\\\"initialized stop_limit\\\") if category\",\n \"min_roof < x.price < max_roof: logger.debug(\\\"success: price corresponding\\\") # bind\",\n \"__init__(self, api, html_div): \\\"\\\"\\\"initialized from div\\\"\\\"\\\" self.api = api if\",\n \"%s\\\" % self.id) logger.debug(\\\"closed pos %s\\\" % self.id) def get_gain(self):\",\n \"return f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click()\",\n \"in the collection\\\") pip = get_pip(mov=self) quant = 1 /\",\n \"['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price))\",\n \"\\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close position\",\n \"search_res(self, res, check_counter=None): \\\"\\\"\\\"search for a res\\\"\\\"\\\" logger.debug(\\\"searching result\\\") result\",\n \"> timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1) logger.info(f\\\"logged in as\",\n \"if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box closed\\\") except Exception as\",\n \"api, product): self.api = api self.product = product self.state =\",\n \"# find approximate price max_roof = self.price + self.price *\",\n \"self.mov = x return x else: logger.debug(\\\"fail: price %f not\",\n \"decode(self, message): \\\"\\\"\\\"decode text pop-up\\\"\\\"\\\" title = self.api.css1(\\\"div.title\\\", message).text text\",\n \"alert-box not closed\\\") break if self.elCss(path['alert-box']): self.css1(path['alert-box']).click() logger.debug(\\\"weekend trading alert-box\",\n \"new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos = self.Position(self, html_div) pos.bind_mov()\",\n \"import exceptions import selenium.common.exceptions # logging import logging logger =\",\n \"Exception: raise exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\") return True def\",\n \"# find in the collection try: unit_value = Glob().theCollector.collection['unit_value'] unit_value_res\",\n \"product): self.product = product self.market = True self.records = []\",\n \"e: raise exceptions.BaseExc(e) def get_price(self, name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html,\",\n \"self.elCss(path['logo']): if time.time() > timeout: logger.critical(\\\"login failed\\\") raise CredentialsException(username) time.sleep(1)\",\n \"name_counter=None): \\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened\",\n \"'-btn']).click() self.mode = mode logger.debug(\\\"mode set\\\") def get_quantity(self): \\\"\\\"\\\"gte current\",\n \"f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\" self.api.css1(self.close_tag).click() try:\",\n \"return None def get_mov_margin(self): \\\"\\\"\\\"get the margin of the movement\\\"\\\"\\\"\",\n \"def get_price(self, name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product\",\n \"self.elCss(path['alert-box']): if time.time() > timeout: logger.warning(\\\"weekend trading alert-box not closed\\\")\",\n \"self.vbro = Display() self.vbro.start() logger.debug(\\\"virtual display launched\\\") except Exception: raise\",\n \"title = self.api.css1(\\\"div.title\\\", message).text text = self.api.css1(\\\"div.text\\\", message).text if title\",\n \"None: dom = self.browser return expect(dom.find_by_css, args=[css_path]) def css1(self, css_path,\",\n \"False\\\"\\\"\\\" name = name.lower() string = string.lower() if counter is\",\n \"= { 'free_funds': 'equity-free', 'account_value': 'equity-total', 'live_result': 'equity-ppl', 'used_margin': 'equity-margin'}\",\n \"name.lower() string = string.lower() if counter is None: if name\",\n \"in string: return True else: return False def get_research_name(self, res):\",\n \"num(self.api.css1(\\\"span.cfd-order-info-item-value\\\").text) def set_mode(self, mode): \\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\" self._check_open()\",\n \"= self.browser return expect(dom.is_element_present_by_xpath, args=[xpath]) def login(self, username, password, mode=\\\"demo\\\"):\",\n \"margin = self.get_mov_margin() logger.debug(f\\\"quant: {quant} - pip: {pip} - margin:\",\n \"[] self.stocks = [] # init globals Glob() def launch(self):\",\n \"get_price(self, name): soup = BeautifulSoup( self.css1(\\\"div.scrollable-area-content\\\").html, \\\"html.parser\\\") for product in\",\n \"None return self.api.css1(\\\"span.instrument-name\\\", res).text def get_result(self, pos): \\\"\\\"\\\"get pos result,\",\n \"= self.find_id() def update(self, soup): \\\"\\\"\\\"update the soup\\\"\\\"\\\" self.soup_data =\",\n \"None: dom = self.browser def _css1(path, domm): \\\"\\\"\\\"virtual local func\\\"\\\"\\\"\",\n \"timeout = time.time() + 30 while not self.elCss(path['logo']): if time.time()\",\n \"a record\\\"\\\"\\\" self.records.append(rec) return self.records class Movement(object): \\\"\\\"\\\"class-storing movement\\\"\\\"\\\" def\",\n \"message).text return num(msg_text) except Exception: if msg_text.lower().find(\\\"higher\\\") != -1: value\",\n \"True def logout(self): \\\"\\\"\\\"logout func (quit browser)\\\"\\\"\\\" try: self.browser.quit() except\",\n \"mode).text) self.price = price return price def get_unit_value(self): \\\"\\\"\\\"get unit\",\n \"if name.lower() in fullname: mark_closed_list = [x for x in\",\n \"tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def close(self): \\\"\\\"\\\"close position via tag\\\"\\\"\\\"\",\n \"logging in timeout = time.time() + 30 while not self.elCss(path['logo']):\",\n \"find_id(self): \\\"\\\"\\\"find pos ID with with given data\\\"\\\"\\\" pos_id =\",\n \"pyvirtualdisplay import Display from bs4 import BeautifulSoup from splinter import\",\n \"__init__(self, product, quantity, mode, price): self.product = product self.quantity =\",\n \"evalxpath = path['res'] + f\\\"[{pos + 1}]\\\" try: res =\",\n \"def elXpath(self, xpath, dom=None): \\\"\\\"\\\"check if element is present by\",\n \"self.api.xpath(path['ok_but'])[0].click() except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\\\"market\",\n \"category == 'gain': self.api.xpath( path['limit-gain-%s' % mode])[0].fill(str(value)) elif category ==\",\n \"def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return f\\\"#{self.id} div.close-icon\\\" def close(self):\",\n \"the service\\\"\\\"\\\" def __init__(self, brow=\\\"firefox\\\"): self.brow_name = brow self.positions =\",\n \"logging.getLogger(\\\"tradingAPI.low_level.bind_mov\\\") mov_list = [x for x in self.api.movements if x.product\",\n \"None return None def new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos\",\n \"= product.select(\\\"span.instrument-name\\\")[0].text.lower() if name.lower() in fullname: mark_closed_list = [x for\",\n \"None def new_pos(self, html_div): \\\"\\\"\\\"factory method pattern\\\"\\\"\\\" pos = self.Position(self,\",\n \"exceptions.BrowserException( self.brow_name, \\\"failed to launch\\\") return True def css(self, css_path,\",\n \"= name.lower() string = string.lower() if counter is None: if\",\n \"datetime.now().isoweekday() in range(5, 8): timeout = time.time() + 10 while\",\n \"__init__(self, product): self.product = product self.market = True self.records =\",\n \"mode): \\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\" self._check_open() if mode not\",\n \"= self.soup_data['id'] self.id = pos_id return pos_id @property def close_tag(self):\",\n \"for x in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select( \\\"div.placeholder\\\")[0].text.lower().find(\\\"close\\\") != -1]\",\n \"hasattr(self, 'quantity'): old_quant == self.quantity self.set_quantity(quant) # update the site\",\n \"Quantity Limit\\\": raise exceptions.MinQuantLimit(num(text)) logger.debug(\\\"decoded message\\\") def decode_update(self, message, value,\",\n \"Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\") return False self.vbro.stop() logger.info(\\\"logged out\\\")\",\n \"x in ['quantity', 'mode'] if hasattr(self, x)): self.api.movements.append(Movement( self.product, self.quantity,\",\n \"self.state = 'closed' logger.debug(\\\"closed window\\\") def confirm(self): \\\"\\\"\\\"confirm the movement\\\"\\\"\\\"\",\n \"pos_id return pos_id @property def close_tag(self): \\\"\\\"\\\"obtain close tag\\\"\\\"\\\" return\",\n \"ID with with given data\\\"\\\"\\\" pos_id = self.soup_data['id'] self.id =\",\n \"get_quantity(self): \\\"\\\"\\\"gte current quantity\\\"\\\"\\\" self._check_open() quant = int(num(self.api.css1(path['quantity']).value)) self.quantity =\",\n \"dom = self.browser return expect(dom.find_by_name, args=[name]) def xpath(self, xpath, dom=None):\",\n \"\\\"\\\"\\\"set mode (buy or sell)\\\"\\\"\\\" self._check_open() if mode not in\",\n \"search_name(self, name, dom=None): \\\"\\\"\\\"name find function abbreviation\\\"\\\"\\\" if dom is\",\n \"locals(): self.set_quantity(old_quant) unit_val = margin / quant self.unit_value = unit_val\",\n \"% self.id) logger.debug(\\\"closed pos %s\\\" % self.id) def get_gain(self): \\\"\\\"\\\"get\",\n \"except selenium.common.exceptions.ElementNotInteractableException: if (self.api.css1('.widget_message div.title').text == 'Market Closed'): logger.error(\\\"market closed,\",\n \"\\\"\\\"\\\"open the window\\\"\\\"\\\" if self.api.css1(path['add-mov']).visible: self.api.css1(path['add-mov']).click() else: self.api.css1('span.dataTable-no-data-action').click() logger.debug(\\\"opened window\\\")\",\n \"mode self.price = price def __repr__(self): return ' - '.join([str(self.product),\",\n \"(quit browser)\\\"\\\"\\\" try: self.browser.quit() except Exception: raise exceptions.BrowserException(self.brow_name, \\\"not started\\\")\",\n \"x)): self.api.movements.append(Movement( self.product, self.quantity, self.mode, self.price)) logger.debug(\\\"%s movement appended to\",\n \"mark_closed_list = [x for x in product.select( \\\"div.quantity-list-input-wrapper\\\") if x.select(\",\n \"= num(self.api.css1( \\\"div.orderdialog div.tradebox-price-%s\\\" % mode).text) self.price = price return\",\n \"self.mode = 'sell' self.price = num(self.soup_data.select(\\\"td.averagePrice\\\")[0].text) self.margin = num(self.soup_data.select(\\\"td.margin\\\")[0].text) self.id\",\n \"= product self.state = 'initialized' self.insfu = False def open(self,\",\n \"False class MovementWindow(object): \\\"\\\"\\\"add movement window\\\"\\\"\\\" def __init__(self, api, product):\",\n \"self.api.css(\\\"div.widget_message\\\") if widg: self.decode(widg[0]) raise exceptions.WidgetException(widg) if all(x for x\"\n]","isConversational":false}}},{"rowIdx":198,"cells":{"ngram":{"kind":"list like","value":["finishes, all files injected will be truncated to zero length.","is specified \"spec.containers[2]\" (container with index 2 in this pod).","be set by external tools to store and retrieve arbitrary","sourceStrategy holds the parameters to the Source build strategy. type:","30 seconds, the Kubelet will send a hard termination signal","into a separate runtime image.' type: complex contains: api_version: description:","type: list contains: str generate_name: description: - GenerateName is an","201 Created or 500 with Reason ServerTimeout indicating a unique","- lastTriggeredImageID is used internally by the ImageChangeController to save","state. Populated by the system. Read-only. type: int initializers: description:","- Kind of the referent. type: str name: description: -","Dockerfile cannot provide compatibility with the 'None' policy. An additional","name of the container that triggered the event) or if","author: OpenShift (@openshift) options: api_key: description: - Token used to","Path of the field to select in the specified API","dict src: description: - Provide a path to a file","exist after this timestamp, until an administrator or automated process","this time. For example, a user may request that a","the namespace of the build unless Namespace is specified. type:","For the Source build strategy, these will be injected into","Read-only. type: str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list',","can observe the deletion. type: complex contains: api_version: description: -","and postfix notation for nested attributes. Arrays are zero-indexed. Fields","type: list contains: name: description: - name defines the name","type: str secret_source: description: - secretSource is a reference to","the build context. For Source builds, this is assumed to","referent. type: str resource_version: description: - Specific resourceVersion to which","of the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies","may be active on a node before the system actively","the referent. type: str resource_version: description: - An opaque value","type: list contains: _from: description: - from is a reference","pipeline. type: list contains: name: description: - Name of the","a selector which must be true for the build pod","by the reason type. type: complex contains: causes: description: -","be persisted to storage and then deleted, ensuring that other","Name field, and may be truncated by the length of","prior to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes","the ConfigMap or it's key must be defined type: bool","to true indicates that the docker build must be executed","optional. type: complex contains: git: description: - Git contains information","list of build configs type: list contains: api_version: description: -","OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\", error=str(exc)) if __name__ == '__main__':","by the length of the suffix required to make the","a double $$, ie: $$(VAR_NAME). Escaped references will never be","the pod created by this build. The pod will be","by the server when a graceful deletion is requested by","responsible for initializing this object. type: str result: description: -","all StatusReasons may provide detailed causes. type: list contains: field:","with the API. type: path kubeconfig: description: - Path to","string will be unchanged. The $(VAR_NAME) syntax can be escaped","to the client will be different than the name passed.","server. They may only be valid for a particular resource","launched from a BuildConfig. If no triggers are defined, a","present - absent username: description: - Provide a username for","clients and passed unmodified back to the server. Populated by","zero-indexed. Fields may appear more than once in an array","if the owner has the \"foregroundDeletion\" finalizer, then the owner","from the OpenShift helper. Logging info is written to KubeObjHelper.log","the openshift client will attempt to load the default configuration","the status StatusReason. type: str kind: description: - The kind","is a reference to an ImageStreamTag that will trigger a","image is pulled from the OpenShift registry, this field does","not guaranteed to conform to any schema except that defined","be used to organize and categorize (scope and select) objects.","with Command. Use Script when a shell script is appropriate","contextDir. type: str source_strategy: description: - sourceStrategy holds the parameters","or be '..' or '.'. type: str context_dir: description: -","UID of the referent. type: str build_api_version: description: - buildAPIVersion","be created, if it does not exist, or patched, if","secret: description: - secret used to validate requests. type: str","on a node If nil, it can be overridden by","a directory. destinationDir can also be empty or equal to","a graceful deletion is requested. Read-only. type: complex contains: {}","optimistic concurrency, change detection, and the watch operation on a","to a piece of an object instead of an entire","value must be positive integer type: int failed_builds_history_limit: description: -","description: - script is a shell script to be run","maximum amount of compute resources allowed. type: complex contains: str,","value, and may reject unrecognized values. type: str code: description:","is a build hook executed after the build output image","(when there is a single resource which can be described).","Dockerfile source type may be used with other options like","defined, a new build can only occur as a result","the API. Use in conjunction with I(username). resource_definition: description: -","type: str reason: description: - A machine-readable description of why","order before this object is visible. When the last pending","- images describes a set of images to be used","runtime image for use. Deprecated: This feature will be removed","strategy is in tech preview. type: complex contains: env: description:","unless Namespace is specified. type: complex contains: api_version: description: -","image entrypoint, or if the image does not have `/bin/sh`,","additional environment variables you want to pass into a builder","minimum amount of compute resources required. If Requests is omitted","description: - Whether or not to verify the API server's","all objects are required to be scoped to a namespace","this resource will be deleted. This field is set by","the entry from the list. If the deletionTimestamp of the","will be allowed to use secrets referenced by the ServiceAccount","any innate Dockerfile replaced in the context dir. type: str","from the build strategy will be used. Only one ImageChangeTrigger","to accept type: str strategy: description: - strategy defines how","the referent. type: str secrets: description: - secrets represents a","receive this binary as input on STDIN. type: complex contains:","username: description: - Provide a username for connecting to the","should convert recognized schemas to the latest internal value, and","after the time indicated in the Retry-After header). Applied only","list contains a dictionary with a I(name) or I(type) attribute,","terminate the build; value must be positive integer type: int","resulting image will be used in the FROM line of","- dockerfile is the raw contents of a Dockerfile which","in those cases the Git repo will have any innate","appropriate collections enabled in the shell. E.g., in the Ruby","\\ up the authentication for cloning private repository. The secret\\","set to an empty map or a map with any","not provided, and no other connection options are provided, the","is typically generated by the server on successful creation of","- secrets is a list of additional secrets that will","a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For","directory. destinationDir must be a directory. destinationDir can also be","to reach the git repository over https type: str no_proxy:","command to be run. type: list contains: str command: description:","str status: description: - status holds any relevant information about","container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and","be deleted from the key-value store until this reference is","to have some well-defined way of referencing a part of","as opaque by clients and . type: str self_link: description:","for setting up the authentication for pulling the Docker images","output image is committed, before it is pushed to a","path ends in /. then the content of the directory","a part of an object.' type: str kind: description: -","description: - source describes the SCM in use. type: complex","name which can be described). type: str retry_after_seconds: description: -","the system will populate this list with the current set","space within each name must be unique. An empty namespace","scripts type: str type: description: - type is the kind","an ImageChange type of trigger type: complex contains: _from: description:","lists, and not reachable by name) after the time in","registry If the image is pulled from the OpenShift registry,","into the future, although it may be shortened or the","resource_version: description: - String that identifies the server's internal version","description: - APIVersion defines the versioned schema of this representation","the images from registry prior to building. type: bool incremental:","type: str github: description: - github contains the parameters for","use. type: complex contains: binary: description: - binary builds accept","a proxy used to reach the git repository over http","presence of network partitions, this object may still exist after","specific generation of the desired state. Populated by the system.","escaped with a double $$, ie: $$(VAR_NAME). Escaped references will","be copied from the builder to the runtime image. sourcePath","- The name of the cluster which the object belongs","arguments are placed immediately after the command to be run.","used to build the Docker image, relative to the root","the Git repo will have any innate Dockerfile replaced in","email of the source control user type: str name: description:","not exist, or patched, if parameter values differ from the","of Source scripts type: str type: description: - type is","from I(~/.kube/config.json). type: path password: description: - Provide a password","to C(present), the object will be created, if it does","defined type: bool field_ref: description: - 'Selects a field of","destinations that will be used only for the build. type:","may not be modified further by any user. type: complex","type: str type: description: - type of build input to","just refers to the root of WORKDIR. Deprecated: This feature","that would be used for setting up the authentication for","that the provided binary input should be considered a single","root of WORKDIR. Deprecated: This feature will be removed in","is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which","suffix. The provided value has the same validation rules as","selector which must be true for the build pod to","uid: description: - UID is the unique in time and","(optionally after the time indicated in the Retry-After header). Applied","force_pull: description: - forcePull describes if the controller should configure","the parameters to the Jenkins Pipeline build strategy. This strategy","last triggered build. type: int kind: description: - Kind is","the event) or if no container name is specified \"spec.containers[2]\"","an optional prefix, used by the server, to generate a","as the Name field, and may be truncated by the","resource that has caused this error, as named by its","is empty there is no information available. A Reason clarifies","binary artifacts created from one build into a separate runtime","description: - 'The field of the resource that has caused","type: str source_secret: description: - \"sourceSecret is the name of","creation idempotence and configuration definition. Cannot be updated. type: str","back to the server. Populated by the system. Read-only. type:","replication controllers and services. type: complex contains: str, str name:","Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is RFC 3339","type: str block_owner_deletion: description: - If true, AND if the","the resource. (when there is a single resource which can","type: str committer: description: - committer is the committer of","an ImageStreamTag, ImageStreamImage, or DockerImage to copy source from. type:","then deleted, ensuring that other clients can observe the deletion.","automatically. Name is primarily intended for creation idempotence and configuration","pod to fit on a node If nil, it can","the root of WORKDIR. Deprecated: This feature will be removed","forcePull describes if the controller should configure the build pod","conform to any schema except that defined by the reason","set to true for GenericWebHook. type: bool secret: description: -","this value may not be unset or be set further","must have non-zero length. type: str value: description: - value","initializers that must execute in order before this object is","an optional location to push the output of this build","to an ImageStreamTag that will trigger a build when updated","use secrets referenced by the ServiceAccount type: str source: description:","build strategy will be used. Only one ImageChangeTrigger with an","Name must be unique within a namespace. Is required when","was created. It is not guaranteed to be set in","stanzas. The Dockerfile source type may be used with other","a zip, tar, or tar.gz file and extract it as","make `ruby`, `bundle` and other binaries available in the PATH.","used to organize and categorize (scope and select) objects. May","of the object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert:","clients can observe the deletion. type: complex contains: api_version: description:","docker image should be pulled type: complex contains: api_version: description:","or automated process can determine the resource is fully terminated.","object is visible. When the last pending initializer is removed,","type: complex contains: args: description: - args is a list","Supported auth methods\\ \\ are: ssh-privatekey.\" type: complex contains: name:","description: - triggers determine how new Builds can be launched","Token used to connect to the API. cert_file: description: -","methods\\ \\ are: ssh-privatekey.\" type: complex contains: name: description: -","ImageChange type of trigger type: complex contains: _from: description: -","- paths is a list of source and destination paths","before it will be removed from the system. Only set","as standard input. This filename may not contain slashes or","Name is not specified. type: str generation: description: - A","be true for the build pod to fit on a","strategy. This strategy is in tech preview. type: complex contains:","completely initialized. Otherwise, the object is considered uninitialized and is","exposed resources, defaults to \"1\" type: str resource: description: -","graceful deletion is requested. Read-only. type: complex contains: {} finalizers:","Examples: \"name\" - the field \"name\" on the current resource","type: path state: description: - Determines if an object should","API. verify_ssl: description: - Whether or not to verify the","used in the FROM line of the Dockerfile for this","determine when objects have changed. May be used for optimistic","type: path context: description: - The name of a context","system actively tries to terminate the build; value must be","run. It may not be specified with Script. This might","reference is made, if any. type: str uid: description: -","by clients and passed unmodified back to the server. Populated","not be found in the time allotted, and the client","bool dockerfile_path: description: - dockerfilePath is the path of the","inside the Docker container. type: bool force_pull: description: - forcePull","all other cases, using Script might be more convenient. type:","str, str revision: description: - revision is the information from","- UID of the resource. (when there is a single","for BuildConfig. type: complex contains: annotations: description: - Annotations is","on the current resource \"items[0].name\" - the field \"name\" on","a reference to an ImageStreamTag, ImageStreamImage, or DockerImage to copy","files copied from the image are placed. type: str source_path:","key file used to authenticate with the API. type: path","to configure the pipeline relative to the root of the","value may not be unset or be set further into","the API. In the presence of network partitions, this object","description: - The group attribute of the resource associated with","- buildArgs contains build arguments that will be resolved in","str script: description: - script is a shell script to","If not provided, and no other connection options are provided,","is set, the initializers struct will be set to nil","the Ruby image, this is necessary to make `ruby`, `bundle`","description: - Token used to connect to the API. cert_file:","str type: description: - type is the kind of build","defines a Jenkins pipeline build. type: str jenkinsfile_path: description: -","a container, it defaults to Limits if that is explicitly","set, graceful deletion of the object has not been requested.","execution. This is optional, if not specified we default to","- httpProxy is a proxy used to reach the git","config file. debug: description: - Enable debug output from the","will be created, if it does not exist, or patched,","that has caused this error, as named by its JSON","for the Build object serialized and passed to the custom","should be considered a single file within the build input.","is the relative directory within the build directory where files","description: - Standard list metadata. type: complex contains: resource_version: description:","or if no container name is specified \"spec.containers[2]\" (container with","- Specify whether the Secret or it's key must be","the object will be persisted to storage and then deleted,","is written to KubeObjHelper.log default: false type: bool force: description:","will be copied, but the directory itself will not be","root of the context (contextDir). If both JenkinsfilePath & Jenkinsfile","object is considered as initialized and visible to all clients.","str no_proxy: description: - noProxy is the list of domains","always pull the images for the builder or only pull","in the Kubernetes config file. debug: description: - Enable debug","be created at the destination. type: str pull_secret: description: -","an ImageStreamTag that will trigger a build when updated It","strategic merge is performed, where individual elements with a matching","an array of causes due to fields having multiple errors.","a value like: \"spec.containers{name}\" (where \"name\" refers to the name","complex contains: binary: description: - binary builds accept a binary","until this reference is removed. Defaults to false. To set","path at which to mount the secret type: str secret_source:","going to ignore it if set in create or update","credentials. Supported auth methods\\ \\ are: ssh-privatekey.\" type: complex contains:","This strategy is in tech preview. type: complex contains: env:","of why this operation is in the \"Failure\" status. If","referent. type: str build_args: description: - buildArgs contains build arguments","is the number of old failed builds to retain. If","authentication for cloning private repository. The secret\\ \\ contains valid","zip, tar, or tar.gz file and extract it as the","path of the Jenkinsfile that will be used to configure","use Command and/or Args. The `-i` flag is needed to","merge is performed, where individual elements with a matching I(name_)","depend on the type of build to run type: str","of the error. If this value is empty there is","of causes due to fields having multiple errors. Optional. Examples:","- script is a shell script to be run with","to be used and\\ \\ value is the base64 encoded","from the source for a specific repo snapshot. This is","referent. type: str type: description: - type of build input","Must be a DNS_LABEL. Cannot be updated. type: str owner_references:","the referent. type: str optional: description: - Specify whether the","= OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The helper","description: - Namespace defines the space within each name must","dir. type: str git: description: - git contains optional information","of build input to accept type: str strategy: description: -","GitLab webhook type of trigger type: complex contains: allow_env: description:","description: - 'Selects a resource of the container: only resources","the provided binary as `/webapp.war` for the builder. If left","experimental policy 'SkipLayers' will avoid commiting new layers in between","str name: description: - Name must be unique within a","or '.'. type: str context_dir: description: - contextDir specifies the","str uri: description: - uri points to the source that","double $$, ie: $$(VAR_NAME). Escaped references will never be expanded,","complex contains: str, str cluster_name: description: - The name of","to \"v1\". type: str field_path: description: - Path of the","cases, using Script might be more convenient. type: list contains:","Source for the environment variable's value. Cannot be used if","description: - Must be empty before the object is deleted","defines the space within each name must be unique. An","to use to run the pod created by this build.","further into the future, although it may be shortened or","str resource_version: description: - Specific resourceVersion to which this reference","description: - ref is the branch/tag/ref to build. type: str","ignored. type: complex contains: str, str output: description: - output","or not to verify the API server's SSL certificates. type:","of this operation. type: str metadata: description: - Standard list","path context: description: - The name of a context found","imageOptimizationPolicy describes what optimizations the system can use when building","the PATH. type: str resources: description: - resources computes resource","type: str push_secret: description: - PushSecret is the name of","to validate requests. type: str gitlab: description: - GitLabWebHook contains","specified contextDir. type: str source_strategy: description: - sourceStrategy holds the","the build will receive a directory with a single file.","spec: description: - spec holds all the input necessary to","a password for connecting to the API. Use in conjunction","- Name of the referent. type: str to: description: -","initializers. Only privileged users may set or modify this list.","only occur as a result of an explicit client build","be pulled type: complex contains: api_version: description: - API version","str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to the","these will be copied into the build directory, where the","is requested. Read-only. type: complex contains: {} finalizers: description: -","information from the source for a specific repo snapshot. This","is a zip, tar, or tar.gz file and extract it","not want to use a shell. In all other cases,","identifying a specific commit type: str committer: description: - committer","order to have the appropriate collections enabled in the shell.","be truncated by the length of the suffix required to","true, AND if the owner has the \"foregroundDeletion\" finalizer, then","config_map_key_ref: description: - Selects a key of a ConfigMap. type:","secret should be available for the build time. For the","in /. then the content of the directory will be","mount the secret type: str secret_source: description: - secretSource is","- name of the source control user type: str commit:","describes if the builder should pull the images from registry","str github: description: - github contains the parameters for a","JenkinsfilePath & Jenkinsfile are both not specified, this defaults to","C(True). default: present choices: - present - absent username: description:","Kubelet will react by sending a graceful termination signal to","and . type: str self_link: description: - SelfLink is a","this object to gracefully terminate before it will be removed","may not be specified with Script. This might be needed","controller. type: list contains: api_version: description: - API version of","override it. type: str status: description: - 'Status of the","status StatusReason (when there is a single name which can","of images to be used to provide source for the","deletion. type: complex contains: api_version: description: - APIVersion defines the","optional. If no From is specified, the From image from","to always pull the images for the builder or only","contain slashes or be '..' or '.'. type: str context_dir:","Only one ImageChangeTrigger with an empty From reference is allowed","the resource is fully terminated. If not set, graceful deletion","of the label. type: str push_secret: description: - PushSecret is","StatusReason. On some operations may differ from the requested resource","object instead of an entire object, this string should contain","specified, otherwise to an implementation-defined value. type: complex contains: str,","description: - allowEnv determines whether the webhook can set environment","to an existing Kubernetes config file. If not provided, and","the kind of build strategy. type: str successful_builds_history_limit: description: -","module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\", error=str(exc)) if __name__","the initializers struct will be set to nil and the","the relative directory within the build directory where files copied","'Images' type: str run_policy: description: - RunPolicy describes how the","operation. One of: \"Success\" or \"Failure\".' type: str labels: description:","- uri points to the source that will be built.","may not be specified with Command. Use Script when a","that will be used to configure the pipeline relative to","description: - lastTriggeredImageID is used internally by the ImageChangeController to","unneeded dependencies installed. The building of the application is still","the data returned is not guaranteed to conform to any","The experimental policy 'SkipLayers' will avoid commiting new layers in","int uid: description: - UID of the resource. (when there","description of the cause of the error. If this value","str owner_references: description: - List of objects depended by this","should not be used type: str ref: description: - ref","a resource and is not allowed to change on PUT","to organize and categorize (scope and select) objects. May match","the status of this operation. type: str metadata: description: -","key must be defined type: bool dockerfile_path: description: - dockerfilePath","the directory where the files from the secret should be","list of initializers that have not yet acted on this","In all other cases, using Script might be more convenient.","and an optional Dockerfile may be specified to override any","by the system. Read-only. type: str reason: description: - A","points to the managing controller. type: bool kind: description: -","appear more than once in an array of causes due","str name: description: - name of the source control user","image. The default policy is 'None' which means the final","considered as initialized and visible to all clients. type: list","complex contains: _from: description: - from is a reference to","destinationDir must be a directory. destinationDir can also be empty","str value: description: - 'Variable references $(VAR_NAME) are expanded using","what optimizations the system can use when building images to","the value unique on the server. If this field is","not set, graceful deletion of the object has not been","such as desiredState.manifest.containers[2]. For example, if the object reference is","will be deleted. This field is set by the server","\"v1\". type: str field_path: description: - Path of the field","will receive this binary as input on STDIN. type: complex","its JSON serialization. May include dot and postfix notation for","is generally assumed to be a tar, gzipped tar, or","Populated by the system. Read-only. type: str uid: description: -","status holds any relevant information about a build config type:","object has not been requested. Populated by the system when","the build pod type: list contains: mount_path: description: - mountPath","- output describes the Docker image the Strategy should produce.","or empty, this object has been completely initialized. Otherwise, the","have changed. Value must be treated as opaque by clients","path to a file containing the YAML definition of the","the image is pulled from the OpenShift registry, this field","Limits describes the maximum amount of compute resources allowed. type:","so there is no module object. All we can do","commit: description: - commit is the commit hash identifying a","int details: description: - Extended data associated with the reason.","images to reduce the final size or time spent building","the name of a Secret that would be used for","the specified API version. type: str resource_field_ref: description: - 'Selects","images from the private Docker registries type: complex contains: name:","of the specified contextDir. type: str source_strategy: description: - sourceStrategy","items: description: - items is a list of build configs","time. For example, a user may request that a pod","strategy. For Docker builds, this is the build context and","retain. If not specified, all failed builds are retained. type:","the referent. type: str resource_version: description: - Specific resourceVersion to","is used. type: list contains: name: description: - name defines","means the final build image will be equivalent to an","and is in UTC. Populated by the system. Read-only. Null","using the previous defined environment variables in the container and","namespace type: complex contains: key: description: - The key of","source. The custom strategy receives this binary as standard input.","this value is empty there is no information available. A","human-readable description of the status of this operation. type: str","last used image ID for build type: str type: description:","type: complex contains: as_file: description: - asFile indicates that the","be specified to override any Dockerfile in the build context.","a user may request that a pod is deleted in","not override it. type: str status: description: - 'Status of","listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description:","are defined, a new build can only occur as a","context. For Source builds, this is assumed to be an","of replication controllers and services. type: complex contains: str, str","set the build will receive a directory with a single","until an administrator or automated process can determine the resource","Dockerfile may be specified to override any Dockerfile in the","will be scheduled for execution. This is optional, if not","determine the resource is fully terminated. If not set, graceful","more convenient. type: list contains: str script: description: - script","to the root of the context (contextDir). type: str env:","is the requested API version for the Build object serialized","and requests.memory) are currently supported.' type: complex contains: container_name: description:","If nil, it can be overridden by default build nodeselector","representing this object. Populated by the system. Read-only. type: str","arguments that are provided to either Command, Script or the","root of the context (contextDir). type: str env: description: -","the referent. type: str build_api_version: description: - buildAPIVersion is the","build when updated It is optional. If no From is","A patch operation results in merging lists and updating dictionaries,","type: list contains: field: description: - 'The field of the","the needed artifacts in the runtime image for use. Deprecated:","the StatusReason failure. Not all StatusReasons may provide detailed causes.","select) objects. May match selectors of replication controllers and services.","the reason type. type: complex contains: causes: description: - The","set of resources. Populated by the system. Read-only. Value must","and destination paths to copy from the image. type: list","over http type: str https_proxy: description: - httpsProxy is a","future release. Use ImageSource to copy binary artifacts created from","resource this object represents. Servers may infer this from the","description: - Specifies the output format of the exposed resources,","it defaults to Limits if that is explicitly specified, otherwise","push_secret: description: - PushSecret is the name of a Secret","The kind attribute of the resource associated with the status","strategy, these will be injected into a container where the","a strategic merge is performed, where individual elements with a","run_policy: description: - RunPolicy describes how the new build created","created at the destination. type: str pull_secret: description: - pullSecret","This filename may not contain slashes or be '..' or","contains: str command: description: - command is the command to","type: str namespace: description: - Namespace of the referent. type:","variables; can only be set to true for GenericWebHook. type:","pullSecret is a reference to a secret to be used","description: - Name of the referent. type: str jenkins_pipeline_strategy: description:","'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels,","list contains: name: description: - name of the process that","destination_dir: description: - destinationDir is the relative directory within the","image ID for build type: str type: description: - type","object to gracefully terminate before it will be removed from","build pod to fit on a node If nil, it","of an explicit client build creation. type: list contains: bitbucket:","- pullSecret is a reference to a secret to be","the \"Failure\" status. If this value is empty there is","of the referent. type: str namespace: description: - Namespace of","strategy will be used. Only one ImageChangeTrigger with an empty","defines the versioned schema of this representation of an object.","the author of a specific commit type: complex contains: email:","reference is removed. Defaults to false. To set this field,","COPY them during docker build. type: str secret: description: -","complex contains: limits: description: - Limits describes the maximum amount","is an unstructured key value map stored with a resource","- Path to a certificate used to authenticate with the","execute the post build hook, for example for running unit","type: str value: description: - 'Variable references $(VAR_NAME) are expanded","contains the parameters for a Bitbucket webhook type of trigger","in this list will point to this controller, with the","contains optional information about git build source type: complex contains:","username for connecting to the API. verify_ssl: description: - Whether","could not be found in the time allotted, and the","of the exposed resources, defaults to \"1\" type: str resource:","specified to override any Dockerfile in the build context. For","created, if it does not exist, or patched, if parameter","build. type: complex contains: api_version: description: - API version of","name passed. This value will also be combined with a","The key to select. type: str name: description: - Name","the path at which to mount the secret type: str","pull_secret: description: - pullSecret is a reference to a secret","will point to this controller, with the controller field set","of an ImageStreamTag, the ImageStreamTag will be looked for in","a Jenkins pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath","system when a graceful deletion is requested. Read-only. type: complex","sources in directory other than root of repository. type: str","Source build strategy. type: complex contains: _from: description: - from","seconds allowed for this object to gracefully terminate before it","parameters intended to define object attributes. type: dict src: description:","a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME)","chosen only to have some well-defined way of referencing a","API version build_config_list: type: complex returned: when I(state) = C(present)","no failing result is set, the initializers struct will be","copied from the builder to the runtime image. sourcePath can","although it may be shortened or the resource may be","description: - type of build input to accept type: str","failed builds to retain. If not specified, all failed builds","for connecting to the API. verify_ssl: description: - Whether or","description: - type is the type of build trigger type:","cannot be resolved, the reference in the input string will","environment variables. If a variable cannot be resolved, the reference","for BuildConfigList. type: complex contains: resource_version: description: - String that","of values. If a list contains a dictionary with a","\"Failure\" status. If this value is empty there is no","will be unchanged. The $(VAR_NAME) syntax can be escaped with","termination signal to the containers in the pod. After that","the name of the container that triggered the event) or","and select) objects. May match selectors of replication controllers and","description: - Name of the environment variable. Must be a","field of the resource that has caused this error, as","type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of","type: str no_cache: description: - noCache if set to true","be described). type: str kind: description: - Kind is a","type: str no_proxy: description: - noProxy is the list of","build image will be equivalent to an image created by","set with the Failure field, the object will be persisted","Name of the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy","the API server's SSL certificates. type: bool requirements: - openshift","that will be included in the build pod type: list","reason: description: - A machine-readable description of why this operation","deleted, if set to C(absent). A patch operation results in","be deleted prior to this time. For example, a user","type: str uri: description: - uri points to the source","of the source control user type: str commit: description: -","description: - Path to a CA certificate used to authenticate","server time when this object was created. It is not","must be unique within a namespace. Is required when creating","committer: description: - committer is the committer of a specific","assemble script runs. Later, when the script finishes, all files","required when creating resources, although some resources may allow a","file from I(~/.kube/config.json). type: path password: description: - Provide a","creation time. This field is a list of initializers that","location to push the output of this build to. Kind","build must be executed with the --no-cache=true flag type: bool","node If nil, it can be overridden by default build","OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The helper failed","that have not yet acted on this object. If nil","parameters for a GitHub webhook type of trigger type: complex","input on STDIN. type: complex contains: as_file: description: - asFile","as the source. The custom strategy receives this binary as","- imageOptimizationPolicy describes what optimizations the system can use when","the FROM may be modified based on your strategy base","description: - UID of the referent. type: str resource_version: description:","or tar.gz file and extract it as the source. The","from your strategy environment will be added after the FROM,","shell script is appropriate to execute the post build hook,","build, you can copy the needed artifacts in the runtime","Requested API version build_config_list: type: complex returned: when I(state) =","type: str kind: description: - Kind is a string value","repository over https type: str no_proxy: description: - noProxy is","be scheduled for execution. This is optional, if not specified","- GitLabWebHook contains the parameters for a GitLab webhook type","type: str items: description: - items is a list of","not directly settable by a client. The resource is expected","status of this operation. type: str metadata: description: - Standard","the build output image is committed, before it is pushed","directory where files copied from the image are placed. type:","in use. type: complex contains: binary: description: - binary builds","and the client should retry (optionally after the time indicated","concurrency, change detection, and the watch operation on a resource","at which to mount the secret type: str secret_source: description:","used to reach the git repository over https type: str","strategy base image and additional ENV stanzas from your strategy","parameters to the Jenkins Pipeline build strategy. This strategy is","strategy. type: complex contains: _from: description: - from is reference","successful builds are retained. type: int triggers: description: - triggers","objects, returning a resource_version representing a consistent version of the","Docker and Source build strategies assume this file is a","and updating dictionaries, with lists being merged into a unique","contains: as_file: description: - asFile indicates that the provided binary","I(username). resource_definition: description: - Provide the YAML definition for the","to zero length. For the Docker build strategy, these will","description: - buildAPIVersion is the requested API version for the","in the build context. For Source builds, this is assumed","sourcePath can be a file or directory. destinationDir must be","test`. If you need control over the image entrypoint, or","a node If nil, it can be overridden by default","type: str name: description: - The name attribute of the","- Map of string keys and values that can be","resources, although some resources may allow a client to request","- 'runtimeImage is an optional image that is used to","or COPY them during docker build. type: str secret: description:","- openshift == 0.3.3 ''' EXAMPLES = ''' ''' RETURN","https type: str no_proxy: description: - noProxy is the list","of the referent. type: str build_api_version: description: - buildAPIVersion is","user needs \"delete\" permission of the owner, otherwise 422 (Unprocessable","values for the cluster. If set to an empty map","be built. The structure of the source will depend on","metadata for BuildConfigList. type: complex contains: resource_version: description: - String","the FROM line of the Dockerfile for this build. type:","associated with the status StatusReason. type: str kind: description: -","\".\", in this case it just refers to the root","str field_path: description: - 'If referring to a piece of","is optional. If no From is specified, the From image","type: bool kind: description: - Kind of the referent. type:","commit type: str type: description: - type of the build","kind attribute of the resource associated with the status StatusReason.","be presented as-is to a reader. type: str reason: description:","has the \"foregroundDeletion\" finalizer, then the owner cannot be deleted","found in the time allotted, and the client should retry","a username for connecting to the API. verify_ssl: description: -","resource to select' type: str secret_key_ref: description: - Selects a","value will also be combined with a unique suffix. The","binary input should be considered a single file within the","ignore it if set in create or update request. type:","of build to run type: str images: description: - images","builds if true. type: bool pull_secret: description: - pullSecret is","a dictionary with a I(name) or I(type) attribute, a strategic","the system when a graceful deletion is requested. Read-only. type:","the input necessary to produce a new build, and the","description: - Path of the field to select in the","str value: description: - value defines the literal value of","secret used to validate requests. type: str github: description: -","of additional secrets that will be included in the build","how new Builds can be launched from a BuildConfig. If","to validate requests. type: str image_change: description: - imageChange contains","of repository. type: str dockerfile: description: - dockerfile is the","the builder image but, post build, you can copy the","FROM may be modified based on your strategy base image","Populated by the system. Read-only. Null for lists. type: complex","the Docker build strategy. type: complex contains: _from: description: -","For example, specifying \"webapp.war\" would place the provided binary as","be built. When this option is specified, the FROM may","If set to an empty map or a map with","not be used type: str ref: description: - ref is","kubeconfig: description: - Path to an existing Kubernetes config file.","necessary to make `ruby`, `bundle` and other binaries available in","is the kind of build strategy. type: str successful_builds_history_limit: description:","- Path to a key file used to authenticate with","a namespace. Is required when creating resources, although some resources","not specified, all successful builds are retained. type: int triggers:","complex contains: _from: description: - from is reference to an","server's internal version of this object that can be used","this would take on a value like: \"spec.containers{name}\" (where \"name\"","- Provide the YAML definition for the object, bypassing any","administrator or automated process can determine the resource is fully","build_args: description: - buildArgs contains build arguments that will be","build. type: complex contains: custom_strategy: description: - customStrategy holds the","elements with a matching I(name_) or I(type) are merged. To","description: - Suggested HTTP return code for this status, 0","client will be different than the name passed. This value","unique. An empty namespace is equivalent to the \"default\" namespace,","of the ServiceAccount to use to run the pod created","is the name of a Secret that would be used","resource of the container: only resources limits and requests (limits.cpu,","not empty. type: complex contains: config_map_key_ref: description: - Selects a","the Dockerfile. See type: list contains: name: description: - Name","for the application exists. This allows to have buildable sources","description: - A machine-readable description of why this operation is","API. key_file: description: - Path to a key file used","have non-zero length. type: str value: description: - value defines","resources computes resource requirements to execute the build. type: complex","description: - github contains the parameters for a GitHub webhook","specific commit type: str type: description: - type of the","- GenerateName is an optional prefix, used by the server,","referent. type: str build_api_version: description: - buildAPIVersion is the requested","objects have changed. Value must be treated as opaque by","'The field of the resource that has caused this error,","message: description: - message is the description of a specific","a timestamp representing the server time when this object was","may be specified to override any Dockerfile in the build","be defined type: bool jenkinsfile: description: - Jenkinsfile defines the","to. Cannot be updated. In CamelCase. type: str metadata: description:","you can copy the needed artifacts in the runtime image","of the resource that has caused this error, as named","provide source for the build type: list contains: _from: description:","the same validation rules as the Name field, and may","no_proxy: description: - noProxy is the list of domains for","the final build image will be equivalent to an image","to generate a unique name ONLY IF the Name field","fully terminated. If not set, graceful deletion of the object","type: list contains: mount_path: description: - mountPath is the path","that the docker build must be executed with the --no-cache=true","builds, this is assumed to be an archive as described","attribute, a strategic merge is performed, where individual elements with","code for the application exists. This allows to have buildable","owner has the \"foregroundDeletion\" finalizer, then the owner cannot be","- API version of the referent. type: str block_owner_deletion: description:","key to select. type: str name: description: - Name of","git build source type: complex contains: http_proxy: description: - httpProxy","option to C(True). default: present choices: - present - absent","environment variable. Must be a C_IDENTIFIER. type: str value: description:","variables you want to pass into a build pipeline. type:","#!/usr/bin/python # -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule,","your build. type: complex contains: name: description: - Name of","complex contains: str, str name: description: - Name must be","signal to the containers in the pod. After that 30","operations provide a snapshot read of the underlying objects, returning","configure the build pod to always pull the images for","'SkipLayersAndWarn' is the same as 'SkipLayers' but simply warns if","One of: \"Success\" or \"Failure\".' type: str labels: description: -","pod to always pull the images for the builder or","for env vars' type: str divisor: description: - Specifies the","ENV stanzas from your strategy environment will be added after","operations. Populated by the system. Read-only. type: str spec: description:","is no information available. A Reason clarifies an HTTP status","The provided value has the same validation rules as the","str type: description: - type is the type of build","into a separate runtime image.' type: list contains: destination_dir: description:","initializers struct will be set to nil and the object","the number of old failed builds to retain. If not","uid: description: - UID of the referent. type: str build_api_version:","JSON serialization. May include dot and postfix notation for nested","valid for a particular resource or set of resources. Populated","after the FROM, but before the rest of your Dockerfile","They may only be valid for a particular resource or","the system. Read-only. type: str spec: description: - spec holds","in this field. Once set, this value may not be","not. Defaults to \"\".' type: str value_from: description: - Source","status: description: - 'Status of the operation. One of: \"Success\"","complex contains: args: description: - args is a list of","the Build object serialized and passed to the custom builder","updated. type: str owner_references: description: - List of objects depended","set further into the future, although it may be shortened","If the deletionTimestamp of the object is non-nil, entries in","generated by the server on successful creation of a resource","str images: description: - images describes a set of images","layers in between each image step, and will fail if","the cluster which the object belongs to. This is used","set by the server when a graceful deletion is requested","complex contains: http_proxy: description: - httpProxy is a proxy used","key. type: str name: description: - Name of the referent.","for the build type: list contains: _from: description: - from","generally assumed to be a tar, gzipped tar, or zip","be '..' or '.'. type: str context_dir: description: - contextDir","yet acted on this object. If nil or empty, this","convert recognized schemas to the latest internal value, and may","exists or not. Defaults to \"\".' type: str value_from: description:","and may reject unrecognized values. type: str kind: description: -","other binaries available in the PATH. type: str resources: description:","computes resource requirements to execute the build. type: complex contains:","the client submits requests to. Cannot be updated. In CamelCase.","changed. May be used for optimistic concurrency, change detection, and","in the build pod type: list contains: mount_path: description: -","of a Dockerfile which should be built. When this option","default configuration file from I(~/.kube/config.json). type: path password: description: -","description: - successfulBuildsHistoryLimit is the number of old successful builds","human-readable description of the cause of the error. This field","supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type:","a GitLab webhook type of trigger type: complex contains: allow_env:","type: complex contains: str, str cluster_name: description: - The name","described above. For Source and Docker builds, if binary.asFile is","about number of last triggered build. type: int kind: description:","version of this object that can be used by clients","variable. Must be a C_IDENTIFIER. type: str value: description: -","non-nil, entries in this list can only be removed. type:","jenkinsfile: description: - Jenkinsfile defines the optional raw contents of","space value for this object. It is typically generated by","is to a container within a pod, this would take","single file within the build input. For example, specifying \"webapp.war\"","to the container and after cleanup, remove the pod from","type: complex contains: limits: description: - Limits describes the maximum","trigger a build when updated It is optional. If no","terminate before it will be removed from the system. Only","Each entry is an identifier for the responsible component that","str command: description: - command is the command to run.","them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an","This field is not set anywhere right now and apiserver","'DockerImage'. This value will be used to look up a","map with any values, default build nodeselector values are ignored.","the current resource \"items[0].name\" - the field \"name\" on the","Secret that would be used for setting up the authentication","- DeletionTimestamp is RFC 3339 date and time at which","image to copy to the build directory. If the source","each image step, and will fail if the Dockerfile cannot","this reference points to the managing controller. type: bool kind:","needed to support CentOS and RHEL images that use Software","type: str env: description: - env contains additional environment variables","str docker_strategy: description: - dockerStrategy holds the parameters to the","For Source builds, this is assumed to be an archive","the authentication for pulling the Docker images from the private","used to configure the pipeline relative to the root of","type: str uid: description: - UID of the referent. type:","be a C_IDENTIFIER. type: str value: description: - 'Variable references","Must be a valid secret key. type: str name: description:","holds the parameters to the Jenkins Pipeline build strategy. This","After that 30 seconds, the Kubelet will send a hard","to the server. Populated by the system. Read-only. type: str","entry is an identifier for the responsible component that will","want to use a shell. In all other cases, using","A machine-readable description of why this operation is in the","is used to distinguish resources with same name and namespace","destination. type: str pull_secret: description: - pullSecret is a reference","exposeDockerSocket will allow running Docker commands (and build Docker images)","images describes a set of images to be used to","list contains: name: description: - name defines the name of","are required to be scoped to a namespace - the","description: - If specified, the time in seconds before the","description: - committer is the committer of a specific commit","the Source build strategy. type: complex contains: _from: description: -","by any user. type: complex contains: pending: description: - Pending","I(resource_definition). type: path ssl_ca_cert: description: - Path to a CA","type: complex contains: binary: description: - binary builds accept a","list contains: destination_dir: description: - destinationDir is the relative directory","schema except that defined by the reason type. type: complex","users can ADD or COPY them during docker build. type:","attributes, and deleted, if set to C(absent). A patch operation","can set environment variables; can only be set to true","and will fail if the Dockerfile cannot provide compatibility with","must be treated as opaque by clients and passed unmodified","to a key file used to authenticate with the API.","- forcePull describes if the controller should configure the build","there is a single resource which can be described). type:","of the referent. type: str to: description: - to defines","be modified further by any user. type: complex contains: pending:","command: description: - command is the command to run. It","structure of the source will depend on the type of","on a resource or set of resources. Clients must treat","the image entrypoint, or if the image does not have","build configuration will be scheduled for execution. This is optional,","having multiple errors. Optional. Examples: \"name\" - the field \"name\"","contains: key: description: - The key to select. type: str","binary as their input. The binary is generally assumed to","description: - lastVersion is used to inform about number of","empty there is no information available. A Reason clarifies an","will fail if the Dockerfile cannot provide compatibility with the","cloning private repository. The secret\\ \\ contains valid credentials for","if binary.asFile is set the build will receive a directory","receive a directory with a single file. contextDir may be","the resource associated with the status StatusReason. type: str kind:","be unique. An empty namespace is equivalent to the \"default\"","part of an object.' type: str kind: description: - Kind","used to reach the git repository over http type: str","- type of the build source, may be one of","network partitions, this object may still exist after this timestamp,","in seconds before the operation should be retried. type: int","str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of old","generic: description: - generic contains the parameters for a Generic","str, str output: description: - output describes the Docker image","contains: container_name: description: - 'Container name: required for volumes, optional","user type: str name: description: - name of the source","the literal value of the label. type: str push_secret: description:","to push to. In the case of an ImageStreamTag, the","is written in terms of, defaults to \"v1\". type: str","cluster_name: description: - The name of the cluster which the","will be replaced, rather than merged. default: false type: bool","in the namespace of the build unless Namespace is specified.","will receive a directory with a single file. contextDir may","needed if the image doesn't have `/bin/sh`, or if you","simply warns if compatibility cannot be preserved. type: str no_cache:","type: bool pull_secret: description: - pullSecret is the name of","if set to C(absent). A patch operation results in merging","description: - sourcePath is the absolute path of the file","\"delete\" permission of the owner, otherwise 422 (Unprocessable Entity) will","build into a separate runtime image.' type: list contains: destination_dir:","the root of the context (contextDir). type: str env: description:","strategy environment will be added after the FROM, but before","opaque value that represents the internal version of this object","operation is in the \"Failure\" status. If this value is","be preserved. type: str no_cache: description: - noCache if set","by the ServiceAccount type: str source: description: - source describes","attribute of the resource associated with the status StatusReason (when","options: api_key: description: - Token used to connect to the","serviceAccount is the name of the ServiceAccount to use to","are merged. To force the replacement of lists, set the","status code but does not override it. type: str status:","will be used. Only one ImageChangeTrigger with an empty From","object is deleted from the registry. Each entry is an","unique on the server. If this field is specified and","are not queryable and should be preserved when modifying objects.","the presence of network partitions, this object may still exist","associated with the StatusReason failure. Not all StatusReasons may provide","Requests describes the minimum amount of compute resources required. If","managing controller. type: list contains: api_version: description: - API version","snapshot read of the underlying objects, returning a resource_version representing","the git repository over http type: str https_proxy: description: -","requests to. Cannot be updated. In CamelCase. type: str message:","reference points to the managing controller. type: bool kind: description:","to run the pod created by this build. The pod","setting up the authentication for pulling the Docker images from","the previous defined environment variables in the container and any","required to be scoped to a namespace - the value","image.' type: complex contains: api_version: description: - API version of","secrets and their destinations that will be used only for","indicates that the docker build must be executed with the","set in happens-before order across separate operations. Clients may not","ConfigMap or it's key must be defined type: bool field_ref:","to observe uninitialized objects. When an object is created, the","push the output of this build to. Kind must be","provide compatibility with the 'None' policy. An additional experimental policy","'runtimeArtifacts specifies a list of source/destination pairs that will be","would be used for setting up the authentication for executing","field \"name\" on the current resource \"items[0].name\" - the field","== 0.3.3 ''' EXAMPLES = ''' ''' RETURN = '''","be active on a node before the system actively tries","is the canonical representation. Not all objects are required to","of this build to. Kind must be one of 'ImageStreamTag'","is specified and the generated name exists, the server will","- RunPolicy describes how the new build created from this","\"Serial\". type: str service_account: description: - serviceAccount is the name","then the content of the directory will be copied, but","set or modify this list. Once it is empty, it","triggers: description: - triggers determine how new Builds can be","a binary as their input. The binary is generally assumed","during docker build. type: str secret: description: - secret is","use Software Collections (SCL), in order to have the appropriate","of a Jenkinsfile which defines a Jenkins pipeline build. type:","a Bitbucket webhook type of trigger type: complex contains: allow_env:","from one build into a separate runtime image.' type: list","an optional duration in seconds, counted from the time when","nil, it can be overridden by default build nodeselector values","used type: str ref: description: - ref is the branch/tag/ref","of source/destination pairs that will be copied from the builder","also be empty or equal to \".\", in this case","replacement of lists, set the I(force) option to C(True). default:","str pull_secret: description: - pullSecret is a reference to a","custom builder type: str env: description: - env contains additional","with the StatusReason failure. Not all StatusReasons may provide detailed","postfix notation for nested attributes. Arrays are zero-indexed. Fields may","the raw contents of a Dockerfile which should be built.","type: bool requirements: - openshift == 0.3.3 ''' EXAMPLES =","str code: description: - Suggested HTTP return code for this","reference to an ImageStreamTag that will trigger a build when","cannot provide compatibility with the 'None' policy. An additional experimental","of trigger type: complex contains: allow_env: description: - allowEnv determines","type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of","with index 2 in this pod). This syntax is chosen","name: description: - Name of the referent. type: str secrets:","describes the maximum amount of compute resources allowed. type: complex","support CentOS and RHEL images that use Software Collections (SCL),","- UID of the referent. type: str build_args: description: -","one build into a separate runtime image.' type: complex contains:","is used to inform about number of last triggered build.","deletion of the object has not been requested. Populated by","description: - Name of the referent. type: str optional: description:","configs type: list contains: api_version: description: - APIVersion defines the","file or directory. destinationDir must be a directory. destinationDir can","unique within a namespace. Is required when creating resources, although","type: complex contains: last_version: description: - lastVersion is used to","executing the Docker push to authentication enabled Docker Registry (or","system. Read-only. type: str uid: description: - UID is the","destinationDir is the directory where the files from the secret","be used for setting up the authentication for executing the","referent. type: str secrets: description: - secrets is a list","conditions when to trigger them. type: complex contains: completion_deadline_seconds: description:","UID of the referent. type: str post_commit: description: - postCommit","this field. Once set, this value may not be unset","Namespace of the referent. type: str resource_version: description: - Specific","- value defines the literal value of the label. type:","in create or update request. type: str creation_timestamp: description: -","details: description: - Extended data associated with the reason. Each","controller. type: bool kind: description: - Kind of the referent.","- git contains optional information about git build source type:","Specific resourceVersion to which this reference is made, if any.","path password: description: - Provide a password for connecting to","the parameters for a GitLab webhook type of trigger type:","metadata: description: - Standard list metadata. type: complex contains: resource_version:","the secret to select from. Must be a valid secret","this field does not need to be set. type: complex","set. type: complex contains: name: description: - Name of the","referent. type: str optional: description: - Specify whether the Secret","invariant at object creation time. This field is a list","Enable debug output from the OpenShift helper. Logging info is","environment variable's value. Cannot be used if value is not","may be shortened or the resource may be deleted prior","before the object is deleted from the registry. Each entry","is C(present), an existing object will updated, and lists will","- Path to a CA certificate used to authenticate with","contains the parameters for a Generic webhook type of trigger","commit type: complex contains: email: description: - email of the","in the list have been deleted, this object will be","to. Cannot be updated. In CamelCase. type: str message: description:","ssh-privatekey.\" type: complex contains: name: description: - Name of the","array of causes due to fields having multiple errors. Optional.","rules as the Name field, and may be truncated by","description: - Provide the YAML definition for the object, bypassing","by clients to determine when objects have changed. May be","relative directory within the build directory where files copied from","with the status StatusReason. type: str kind: description: - The","ConfigMap. type: complex contains: key: description: - The key to","prior to building. type: bool incremental: description: - incremental flag","a list of source/destination pairs that will be copied from","pod, this would take on a value like: \"spec.containers{name}\" (where","may infer this from the endpoint the client submits requests","description: - imageChange contains parameters for an ImageChange type of","the system. Read-only. type: str ''' def main(): try: module","the source path ends in /. then the content of","the \"foregroundDeletion\" finalizer, then the owner cannot be deleted from","to execute the post build hook, for example for running","type: str post_commit: description: - postCommit is a build hook","used image ID for build type: str type: description: -","where individual elements with a matching I(name_) or I(type) are","of an object instead of an entire object, this string","categorize (scope and select) objects. May match selectors of replication","source control user type: str commit: description: - commit is","set this value. It is represented in RFC3339 form and","of the referent. type: str block_owner_deletion: description: - If true,","old successful builds to retain. If not specified, all successful","OpenShift helper. Logging info is written to KubeObjHelper.log default: false","causes due to fields having multiple errors. Optional. Examples: \"name\"","written to KubeObjHelper.log default: false type: bool force: description: -","I(state) = C(present) contains: api_version: description: - APIVersion defines the","object. Servers should convert recognized schemas to the latest internal","by the system. Read-only. Null for lists. type: complex contains:","new build can only occur as a result of an","name: description: - Name of the referent. type: str source_secret:","the referent. type: str secrets: description: - secrets is a","not be specified with Script. This might be needed if","set to true. There cannot be more than one managing","int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old","Secret or it's key must be defined type: bool force_pull:","longer visible from resource lists, and not reachable by name)","and the conditions when to trigger them. type: complex contains:","of initializers. Only privileged users may set or modify this","description: - Determines if an object should be created, patched,","Name of the referent. type: str optional: description: - Specify","map stored with a resource that may be set by","instead of an entire object, this string should contain a","ImageStreamTag, ImageStreamImage, or DockerImage to copy source from. type: complex","Read-only. type: str reason: description: - A machine-readable description of","all failed builds are retained. type: int node_selector: description: -","the server, to generate a unique name ONLY IF the","garbage collected. If this object is managed by a controller,","httpsProxy is a proxy used to reach the git repository","it may not be modified further by any user. type:","for in the namespace of the build unless Namespace is","src: description: - Provide a path to a file containing","the branch/tag/ref to build. type: str uri: description: - uri","group attribute of the resource associated with the status StatusReason.","struct will be set to nil and the object is","- BitbucketWebHook contains the parameters for a Bitbucket webhook type","str secrets: description: - secrets represents a list of secrets","from is a reference to an ImageStreamTag, ImageStreamImage, or DockerImage","field has not been provided. If this field is used,","to which this reference is made, if any. type: str","a 409 - instead, it will either return 201 Created","labels: description: - Map of string keys and values that","They are not queryable and should be preserved when modifying","array includes more details associated with the StatusReason failure. Not","be set in happens-before order across separate operations. Clients may","Script might be more convenient. type: list contains: str script:","node_selector: description: - nodeSelector is a selector which must be","source type may be used with other options like git","str runtime_artifacts: description: - 'runtimeArtifacts specifies a list of source/destination","build to do incremental builds if true. type: bool pull_secret:","react by sending a graceful termination signal to the containers","of the source control user type: str name: description: -","Builds can be launched from a BuildConfig. If no triggers","name could not be found in the time allotted, and","complex contains: causes: description: - The Causes array includes more","object. Populated by the system. Read-only. type: str uid: description:","returned is not guaranteed to conform to any schema except","a Docker image repository to push to. In the case","- If result is set with the Failure field, the","identifies the server's internal version of this object that can","objects. May match selectors of replication controllers and services. type:","terminated. If not set, graceful deletion of the object has","script is appropriate to execute the post build hook, for","merging lists and updating dictionaries, with lists being merged into","is the optional path of the Jenkinsfile that will be","doesn't have `/bin/sh`, or if you do not want to","description: - UID of the resource. (when there is a","is a reference to the secret type: complex contains: name:","str no_cache: description: - noCache if set to true indicates","of the desired state. Populated by the system. Read-only. type:","type: list contains: name: description: - name of the process","script is a shell script to be run with `/bin/sh","For example, if the object reference is to a container","can be escaped with a double $$, ie: $$(VAR_NAME). Escaped","retry (optionally after the time indicated in the Retry-After header).","entry from the list. If the deletionTimestamp of the object","contains: name: description: - Name of the environment variable. Must","anywhere right now and apiserver is going to ignore it","the build directory where files copied from the image are","- committer is the committer of a specific commit type:","the existing object's attributes, and deleted, if set to C(absent).","object represents. Servers may infer this from the endpoint the","where the Dockerfile is located, so users can ADD or","have been deleted, this object will be garbage collected. If","unset or be set further into the future, although it","api_version: type: string description: Requested API version build_config_list: type: complex","example, if the object reference is to a container within","field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName,","application exists. This allows to have buildable sources in directory","not be specified with Command. Use Script when a shell","deletion_grace_period_seconds: description: - Number of seconds allowed for this object","and retrieve arbitrary metadata. They are not queryable and should","- the field \"name\" on the current resource \"items[0].name\" -","is RFC 3339 date and time at which this resource","status.podIP.' type: complex contains: api_version: description: - Version of the","also be combined with a unique suffix. The provided value","in the context dir. type: str git: description: - git","- 'Variable references $(VAR_NAME) are expanded using the previous defined","includes more details associated with the StatusReason failure. Not all","non-zero length. type: str value: description: - value defines the","description: - name defines the name of the label. It","description: - revision is the information from the source for","is removed, and no failing result is set, the initializers","internal value, and may reject unrecognized values. type: str kind:","watch operation on a resource or set of resources. Clients","where the source code for the application exists. This allows","-*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION","not allowed to change on PUT operations. Populated by the","successfulBuildsHistoryLimit is the number of old successful builds to retain.","the time when a build pod gets scheduled in the","be one of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str","for build type: str type: description: - type is the","to define object attributes. type: dict src: description: - Provide","name: description: - Name of the referent. type: str jenkins_pipeline_strategy:","context found in the Kubernetes config file. debug: description: -","pulled from the OpenShift registry, this field does not need","500 with Reason ServerTimeout indicating a unique name could not","the time indicated in the Retry-After header). Applied only if","compute resources allowed. type: complex contains: str, str requests: description:","description: - The name of a context found in the","time indicated in the Retry-After header). Applied only if Name","Specify whether the ConfigMap or it's key must be defined","strategy receives this binary as standard input. This filename may","control over the image entrypoint, or if the image does","description: - UID is the unique in time and space","provide detailed causes. type: list contains: field: description: - 'The","type: str message: description: - A human-readable description of the","a list of additional secrets that will be included in","optional information about git build source type: complex contains: http_proxy:","to the root of WORKDIR. Deprecated: This feature will be","false type: bool force: description: - If set to C(True),","if true. type: bool pull_secret: description: - pullSecret is the","removed from the system. Only set when deletionTimestamp is also","of domains for which the proxy should not be used","the server's internal version of this object that can be","key_file: description: - Path to a key file used to","system. Read-only. Value must be treated as opaque by clients","determines whether the webhook can set environment variables; can only","path kubeconfig: description: - Path to an existing Kubernetes config","description: - asFile indicates that the provided binary input should","you want to pass into a builder container. type: list","the Custom build strategy type: complex contains: _from: description: -","is a reference to an ImageStreamTag, ImageStreamImage, or DockerImage to","ImageSource to copy binary artifacts created from one build into","Not all objects are required to be scoped to a","used to validate requests. type: str gitlab: description: - GitLabWebHook","name: description: - name of the process that is responsible","str, str name: description: - Name must be unique within","a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations,","OpenShift (@openshift) options: api_key: description: - Token used to connect","is going to ignore it if set in create or","metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version:","to build the Docker image, relative to the root of","will not be created at the destination. type: str runtime_image:","- A human-readable description of the status of this operation.","with same name and namespace in different clusters. This field","should be preserved when modifying objects. type: complex contains: str,","The $(VAR_NAME) syntax can be escaped with a double $$,","- present - absent username: description: - Provide a username","type: str value: description: - value defines the literal value","should be available for the build time. For the Source","unrecognized values. type: str code: description: - Suggested HTTP return","secret in the pod's namespace type: complex contains: key: description:","specified we default to \"Serial\". type: str service_account: description: -","single resource which can be described). type: str kind: description:","the client should retry (optionally after the time indicated in","the Jenkinsfile that will be used to configure the pipeline","ServerTimeout indicating a unique name could not be found in","the field to select in the specified API version. type:","Command and/or Args. The `-i` flag is needed to support","not be created at the destination. type: str pull_secret: description:","guaranteed to be set in happens-before order across separate operations.","field access statement, such as desiredState.manifest.containers[2]. For example, if the","if the controller should configure the build pod to always","override any Dockerfile in the build context. For Source builds,","label. type: str push_secret: description: - PushSecret is the name","a shell script to be run with `/bin/sh -ic`. It","if you do not want to use a shell. In","them during docker build. type: str secret: description: - secret","empty, it may not be modified further by any user.","If both JenkinsfilePath & Jenkinsfile are both not specified, this","str namespace: description: - Namespace of the referent. type: str","parameters for a Generic webhook type of trigger type: complex","to look up a Docker image repository to push to.","provided to either Command, Script or the Docker image's default","build configs type: list contains: api_version: description: - APIVersion defines","this object. type: str result: description: - If result is","node before the system actively tries to terminate the build;","(in list/watch and get calls) from clients that haven't explicitly","optional location to push the output of this build to.","be used if value is not empty. type: complex contains:","must be defined type: bool field_ref: description: - 'Selects a","metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex","- secret used to validate requests. type: str image_change: description:","other options like git - in those cases the Git","each name must be unique. An empty namespace is equivalent","image step, and will fail if the Dockerfile cannot provide","preserved. type: str no_cache: description: - noCache if set to","pipeline relative to the root of the context (contextDir). If","type: str owner_references: description: - List of objects depended by","is specified. type: complex contains: api_version: description: - API version","by its JSON serialization. May include dot and postfix notation","resource_version: description: - Specific resourceVersion to which this reference is","Path to a key file used to authenticate with the","List operations provide a snapshot read of the underlying objects,","a single file within the build input. For example, specifying","that haven't explicitly asked to observe uninitialized objects. When an","type: str generic: description: - generic contains the parameters for","str image_change: description: - imageChange contains parameters for an ImageChange","defined type: bool jenkinsfile: description: - Jenkinsfile defines the optional","operation on a resource or set of resources. Clients must","directory itself will not be created at the destination. type:","depended by this object. If ALL objects in the list","date and time at which this resource will be deleted.","scheduled in the system, that the build may be active","will be removed in a future release. Use ImageSource to","is optional. type: complex contains: git: description: - Git contains","from inside the Docker container. type: bool force_pull: description: -","a node before the system actively tries to terminate the","field_path: description: - 'If referring to a piece of an","'Required: resource to select' type: str secret_key_ref: description: - Selects","for this object to gracefully terminate before it will be","bool secret: description: - secret used to validate requests. type:","the list. If the deletionTimestamp of the object is non-nil,","type: complex contains: annotations: description: - Annotations is an unstructured","namespace - the value of this field for those objects","type: complex returned: when I(state) = C(present) contains: api_version: description:","location of Source scripts type: str type: description: - type","a resource of the container: only resources limits and requests","define object attributes. type: dict src: description: - Provide a","Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\", error=str(exc))","referent. type: str uid: description: - UID of the referent.","the resource that has caused this error, as named by","be returned. type: bool controller: description: - If true, this","of the environment variable. Must be a C_IDENTIFIER. type: str","the deletionTimestamp of the object is non-nil, entries in this","contains: field: description: - 'The field of the resource that","gitlab: description: - GitLabWebHook contains the parameters for a GitLab","str build_args: description: - buildArgs contains build arguments that will","API. Use in conjunction with I(username). resource_definition: description: - Provide","connection options are provided, the openshift client will attempt to","an implementation-defined value. type: complex contains: str, str revision: description:","source from. type: complex contains: api_version: description: - API version","directory with a single file. contextDir may be used when","description: Requested API version build_config_list: type: complex returned: when I(state)","is raise the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException","& Jenkinsfile are both not specified, this defaults to Jenkinsfile","entry in \"items\"' type: str message: description: - A human-readable","In the case of an ImageStreamTag, the ImageStreamTag will be","value, and may reject unrecognized values. type: str kind: description:","which must be true for the build pod to fit","source_secret: description: - \"sourceSecret is the name of a Secret","builds accept a binary as their input. The binary is","will updated, and lists will be replaced, rather than merged.","define its own extended details. This field is optional and","in a build configuration. type: complex contains: api_version: description: -","cause of the error. If this value is empty there","operation results in merging lists and updating dictionaries, with lists","value_from: description: - Source for the environment variable's value. Cannot","type: str images: description: - images describes a set of","secret used to validate requests. type: str generic: description: -","to an image created by the Docker build API. The","be removed. type: list contains: str generate_name: description: - GenerateName","This might be needed if the image doesn't have `/bin/sh`,","list of initializers that must execute in order before this","image for use. Deprecated: This feature will be removed in","need control over the image entrypoint, or if the image","version for the Build object serialized and passed to the","type: complex contains: key: description: - The key to select.","type: str secrets: description: - secrets is a list of","graceful deletion is requested by the user, and is not","UID of the referent. type: str resource_version: description: - An","it can be overridden by default build nodeselector values for","Provide a path to a file containing the YAML definition","contains: author: description: - author is the author of a","than one managing controller. type: list contains: api_version: description: -","Determines if an object should be created, patched, or deleted.","description: - Name of the referent. type: str source_secret: description:","description: - nodeSelector is a selector which must be true","as 'SkipLayers' but simply warns if compatibility cannot be preserved.","be used. Only one ImageChangeTrigger with an empty From reference","used to authenticate with the API. type: path kubeconfig: description:","user, and is not directly settable by a client. The","of the referent. type: str field_path: description: - 'If referring","type: str resource_field_ref: description: - 'Selects a resource of the","the container and any service environment variables. If a variable","be needed if the image doesn't have `/bin/sh`, or if","that is explicitly specified, otherwise to an implementation-defined value. type:","build strategy type: complex contains: _from: description: - from is","internal version of this object that can be used by","owner_references: description: - List of objects depended by this object.","description: - output describes the Docker image the Strategy should","name: description: - Name of the referent. type: str to:","when a shell script is appropriate to execute the post","StatusReason. type: str kind: description: - The kind attribute of","be treated as opaque by clients and . type: str","from. type: complex contains: api_version: description: - API version of","the Source build strategy, these will be injected into a","required for volumes, optional for env vars' type: str divisor:","by default build nodeselector values for the cluster. If set","the process that is responsible for initializing this object. type:","in directory other than root of repository. type: str dockerfile:","and is hidden (in list/watch and get calls) from clients","''' api_version: type: string description: Requested API version build_config_list: type:","updated It is optional. If no From is specified, the","code but does not override it. type: str status: description:","seconds before the operation should be retried. type: int uid:","description: - Source for the environment variable's value. Cannot be","is represented in RFC3339 form and is in UTC. Populated","for cloning private repository. The secret\\ \\ contains valid credentials","to the Jenkins Pipeline build strategy. This strategy is in","the error. If this value is empty there is no","- CreationTimestamp is a timestamp representing the server time when","private Docker registries type: complex contains: name: description: - Name","Name of the referent. type: str uid: description: - UID","type: str message: description: - message is the description of","'SkipLayers' but simply warns if compatibility cannot be preserved. type:","to. Kind must be one of 'ImageStreamTag' or 'DockerImage'. This","tar, gzipped tar, or zip file depending on the strategy.","that you want to use in your build. type: complex","Git contains information about git-based build source type: complex contains:","recognized schemas to the latest internal value, and may reject","of the referent. type: str env: description: - env contains","(when there is a single name which can be described).","output from the OpenShift helper. Logging info is written to","environment variables in the container and any service environment variables.","- Specifies the output format of the exposed resources, defaults","value: description: - value defines the literal value of the","SSL certificates. type: bool requirements: - openshift == 0.3.3 '''","true for the build pod to fit on a node","completionDeadlineSeconds is an optional duration in seconds, counted from the","destination. type: str runtime_image: description: - 'runtimeImage is an optional","GenerateName is an optional prefix, used by the server, to","existing object's attributes, and deleted, if set to C(absent). A","of the secret to select from. Must be a valid","The group attribute of the resource associated with the status","submits requests to. Cannot be updated. In CamelCase. type: str","- UID of the referent. type: str last_triggered_image_id: description: -","If there are multiple labels with the same name then","if Name is not specified. type: str generation: description: -","The key of the secret to select from. Must be","for volumes, optional for env vars' type: str divisor: description:","type of trigger type: complex contains: allow_env: description: - allowEnv","the case of an ImageStreamTag, the ImageStreamTag will be looked","Deprecated: This feature will be removed in a future release.","the Dockerfile for this build. type: complex contains: api_version: description:","the build context and an optional Dockerfile may be specified","creation of a resource and is not allowed to change","over the image entrypoint, or if the image does not","failed builds are retained. type: int node_selector: description: - nodeSelector","have the appropriate collections enabled in the shell. E.g., in","type: str self_link: description: - SelfLink is a URL representing","- 'runtimeArtifacts specifies a list of source/destination pairs that will","of the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is","description: - Number of seconds allowed for this object to","for which the proxy should not be used type: str","resource may be deleted prior to this time. For example,","type: path password: description: - Provide a password for connecting","to make `ruby`, `bundle` and other binaries available in the","references $(VAR_NAME) are expanded using the previous defined environment variables","description: - type is the kind of build strategy. type:","updated. In CamelCase. type: str metadata: description: - metadata for","referent. type: str namespace: description: - Namespace of the referent.","the final size or time spent building the image. The","has caused this error, as named by its JSON serialization.","the SCM in use. type: complex contains: binary: description: -","result is set, the initializers struct will be set to","git-based build source type: complex contains: author: description: - author","caused this error, as named by its JSON serialization. May","description: - Path to an existing Kubernetes config file. If","is the author of a specific commit type: complex contains:","returned to the client will be different than the name","GitHub webhook type of trigger type: complex contains: allow_env: description:","it is not present locally type: bool pull_secret: description: -","ServiceAccount to use to run the pod created by this","may not be unset or be set further into the","pass into a build pipeline. type: list contains: name: description:","a list of initializers that have not yet acted on","creating resources, although some resources may allow a client to","type: complex contains: _from: description: - from is reference to","type: str ref: description: - ref is the branch/tag/ref to","ImageChangeController to save last used image ID for build type:","timestamp representing the server time when this object was created.","or set of resources. Populated by the system. Read-only. Value","where the files from the secret should be available for","in the FROM line of the Dockerfile for this build.","immediately after the command to be run. type: list contains:","version of the listed objects. version_added: 2.3.0 author: OpenShift (@openshift)","SCM in use. type: complex contains: binary: description: - binary","creation. type: list contains: bitbucket: description: - BitbucketWebHook contains the","In CamelCase. type: str metadata: description: - metadata for BuildConfigList.","deletion is requested. Read-only. type: complex contains: {} finalizers: description:","reader. type: str reason: description: - A machine-readable description of","to the Source build strategy. type: complex contains: _from: description:","a Generic webhook type of trigger type: complex contains: allow_env:","- Limits describes the maximum amount of compute resources allowed.","source type: complex contains: http_proxy: description: - httpProxy is a","the base64 encoded credentials. Supported auth methods\\ \\ are: ssh-privatekey.\"","description: - Provide a URL for acessing the Kubernetes API.","based on your strategy base image and additional ENV stanzas","is the committer of a specific commit type: complex contains:","containers in the pod. After that 30 seconds, the Kubelet","of last triggered build. type: int kind: description: - Kind","secrets: description: - secrets is a list of additional secrets","to the secret type: complex contains: name: description: - Name","as opaque by clients and passed unmodified back to the","and services. type: complex contains: str, str name: description: -","- UID of the referent. type: str scripts: description: -","to authenticate with the API. type: path kubeconfig: description: -","to run an application without unneeded dependencies installed. The building","uid: description: - UID of the referent. type: str build_args:","registry prior to building. type: bool incremental: description: - incremental","the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the","type: description: - type is the kind of build strategy.","int deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date and","the image. type: list contains: destination_dir: description: - destinationDir is","api_version: description: - Version of the schema the FieldPath is","client to request the generation of an appropriate name automatically.","placed immediately after the command to be run. type: list","runtime_artifacts: description: - 'runtimeArtifacts specifies a list of source/destination pairs","the authentication for cloning private repository. The secret\\ \\ contains","not specified, all failed builds are retained. type: int node_selector:","password for connecting to the API. Use in conjunction with","Each reason may define its own extended details. This field","triggered build. type: int kind: description: - Kind is a","- Kind is a string value representing the REST resource","- Extended data associated with the reason. Each reason may","visible from resource lists, and not reachable by name) after","parameters for a GitLab webhook type of trigger type: complex","updated. type: str namespace: description: - Namespace defines the space","individual elements with a matching I(name_) or I(type) are merged.","of the referent. type: str name: description: - Name of","builder should pull the images from registry prior to building.","object's attributes, and deleted, if set to C(absent). A patch","is not guaranteed to conform to any schema except that","allowed to change on PUT operations. Populated by the system.","complex contains: git: description: - Git contains information about git-based","name is specified \"spec.containers[2]\" (container with index 2 in this","the application is still done in the builder image but,","BuildConfig. If no triggers are defined, a new build can","to a container within a pod, this would take on","set the I(force) option to C(True). default: present choices: -","size or time spent building the image. The default policy","str committer: description: - committer is the committer of a","PushSecret is the name of a Secret that would be","set to C(True), and I(state) is C(present), an existing object","imageLabels define a list of labels that are applied to","to true for GenericWebHook. type: bool secret: description: - secret","= ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve","list of labels that are applied to the resulting image.","On some operations may differ from the requested resource Kind.","and categorize (scope and select) objects. May match selectors of","not reachable by name) after the time in this field.","can only occur as a result of an explicit client","be a valid secret key. type: str name: description: -","force: description: - If set to C(True), and I(state) is","type: list contains: bitbucket: description: - BitbucketWebHook contains the parameters","of old failed builds to retain. If not specified, all","the value of this field for those objects will be","as exc: module.fail_json(msg=\"Module failed!\", error=str(exc)) if __name__ == '__main__': main()","integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number","this operation is in the \"Failure\" status. If this value","object that can be used by clients to determine when","be injected into a container where the assemble script runs.","a key of a ConfigMap. type: complex contains: key: description:","deletionTimestamp is also set. May only be shortened. Read-only. type:","from a BuildConfig. If no triggers are defined, a new","description: - httpProxy is a proxy used to reach the","line of the Dockerfile for this build. type: complex contains:","str name: description: - Name of the referent. type: str","description: - git contains optional information about git build source","Namespace is specified. type: complex contains: api_version: description: - API","build configuration. type: complex contains: api_version: description: - API version","visible. When the last pending initializer is removed, and no","for nested attributes. Arrays are zero-indexed. Fields may appear more","description: - noCache if set to true indicates that the","description: - Selects a key of a ConfigMap. type: complex","to copy binary artifacts created from one build into a","type of trigger type: complex contains: _from: description: - from","(container with index 2 in this pod). This syntax is","example, specifying \"webapp.war\" would place the provided binary as `/webapp.war`","by a client. The resource is expected to be deleted","secret: description: - secret is a reference to an existing","existing object will updated, and lists will be replaced, rather","build. type: str secret: description: - secret is a reference","to \".\", in this case it just refers to the","str type: description: - type of build input to accept","this reference is made, if any. type: str uid: description:","as desiredState.manifest.containers[2]. For example, if the object reference is to","created, the system will populate this list with the current","be scoped to a namespace - the value of this","the watch operation on a resource or set of resources.","to the server. They may only be valid for a","to false. To set this field, a user needs \"delete\"","executed with the --no-cache=true flag type: bool pull_secret: description: -","and the data returned is not guaranteed to conform to","the system, that the build may be active on a","This value will also be combined with a unique suffix.","namespace, but \"default\" is the canonical representation. Not all objects","merged into a unique set of values. If a list","contains: str, str name: description: - Name must be unique","the parameters for a Bitbucket webhook type of trigger type:","rather than merged. default: false type: bool host: description: -","used for setting up the authentication for executing the Docker","of the cause of the error. This field may be","contains: env: description: - env contains additional environment variables you","do is raise the error. raise Exception(exc.message) try: module.execute_module() except","expose_docker_socket: description: - exposeDockerSocket will allow running Docker commands (and","ref is the branch/tag/ref to build. type: str uri: description:","terms of, defaults to \"v1\". type: str field_path: description: -","FROM line of the Dockerfile for this build. type: complex","for optimistic concurrency, change detection, and the watch operation on","and no other connection options are provided, the openshift client","If true, AND if the owner has the \"foregroundDeletion\" finalizer,","description: - List of objects depended by this object. If","on the first array entry in \"items\"' type: str message:","owner, otherwise 422 (Unprocessable Entity) will be returned. type: bool","used to distinguish resources with same name and namespace in","prefix, used by the server, to generate a unique name","referent. type: str source_secret: description: - \"sourceSecret is the name","If not specified, all failed builds are retained. type: int","type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a list of","Dockerfile for this build. type: complex contains: api_version: description: -","include dot and postfix notation for nested attributes. Arrays are","type: str context_dir: description: - contextDir specifies the sub-directory where","try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: #","for lists. type: complex contains: {} deletion_grace_period_seconds: description: - Number","to an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker","the From image from the build strategy will be used.","secrets referenced by the ServiceAccount type: str source: description: -","the same name then the last one in the list","determine when objects have changed. Value must be treated as","may allow a client to request the generation of an","server, to generate a unique name ONLY IF the Name","complex contains: api_version: description: - Version of the schema the","it's key must be defined type: bool jenkinsfile: description: -","ADD or COPY them during docker build. type: str secret:","a specific commit type: str type: description: - type of","causes: description: - The Causes array includes more details associated","AND if the owner has the \"foregroundDeletion\" finalizer, then the","(scope and select) objects. May match selectors of replication controllers","build; value must be positive integer type: int failed_builds_history_limit: description:","- name defines the name of the label. It must","description: - args is a list of arguments that are","Dockerfile in the build context. For Source builds, this is","be updated. type: str namespace: description: - Namespace defines the","a client. The resource is expected to be deleted (no","Source build to do incremental builds if true. type: bool","new build created from this build configuration will be scheduled","contains the parameters for a GitLab webhook type of trigger","type: description: - type of build input to accept type:","customStrategy holds the parameters to the Custom build strategy type:","is omitted for a container, it defaults to Limits if","at which this resource will be deleted. This field is","the system. Read-only. type: str uid: description: - UID is","will react by sending a graceful termination signal to the","that will be used only for the build. type: list","to an ImageStreamTag, ImageStreamImage, or DockerImage to copy source from.","if set to true indicates that the docker build must","those objects will be empty. Must be a DNS_LABEL. Cannot","available for the build time. For the Source build strategy,","a build pipeline. type: list contains: name: description: - Name","JenkinsfilePath is the optional path of the Jenkinsfile that will","on STDIN. type: complex contains: as_file: description: - asFile indicates","key value map stored with a resource that may be","str requests: description: - Requests describes the minimum amount of","object was created. It is not guaranteed to be set","the versioned schema of this representation of an object. Servers","with the status StatusReason (when there is a single name","the docker image should be pulled the resulting image will","api_version: description: - APIVersion defines the versioned schema of this","should produce. type: complex contains: image_labels: description: - imageLabels define","set this field, a user needs \"delete\" permission of the","for setting up the authentication for executing the Docker push","when creating resources, although some resources may allow a client","str metadata: description: - metadata for BuildConfigList. type: complex contains:","value has the same validation rules as the Name field,","whether the Secret or it's key must be defined type:","(Unprocessable Entity) will be returned. type: bool controller: description: -","in the system, that the build may be active on","set to true indicates that the docker build must be","sourcePath is the absolute path of the file or directory","ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of","files injected will be truncated to zero length. For the","assumed to be a tar, gzipped tar, or zip file","validate requests. type: str gitlab: description: - GitLabWebHook contains the","a namespace - the value of this field for those","allowEnv determines whether the webhook can set environment variables; can","Jenkins pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath is","feature will be removed in a future release. Use ImageSource","secrets represents a list of secrets and their destinations that","description: - GitLabWebHook contains the parameters for a GitLab webhook","description: - from is a reference to an ImageStreamTag that","value, and may reject unrecognized values. type: str items: description:","For the Docker build strategy, these will be copied into","an optional Dockerfile may be specified to override any Dockerfile","- resources computes resource requirements to execute the build. type:","convenient. type: list contains: str script: description: - script is","of the label. It must have non-zero length. type: str","in this list can only be removed. type: list contains:","From reference is allowed in a build configuration. type: complex","empty before the object is deleted from the registry. Each","source describes the SCM in use. type: complex contains: binary:","It is optional. If no From is specified, the From","container and any service environment variables. If a variable cannot","number of old failed builds to retain. If not specified,","config file. If not provided, and no other connection options","is hidden (in list/watch and get calls) from clients that","Whether or not to verify the API server's SSL certificates.","retain. If not specified, all successful builds are retained. type:","variable exists or not. Defaults to \"\".' type: str value_from:","resource that may be set by external tools to store","change detection, and the watch operation on a resource or","this reference is removed. Defaults to false. To set this","May be used for optimistic concurrency, change detection, and the","metadata. type: complex contains: resource_version: description: - String that identifies","must be one of 'ImageStreamTag' or 'DockerImage'. This value will","objects will be empty. Must be a DNS_LABEL. Cannot be","in the Dockerfile. See type: list contains: name: description: -","how the new build created from this build configuration will","config type: complex contains: last_version: description: - lastVersion is used","git contains optional information about git build source type: complex","a Jenkinsfile which defines a Jenkins pipeline build. type: str","certificate used to authenticate with the API. type: path state:","or 'DockerImage'. This value will be used to look up","\"name\" on the current resource \"items[0].name\" - the field \"name\"","files from the secret should be available for the build","Servers may infer this from the endpoint the client submits","is not guaranteed to be set in happens-before order across","trigger type: complex contains: allow_env: description: - allowEnv determines whether","a reference to an ImageStreamTag that will trigger a build","an existing object will updated, and lists will be replaced,","only be shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp","created from this build configuration will be scheduled for execution.","and any service environment variables. If a variable cannot be","Name of the referent. type: str secrets: description: - secrets","- secret used to validate requests. type: str generic: description:","= C(present) contains: api_version: description: - APIVersion defines the versioned","DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: -","build to. Kind must be one of 'ImageStreamTag' or 'DockerImage'.","version. type: str resource_field_ref: description: - 'Selects a resource of","namespace. Is required when creating resources, although some resources may","is removed. Defaults to false. To set this field, a","- UID of the referent. type: str post_commit: description: -","RFC3339 form and is in UTC. Populated by the system.","- 'Container name: required for volumes, optional for env vars'","typically generated by the server on successful creation of a","str commit: description: - commit is the commit hash identifying","a key file used to authenticate with the API. type:","an optional image that is used to run an application","number representing a specific generation of the desired state. Populated","shell. E.g., in the Ruby image, this is necessary to","selectors of replication controllers and services. type: complex contains: str,","to a file containing the YAML definition of the object.","description: - email of the source control user type: str","source code for the application exists. This allows to have","the referent. type: str env: description: - env contains additional","a variable cannot be resolved, the reference in the input","that represents the internal version of this object that can","build type: str type: description: - type is the type","unique set of values. If a list contains a dictionary","fields having multiple errors. Optional. Examples: \"name\" - the field","name of the process that is responsible for initializing this","present choices: - present - absent username: description: - Provide","specific repo snapshot. This is optional. type: complex contains: git:","a shell. In all other cases, using Script might be","described). type: str kind: description: - Kind is a string","str, str requests: description: - Requests describes the minimum amount","value for this object. It is typically generated by the","allowed. type: complex contains: str, str requests: description: - Requests","or zip file depending on the strategy. For Docker builds,","or it's key must be defined type: bool force_pull: description:","build. type: complex contains: limits: description: - Limits describes the","YAML definition of the object. Mutually exclusive with I(resource_definition). type:","description: - dockerStrategy holds the parameters to the Docker build","requests. type: str gitlab: description: - GitLabWebHook contains the parameters","not be created at the destination. type: str runtime_image: description:","For Source and Docker builds, if binary.asFile is set the","Docker image's default entrypoint. The arguments are placed immediately after","- exposeDockerSocket will allow running Docker commands (and build Docker","be overridden by default build nodeselector values for the cluster.","the FieldPath is written in terms of, defaults to \"v1\".","primarily intended for creation idempotence and configuration definition. Cannot be","a separate runtime image.' type: list contains: destination_dir: description: -","for a container, it defaults to Limits if that is","If this field is specified and the generated name exists,","implementation-defined value. type: complex contains: str, str revision: description: -","contains: config_map_key_ref: description: - Selects a key of a ConfigMap.","Is required when creating resources, although some resources may allow","overridden by default build nodeselector values for the cluster. If","build. type: str uri: description: - uri points to the","(contextDir). If both JenkinsfilePath & Jenkinsfile are both not specified,","controller, then an entry in this list will point to","the image. The default policy is 'None' which means the","defined type: bool force_pull: description: - forcePull describes if the","within the build input. For example, specifying \"webapp.war\" would place","Cannot be updated. In CamelCase. type: str metadata: description: -","DNS_LABEL. Cannot be updated. type: str owner_references: description: - List","to a registry. type: complex contains: args: description: - args","new build, and the conditions when to trigger them. type:","it does not exist, or patched, if parameter values differ","type: str kind: description: - The kind attribute of the","by the user, and is not directly settable by a","resource requirements to execute the build. type: complex contains: limits:","description: - A human-readable description of the cause of the","OpenShiftAnsibleException as exc: # The helper failed to init, so","contains valid credentials for remote repository, where the\\ \\ data's","or if the image does not have `/bin/sh`, use Command","version of the referent. type: str field_path: description: - 'If","system. Read-only. type: str spec: description: - spec holds all","pushed to a registry. type: complex contains: args: description: -","list of secrets and their destinations that will be used","right now and apiserver is going to ignore it if","type: bool jenkinsfile: description: - Jenkinsfile defines the optional raw","key of a ConfigMap. type: complex contains: key: description: -","false. To set this field, a user needs \"delete\" permission","is optional and the data returned is not guaranteed to","type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the","to reduce the final size or time spent building the","list contains: name: description: - Name of the environment variable.","type: complex contains: git: description: - Git contains information about","C(absent). A patch operation results in merging lists and updating","When this option is specified, the FROM may be modified","returned: when I(state) = C(present) contains: api_version: description: - APIVersion","merged. To force the replacement of lists, set the I(force)","- The name of a context found in the Kubernetes","visible to all clients. type: list contains: name: description: -","available in the PATH. type: str resources: description: - resources","or I(type) are merged. To force the replacement of lists,","information available. A Reason clarifies an HTTP status code but","author of a specific commit type: complex contains: email: description:","Kind must be one of 'ImageStreamTag' or 'DockerImage'. This value","type: str field_path: description: - 'If referring to a piece","resource: description: - 'Required: resource to select' type: str secret_key_ref:","RETURN = ''' api_version: type: string description: Requested API version","the error. This field may be presented as-is to a","value is not empty. type: complex contains: config_map_key_ref: description: -","valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example,","used and\\ \\ value is the base64 encoded credentials. Supported","contains: destination_dir: description: - destinationDir is the directory where the","auth methods\\ \\ are: ssh-privatekey.\" type: complex contains: name: description:","set to C(present), the object will be created, if it","optional path of the Jenkinsfile that will be used to","be preserved when modifying objects. type: complex contains: str, str","context and an optional Dockerfile may be specified to override","injected will be truncated to zero length. For the Docker","intended for creation idempotence and configuration definition. Cannot be updated.","defaults to \"1\" type: str resource: description: - 'Required: resource","and passed to the custom builder type: str env: description:","be pulled the resulting image will be used in the","Registry (or Docker Hub). type: complex contains: name: description: -","str message: description: - A human-readable description of the status","registry. type: complex contains: args: description: - args is a","- serviceAccount is the name of the ServiceAccount to use","absolute path of the file or directory inside the image","secret_key_ref: description: - Selects a key of a secret in","= ''' api_version: type: string description: Requested API version build_config_list:","imageChange contains parameters for an ImageChange type of trigger type:","has not been provided. If this field is used, the","is also set. May only be shortened. Read-only. type: int","resource is fully terminated. If not set, graceful deletion of","resource. (when there is a single resource which can be","- The key to select. type: str name: description: -","of the error. This field may be presented as-is to","to retain. If not specified, all successful builds are retained.","object.' type: str kind: description: - Kind of the referent.",". type: str self_link: description: - SelfLink is a URL","to the API. Use in conjunction with I(username). resource_definition: description:","helper failed to init, so there is no module object.","contents of a Dockerfile which should be built. When this","I(force) option to C(True). default: present choices: - present -","be added after the FROM, but before the rest of","\"spec.containers[2]\" (container with index 2 in this pod). This syntax","value that represents the internal version of this object that","image. sourcePath can be a file or directory. destinationDir must","is used to run an application without unneeded dependencies installed.","\"name\" on the first array entry in \"items\"' type: str","pull the image from a registry If the image is","seconds. The Kubelet will react by sending a graceful termination","- An initializer is a controller which enforces some system","dockerfile is the raw contents of a Dockerfile which should","exists, the server will NOT return a 409 - instead,","with the 'None' policy. An additional experimental policy 'SkipLayersAndWarn' is","parameters to the Docker build strategy. type: complex contains: _from:","value. type: complex contains: str, str revision: description: - revision","description: - A human-readable description of the status of this","can be overridden by default build nodeselector values for the","authentication method to be used and\\ \\ value is the","of the object has not been requested. Populated by the","send a hard termination signal (SIGKILL) to the container and","pod from the API. In the presence of network partitions,","type: complex contains: resource_version: description: - String that identifies the","An initializer is a controller which enforces some system invariant","Optional. Examples: \"name\" - the field \"name\" on the current","this case it just refers to the root of WORKDIR.","the owner, otherwise 422 (Unprocessable Entity) will be returned. type:","the optional raw contents of a Jenkinsfile which defines a","requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name:","before the operation should be retried. type: int uid: description:","spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: -","description: - buildArgs contains build arguments that will be resolved","# -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException","contains: last_version: description: - lastVersion is used to inform about","list of additional secrets that will be included in the","the resource may be deleted prior to this time. For","are retained. type: int node_selector: description: - nodeSelector is a","pairs that will be copied from the builder to the","for this build. type: complex contains: api_version: description: - API","description: - \"sourceSecret is the name of a Secret that","in 30 seconds. The Kubelet will react by sending a","gets scheduled in the system, that the build may be","may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' type:","version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description: - Token","path of the Dockerfile that will be used to build","kind of build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit","directory will be copied, but the directory itself will not","is non-nil, entries in this list can only be removed.","merged. default: false type: bool host: description: - Provide a","description: - from is a reference to an ImageStreamTag, ImageStreamImage,","Version of the schema the FieldPath is written in terms","is requested by the user, and is not directly settable","some operations may differ from the requested resource Kind. type:","set to C(absent). A patch operation results in merging lists","an empty map or a map with any values, default","422 (Unprocessable Entity) will be returned. type: bool controller: description:","tar, or tar.gz file and extract it as the source.","of a ConfigMap. type: complex contains: key: description: - The","of a resource and is not allowed to change on","pull if it is not present locally type: bool pull_secret:","or be set further into the future, although it may","be expanded, regardless of whether the variable exists or not.","info is written to KubeObjHelper.log default: false type: bool force:","to a CA certificate used to authenticate with the API.","description: - 'Container name: required for volumes, optional for env","to validate requests. type: str github: description: - github contains","field is specified and the generated name exists, the server","are currently supported.' type: complex contains: container_name: description: - 'Container","str resource: description: - 'Required: resource to select' type: str","In CamelCase. type: str metadata: description: - metadata for BuildConfig.","application without unneeded dependencies installed. The building of the application","pending initializer is removed, and no failing result is set,","source_path: description: - sourcePath is the absolute path of the","scripts is the location of Source scripts type: str type:","registry. Each entry is an identifier for the responsible component","system. Read-only. Null for lists. type: complex contains: {} deletion_grace_period_seconds:","a file or directory. destinationDir must be a directory. destinationDir","expected to be deleted (no longer visible from resource lists,","Path to an existing Kubernetes config file. If not provided,","example, a user may request that a pod is deleted","jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to the Jenkins","github: description: - github contains the parameters for a GitHub","additional secrets that will be included in the build pod","describes what optimizations the system can use when building images","receives this binary as standard input. This filename may not","system. Only set when deletionTimestamp is also set. May only","returned. type: bool controller: description: - If true, this reference","'Container name: required for volumes, optional for env vars' type:","must be defined type: bool jenkinsfile: description: - Jenkinsfile defines","str ref: description: - ref is the branch/tag/ref to build.","from the endpoint the client submits requests to. Cannot be","registry, this field does not need to be set. type:","Kubelet will send a hard termination signal (SIGKILL) to the","'If referring to a piece of an object instead of","true. There cannot be more than one managing controller. type:","select in the specified API version. type: str resource_field_ref: description:","machine-readable description of the cause of the error. If this","the Docker build API. The experimental policy 'SkipLayers' will avoid","API version for the Build object serialized and passed to","- from is a reference to an ImageStreamTag, ImageStreamImage, or","be created at the destination. type: str runtime_image: description: -","after cleanup, remove the pod from the API. In the","the docker build must be executed with the --no-cache=true flag","- to defines an optional location to push the output","is a list of initializers that must execute in order","empty. Must be a DNS_LABEL. Cannot be updated. type: str","produce a new build, and the conditions when to trigger","type: complex contains: custom_strategy: description: - customStrategy holds the parameters","may define its own extended details. This field is optional","dockerfilePath is the path of the Dockerfile that will be","additional environment variables you want to pass into a build","future, although it may be shortened or the resource may","filename may not contain slashes or be '..' or '.'.","be used to build the Docker image, relative to the","input necessary to produce a new build, and the conditions","refers to the name of the container that triggered the","by the server on successful creation of a resource and","save last used image ID for build type: str type:","the build may be active on a node before the","Fields may appear more than once in an array of","system. Read-only. type: str self_link: description: - SelfLink is a","values, default build nodeselector values are ignored. type: complex contains:","description: - 'Required: resource to select' type: str secret_key_ref: description:","format of the exposed resources, defaults to \"1\" type: str","contains: http_proxy: description: - httpProxy is a proxy used to","cleanup, remove the pod from the API. In the presence","is a proxy used to reach the git repository over","removed. Defaults to false. To set this field, a user","consistent version of the listed objects. version_added: 2.3.0 author: OpenShift","cluster which the object belongs to. This is used to","is a timestamp representing the server time when this object","occur as a result of an explicit client build creation.","group: description: - The group attribute of the resource associated","the container and after cleanup, remove the pod from the","This feature will be removed in a future release. Use","that use Software Collections (SCL), in order to have the","empty namespace is equivalent to the \"default\" namespace, but \"default\"","any modules parameters intended to define object attributes. type: dict","The name of the cluster which the object belongs to.","- triggers determine how new Builds can be launched from","if compatibility cannot be preserved. type: str no_cache: description: -","annotations: description: - Annotations is an unstructured key value map","for the build. type: list contains: destination_dir: description: - destinationDir","str output: description: - output describes the Docker image the","str push_secret: description: - PushSecret is the name of a","resources. Clients must treat these values as opaque and passed","default to \"Serial\". type: str service_account: description: - serviceAccount is","description: - 'If referring to a piece of an object","which can be described). type: str retry_after_seconds: description: - If","namespace is equivalent to the \"default\" namespace, but \"default\" is","successful creation of a resource and is not allowed to","to execute the build. type: complex contains: limits: description: -","return code for this status, 0 if not set. type:","Use in conjunction with I(username). resource_definition: description: - Provide the","request the generation of an appropriate name automatically. Name is","object is managed by a controller, then an entry in","store and retrieve arbitrary metadata. They are not queryable and","int triggers: description: - triggers determine how new Builds can","deleted, this object will be garbage collected. If this object","be used and\\ \\ value is the base64 encoded credentials.","this object is managed by a controller, then an entry","- mountPath is the path at which to mount the","be garbage collected. If this object is managed by a","will populate this list with the current set of initializers.","defines how to perform a build. type: complex contains: custom_strategy:","bool host: description: - Provide a URL for acessing the","new Builds can be launched from a BuildConfig. If no","raw contents of a Dockerfile which should be built. When","registries type: complex contains: name: description: - Name of the","KubeObjHelper.log default: false type: bool force: description: - If set","will be resolved in the Dockerfile. See type: list contains:","will allow running Docker commands (and build Docker images) from","defined by the reason type. type: complex contains: causes: description:","When set to C(present), the object will be created, if","The name of a context found in the Kubernetes config","created by this build. The pod will be allowed to","type is the type of build trigger type: str status:","does not need to be set. type: complex contains: name:","a DNS_LABEL. Cannot be updated. type: str owner_references: description: -","object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description: -","contains: annotations: description: - Annotations is an unstructured key value","set, the initializers struct will be set to nil and","name exists, the server will NOT return a 409 -","be treated as opaque by clients and passed unmodified back","deleted, ensuring that other clients can observe the deletion. type:","containing the YAML definition of the object. Mutually exclusive with","that defined by the reason type. type: complex contains: causes:","ImageStreamTag, the ImageStreamTag will be looked for in the namespace","The Kubelet will react by sending a graceful termination signal","this build configuration will be scheduled for execution. This is","the command to run. It may not be specified with","indicated in the Retry-After header). Applied only if Name is","name: description: - Name of the environment variable. Must be","are: ssh-privatekey.\" type: complex contains: name: description: - Name of","- type is the kind of build strategy. type: str","when modifying objects. type: complex contains: str, str cluster_name: description:","str scripts: description: - scripts is the location of Source","complex contains: annotations: description: - Annotations is an unstructured key","secret to select from. Must be a valid secret key.","must be defined type: bool expose_docker_socket: description: - exposeDockerSocket will","acessing the Kubernetes API. key_file: description: - Path to a","referent. type: str field_path: description: - 'If referring to a","you need control over the image entrypoint, or if the","into the build directory, where the Dockerfile is located, so","is a single name which can be described). type: str","Namespace defines the space within each name must be unique.","done in the builder image but, post build, you can","from the list. If the deletionTimestamp of the object is","pod. After that 30 seconds, the Kubelet will send a","may be modified based on your strategy base image and","generation of the desired state. Populated by the system. Read-only.","description: - SelfLink is a URL representing this object. Populated","a particular resource or set of resources. Populated by the","is made, if any. type: str uid: description: - UID","to be run. type: list contains: str command: description: -","then the owner cannot be deleted from the key-value store","name: description: - The name attribute of the resource associated","shell. In all other cases, using Script might be more","webhook can set environment variables; can only be set to","removed in a future release. Use ImageSource to copy binary","Applied only if Name is not specified. type: str generation:","`bundle` and other binaries available in the PATH. type: str","or the Docker image's default entrypoint. The arguments are placed","base64 encoded credentials. Supported auth methods\\ \\ are: ssh-privatekey.\" type:","this error, as named by its JSON serialization. May include","The secret\\ \\ contains valid credentials for remote repository, where","description: - Limits describes the maximum amount of compute resources","validate requests. type: str generic: description: - generic contains the","considered a single file within the build input. For example,","reference to an ImageStreamTag, ImageStreamImage, or DockerImage to copy source","details. This field is optional and the data returned is","0 if not set. type: int details: description: - Extended","This field may be presented as-is to a reader. type:","int initializers: description: - An initializer is a controller which","commiting new layers in between each image step, and will","Arrays are zero-indexed. Fields may appear more than once in","optional, if not specified we default to \"Serial\". type: str","description: - Name must be unique within a namespace. Is","there is a single name which can be described). type:","is in tech preview. type: complex contains: env: description: -","description: - Requests describes the minimum amount of compute resources","for the builder. If left empty, the Docker and Source","The pod will be allowed to use secrets referenced by","to be scoped to a namespace - the value of","description: - JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline","I(type) attribute, a strategic merge is performed, where individual elements","binaries available in the PATH. type: str resources: description: -","patched, if parameter values differ from the existing object's attributes,","field may be presented as-is to a reader. type: str","metadata: description: - metadata for BuildConfig. type: complex contains: annotations:","build. type: str jenkinsfile_path: description: - JenkinsfilePath is the optional","- metadata for BuildConfig. type: complex contains: annotations: description: -","hash identifying a specific commit type: str committer: description: -","resource will be deleted. This field is set by the","empty, this object has been completely initialized. Otherwise, the object","openshift == 0.3.3 ''' EXAMPLES = ''' ''' RETURN =","name: description: - Name must be unique within a namespace.","with an empty From reference is allowed in a build","field, and may be truncated by the length of the","ends in /. then the content of the directory will","description: - paths is a list of source and destination","built. The structure of the source will depend on the","are expanded using the previous defined environment variables in the","description: - dockerfilePath is the path of the Dockerfile that","name of a Secret that would be used for setting\\","true, this reference points to the managing controller. type: bool","is set the build will receive a directory with a","the build input. For example, specifying \"webapp.war\" would place the","take on a value like: \"spec.containers{name}\" (where \"name\" refers to","by sending a graceful termination signal to the containers in","create or update request. type: str creation_timestamp: description: - CreationTimestamp","in the container and any service environment variables. If a","in order to have the appropriate collections enabled in the","be a DNS_LABEL. Cannot be updated. type: str owner_references: description:","with lists being merged into a unique set of values.","- Must be empty before the object is deleted from","than root of repository. type: str dockerfile: description: - dockerfile","timestamp, until an administrator or automated process can determine the","clusters. This field is not set anywhere right now and","debug output from the OpenShift helper. Logging info is written","necessary to produce a new build, and the conditions when","a list of labels that are applied to the resulting","unique name could not be found in the time allotted,","Entity) will be returned. type: bool controller: description: - If","description: - completionDeadlineSeconds is an optional duration in seconds, counted","modifying objects. type: complex contains: str, str cluster_name: description: -","type of the build source, may be one of 'Source',","when to trigger them. type: complex contains: completion_deadline_seconds: description: -","initialized and visible to all clients. type: list contains: name:","str to: description: - to defines an optional location to","running Docker commands (and build Docker images) from inside the","str last_triggered_image_id: description: - lastTriggeredImageID is used internally by the","openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a list of","must be unique. An empty namespace is equivalent to the","copy source from. type: complex contains: api_version: description: - API","incremental: description: - incremental flag forces the Source build to","Number of seconds allowed for this object to gracefully terminate","build Docker images) from inside the Docker container. type: bool","the user, and is not directly settable by a client.","of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName,","Created or 500 with Reason ServerTimeout indicating a unique name","- name of the process that is responsible for initializing","be specified with Command. Use Script when a shell script","separate runtime image.' type: list contains: destination_dir: description: - destinationDir","the name passed. This value will also be combined with","error. If this value is empty there is no information","the resource associated with the status StatusReason (when there is","type: str retry_after_seconds: description: - If specified, the time in","be escaped with a double $$, ie: $$(VAR_NAME). Escaped references","API. In the presence of network partitions, this object may","ensuring that other clients can observe the deletion. type: complex","C(present), an existing object will updated, and lists will be","type: str namespace: description: - Namespace defines the space within","List of objects depended by this object. If ALL objects","type: str build_args: description: - buildArgs contains build arguments that","accept type: str strategy: description: - strategy defines how to","for this status, 0 if not set. type: int details:","or it's key must be defined type: bool dockerfile_path: description:","image should be pulled the resulting image will be used","is not allowed to change on PUT operations. Populated by","resource lists, and not reachable by name) after the time","key must be defined type: bool jenkinsfile: description: - Jenkinsfile","description: - customStrategy holds the parameters to the Custom build","to make the value unique on the server. If this","initializer is removed, and no failing result is set, the","information available. type: str group: description: - The group attribute","pass into a builder container. type: list contains: name: description:","is empty there is no information available. type: str group:","and may reject unrecognized values. type: str items: description: -","required to make the value unique on the server. If","is the same as 'SkipLayers' but simply warns if compatibility","description: - UID of the referent. type: str last_triggered_image_id: description:","only to have some well-defined way of referencing a part","- An opaque value that represents the internal version of","of the referent. type: str resource_version: description: - An opaque","of the resource associated with the status StatusReason. type: str","of the cluster which the object belongs to. This is","path ssl_ca_cert: description: - Path to a CA certificate used","the rest of your Dockerfile stanzas. The Dockerfile source type","be updated. In CamelCase. type: str metadata: description: - metadata","UID of the referent. type: str env: description: - env","the label. It must have non-zero length. type: str value:","context (contextDir). If both JenkinsfilePath & Jenkinsfile are both not","- PushSecret is the name of a Secret that would","Once set, this value may not be unset or be","is used internally by the ImageChangeController to save last used","If left empty, the Docker and Source build strategies assume","not guaranteed to be set in happens-before order across separate","strategy: description: - strategy defines how to perform a build.","- 'If referring to a piece of an object instead","Otherwise, the object is considered uninitialized and is hidden (in","may not set this value. It is represented in RFC3339","BuildConfigList description: - Retrieve a list of build_configs. List operations","field is optional and the data returned is not guaranteed","retrieve arbitrary metadata. They are not queryable and should be","From image from the build strategy will be used. Only","description: - API version of the referent. type: str field_path:","Reason clarifies an HTTP status code but does not override","noCache if set to true indicates that the docker build","description: - strategy defines how to perform a build. type:","unique suffix. The provided value has the same validation rules","only be set to true for GenericWebHook. type: bool secret:","field is used, the name returned to the client will","an object. Servers should convert recognized schemas to the latest","- secret used to validate requests. type: str github: description:","- allowEnv determines whether the webhook can set environment variables;","be included in the build pod type: list contains: mount_path:","removed. type: list contains: str generate_name: description: - GenerateName is","str resources: description: - resources computes resource requirements to execute","of the status of this operation. type: str metadata: description:","openshift client will attempt to load the default configuration file","commit type: str committer: description: - committer is the committer","Name of the referent. type: str source_secret: description: - \"sourceSecret","appropriate to execute the post build hook, for example for","The `-i` flag is needed to support CentOS and RHEL","(no longer visible from resource lists, and not reachable by","use to run the pod created by this build. The","resource Kind. type: str name: description: - The name attribute","which defines a Jenkins pipeline build. type: str jenkinsfile_path: description:","`rake test`. If you need control over the image entrypoint,","object is considered uninitialized and is hidden (in list/watch and","name of a Secret that would be used for setting","raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\",","PUT operations. Populated by the system. Read-only. type: str spec:","to this time. For example, a user may request that","This is optional, if not specified we default to \"Serial\".","a list of initializers that must execute in order before","to ignore it if set in create or update request.","- Requests describes the minimum amount of compute resources required.","the system. Read-only. type: str self_link: description: - SelfLink is","the pod. After that 30 seconds, the Kubelet will send","retained. type: int triggers: description: - triggers determine how new","a result of an explicit client build creation. type: list","the build time. For the Source build strategy, these will","endpoint the client submits requests to. Cannot be updated. In","preserved when modifying objects. type: complex contains: str, str cluster_name:","nil or empty, this object has been completely initialized. Otherwise,","Collections (SCL), in order to have the appropriate collections enabled","will be empty. Must be a DNS_LABEL. Cannot be updated.","the new build created from this build configuration will be","set. May only be shortened. Read-only. type: int deletion_timestamp: description:","''' EXAMPLES = ''' ''' RETURN = ''' api_version: type:","git repository over http type: str https_proxy: description: - httpsProxy","\"items\"' type: str message: description: - A human-readable description of","the server. They may only be valid for a particular","It is typically generated by the server on successful creation","referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters","build input to accept type: str strategy: description: - strategy","the application exists. This allows to have buildable sources in","environment will be added after the FROM, but before the","type: int details: description: - Extended data associated with the","object. If nil or empty, this object has been completely","a hard termination signal (SIGKILL) to the container and after","with Reason ServerTimeout indicating a unique name could not be","after the command to be run. type: list contains: str","with `rake test`. If you need control over the image","type: str git: description: - git contains optional information about","inside the image to copy to the build directory. If","name of the source control user type: str message: description:","shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is RFC","initializing this object. type: str result: description: - If result","instead, it will either return 201 Created or 500 with","may only be valid for a particular resource or set","remote repository, where the\\ \\ data's key represent the authentication","one build into a separate runtime image.' type: list contains:","UID of the referent. type: str build_args: description: - buildArgs","avoid commiting new layers in between each image step, and","on the type of build to run type: str images:","description: - items is a list of build configs type:","may be used with other options like git - in","it if set in create or update request. type: str","type of build to run type: str images: description: -","as_file: description: - asFile indicates that the provided binary input","compatibility with the 'None' policy. An additional experimental policy 'SkipLayersAndWarn'","of resources. Clients must treat these values as opaque and","source that will be built. The structure of the source","file and extract it as the source. The custom strategy","the cluster. If set to an empty map or a","attribute of the resource associated with the status StatusReason. On","for the responsible component that will remove the entry from","of the field to select in the specified API version.","the builder should pull the images from registry prior to","if not specified we default to \"Serial\". type: str service_account:","requested by the user, and is not directly settable by","file. debug: description: - Enable debug output from the OpenShift","{} finalizers: description: - Must be empty before the object","a snapshot read of the underlying objects, returning a resource_version","list contains: field: description: - 'The field of the resource","controller field set to true. There cannot be more than","we can do is raise the error. raise Exception(exc.message) try:","resource associated with the status StatusReason (when there is a","otherwise to an implementation-defined value. type: complex contains: str, str","of the source will depend on the type of build","str resource_version: description: - An opaque value that represents the","be used for optimistic concurrency, change detection, and the watch","with Script. This might be needed if the image doesn't","type: bool controller: description: - If true, this reference points","in the pod. After that 30 seconds, the Kubelet will","list contains: api_version: description: - API version of the referent.","holds all the input necessary to produce a new build,","further by any user. type: complex contains: pending: description: -","int node_selector: description: - nodeSelector is a selector which must","representation of an object. Servers should convert recognized schemas to","- type is the type of build trigger type: str","- message is the description of a specific commit type:","`-i` flag is needed to support CentOS and RHEL images","is the unique in time and space value for this","default: false type: bool force: description: - If set to","is considered uninitialized and is hidden (in list/watch and get","the object is deleted from the registry. Each entry is","- contextDir specifies the sub-directory where the source code for","be combined with a unique suffix. The provided value has","from a registry If the image is pulled from the","from the system. Only set when deletionTimestamp is also set.","the command to be run. type: list contains: str command:","other than root of repository. type: str dockerfile: description: -","is an optional prefix, used by the server, to generate","reference in the input string will be unchanged. The $(VAR_NAME)","the referent. type: str build_args: description: - buildArgs contains build","the future, although it may be shortened or the resource","will be built. The structure of the source will depend","representing the server time when this object was created. It","it's key must be defined type: bool dockerfile_path: description: -","complex contains: str, str requests: description: - Requests describes the","in the \"Failure\" status. If this value is empty there","description: - secrets represents a list of secrets and their","for those objects will be empty. Must be a DNS_LABEL.","- Provide a URL for acessing the Kubernetes API. key_file:","- A human-readable description of the cause of the error.","are placed immediately after the command to be run. type:","to Limits if that is explicitly specified, otherwise to an","the pod's namespace type: complex contains: key: description: - The","set, this value may not be unset or be set","replaced in the context dir. type: str git: description: -","is the base64 encoded credentials. Supported auth methods\\ \\ are:","reference is allowed in a build configuration. type: complex contains:","This value will be used to look up a Docker","generate_name: description: - GenerateName is an optional prefix, used by","when I(state) = C(present) contains: api_version: description: - APIVersion defines","requests: description: - Requests describes the minimum amount of compute","strategy defines how to perform a build. type: complex contains:","the referent. type: str block_owner_deletion: description: - If true, AND","do incremental builds if true. type: bool pull_secret: description: -","Provide a password for connecting to the API. Use in","in between each image step, and will fail if the","to gracefully terminate before it will be removed from the","of a Secret that would be used for setting up","single name which can be described). type: str retry_after_seconds: description:","the Docker container. type: bool force_pull: description: - forcePull describes","for the cluster. If set to an empty map or","_from: description: - from is reference to an DockerImage, ImageStreamTag,","Docker build strategy. type: complex contains: _from: description: - from","both JenkinsfilePath & Jenkinsfile are both not specified, this defaults","must be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit","might be needed if the image doesn't have `/bin/sh`, or","the reference in the input string will be unchanged. The","of the directory will be copied, but the directory itself","be different than the name passed. This value will also","deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date and time","type may be used with other options like git -","source for the build type: list contains: _from: description: -","inform about number of last triggered build. type: int kind:","bool force_pull: description: - forcePull describes if the builder should","contains: name: description: - Name of the referent. type: str","repository, where the\\ \\ data's key represent the authentication method","contains: str generate_name: description: - GenerateName is an optional prefix,","will be used to look up a Docker image repository","for the environment variable's value. Cannot be used if value","Read-only. type: str uid: description: - UID is the unique","is the directory where the files from the secret should","name of the cluster which the object belongs to. This","description: - The kind attribute of the resource associated with","to the API. verify_ssl: description: - Whether or not to","build_configs. List operations provide a snapshot read of the underlying","path of the file or directory inside the image to","used when an archive is provided. Custom builds will receive","reference to an existing secret that you want to use","From is specified, the From image from the build strategy","- Name of the referent. type: str namespace: description: -","of a specific commit type: complex contains: email: description: -","but the directory itself will not be created at the","to \"Serial\". type: str service_account: description: - serviceAccount is the","OpenShift BuildConfigList description: - Retrieve a list of build_configs. List","now and apiserver is going to ignore it if set","It is not guaranteed to be set in happens-before order","runtime_image: description: - 'runtimeImage is an optional image that is","will be different than the name passed. This value will","entire object, this string should contain a valid JSON/Go field","idempotence and configuration definition. Cannot be updated. type: str namespace:","If this value is empty there is no information available.","Populated by the system. Read-only. type: str ''' def main():","string value representing the REST resource this object represents. Servers","or update request. type: str creation_timestamp: description: - CreationTimestamp is","the source code for the application exists. This allows to","reference is to a container within a pod, this would","image will be used in the FROM line of the","values differ from the existing object's attributes, and deleted, if","the Docker build strategy, these will be copied into the","type: str type: description: - type of the build source,","UID of the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID","used by clients to determine when objects have changed. Value","secret to be used to pull the image from a","innate Dockerfile replaced in the context dir. type: str git:","an archive as described above. For Source and Docker builds,","- Path of the field to select in the specified","webhook type of trigger type: complex contains: allow_env: description: -","separate operations. Clients may not set this value. It is","like git - in those cases the Git repo will","- If specified, the time in seconds before the operation","build directory where files copied from the image are placed.","value. Cannot be used if value is not empty. type:","description: - Jenkinsfile defines the optional raw contents of a","`ruby`, `bundle` and other binaries available in the PATH. type:","nested attributes. Arrays are zero-indexed. Fields may appear more than","paths is a list of source and destination paths to","for a GitLab webhook type of trigger type: complex contains:","is chosen only to have some well-defined way of referencing","If you need control over the image entrypoint, or if","be used for setting up the authentication for pulling the","type: bool expose_docker_socket: description: - exposeDockerSocket will allow running Docker","to do incremental builds if true. type: bool pull_secret: description:","this representation of an object. Servers should convert recognized schemas","except that defined by the reason type. type: complex contains:","allowed for this object to gracefully terminate before it will","images: description: - images describes a set of images to","server when a graceful deletion is requested by the user,","api_key: description: - Token used to connect to the API.","of network partitions, this object may still exist after this","to support CentOS and RHEL images that use Software Collections","is deleted from the registry. Each entry is an identifier","of an entire object, this string should contain a valid","is set with the Failure field, the object will be","configuration file from I(~/.kube/config.json). type: path password: description: - Provide","shell script to be run with `/bin/sh -ic`. It may","contains: api_version: description: - Version of the schema the FieldPath","fit on a node If nil, it can be overridden","are both not specified, this defaults to Jenkinsfile in the","optional duration in seconds, counted from the time when a","to be a tar, gzipped tar, or zip file depending","to determine when objects have changed. Value must be treated","a map with any values, default build nodeselector values are","where files copied from the image are placed. type: str","authentication enabled Docker Registry (or Docker Hub). type: complex contains:","\"foregroundDeletion\" finalizer, then the owner cannot be deleted from the","base image and additional ENV stanzas from your strategy environment","this string should contain a valid JSON/Go field access statement,","details associated with the StatusReason failure. Not all StatusReasons may","description: - 'Variable references $(VAR_NAME) are expanded using the previous","with the Failure field, the object will be persisted to","type: bool force: description: - If set to C(True), and","unique name ONLY IF the Name field has not been","would take on a value like: \"spec.containers{name}\" (where \"name\" refers","contains: str, str requests: description: - Requests describes the minimum","author: description: - author is the author of a specific","a specific generation of the desired state. Populated by the","build_config_list: type: complex returned: when I(state) = C(present) contains: api_version:","will have any innate Dockerfile replaced in the context dir.","a new build can only occur as a result of","metadata. They are not queryable and should be preserved when","be available for the build time. For the Source build","- Retrieve a list of build_configs. List operations provide a","a container where the assemble script runs. Later, when the","list contains: str script: description: - script is a shell","input to accept type: str strategy: description: - strategy defines","resolved in the Dockerfile. See type: list contains: name: description:","str gitlab: description: - GitLabWebHook contains the parameters for a","also set. May only be shortened. Read-only. type: int deletion_timestamp:","authenticate with the API. type: path context: description: - The","build input. For example, specifying \"webapp.war\" would place the provided","provided value has the same validation rules as the Name","system. Read-only. type: str ''' def main(): try: module =","type: complex contains: str, str output: description: - output describes","description: - JenkinsfilePath is the optional path of the Jenkinsfile","collections enabled in the shell. E.g., in the Ruby image,","trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is","last pending initializer is removed, and no failing result is","reason may define its own extended details. This field is","- 'Selects a resource of the container: only resources limits","the number of old successful builds to retain. If not","- Provide a username for connecting to the API. verify_ssl:","description: - Name of the referent. type: str secrets: description:","will be truncated to zero length. For the Docker build","enabled Docker Registry (or Docker Hub). type: complex contains: name:","the private Docker registries type: complex contains: name: description: -","and the generated name exists, the server will NOT return","which means the final build image will be equivalent to","the appropriate collections enabled in the shell. E.g., in the","specified, the time in seconds before the operation should be","type: bool field_ref: description: - 'Selects a field of the","is a list of initializers that have not yet acted","is an optional image that is used to run an","type: str strategy: description: - strategy defines how to perform","of the container: only resources limits and requests (limits.cpu, limits.memory,","where the assemble script runs. Later, when the script finishes,","- Path to an existing Kubernetes config file. If not","images to be used to provide source for the build","any service environment variables. If a variable cannot be resolved,","- Annotations is an unstructured key value map stored with","pod type: list contains: mount_path: description: - mountPath is the","post build hook, for example for running unit tests with","values that can be used to organize and categorize (scope","kind: description: - Kind is a string value representing the","container. type: bool force_pull: description: - forcePull describes if the","from registry prior to building. type: bool incremental: description: -","ImageStreamImage from which the docker image should be pulled type:","utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = '''","has the same validation rules as the Name field, and","Name field has not been provided. If this field is","the root of the specified contextDir. type: str source_strategy: description:","Read-only. type: str spec: description: - spec holds all the","Kind. type: str name: description: - The name attribute of","slashes or be '..' or '.'. type: str context_dir: description:","contextDir specifies the sub-directory where the source code for the","to the containers in the pod. After that 30 seconds,","from this build configuration will be scheduled for execution. This","namespace: description: - Namespace defines the space within each name","information about git-based build source type: complex contains: author: description:","build. type: complex contains: name: description: - Name of the","list with the current set of initializers. Only privileged users","Specifies the output format of the exposed resources, defaults to","from resource lists, and not reachable by name) after the","and passed unmodified back to the server. Populated by the","\\ value is the base64 encoded credentials. Supported auth methods\\","is optional, if not specified we default to \"Serial\". type:","the provided binary input should be considered a single file","the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a","well-defined way of referencing a part of an object.' type:","description: - uri points to the source that will be","for the builder or only pull if it is not","the Jenkins Pipeline build strategy. This strategy is in tech","added after the FROM, but before the rest of your","keys and values that can be used to organize and","after this timestamp, until an administrator or automated process can","if the object reference is to a container within a","Kubernetes API. key_file: description: - Path to a key file","will be used to configure the pipeline relative to the","the 'None' policy. An additional experimental policy 'SkipLayersAndWarn' is the","amount of compute resources required. If Requests is omitted for","the build; value must be positive integer type: int failed_builds_history_limit:","used for optimistic concurrency, change detection, and the watch operation","- generic contains the parameters for a Generic webhook type","conjunction with I(username). resource_definition: description: - Provide the YAML definition","deleted. When set to C(present), the object will be created,","type: str commit: description: - commit is the commit hash","of arguments that are provided to either Command, Script or","not need to be set. type: complex contains: name: description:","Kind of the referent. type: str name: description: - Name","the pipeline relative to the root of the context (contextDir).","between each image step, and will fail if the Dockerfile","the referent. type: str namespace: description: - Namespace of the","build context. For Source builds, this is assumed to be","cases the Git repo will have any innate Dockerfile replaced","bool pull_secret: description: - pullSecret is the name of a","by the system. Read-only. type: str ''' def main(): try:","some system invariant at object creation time. This field is","hook, for example for running unit tests with `rake test`.","- UID is the unique in time and space value","latest internal value, and may reject unrecognized values. type: str","client will attempt to load the default configuration file from","type: str spec: description: - spec holds all the input","to retain. If not specified, all failed builds are retained.","str context_dir: description: - contextDir specifies the sub-directory where the","setting up the authentication for executing the Docker push to","from registry prior to building. type: bool image_optimization_policy: description: -","truncated to zero length. For the Docker build strategy, these","want to use in your build. type: complex contains: name:","branch/tag/ref to build. type: str uri: description: - uri points","Secret that would be used for setting\\ \\ up the","the underlying objects, returning a resource_version representing a consistent version","Jenkinsfile defines the optional raw contents of a Jenkinsfile which","to mount the secret type: str secret_source: description: - secretSource","from is a reference to an ImageStreamTag that will trigger","determine how new Builds can be launched from a BuildConfig.","be copied, but the directory itself will not be created","be considered a single file within the build input. For","- \"sourceSecret is the name of a Secret that would","type: str divisor: description: - Specifies the output format of","type: complex contains: str, str revision: description: - revision is","is a list of additional secrets that will be included","deleted in 30 seconds. The Kubelet will react by sending","runtime image.' type: complex contains: api_version: description: - API version","matching I(name_) or I(type) are merged. To force the replacement","deleted from the registry. Each entry is an identifier for","name: description: - name defines the name of the label.","It may not be specified with Command. Use Script when","and no failing result is set, the initializers struct will","place the provided binary as `/webapp.war` for the builder. If","resource_version: description: - An opaque value that represents the internal","referent. type: str secrets: description: - secrets represents a list","of the resource associated with the status StatusReason (when there","Requests is omitted for a container, it defaults to Limits","output: description: - output describes the Docker image the Strategy","- postCommit is a build hook executed after the build","to any schema except that defined by the reason type.","or ImageStreamImage from which the docker image should be pulled","type: bool force_pull: description: - forcePull describes if the builder","the name returned to the client will be different than","explicitly asked to observe uninitialized objects. When an object is","directory. destinationDir can also be empty or equal to \".\",","a shell script is appropriate to execute the post build","complex contains: str, str revision: description: - revision is the","\"spec.containers{name}\" (where \"name\" refers to the name of the container","to select in the specified API version. type: str resource_field_ref:","directory, where the Dockerfile is located, so users can ADD","Command. Use Script when a shell script is appropriate to","SelfLink is a URL representing this object. Populated by the","is the number of old successful builds to retain. If","contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2].","this pod). This syntax is chosen only to have some","name of the source control user type: str commit: description:","run. type: list contains: str command: description: - command is","specific commit type: str committer: description: - committer is the","deletion is requested by the user, and is not directly","that the build may be active on a node before","this from the endpoint the client submits requests to. Cannot","contains: key: description: - The key of the secret to","the information from the source for a specific repo snapshot.","and\\ \\ value is the base64 encoded credentials. Supported auth","specified API version. type: str resource_field_ref: description: - 'Selects a","Later, when the script finishes, all files injected will be","Custom builds will receive this binary as input on STDIN.","should be pulled the resulting image will be used in","source type: complex contains: author: description: - author is the","may still exist after this timestamp, until an administrator or","trigger type: str status: description: - status holds any relevant","description: - env contains additional environment variables you want to","UID of the referent. type: str paths: description: - paths","for the build pod to fit on a node If","storage and then deleted, ensuring that other clients can observe","This syntax is chosen only to have some well-defined way","to this controller, with the controller field set to true.","file within the build input. For example, specifying \"webapp.war\" would","would be used for setting up the authentication for pulling","image_change: description: - imageChange contains parameters for an ImageChange type","a unique suffix. The provided value has the same validation","the label. type: str push_secret: description: - PushSecret is the","specified, the FROM may be modified based on your strategy","to provide source for the build type: list contains: _from:","fail if the Dockerfile cannot provide compatibility with the 'None'","true for GenericWebHook. type: bool secret: description: - secret used","result of an explicit client build creation. type: list contains:","versioned schema of this representation of an object. Servers should","description: - Kind is a string value representing the REST","It is represented in RFC3339 form and is in UTC.","extract it as the source. The custom strategy receives this","that may be set by external tools to store and","exc: # The helper failed to init, so there is","an object is created, the system will populate this list","in RFC3339 form and is in UTC. Populated by the","not be unset or be set further into the future,","initializers: description: - An initializer is a controller which enforces","field set to true. There cannot be more than one","incremental builds if true. type: bool pull_secret: description: - pullSecret","name and namespace in different clusters. This field is not","A machine-readable description of the cause of the error. If","defined environment variables in the container and any service environment","type: str paths: description: - paths is a list of","particular resource or set of resources. Populated by the system.","to authenticate with the API. type: path context: description: -","type: complex contains: api_version: description: - APIVersion defines the versioned","the REST resource this object represents. Servers may infer this","to request the generation of an appropriate name automatically. Name","description: - 'Selects a field of the pod: supports metadata.name,","clients that haven't explicitly asked to observe uninitialized objects. When","contains: pending: description: - Pending is a list of initializers","for pulling the Docker images from the private Docker registries","provided binary as `/webapp.war` for the builder. If left empty,","- completionDeadlineSeconds is an optional duration in seconds, counted from","binary as input on STDIN. type: complex contains: as_file: description:","a list of build_configs. List operations provide a snapshot read","that will remove the entry from the list. If the","be used to provide source for the build type: list","complex contains: container_name: description: - 'Container name: required for volumes,","Only privileged users may set or modify this list. Once","from the image. type: list contains: destination_dir: description: - destinationDir","the object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description:","use when building images to reduce the final size or","differ from the existing object's attributes, and deleted, if set","the system can use when building images to reduce the","user may request that a pod is deleted in 30","may reject unrecognized values. type: str code: description: - Suggested","contains: api_version: description: - API version of the referent. type:","there is no information available. A Reason clarifies an HTTP","value is empty there is no information available. A Reason","description: - dockerfile is the raw contents of a Dockerfile","combined with a unique suffix. The provided value has the","will either return 201 Created or 500 with Reason ServerTimeout","the authentication for executing the Docker push to authentication enabled","can determine the resource is fully terminated. If not set,","describes the SCM in use. type: complex contains: binary: description:","object attributes. type: dict src: description: - Provide a path","refers to the root of WORKDIR. Deprecated: This feature will","configure the pipeline relative to the root of the context","archive is provided. Custom builds will receive this binary as","object should be created, patched, or deleted. When set to","referencing a part of an object.' type: str kind: description:","path state: description: - Determines if an object should be","be described). type: str retry_after_seconds: description: - If specified, the","their input. The binary is generally assumed to be a","type: str source_path: description: - sourcePath is the absolute path","''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException","get calls) from clients that haven't explicitly asked to observe","executed after the build output image is committed, before it","to pass into a build pipeline. type: list contains: name:","- ref is the branch/tag/ref to build. type: str uri:","the Docker image's default entrypoint. The arguments are placed immediately","due to fields having multiple errors. Optional. Examples: \"name\" -","than once in an array of causes due to fields","list of build_configs. List operations provide a snapshot read of","no other connection options are provided, the openshift client will","a unique name could not be found in the time","the Strategy should produce. type: complex contains: image_labels: description: -","the images from registry prior to building. type: bool image_optimization_policy:","component that will remove the entry from the list. If","it's key must be defined type: bool field_ref: description: -","'None' policy. An additional experimental policy 'SkipLayersAndWarn' is the same","type: str type: description: - type is the type of","option is specified, the FROM may be modified based on","clients to determine when objects have changed. May be used","a ConfigMap. type: complex contains: key: description: - The key","type: int uid: description: - UID of the resource. (when","select from. Must be a valid secret key. type: str","allotted, and the client should retry (optionally after the time","of the Jenkinsfile that will be used to configure the","name) after the time in this field. Once set, this","to. In the case of an ImageStreamTag, the ImageStreamTag will","to authentication enabled Docker Registry (or Docker Hub). type: complex","of, defaults to \"v1\". type: str field_path: description: - Path","or I(type) attribute, a strategic merge is performed, where individual","have changed. May be used for optimistic concurrency, change detection,","a build hook executed after the build output image is","description: - A machine-readable description of the cause of the","and other binaries available in the PATH. type: str resources:","used to connect to the API. cert_file: description: - Path","only be removed. type: list contains: str generate_name: description: -","pull the images from registry prior to building. type: bool","once in an array of causes due to fields having","the system. Only set when deletionTimestamp is also set. May","the space within each name must be unique. An empty","an empty From reference is allowed in a build configuration.","field is not set anywhere right now and apiserver is","- Name of the referent. type: str runtime_artifacts: description: -","be one of 'ImageStreamTag' or 'DockerImage'. This value will be","required. If Requests is omitted for a container, it defaults","contains: custom_strategy: description: - customStrategy holds the parameters to the","to copy source from. type: complex contains: api_version: description: -","is visible. When the last pending initializer is removed, and","process can determine the resource is fully terminated. If not","parameters to the Custom build strategy type: complex contains: _from:","if not set. type: int details: description: - Extended data","schema the FieldPath is written in terms of, defaults to","- secretSource is a reference to the secret type: complex","the committer of a specific commit type: complex contains: email:","namespace in different clusters. This field is not set anywhere","type: complex contains: allow_env: description: - allowEnv determines whether the","complex contains: as_file: description: - asFile indicates that the provided","Read-only. Value must be treated as opaque by clients and","considered uninitialized and is hidden (in list/watch and get calls)","reference to the secret type: complex contains: name: description: -","machine-readable description of why this operation is in the \"Failure\"","Ruby image, this is necessary to make `ruby`, `bundle` and","to true. There cannot be more than one managing controller.","''' ''' RETURN = ''' api_version: type: string description: Requested","this build to. Kind must be one of 'ImageStreamTag' or","should be created, patched, or deleted. When set to C(present),","to the Custom build strategy type: complex contains: _from: description:","up the authentication for pulling the Docker images from the","divisor: description: - Specifies the output format of the exposed","key: description: - The key of the secret to select","name attribute of the resource associated with the status StatusReason","set when deletionTimestamp is also set. May only be shortened.","limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently","the server. Populated by the system. Read-only. type: str self_link:","description: - Extended data associated with the reason. Each reason","of referencing a part of an object.' type: str kind:","different clusters. This field is not set anywhere right now","to. This is used to distinguish resources with same name","uid: description: - UID of the referent. type: str resource_version:","input string will be unchanged. The $(VAR_NAME) syntax can be","with `/bin/sh -ic`. It may not be specified with Command.","self_link: description: - SelfLink is a URL representing this object.","time when this object was created. It is not guaranteed","complex contains: {} finalizers: description: - Must be empty before","so users can ADD or COPY them during docker build.","type: str uid: description: - UID is the unique in","message is the description of a specific commit type: str","type: int node_selector: description: - nodeSelector is a selector which","URL for acessing the Kubernetes API. key_file: description: - Path","referent. type: str paths: description: - paths is a list","- Name of the referent. type: str docker_strategy: description: -","being merged into a unique set of values. If a","must be treated as opaque by clients and . type:","error. This field may be presented as-is to a reader.","for the object, bypassing any modules parameters intended to define","type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional","result: description: - If result is set with the Failure","REST resource this object represents. Servers may infer this from","reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which the","and may be truncated by the length of the suffix","available. A Reason clarifies an HTTP status code but does","length. For the Docker build strategy, these will be copied","tech preview. type: complex contains: env: description: - env contains","str divisor: description: - Specifies the output format of the","queryable and should be preserved when modifying objects. type: complex","a registry. type: complex contains: args: description: - args is","the managing controller. type: bool kind: description: - Kind of","str generate_name: description: - GenerateName is an optional prefix, used","be valid for a particular resource or set of resources.","- The Causes array includes more details associated with the","- 'Required: resource to select' type: str secret_key_ref: description: -","bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the system","of the referent. type: str secrets: description: - secrets represents","been deleted, this object will be garbage collected. If this","store until this reference is removed. Defaults to false. To","GenericWebHook. type: bool secret: description: - secret used to validate","description: - metadata for BuildConfigList. type: complex contains: resource_version: description:","the object, bypassing any modules parameters intended to define object","a I(name) or I(type) attribute, a strategic merge is performed,","to validate requests. type: str generic: description: - generic contains","description: - CreationTimestamp is a timestamp representing the server time","can use when building images to reduce the final size","It must have non-zero length. type: str value: description: -","complex contains: last_version: description: - lastVersion is used to inform","to the Docker build strategy. type: complex contains: _from: description:","to C(True). default: present choices: - present - absent username:","set of initializers. Only privileged users may set or modify","object serialized and passed to the custom builder type: str","the build strategy will be used. Only one ImageChangeTrigger with","and the object is considered as initialized and visible to","the Secret or it's key must be defined type: bool","description: - Specify whether the Secret or it's key must","and may reject unrecognized values. type: str code: description: -","version of the referent. type: str block_owner_deletion: description: - If","complex contains: config_map_key_ref: description: - Selects a key of a","Read-only. Null for lists. type: complex contains: {} deletion_grace_period_seconds: description:","complex contains: email: description: - email of the source control","description: - noProxy is the list of domains for which","an existing Kubernetes config file. If not provided, and no","to verify the API server's SSL certificates. type: bool requirements:","Failure field, the object will be persisted to storage and","cause of the error. This field may be presented as-is","provided. If this field is used, the name returned to","I(~/.kube/config.json). type: path password: description: - Provide a password for","that 30 seconds, the Kubelet will send a hard termination","key must be defined type: bool field_ref: description: - 'Selects","contains additional environment variables you want to pass into a","stored with a resource that may be set by external","look up a Docker image repository to push to. In","the operation should be retried. type: int uid: description: -","ImageStreamTag, or ImageStreamImage from which the docker image should be","API version. type: str resource_field_ref: description: - 'Selects a resource","is fully terminated. If not set, graceful deletion of the","changed. Value must be treated as opaque by clients and","clients to determine when objects have changed. Value must be","bool requirements: - openshift == 0.3.3 ''' EXAMPLES = '''","source for a specific repo snapshot. This is optional. type:","URL representing this object. Populated by the system. Read-only. type:","will be persisted to storage and then deleted, ensuring that","described). type: str retry_after_seconds: description: - If specified, the time","builds will receive this binary as input on STDIN. type:","of the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds","referenced by the ServiceAccount type: str source: description: - source","created. It is not guaranteed to be set in happens-before","or deleted. When set to C(present), the object will be","can only be removed. type: list contains: str generate_name: description:","cannot be more than one managing controller. type: list contains:","build config type: complex contains: last_version: description: - lastVersion is","the Docker and Source build strategies assume this file is","up the authentication for cloning private repository. The secret\\ \\","named by its JSON serialization. May include dot and postfix","code for this status, 0 if not set. type: int","description: - DeletionTimestamp is RFC 3339 date and time at","committer is the committer of a specific commit type: complex","by the Docker build API. The experimental policy 'SkipLayers' will","defines an optional location to push the output of this","type: str secret: description: - secret is a reference to","to use a shell. In all other cases, using Script","list contains: str generate_name: description: - GenerateName is an optional","name must be unique. An empty namespace is equivalent to","the runtime image for use. Deprecated: This feature will be","objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description: -","provided, and no other connection options are provided, the openshift","perform a build. type: complex contains: custom_strategy: description: - customStrategy","a key of a secret in the pod's namespace type:","to storage and then deleted, ensuring that other clients can","`/bin/sh -ic`. It may not be specified with Command. Use","description: - destinationDir is the directory where the files from","is not specified. type: str generation: description: - A sequence","lastTriggeredImageID is used internally by the ImageChangeController to save last","definition for the object, bypassing any modules parameters intended to","script: description: - script is a shell script to be","dockerfile: description: - dockerfile is the raw contents of a","'Variable references $(VAR_NAME) are expanded using the previous defined environment","build pod gets scheduled in the system, that the build","status. If this value is empty there is no information","resource or set of resources. Clients must treat these values","set. type: int details: description: - Extended data associated with","for a Bitbucket webhook type of trigger type: complex contains:","- lastVersion is used to inform about number of last","contains: image_labels: description: - imageLabels define a list of labels","new layers in between each image step, and will fail","result is set with the Failure field, the object will","by the ImageChangeController to save last used image ID for","description: - images describes a set of images to be","\"name\" refers to the name of the container that triggered","CamelCase. type: str metadata: description: - metadata for BuildConfig. type:","contents of a Jenkinsfile which defines a Jenkins pipeline build.","be modified based on your strategy base image and additional","to the source that will be built. The structure of","is the absolute path of the file or directory inside","uri: description: - uri points to the source that will","of the container that triggered the event) or if no","Docker build strategy, these will be copied into the build","directory within the build directory where files copied from the","ImageStreamImage from which the docker image should be pulled the","the system. Read-only. Value must be treated as opaque by","description: - If true, AND if the owner has the","time in seconds before the operation should be retried. type:","allow running Docker commands (and build Docker images) from inside","Clients must treat these values as opaque and passed unmodified","copy binary artifacts created from one build into a separate","from clients that haven't explicitly asked to observe uninitialized objects.","OpenShift registry, this field does not need to be set.","at object creation time. This field is a list of","of resources. Populated by the system. Read-only. Value must be","message: description: - A human-readable description of the status of","The arguments are placed immediately after the command to be","description: - Enable debug output from the OpenShift helper. Logging","Generic webhook type of trigger type: complex contains: allow_env: description:","will never be expanded, regardless of whether the variable exists","- from is a reference to an ImageStreamTag that will","- The key of the secret to select from. Must","have any innate Dockerfile replaced in the context dir. type:","trigger type: complex contains: _from: description: - from is a","schemas to the latest internal value, and may reject unrecognized","authentication for executing the Docker push to authentication enabled Docker","and RHEL images that use Software Collections (SCL), in order","for the build time. For the Source build strategy, these","str https_proxy: description: - httpsProxy is a proxy used to","a file containing the YAML definition of the object. Mutually","represents. Servers may infer this from the endpoint the client","contains: name: description: - name defines the name of the","time. This field is a list of initializers that have","may be used when an archive is provided. Custom builds","list contains: _from: description: - from is a reference to","resource associated with the status StatusReason. type: str kind: description:","will be garbage collected. If this object is managed by","destinationDir can also be empty or equal to \".\", in","and should be preserved when modifying objects. type: complex contains:","Docker images) from inside the Docker container. type: bool force_pull:","the source. The custom strategy receives this binary as standard","and deleted, if set to C(absent). A patch operation results","opaque by clients and passed unmodified back to the server.","the variable exists or not. Defaults to \"\".' type: str","parameters for a Bitbucket webhook type of trigger type: complex","artifacts created from one build into a separate runtime image.'","the name of the label. It must have non-zero length.","notation for nested attributes. Arrays are zero-indexed. Fields may appear","type: str runtime_image: description: - 'runtimeImage is an optional image","- UID of the referent. type: str env: description: -","type: complex contains: pending: description: - Pending is a list","tar.gz file and extract it as the source. The custom","Selects a key of a secret in the pod's namespace","input should be considered a single file within the build","type: str resource_version: description: - Specific resourceVersion to which this","this binary as input on STDIN. type: complex contains: as_file:","CentOS and RHEL images that use Software Collections (SCL), in","associated with the status StatusReason. On some operations may differ","is a shell script to be run with `/bin/sh -ic`.","privileged users may set or modify this list. Once it","type: list contains: str script: description: - script is a","valid credentials for remote repository, where the\\ \\ data's key","across separate operations. Clients may not set this value. It","modified further by any user. type: complex contains: pending: description:","is the description of a specific commit type: str type:","uid: description: - UID of the referent. type: str post_commit:","within a pod, this would take on a value like:","coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION =","Selects a key of a ConfigMap. type: complex contains: key:","If nil or empty, this object has been completely initialized.","build into a separate runtime image.' type: complex contains: api_version:","the Docker images from the private Docker registries type: complex","build unless Namespace is specified. type: complex contains: api_version: description:","- sourcePath is the absolute path of the file or","failed to init, so there is no module object. All","CA certificate used to authenticate with the API. type: path","list have been deleted, this object will be garbage collected.","run with `/bin/sh -ic`. It may not be specified with","str env: description: - env contains additional environment variables you","http_proxy: description: - httpProxy is a proxy used to reach","Secret or it's key must be defined type: bool dockerfile_path:","I(name_) or I(type) are merged. To force the replacement of","of this representation of an object. Servers should convert recognized","API. The experimental policy 'SkipLayers' will avoid commiting new layers","ImageStreamTag will be looked for in the namespace of the","on the strategy. For Docker builds, this is the build","by a controller, then an entry in this list will","in the root of the specified contextDir. type: str source_strategy:","description of a specific commit type: str type: description: -","the name of the ServiceAccount to use to run the","observe uninitialized objects. When an object is created, the system","must be a directory. destinationDir can also be empty or","users may set or modify this list. Once it is","resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are","be an archive as described above. For Source and Docker","Retrieve a list of build_configs. List operations provide a snapshot","still done in the builder image but, post build, you","been provided. If this field is used, the name returned","str git: description: - git contains optional information about git","found in the Kubernetes config file. debug: description: - Enable","str post_commit: description: - postCommit is a build hook executed","Cannot be updated. type: str namespace: description: - Namespace defines","description: - RunPolicy describes how the new build created from","list contains: str command: description: - command is the command","the content of the directory will be copied, but the","to have the appropriate collections enabled in the shell. E.g.,","A Reason clarifies an HTTP status code but does not","description: - Name of the referent. type: str type: description:","triggers determine how new Builds can be launched from a","description: - metadata for BuildConfig. type: complex contains: annotations: description:","API. type: path kubeconfig: description: - Path to an existing","the system. Read-only. type: str reason: description: - A machine-readable","Docker registries type: complex contains: name: description: - Name of","connecting to the API. Use in conjunction with I(username). resource_definition:","description: - Namespace of the referent. type: str resource_version: description:","GitLabWebHook contains the parameters for a GitLab webhook type of","RFC 3339 date and time at which this resource will","be used to pull the image from a registry If","be launched from a BuildConfig. If no triggers are defined,","the server. If this field is specified and the generated","See type: list contains: name: description: - Name of the","container and after cleanup, remove the pod from the API.","by the system. Read-only. type: str spec: description: - spec","used to authenticate with the API. type: path state: description:","force_pull: description: - forcePull describes if the builder should pull","the Docker push to authentication enabled Docker Registry (or Docker","system can use when building images to reduce the final","(limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex","of this object that can be used by clients to","container within a pod, this would take on a value","authenticate with the API. type: path state: description: - Determines","image will be equivalent to an image created by the","is appropriate to execute the post build hook, for example","description: - command is the command to run. It may","a Dockerfile which should be built. When this option is","this list with the current set of initializers. Only privileged","source will depend on the type of build to run","May match selectors of replication controllers and services. type: complex","To set this field, a user needs \"delete\" permission of","description: - serviceAccount is the name of the ServiceAccount to","to be an archive as described above. For Source and","complex contains: allow_env: description: - allowEnv determines whether the webhook","the client will be different than the name passed. This","description: - Specify whether the ConfigMap or it's key must","of the schema the FieldPath is written in terms of,","placed. type: str source_path: description: - sourcePath is the absolute","can be a file or directory. destinationDir must be a","secrets is a list of additional secrets that will be","Pipeline build strategy. This strategy is in tech preview. type:","referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a list","positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the","to save last used image ID for build type: str","retained. type: int node_selector: description: - nodeSelector is a selector","forcePull describes if the builder should pull the images from","is not directly settable by a client. The resource is","any user. type: complex contains: pending: description: - Pending is","secret type: str secret_source: description: - secretSource is a reference","access statement, such as desiredState.manifest.containers[2]. For example, if the object","in the shell. E.g., in the Ruby image, this is","of build configs type: list contains: api_version: description: - APIVersion","is the location of Source scripts type: str type: description:","the parameters for a GitHub webhook type of trigger type:","as exc: # The helper failed to init, so there","output of this build to. Kind must be one of","and values that can be used to organize and categorize","spent building the image. The default policy is 'None' which","kind: description: - The kind attribute of the resource associated","lastVersion is used to inform about number of last triggered","description: - incremental flag forces the Source build to do","type is the kind of build strategy. type: str successful_builds_history_limit:","the generation of an appropriate name automatically. Name is primarily","causes. type: list contains: field: description: - 'The field of","resources with same name and namespace in different clusters. This","Must be empty before the object is deleted from the","will be looked for in the namespace of the build","in the pod's namespace type: complex contains: key: description: -","present locally type: bool pull_secret: description: - pullSecret is the","API. cert_file: description: - Path to a certificate used to","host: description: - Provide a URL for acessing the Kubernetes","value is the base64 encoded credentials. Supported auth methods\\ \\","Limits if that is explicitly specified, otherwise to an implementation-defined","type: description: - type is the type of build trigger","the schema the FieldPath is written in terms of, defaults","controllers and services. type: complex contains: str, str name: description:","- List of objects depended by this object. If ALL","Causes array includes more details associated with the StatusReason failure.","the input string will be unchanged. The $(VAR_NAME) syntax can","images that use Software Collections (SCL), in order to have","- A sequence number representing a specific generation of the","of an object. Servers should convert recognized schemas to the","resource_definition: description: - Provide the YAML definition for the object,","may request that a pod is deleted in 30 seconds.","- destinationDir is the directory where the files from the","the last one in the list is used. type: list","bypassing any modules parameters intended to define object attributes. type:","desiredState.manifest.containers[2]. For example, if the object reference is to a","representing the REST resource this object represents. Servers may infer","short_description: OpenShift BuildConfigList description: - Retrieve a list of build_configs.","contains the parameters for a GitHub webhook type of trigger","str optional: description: - Specify whether the Secret or it's","Script or the Docker image's default entrypoint. The arguments are","created by the Docker build API. The experimental policy 'SkipLayers'","type: int initializers: description: - An initializer is a controller","Dockerfile that will be used to build the Docker image,","complex contains: {} deletion_grace_period_seconds: description: - Number of seconds allowed","Logging info is written to KubeObjHelper.log default: false type: bool","be defined type: bool force_pull: description: - forcePull describes if","builder image but, post build, you can copy the needed","This is used to distinguish resources with same name and","it's key must be defined type: bool expose_docker_socket: description: -","which should be built. When this option is specified, the","of a Secret that would be used for setting\\ \\","want to pass into a builder container. type: list contains:","image. type: list contains: destination_dir: description: - destinationDir is the","the pod from the API. In the presence of network","this list will point to this controller, with the controller","the --no-cache=true flag type: bool pull_secret: description: - pullSecret is","on PUT operations. Populated by the system. Read-only. type: str","source path ends in /. then the content of the","successful builds to retain. If not specified, all successful builds","for a Generic webhook type of trigger type: complex contains:","The default policy is 'None' which means the final build","file or directory inside the image to copy to the","a reference to the secret type: complex contains: name: description:","the ImageChangeController to save last used image ID for build","will not be created at the destination. type: str pull_secret:","Docker image, relative to the root of the context (contextDir).","omitted for a container, it defaults to Limits if that","- Selects a key of a ConfigMap. type: complex contains:","of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy: description:","empty or equal to \".\", in this case it just","object is non-nil, entries in this list can only be","description: - pullSecret is the name of a Secret that","Use ImageSource to copy binary artifacts created from one build","the listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key:","- APIVersion defines the versioned schema of this representation of","the FROM, but before the rest of your Dockerfile stanzas.","the environment variable. Must be a C_IDENTIFIER. type: str value:","the time in seconds before the operation should be retried.","repo snapshot. This is optional. type: complex contains: git: description:","type: str group: description: - The group attribute of the","status StatusReason. On some operations may differ from the requested","to perform a build. type: complex contains: custom_strategy: description: -","contains: {} finalizers: description: - Must be empty before the","of this field for those objects will be empty. Must","the system actively tries to terminate the build; value must","is expected to be deleted (no longer visible from resource","for running unit tests with `rake test`. If you need","as-is to a reader. type: str reason: description: - A","Name is primarily intended for creation idempotence and configuration definition.","otherwise 422 (Unprocessable Entity) will be returned. type: bool controller:","the owner cannot be deleted from the key-value store until","returning a resource_version representing a consistent version of the listed","the OpenShift registry, this field does not need to be","after the build output image is committed, before it is","docker_strategy: description: - dockerStrategy holds the parameters to the Docker","requested resource Kind. type: str name: description: - The name","deleted (no longer visible from resource lists, and not reachable","the object is considered uninitialized and is hidden (in list/watch","needed artifacts in the runtime image for use. Deprecated: This","- UID of the referent. type: str resource_version: description: -","not specified. type: str generation: description: - A sequence number","of the Dockerfile for this build. type: complex contains: api_version:","written in terms of, defaults to \"v1\". type: str field_path:","credentials for remote repository, where the\\ \\ data's key represent","this object. Populated by the system. Read-only. type: str reason:","failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old failed","ssl_ca_cert: description: - Path to a CA certificate used to","seconds, the Kubelet will send a hard termination signal (SIGKILL)","a single file. contextDir may be used when an archive","name then the last one in the list is used.","status, 0 if not set. type: int details: description: -","destinationDir is the relative directory within the build directory where","the script finishes, all files injected will be truncated to","same name then the last one in the list is","the source for a specific repo snapshot. This is optional.","type: str secret_key_ref: description: - Selects a key of a","graceful termination signal to the containers in the pod. After","Hub). type: complex contains: name: description: - Name of the","a registry If the image is pulled from the OpenShift","a resource or set of resources. Clients must treat these","specified, all successful builds are retained. type: int triggers: description:","Source build strategies assume this file is a zip, tar,","in the builder image but, post build, you can copy","is the command to run. It may not be specified","jenkinsfile_path: description: - JenkinsfilePath is the optional path of the","in happens-before order across separate operations. Clients may not set","the build type: list contains: _from: description: - from is","copied from the image are placed. type: str source_path: description:","and the watch operation on a resource or set of","build nodeselector values for the cluster. If set to an","are placed. type: str source_path: description: - sourcePath is the","reason: description: - A machine-readable description of the cause of","running unit tests with `rake test`. If you need control","connect to the API. cert_file: description: - Path to a","must be true for the build pod to fit on","incremental flag forces the Source build to do incremental builds","there are multiple labels with the same name then the","when a graceful deletion is requested by the user, and","retry_after_seconds: description: - If specified, the time in seconds before","the build. type: complex contains: limits: description: - Limits describes","the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu","values. If a list contains a dictionary with a I(name)","object has been completely initialized. Otherwise, the object is considered","of the context (contextDir). type: str env: description: - env","values. type: str items: description: - items is a list","is provided. Custom builds will receive this binary as input","type: str service_account: description: - serviceAccount is the name of","the context dir. type: str git: description: - git contains","or modify this list. Once it is empty, it may","str spec: description: - spec holds all the input necessary","used to validate requests. type: str github: description: - github","initializer is a controller which enforces some system invariant at","file is a zip, tar, or tar.gz file and extract","resourceVersion to which this reference is made, if any. type:","str type: description: - type of the build source, may","equal to \".\", in this case it just refers to","whether the ConfigMap or it's key must be defined type:","String that identifies the server's internal version of this object","or only pull if it is not present locally type:","to be deleted (no longer visible from resource lists, and","the YAML definition for the object, bypassing any modules parameters","list contains: mount_path: description: - mountPath is the path at","- type of build input to accept type: str strategy:","description: - If result is set with the Failure field,","output format of the exposed resources, defaults to \"1\" type:","to the managing controller. type: bool kind: description: - Kind","the containers in the pod. After that 30 seconds, the","key represent the authentication method to be used and\\ \\","describes the Docker image the Strategy should produce. type: complex","the referent. type: str scripts: description: - scripts is the","an application without unneeded dependencies installed. The building of the","- instead, it will either return 201 Created or 500","the Docker image the Strategy should produce. type: complex contains:","referent. type: str resource_version: description: - An opaque value that","the Dockerfile is located, so users can ADD or COPY","a context found in the Kubernetes config file. debug: description:","managing controller. type: bool kind: description: - Kind of the","be used when an archive is provided. Custom builds will","a valid secret key. type: str name: description: - Name","to reach the git repository over http type: str https_proxy:","will be used to build the Docker image, relative to","description: - type of the build source, may be one","args is a list of arguments that are provided to","all clients. type: list contains: name: description: - name of","to trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds","that are applied to the resulting image. If there are","points to the source that will be built. The structure","the latest internal value, and may reject unrecognized values. type:","If true, this reference points to the managing controller. type:","type: str code: description: - Suggested HTTP return code for","set by external tools to store and retrieve arbitrary metadata.","ImageStreamTag that will trigger a build when updated It is","describes a set of images to be used to provide","holds the parameters to the Docker build strategy. type: complex","build. The pod will be allowed to use secrets referenced","In the presence of network partitions, this object may still","on your strategy base image and additional ENV stanzas from","type: str field_path: description: - Path of the field to","of the resource. (when there is a single resource which","Jenkins Pipeline build strategy. This strategy is in tech preview.","resources may allow a client to request the generation of","type: path kubeconfig: description: - Path to an existing Kubernetes","regardless of whether the variable exists or not. Defaults to","Script. This might be needed if the image doesn't have","before the system actively tries to terminate the build; value","binary builds accept a binary as their input. The binary","of the context (contextDir). If both JenkinsfilePath & Jenkinsfile are","is a URL representing this object. Populated by the system.","type: string description: Requested API version build_config_list: type: complex returned:","be set further into the future, although it may be","secret that you want to use in your build. type:","by the system when a graceful deletion is requested. Read-only.","type: str secrets: description: - secrets represents a list of","that will be resolved in the Dockerfile. See type: list","the docker image should be pulled type: complex contains: api_version:","= ''' ''' RETURN = ''' api_version: type: string description:","image and additional ENV stanzas from your strategy environment will","image repository to push to. In the case of an","of the referent. type: str type: description: - type of","build API. The experimental policy 'SkipLayers' will avoid commiting new","with the --no-cache=true flag type: bool pull_secret: description: - pullSecret","you want to pass into a build pipeline. type: list","may be presented as-is to a reader. type: str reason:","complex contains: key: description: - The key of the secret","opaque by clients and . type: str self_link: description: -","type: str docker_strategy: description: - dockerStrategy holds the parameters to","description: - 'runtimeImage is an optional image that is used","of objects depended by this object. If ALL objects in","contains: mount_path: description: - mountPath is the path at which","dot and postfix notation for nested attributes. Arrays are zero-indexed.","a list of build configs type: list contains: api_version: description:","is a single resource which can be described). type: str","contains: args: description: - args is a list of arguments","policy is 'None' which means the final build image will","the operation. One of: \"Success\" or \"Failure\".' type: str labels:","locally type: bool pull_secret: description: - pullSecret is the name","client submits requests to. Cannot be updated. In CamelCase. type:","process that is responsible for initializing this object. type: str","\"1\" type: str resource: description: - 'Required: resource to select'","type: complex contains: key: description: - The key of the","description: - pullSecret is a reference to a secret to","it just refers to the root of WORKDIR. Deprecated: This","the default configuration file from I(~/.kube/config.json). type: path password: description:","permission of the owner, otherwise 422 (Unprocessable Entity) will be","description: - exposeDockerSocket will allow running Docker commands (and build","a Secret that would be used for setting\\ \\ up","allow_env: description: - allowEnv determines whether the webhook can set","generated name exists, the server will NOT return a 409","Cannot be updated. type: str owner_references: description: - List of","match selectors of replication controllers and services. type: complex contains:","is located, so users can ADD or COPY them during","the optional path of the Jenkinsfile that will be used","message: description: - A human-readable description of the cause of","be removed in a future release. Use ImageSource to copy","be empty before the object is deleted from the registry.","str dockerfile: description: - dockerfile is the raw contents of","way of referencing a part of an object.' type: str","requested. Populated by the system when a graceful deletion is","name: description: - Name of the referent. type: str runtime_artifacts:","name of the ServiceAccount to use to run the pod","load the default configuration file from I(~/.kube/config.json). type: path password:","holds any relevant information about a build config type: complex","file depending on the strategy. For Docker builds, this is","image from the build strategy will be used. Only one","committed, before it is pushed to a registry. type: complex","str uid: description: - UID of the referent. type: str","asFile indicates that the provided binary input should be considered","previous defined environment variables in the container and any service","- strategy defines how to perform a build. type: complex","module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The","\"Success\" or \"Failure\".' type: str labels: description: - Map of","representation. Not all objects are required to be scoped to","proxy used to reach the git repository over https type:","type: list contains: name: description: - Name of the environment","contains: str, str cluster_name: description: - The name of the","Command, Script or the Docker image's default entrypoint. The arguments","contains information about git-based build source type: complex contains: author:","be used by clients to determine when objects have changed.","str self_link: description: - SelfLink is a URL representing this","$$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless","observe the deletion. type: complex contains: api_version: description: - APIVersion","of the object is non-nil, entries in this list can","For Docker builds, this is the build context and an","to init, so there is no module object. All we","and passed unmodified back to the server. They may only","type: str gitlab: description: - GitLabWebHook contains the parameters for","be defined type: bool dockerfile_path: description: - dockerfilePath is the","optimizations the system can use when building images to reduce","values. type: str code: description: - Suggested HTTP return code","the generated name exists, the server will NOT return a","Build object serialized and passed to the custom builder type:","namespace of the build unless Namespace is specified. type: complex","may provide detailed causes. type: list contains: field: description: -","str status: description: - 'Status of the operation. One of:","nodeselector values for the cluster. If set to an empty","return 201 Created or 500 with Reason ServerTimeout indicating a","strategy, these will be copied into the build directory, where","be set to true for GenericWebHook. type: bool secret: description:","passed unmodified back to the server. They may only be","YAML definition for the object, bypassing any modules parameters intended","by this object. If ALL objects in the list have","in a future release. Use ImageSource to copy binary artifacts","Secret or it's key must be defined type: bool expose_docker_socket:","$(VAR_NAME) syntax can be escaped with a double $$, ie:","used to validate requests. type: str generic: description: - generic","defined type: bool expose_docker_socket: description: - exposeDockerSocket will allow running","type: complex contains: author: description: - author is the author","type: str scripts: description: - scripts is the location of","to: description: - to defines an optional location to push","the Source build to do incremental builds if true. type:","the secret should be available for the build time. For","type: str pull_secret: description: - pullSecret is a reference to","description: - UID of the referent. type: str paths: description:","DockerImage to copy source from. type: complex contains: api_version: description:","specified with Script. This might be needed if the image","the\\ \\ data's key represent the authentication method to be","any. type: str uid: description: - UID of the referent.","str strategy: description: - strategy defines how to perform a","run the pod created by this build. The pod will","API version of the referent. type: str block_owner_deletion: description: -","of the cause of the error. If this value is","represents the internal version of this object that can be","the builder to the runtime image. sourcePath can be a","the server time when this object was created. It is","cluster. If set to an empty map or a map","extended details. This field is optional and the data returned","OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description:","be used with other options like git - in those","# The helper failed to init, so there is no","client should retry (optionally after the time indicated in the","https_proxy: description: - httpsProxy is a proxy used to reach","pod's namespace type: complex contains: key: description: - The key","this object has been completely initialized. Otherwise, the object is","author is the author of a specific commit type: complex","image doesn't have `/bin/sh`, or if you do not want","str reason: description: - A machine-readable description of why this","copied, but the directory itself will not be created at","the absolute path of the file or directory inside the","if it is not present locally type: bool pull_secret: description:","contains: name: description: - name of the process that is","should be built. When this option is specified, the FROM","2 in this pod). This syntax is chosen only to","case it just refers to the root of WORKDIR. Deprecated:","referent. type: str docker_strategy: description: - dockerStrategy holds the parameters","type: str https_proxy: description: - httpsProxy is a proxy used","the server on successful creation of a resource and is","contains: binary: description: - binary builds accept a binary as","it will be removed from the system. Only set when","script finishes, all files injected will be truncated to zero","- Name of the referent. type: str optional: description: -","contextDir may be used when an archive is provided. Custom","- failedBuildsHistoryLimit is the number of old failed builds to","or it's key must be defined type: bool jenkinsfile: description:","complex contains: resource_version: description: - String that identifies the server's","custom strategy receives this binary as standard input. This filename","is a list of build configs type: list contains: api_version:","map or a map with any values, default build nodeselector","last one in the list is used. type: list contains:","is in the \"Failure\" status. If this value is empty","An empty namespace is equivalent to the \"default\" namespace, but","preview. type: complex contains: env: description: - env contains additional","ImageChangeTrigger with an empty From reference is allowed in a","description: - Kind of the referent. type: str name: description:","build creation. type: list contains: bitbucket: description: - BitbucketWebHook contains","is the path at which to mount the secret type:","hard termination signal (SIGKILL) to the container and after cleanup,","post_commit: description: - postCommit is a build hook executed after","operation should be retried. type: int uid: description: - UID","is a string value representing the REST resource this object","serialized and passed to the custom builder type: str env:","responsible component that will remove the entry from the list.","in seconds, counted from the time when a build pod","For example, a user may request that a pod is","- dockerfilePath is the path of the Dockerfile that will","build hook executed after the build output image is committed,","commands (and build Docker images) from inside the Docker container.","those cases the Git repo will have any innate Dockerfile","be equivalent to an image created by the Docker build","used to pull the image from a registry If the","field_ref: description: - 'Selects a field of the pod: supports","object will be created, if it does not exist, or","`/webapp.war` for the builder. If left empty, the Docker and","builds to retain. If not specified, all failed builds are","it as the source. The custom strategy receives this binary","actively tries to terminate the build; value must be positive","str namespace: description: - Namespace defines the space within each","rest of your Dockerfile stanzas. The Dockerfile source type may","optional prefix, used by the server, to generate a unique","passed to the custom builder type: str env: description: -","string description: Requested API version build_config_list: type: complex returned: when","of the referent. type: str optional: description: - Specify whether","updated, and lists will be replaced, rather than merged. default:","service_account: description: - serviceAccount is the name of the ServiceAccount","limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains:","the builder. If left empty, the Docker and Source build","can also be empty or equal to \".\", in this","Docker container. type: bool force_pull: description: - forcePull describes if","never be expanded, regardless of whether the variable exists or","associated with the status StatusReason (when there is a single","type: int deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date","requested API version for the Build object serialized and passed","defines the literal value of the label. type: str push_secret:","this field, a user needs \"delete\" permission of the owner,","key of a secret in the pod's namespace type: complex","Name of the referent. type: str type: description: - type","type: complex contains: env: description: - env contains additional environment","as described above. For Source and Docker builds, if binary.asFile","to distinguish resources with same name and namespace in different","type: str status: description: - 'Status of the operation. One","into a unique set of values. If a list contains","use in your build. type: complex contains: name: description: -","contains: bitbucket: description: - BitbucketWebHook contains the parameters for a","provide a snapshot read of the underlying objects, returning a","its own extended details. This field is optional and the","the same as 'SkipLayers' but simply warns if compatibility cannot","the list of domains for which the proxy should not","to the root of the context (contextDir). If both JenkinsfilePath","referent. type: str env: description: - env contains additional environment","- Specific resourceVersion to which this reference is made, if","the Docker image, relative to the root of the context","- spec holds all the input necessary to produce a","type: complex contains: {} deletion_grace_period_seconds: description: - Number of seconds","is the branch/tag/ref to build. type: str uri: description: -","to authenticate with the API. type: path state: description: -","variable's value. Cannot be used if value is not empty.","If not specified, all successful builds are retained. type: int","change on PUT operations. Populated by the system. Read-only. type:","RHEL images that use Software Collections (SCL), in order to","flag is needed to support CentOS and RHEL images that","an object.' type: str kind: description: - Kind of the","secretSource is a reference to the secret type: complex contains:","description: - The name attribute of the resource associated with","object. Populated by the system. Read-only. type: str ''' def","binary: description: - binary builds accept a binary as their","the cause of the error. If this value is empty","The Causes array includes more details associated with the StatusReason","pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath is the","type of build input to accept type: str strategy: description:","from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from","has been completely initialized. Otherwise, the object is considered uninitialized","the output format of the exposed resources, defaults to \"1\"","the controller field set to true. There cannot be more","description: - Version of the schema the FieldPath is written","build_api_version: description: - buildAPIVersion is the requested API version for","contains: destination_dir: description: - destinationDir is the relative directory within","is performed, where individual elements with a matching I(name_) or","list metadata. type: complex contains: resource_version: description: - String that","type: complex contains: _from: description: - from is a reference","be defined type: bool field_ref: description: - 'Selects a field","WORKDIR. Deprecated: This feature will be removed in a future","CamelCase. type: str message: description: - A human-readable description of","failure. Not all StatusReasons may provide detailed causes. type: list","is primarily intended for creation idempotence and configuration definition. Cannot","to a namespace - the value of this field for","destination_dir: description: - destinationDir is the directory where the files","or patched, if parameter values differ from the existing object's","flag forces the Source build to do incremental builds if","root of repository. type: str dockerfile: description: - dockerfile is","init, so there is no module object. All we can","information about a build config type: complex contains: last_version: description:","for this object. It is typically generated by the server","revision is the information from the source for a specific","of the listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options:","pod will be allowed to use secrets referenced by the","optional: description: - Specify whether the ConfigMap or it's key","C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are","a GitHub webhook type of trigger type: complex contains: allow_env:","the source control user type: str name: description: - name","pull_secret: description: - pullSecret is the name of a Secret","- scripts is the location of Source scripts type: str","list will point to this controller, with the controller field","is still done in the builder image but, post build,","the ServiceAccount to use to run the pod created by","warns if compatibility cannot be preserved. type: str no_cache: description:","references will never be expanded, regardless of whether the variable","`/bin/sh`, use Command and/or Args. The `-i` flag is needed","bitbucket: description: - BitbucketWebHook contains the parameters for a Bitbucket","an entire object, this string should contain a valid JSON/Go","the API. verify_ssl: description: - Whether or not to verify","and Source build strategies assume this file is a zip,","can be used to organize and categorize (scope and select)","str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except","associated with the reason. Each reason may define its own","will be copied from the builder to the runtime image.","bool jenkinsfile: description: - Jenkinsfile defines the optional raw contents","detection, and the watch operation on a resource or set","Path to a certificate used to authenticate with the API.","or set of resources. Clients must treat these values as","- 'The field of the resource that has caused this","/. then the content of the directory will be copied,","destination paths to copy from the image. type: list contains:","\"webapp.war\" would place the provided binary as `/webapp.war` for the","the secret type: str secret_source: description: - secretSource is a","which the docker image should be pulled the resulting image","type: str optional: description: - Specify whether the ConfigMap or","the referent. type: str source_secret: description: - \"sourceSecret is the","type: str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1')","script runs. Later, when the script finishes, all files injected","the resource associated with the status StatusReason. On some operations","a specific repo snapshot. This is optional. type: complex contains:","raw contents of a Jenkinsfile which defines a Jenkins pipeline","failing result is set, the initializers struct will be set","build strategy, these will be injected into a container where","values as opaque and passed unmodified back to the server.","binary.asFile is set the build will receive a directory with","server. If this field is specified and the generated name","repo will have any innate Dockerfile replaced in the context","in this case it just refers to the root of","may reject unrecognized values. type: str items: description: - items","be found in the time allotted, and the client should","that a pod is deleted in 30 seconds. The Kubelet","except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\", error=str(exc)) if __name__ ==","unique in time and space value for this object. It","about git-based build source type: complex contains: author: description: -","namespace: description: - Namespace of the referent. type: str resource_version:","empty, the Docker and Source build strategies assume this file","description: - mountPath is the path at which to mount","labels with the same name then the last one in","build strategies assume this file is a zip, tar, or","Read-only. type: str self_link: description: - SelfLink is a URL","or time spent building the image. The default policy is","must execute in order before this object is visible. When","type: int triggers: description: - triggers determine how new Builds","uninitialized objects. When an object is created, the system will","binary is generally assumed to be a tar, gzipped tar,","can be launched from a BuildConfig. If no triggers are","the Name field has not been provided. If this field","description: - author is the author of a specific commit","buildAPIVersion is the requested API version for the Build object","value representing the REST resource this object represents. Servers may","'SkipLayers' will avoid commiting new layers in between each image","set anywhere right now and apiserver is going to ignore","of WORKDIR. Deprecated: This feature will be removed in a","by clients and . type: str self_link: description: - SelfLink","to copy from the image. type: list contains: destination_dir: description:","the parameters for a Generic webhook type of trigger type:","into a builder container. type: list contains: name: description: -","of old successful builds to retain. If not specified, all","reach the git repository over http type: str https_proxy: description:","API. type: path state: description: - Determines if an object","of the suffix required to make the value unique on","of the Dockerfile that will be used to build the","graceful deletion of the object has not been requested. Populated","a specific commit type: str committer: description: - committer is","or it's key must be defined type: bool expose_docker_socket: description:","of source and destination paths to copy from the image.","acted on this object. If nil or empty, this object","to connect to the API. cert_file: description: - Path to","to a reader. type: str reason: description: - A machine-readable","Kubernetes config file. debug: description: - Enable debug output from","if that is explicitly specified, otherwise to an implementation-defined value.","injected into a container where the assemble script runs. Later,","builder to the runtime image. sourcePath can be a file","of the build unless Namespace is specified. type: complex contains:","value. It is represented in RFC3339 form and is in","- Namespace of the referent. type: str resource_version: description: -","the field \"name\" on the current resource \"items[0].name\" - the","generate a unique name ONLY IF the Name field has","and is not allowed to change on PUT operations. Populated","Dockerfile stanzas. The Dockerfile source type may be used with","compute resources required. If Requests is omitted for a container,","which this resource will be deleted. This field is set","be defined type: bool expose_docker_socket: description: - exposeDockerSocket will allow","source/destination pairs that will be copied from the builder to","type: str source_strategy: description: - sourceStrategy holds the parameters to","description: - forcePull describes if the builder should pull the","gracefully terminate before it will be removed from the system.","If the image is pulled from the OpenShift registry, this","Strategy should produce. type: complex contains: image_labels: description: - imageLabels","Bitbucket webhook type of trigger type: complex contains: allow_env: description:","reach the git repository over https type: str no_proxy: description:","will NOT return a 409 - instead, it will either","requirements to execute the build. type: complex contains: limits: description:","- commit is the commit hash identifying a specific commit","of the referent. type: str paths: description: - paths is","seconds, counted from the time when a build pod gets","be used type: str ref: description: - ref is the","builder. If left empty, the Docker and Source build strategies","the output of this build to. Kind must be one","script to be run with `/bin/sh -ic`. It may not","- the field \"name\" on the first array entry in","no triggers are defined, a new build can only occur","noProxy is the list of domains for which the proxy","policy. An additional experimental policy 'SkipLayersAndWarn' is the same as","type: complex contains: config_map_key_ref: description: - Selects a key of","your Dockerfile stanzas. The Dockerfile source type may be used","would place the provided binary as `/webapp.war` for the builder.","accept a binary as their input. The binary is generally","build arguments that will be resolved in the Dockerfile. See","object is created, the system will populate this list with","type: str reason: description: - A machine-readable description of the","asked to observe uninitialized objects. When an object is created,","commit hash identifying a specific commit type: str committer: description:","source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images'","- in those cases the Git repo will have any","by the system. Read-only. type: str self_link: description: - SelfLink","the current set of initializers. Only privileged users may set","will trigger a build when updated It is optional. If","or if you do not want to use a shell.","for initializing this object. type: str result: description: - If","or not. Defaults to \"\".' type: str value_from: description: -","description: - The key of the secret to select from.","If no From is specified, the From image from the","input. For example, specifying \"webapp.war\" would place the provided binary","mount_path: description: - mountPath is the path at which to","define a list of labels that are applied to the","image but, post build, you can copy the needed artifacts","by external tools to store and retrieve arbitrary metadata. They","- revision is the information from the source for a","contains parameters for an ImageChange type of trigger type: complex","image created by the Docker build API. The experimental policy","the replacement of lists, set the I(force) option to C(True).","type: complex contains: api_version: description: - API version of the","flag type: bool pull_secret: description: - pullSecret is the name","in the PATH. type: str resources: description: - resources computes","method to be used and\\ \\ value is the base64","be a directory. destinationDir can also be empty or equal","tries to terminate the build; value must be positive integer","length of the suffix required to make the value unique","UID of the referent. type: str scripts: description: - scripts","list/watch and get calls) from clients that haven't explicitly asked","a specific commit type: complex contains: email: description: - email","requested. Read-only. type: complex contains: {} finalizers: description: - Must","want to pass into a build pipeline. type: list contains:","description: - UID of the referent. type: str post_commit: description:","str revision: description: - revision is the information from the","the Dockerfile that will be used to build the Docker","with the current set of initializers. Only privileged users may","source control user type: str message: description: - message is","to the build directory. If the source path ends in","with I(username). resource_definition: description: - Provide the YAML definition for","str message: description: - A human-readable description of the cause","results in merging lists and updating dictionaries, with lists being","is in UTC. Populated by the system. Read-only. Null for","pod). This syntax is chosen only to have some well-defined","other connection options are provided, the openshift client will attempt","object. Populated by the system. Read-only. type: str reason: description:","represented in RFC3339 form and is in UTC. Populated by","to the custom builder type: str env: description: - env","more details associated with the StatusReason failure. Not all StatusReasons","a controller, then an entry in this list will point","IF the Name field has not been provided. If this","build directory. If the source path ends in /. then","bool field_ref: description: - 'Selects a field of the pod:","be used in the FROM line of the Dockerfile for","will be equivalent to an image created by the Docker","certificates. type: bool requirements: - openshift == 0.3.3 ''' EXAMPLES","description: - Git contains information about git-based build source type:","services. type: complex contains: str, str name: description: - Name","I(state) is C(present), an existing object will updated, and lists","of the owner, otherwise 422 (Unprocessable Entity) will be returned.","description: - Name of the referent. type: str namespace: description:","default policy is 'None' which means the final build image","attempt to load the default configuration file from I(~/.kube/config.json). type:","type: str metadata: description: - metadata for BuildConfig. type: complex","building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations","resource \"items[0].name\" - the field \"name\" on the first array","attribute of the resource associated with the status StatusReason. type:","release. Use ImageSource to copy binary artifacts created from one","builder type: str env: description: - env contains additional environment","the API. cert_file: description: - Path to a certificate used","reason type. type: complex contains: causes: description: - The Causes","in an array of causes due to fields having multiple","be truncated to zero length. For the Docker build strategy,","attributes. type: dict src: description: - Provide a path to","updating dictionaries, with lists being merged into a unique set","number of old successful builds to retain. If not specified,","to KubeObjHelper.log default: false type: bool force: description: - If","the build directory. If the source path ends in /.","description: - The name of the cluster which the object","this option is specified, the FROM may be modified based","- pullSecret is the name of a Secret that would","snapshot. This is optional. type: complex contains: git: description: -","of the process that is responsible for initializing this object.","internal value, and may reject unrecognized values. type: str items:","description of the status of this operation. type: str metadata:","the referent. type: str post_commit: description: - postCommit is a","a consistent version of the listed objects. version_added: 2.3.0 author:","BuildConfigList. type: complex contains: resource_version: description: - String that identifies","of an appropriate name automatically. Name is primarily intended for","description: - A sequence number representing a specific generation of","variables in the container and any service environment variables. If","if it does not exist, or patched, if parameter values","entrypoint, or if the image does not have `/bin/sh`, use","type: list contains: destination_dir: description: - destinationDir is the directory","source control user type: str name: description: - name of","value like: \"spec.containers{name}\" (where \"name\" refers to the name of","optional Dockerfile may be specified to override any Dockerfile in","pulled type: complex contains: api_version: description: - API version of","desired state. Populated by the system. Read-only. type: int initializers:","sequence number representing a specific generation of the desired state.","An additional experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers'","If the source path ends in /. then the content","the image are placed. type: str source_path: description: - sourcePath","system invariant at object creation time. This field is a","str optional: description: - Specify whether the ConfigMap or it's","unit tests with `rake test`. If you need control over","directory. If the source path ends in /. then the","reduce the final size or time spent building the image.","Git repo will have any innate Dockerfile replaced in the","these will be injected into a container where the assemble","happens-before order across separate operations. Clients may not set this","field, a user needs \"delete\" permission of the owner, otherwise","the field \"name\" on the first array entry in \"items\"'","requests. type: str github: description: - github contains the parameters","the location of Source scripts type: str type: description: -","default: present choices: - present - absent username: description: -","limits: description: - Limits describes the maximum amount of compute","can be described). type: str retry_after_seconds: description: - If specified,","of labels that are applied to the resulting image. If","as their input. The binary is generally assumed to be","from the builder to the runtime image. sourcePath can be","\"sourceSecret is the name of a Secret that would be","contains: str, str revision: description: - revision is the information","description: - imageLabels define a list of labels that are","builder or only pull if it is not present locally","into a build pipeline. type: list contains: name: description: -","this object was created. It is not guaranteed to be","description: - value defines the literal value of the label.","resource which can be described). type: str kind: description: -","To force the replacement of lists, set the I(force) option","resource is expected to be deleted (no longer visible from","system. Read-only. type: int initializers: description: - An initializer is","- asFile indicates that the provided binary input should be","data returned is not guaranteed to conform to any schema","object. All we can do is raise the error. raise","build context and an optional Dockerfile may be specified to","operations may differ from the requested resource Kind. type: str","DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image should","complex contains: env: description: - env contains additional environment variables","which the proxy should not be used type: str ref:","indicating a unique name could not be found in the","str labels: description: - Map of string keys and values","'..' or '.'. type: str context_dir: description: - contextDir specifies","point to this controller, with the controller field set to","a tar, gzipped tar, or zip file depending on the","options like git - in those cases the Git repo","a single resource which can be described). type: str kind:","is the commit hash identifying a specific commit type: str","list contains: api_version: description: - APIVersion defines the versioned schema","allows to have buildable sources in directory other than root","on successful creation of a resource and is not allowed","depending on the strategy. For Docker builds, this is the","after the time in this field. Once set, this value","if parameter values differ from the existing object's attributes, and","represent the authentication method to be used and\\ \\ value","used to provide source for the build type: list contains:","directory where the files from the secret should be available","is allowed in a build configuration. type: complex contains: api_version:","to produce a new build, and the conditions when to","type: complex contains: {} finalizers: description: - Must be empty","docker build. type: str secret: description: - secret is a","of a secret in the pod's namespace type: complex contains:","either Command, Script or the Docker image's default entrypoint. The","have `/bin/sh`, use Command and/or Args. The `-i` flag is","one of 'ImageStreamTag' or 'DockerImage'. This value will be used","and additional ENV stanzas from your strategy environment will be","only pull if it is not present locally type: bool","organize and categorize (scope and select) objects. May match selectors","to be set in happens-before order across separate operations. Clients","used by clients to determine when objects have changed. May","is empty, it may not be modified further by any","to the latest internal value, and may reject unrecognized values.","with a single file. contextDir may be used when an","absent username: description: - Provide a username for connecting to","Annotations is an unstructured key value map stored with a","for a GitHub webhook type of trigger type: complex contains:","other clients can observe the deletion. type: complex contains: api_version:","labels that are applied to the resulting image. If there","additional experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but","the endpoint the client submits requests to. Cannot be updated.","description: - Provide a path to a file containing the","the canonical representation. Not all objects are required to be","of a specific commit type: str type: description: - type","if the image doesn't have `/bin/sh`, or if you do","type: complex contains: name: description: - Name of the referent.","type: str creation_timestamp: description: - CreationTimestamp is a timestamp representing","been completely initialized. Otherwise, the object is considered uninitialized and","str secret: description: - secret is a reference to an","'Binary', or 'Images' type: str run_policy: description: - RunPolicy describes","any Dockerfile in the build context. For Source builds, this","a reference to an existing secret that you want to","other cases, using Script might be more convenient. type: list","value is empty there is no information available. type: str","directly settable by a client. The resource is expected to","str source_strategy: description: - sourceStrategy holds the parameters to the","str generation: description: - A sequence number representing a specific","bool incremental: description: - incremental flag forces the Source build","referent. type: str name: description: - Name of the referent.","type: list contains: api_version: description: - APIVersion defines the versioned","ALL objects in the list have been deleted, this object","type: str build_api_version: description: - buildAPIVersion is the requested API","Docker image repository to push to. In the case of","either return 201 Created or 500 with Reason ServerTimeout indicating","the referent. type: str paths: description: - paths is a","type: str image_change: description: - imageChange contains parameters for an","is considered as initialized and visible to all clients. type:","reject unrecognized values. type: str kind: description: - Kind is","complex contains: key: description: - The key to select. type:","that identifies the server's internal version of this object that","index 2 in this pod). This syntax is chosen only","this object is visible. When the last pending initializer is","dockerStrategy holds the parameters to the Docker build strategy. type:","description: - sourceStrategy holds the parameters to the Source build","used to look up a Docker image repository to push","existing Kubernetes config file. If not provided, and no other","secret key. type: str name: description: - Name of the","C(present) contains: api_version: description: - APIVersion defines the versioned schema","is responsible for initializing this object. type: str result: description:","a list contains a dictionary with a I(name) or I(type)","Populated by the system. Read-only. Value must be treated as","is used, the name returned to the client will be","or a map with any values, default build nodeselector values","referent. type: str block_owner_deletion: description: - If true, AND if","Docker Hub). type: complex contains: name: description: - Name of","name: required for volumes, optional for env vars' type: str","to use secrets referenced by the ServiceAccount type: str source:","representing a consistent version of the listed objects. version_added: 2.3.0","complex contains: api_version: description: - API version of the referent.","to be run with `/bin/sh -ic`. It may not be","- successfulBuildsHistoryLimit is the number of old successful builds to","are retained. type: int triggers: description: - triggers determine how","this object will be garbage collected. If this object is","description: - UID of the referent. type: str build_api_version: description:","to override any Dockerfile in the build context. For Source","be resolved, the reference in the input string will be","the assemble script runs. Later, when the script finishes, all","used for setting up the authentication for pulling the Docker","or directory. destinationDir must be a directory. destinationDir can also","builds are retained. type: int triggers: description: - triggers determine","server. Populated by the system. Read-only. type: str self_link: description:","and time at which this resource will be deleted. This","ImageStreamImage, or DockerImage to copy source from. type: complex contains:","options are provided, the openshift client will attempt to load","Docker builds, if binary.asFile is set the build will receive","with the status StatusReason. On some operations may differ from","with the controller field set to true. There cannot be","the length of the suffix required to make the value","All we can do is raise the error. raise Exception(exc.message)","reachable by name) after the time in this field. Once","the type of build to run type: str images: description:","using Script might be more convenient. type: list contains: str","be empty or equal to \".\", in this case it","name returned to the client will be different than the","an unstructured key value map stored with a resource that","zip file depending on the strategy. For Docker builds, this","in order before this object is visible. When the last","arguments that will be resolved in the Dockerfile. See type:","this object represents. Servers may infer this from the endpoint","complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration","- 'Selects a field of the pod: supports metadata.name, metadata.namespace,","string should contain a valid JSON/Go field access statement, such","str kind: description: - Kind of the referent. type: str","from the time when a build pod gets scheduled in","identifier for the responsible component that will remove the entry","object belongs to. This is used to distinguish resources with","object. It is typically generated by the server on successful","type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to","description: - UID of the referent. type: str env: description:","in tech preview. type: complex contains: env: description: - env","type: bool dockerfile_path: description: - dockerfilePath is the path of","try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module failed!\", error=str(exc)) if","build strategy, these will be copied into the build directory,","build strategy. type: complex contains: _from: description: - from is","- API version of the referent. type: str field_path: description:","BitbucketWebHook contains the parameters for a Bitbucket webhook type of","complex contains: pending: description: - Pending is a list of","committer of a specific commit type: complex contains: email: description:","internally by the ImageChangeController to save last used image ID","secret\\ \\ contains valid credentials for remote repository, where the\\","- A machine-readable description of the cause of the error.","can ADD or COPY them during docker build. type: str","May only be shortened. Read-only. type: int deletion_timestamp: description: -","the desired state. Populated by the system. Read-only. type: int","type: str jenkinsfile_path: description: - JenkinsfilePath is the optional path","secrets that will be included in the build pod type:","same name and namespace in different clusters. This field is","the \"default\" namespace, but \"default\" is the canonical representation. Not","which enforces some system invariant at object creation time. This","do not want to use a shell. In all other","description: - scripts is the location of Source scripts type:","field for those objects will be empty. Must be a","can do is raise the error. raise Exception(exc.message) try: module.execute_module()","unmodified back to the server. They may only be valid","git: description: - Git contains information about git-based build source","If set to C(True), and I(state) is C(present), an existing","errors. Optional. Examples: \"name\" - the field \"name\" on the","set environment variables; can only be set to true for","be shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is","- github contains the parameters for a GitHub webhook type","cert_file: description: - Path to a certificate used to authenticate","configuration definition. Cannot be updated. type: str namespace: description: -","If this field is used, the name returned to the","gzipped tar, or zip file depending on the strategy. For","If a list contains a dictionary with a I(name) or","current set of initializers. Only privileged users may set or","secret is a reference to an existing secret that you","still exist after this timestamp, until an administrator or automated","is not present locally type: bool pull_secret: description: - pullSecret","type: str status: description: - status holds any relevant information","application is still done in the builder image but, post","for use. Deprecated: This feature will be removed in a","Dockerfile is located, so users can ADD or COPY them","this field is specified and the generated name exists, the","an image created by the Docker build API. The experimental","final size or time spent building the image. The default","holds the parameters to the Source build strategy. type: complex","no module object. All we can do is raise the","for connecting to the API. Use in conjunction with I(username).","contains: api_version: description: - APIVersion defines the versioned schema of","although some resources may allow a client to request the","active on a node before the system actively tries to","Only set when deletionTimestamp is also set. May only be","name: description: - Name of the referent. type: str namespace:","order across separate operations. Clients may not set this value.","(or Docker Hub). type: complex contains: name: description: - Name","be retried. type: int uid: description: - UID of the","and I(state) is C(present), an existing object will updated, and","specified \"spec.containers[2]\" (container with index 2 in this pod). This","be replaced, rather than merged. default: false type: bool host:","or the resource may be deleted prior to this time.","this field for those objects will be empty. Must be","is a list of arguments that are provided to either","of the source control user type: str message: description: -","Jenkinsfile in the root of the specified contextDir. type: str","whether the variable exists or not. Defaults to \"\".' type:","- incremental flag forces the Source build to do incremental","or DockerImage to copy source from. type: complex contains: api_version:","to be used to pull the image from a registry","field: description: - 'The field of the resource that has","Retry-After header). Applied only if Name is not specified. type:","or it's key must be defined type: bool field_ref: description:","before it is pushed to a registry. type: complex contains:","build. type: list contains: destination_dir: description: - destinationDir is the","duration in seconds, counted from the time when a build","no From is specified, the From image from the build","NOT return a 409 - instead, it will either return","- buildAPIVersion is the requested API version for the Build","a path to a file containing the YAML definition of","be specified with Script. This might be needed if the","this build. The pod will be allowed to use secrets","select' type: str secret_key_ref: description: - Selects a key of","Source builds, this is assumed to be an archive as","true indicates that the docker build must be executed with","to fit on a node If nil, it can be","infer this from the endpoint the client submits requests to.","only be valid for a particular resource or set of","directory other than root of repository. type: str dockerfile: description:","resources allowed. type: complex contains: str, str requests: description: -","variables. If a variable cannot be resolved, the reference in","The helper failed to init, so there is no module","type: str name: description: - Name of the referent. type:","runs. Later, when the script finishes, all files injected will","as a result of an explicit client build creation. type:","is the build context and an optional Dockerfile may be","UID is the unique in time and space value for","from one build into a separate runtime image.' type: complex","last_version: description: - lastVersion is used to inform about number","when building images to reduce the final size or time","unrecognized values. type: str kind: description: - Kind is a","of the referent. type: str resource_version: description: - Specific resourceVersion","- Provide a path to a file containing the YAML","be unchanged. The $(VAR_NAME) syntax can be escaped with a","will be injected into a container where the assemble script","the git repository over https type: str no_proxy: description: -","to load the default configuration file from I(~/.kube/config.json). type: path","be more than one managing controller. type: list contains: api_version:","output describes the Docker image the Strategy should produce. type:","removed, and no failing result is set, the initializers struct","objects have changed. May be used for optimistic concurrency, change","Docker build API. The experimental policy 'SkipLayers' will avoid commiting","used to validate requests. type: str image_change: description: - imageChange","Script when a shell script is appropriate to execute the","not have `/bin/sh`, use Command and/or Args. The `-i` flag","authenticate with the API. type: path kubeconfig: description: - Path","of: \"Success\" or \"Failure\".' type: str labels: description: - Map","policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but simply warns","need to be set. type: complex contains: name: description: -","defines the name of the label. It must have non-zero","specified, the From image from the build strategy will be","the last pending initializer is removed, and no failing result","input. The binary is generally assumed to be a tar,","- source describes the SCM in use. type: complex contains:","a list of arguments that are provided to either Command,","description: - API version of the referent. type: str block_owner_deletion:","syntax is chosen only to have some well-defined way of","the parameters to the Docker build strategy. type: complex contains:","- noCache if set to true indicates that the docker","on a value like: \"spec.containers{name}\" (where \"name\" refers to the","the maximum amount of compute resources allowed. type: complex contains:","- dockerStrategy holds the parameters to the Docker build strategy.","with other options like git - in those cases the","DeletionTimestamp is RFC 3339 date and time at which this","be updated. In CamelCase. type: str message: description: - A","completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in seconds,","that must execute in order before this object is visible.","built. When this option is specified, the FROM may be","the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc:","server's SSL certificates. type: bool requirements: - openshift == 0.3.3","- A machine-readable description of why this operation is in","of seconds allowed for this object to gracefully terminate before","any schema except that defined by the reason type. type:","list of source/destination pairs that will be copied from the","generation of an appropriate name automatically. Name is primarily intended","builds to retain. If not specified, all successful builds are","above. For Source and Docker builds, if binary.asFile is set","the YAML definition of the object. Mutually exclusive with I(resource_definition).","list. Once it is empty, it may not be modified","- imageLabels define a list of labels that are applied","Dockerfile. See type: list contains: name: description: - Name of","deletionTimestamp of the object is non-nil, entries in this list","system. Read-only. type: str reason: description: - A machine-readable description","or directory inside the image to copy to the build","not set anywhere right now and apiserver is going to","may differ from the requested resource Kind. type: str name:","included in the build pod type: list contains: mount_path: description:","field is set by the server when a graceful deletion","email: description: - email of the source control user type:","be more convenient. type: list contains: str script: description: -","of initializers that must execute in order before this object","retried. type: int uid: description: - UID of the resource.","have some well-defined way of referencing a part of an","if an object should be created, patched, or deleted. When","''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a","the strategy. For Docker builds, this is the build context","description: - If true, this reference points to the managing","which the docker image should be pulled type: complex contains:","specified. type: complex contains: api_version: description: - API version of","type: str dockerfile: description: - dockerfile is the raw contents","any relevant information about a build config type: complex contains:","FieldPath is written in terms of, defaults to \"v1\". type:","allowed in a build configuration. type: complex contains: api_version: description:","type of build trigger type: str status: description: - status","uninitialized and is hidden (in list/watch and get calls) from","str name: description: - The name attribute of the resource","is not set anywhere right now and apiserver is going","in merging lists and updating dictionaries, with lists being merged","deleted prior to this time. For example, a user may","github contains the parameters for a GitHub webhook type of","description: - BitbucketWebHook contains the parameters for a Bitbucket webhook","that is used to run an application without unneeded dependencies","generation: description: - A sequence number representing a specific generation","build source type: complex contains: author: description: - author is","additional ENV stanzas from your strategy environment will be added","be used for setting\\ \\ up the authentication for cloning","generic contains the parameters for a Generic webhook type of","description: - Selects a key of a secret in the","- UID of the referent. type: str build_api_version: description: -","verify the API server's SSL certificates. type: bool requirements: -","value unique on the server. If this field is specified","- secret is a reference to an existing secret that","managed by a controller, then an entry in this list","initialized. Otherwise, the object is considered uninitialized and is hidden","This field is a list of initializers that have not","located, so users can ADD or COPY them during docker","str field_path: description: - Path of the field to select","runtime image. sourcePath can be a file or directory. destinationDir","description: - generic contains the parameters for a Generic webhook","used with other options like git - in those cases","operation. type: str metadata: description: - Standard list metadata. type:","created at the destination. type: str runtime_image: description: - 'runtimeImage","description: - imageOptimizationPolicy describes what optimizations the system can use","the context (contextDir). type: str env: description: - env contains","the commit hash identifying a specific commit type: str committer:","controller should configure the build pod to always pull the","explicit client build creation. type: list contains: bitbucket: description: -","a unique name ONLY IF the Name field has not","$(VAR_NAME) are expanded using the previous defined environment variables in","that would be used for setting\\ \\ up the authentication","image, relative to the root of the context (contextDir). type:","treated as opaque by clients and passed unmodified back to","the source control user type: str commit: description: - commit","back to the server. They may only be valid for","-*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module:","the shell. E.g., in the Ruby image, this is necessary","uid: description: - UID of the referent. type: str env:","remove the pod from the API. In the presence of","uid: description: - UID of the resource. (when there is","time and space value for this object. It is typically","specifies the sub-directory where the source code for the application","Defaults to false. To set this field, a user needs","to select. type: str name: description: - Name of the","this status, 0 if not set. type: int details: description:","description: - failedBuildsHistoryLimit is the number of old failed builds","(and build Docker images) from inside the Docker container. type:","When the last pending initializer is removed, and no failing","building of the application is still done in the builder","uid: description: - UID of the referent. type: str last_triggered_image_id:","to change on PUT operations. Populated by the system. Read-only.","this object. If ALL objects in the list have been","build pod type: list contains: mount_path: description: - mountPath is","str retry_after_seconds: description: - If specified, the time in seconds","cannot be preserved. type: str no_cache: description: - noCache if","validate requests. type: str image_change: description: - imageChange contains parameters","in terms of, defaults to \"v1\". type: str field_path: description:","standard input. This filename may not contain slashes or be","build the Docker image, relative to the root of the","may be deleted prior to this time. For example, a","to a certificate used to authenticate with the API. type:","of string keys and values that can be used to","field_path: description: - Path of the field to select in","modules parameters intended to define object attributes. type: dict src:","Provide the YAML definition for the object, bypassing any modules","triggers are defined, a new build can only occur as","value map stored with a resource that may be set","looked for in the namespace of the build unless Namespace","as named by its JSON serialization. May include dot and","in \"items\"' type: str message: description: - A human-readable description","user. type: complex contains: pending: description: - Pending is a","which this reference is made, if any. type: str uid:","ref: description: - ref is the branch/tag/ref to build. type:","repository over http type: str https_proxy: description: - httpsProxy is","the Failure field, the object will be persisted to storage","- httpsProxy is a proxy used to reach the git","str service_account: description: - serviceAccount is the name of the","the build pod to always pull the images for the","default build nodeselector values are ignored. type: complex contains: str,","case of an ImageStreamTag, the ImageStreamTag will be looked for","this defaults to Jenkinsfile in the root of the specified","this object may still exist after this timestamp, until an","existing secret that you want to use in your build.","buildable sources in directory other than root of repository. type:","a builder container. type: list contains: name: description: - Name","git: description: - git contains optional information about git build","StatusReason (when there is a single name which can be","not present locally type: bool pull_secret: description: - pullSecret is","about git build source type: complex contains: http_proxy: description: -","the Dockerfile cannot provide compatibility with the 'None' policy. An","an explicit client build creation. type: list contains: bitbucket: description:","as initialized and visible to all clients. type: list contains:","str source: description: - source describes the SCM in use.","description: - httpsProxy is a proxy used to reach the","environment variables you want to pass into a builder container.","to pass into a builder container. type: list contains: name:","from which the docker image should be pulled the resulting","EXAMPLES = ''' ''' RETURN = ''' api_version: type: string","build can only occur as a result of an explicit","if the image does not have `/bin/sh`, use Command and/or","the referent. type: str to: description: - to defines an","StatusReasons may provide detailed causes. type: list contains: field: description:","build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the","CamelCase. type: str metadata: description: - metadata for BuildConfigList. type:","used. Only one ImageChangeTrigger with an empty From reference is","is pushed to a registry. type: complex contains: args: description:","be set to nil and the object is considered as","object creation time. This field is a list of initializers","used only for the build. type: list contains: destination_dir: description:","status StatusReason. type: str kind: description: - The kind attribute","force the replacement of lists, set the I(force) option to","Dockerfile which should be built. When this option is specified,","to \"\".' type: str value_from: description: - Source for the","are zero-indexed. Fields may appear more than once in an","type: bool force_pull: description: - forcePull describes if the controller","values are ignored. type: complex contains: str, str output: description:","API version of the referent. type: str field_path: description: -","Kubernetes config file. If not provided, and no other connection","there is no information available. type: str group: description: -","a directory with a single file. contextDir may be used","secrets: description: - secrets represents a list of secrets and","is an identifier for the responsible component that will remove","are multiple labels with the same name then the last","entrypoint. The arguments are placed immediately after the command to","If ALL objects in the list have been deleted, this","and/or Args. The `-i` flag is needed to support CentOS","the image to copy to the build directory. If the","and lists will be replaced, rather than merged. default: false","variables you want to pass into a builder container. type:","type: str type: description: - type is the kind of","expanded using the previous defined environment variables in the container","'ImageStreamTag' or 'DockerImage'. This value will be used to look","that will be copied from the builder to the runtime","- Source for the environment variable's value. Cannot be used","the API. type: path kubeconfig: description: - Path to an","verify_ssl: description: - Whether or not to verify the API","private repository. The secret\\ \\ contains valid credentials for remote","is pulled from the OpenShift registry, this field does not","schema of this representation of an object. Servers should convert","str paths: description: - paths is a list of source","experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but simply","contains: str, str output: description: - output describes the Docker","object will updated, and lists will be replaced, rather than","the images for the builder or only pull if it","-ic`. It may not be specified with Command. Use Script","str secrets: description: - secrets is a list of additional","attributes. Arrays are zero-indexed. Fields may appear more than once","must be executed with the --no-cache=true flag type: bool pull_secret:","not specified, this defaults to Jenkinsfile in the root of","description: - forcePull describes if the controller should configure the","this file is a zip, tar, or tar.gz file and","A human-readable description of the status of this operation. type:","'Dockerfile', 'Binary', or 'Images' type: str run_policy: description: - RunPolicy","when the script finishes, all files injected will be truncated","passed. This value will also be combined with a unique","description: - destinationDir is the relative directory within the build","with a double $$, ie: $$(VAR_NAME). Escaped references will never","`/bin/sh`, or if you do not want to use a","remove the entry from the list. If the deletionTimestamp of","\"default\" namespace, but \"default\" is the canonical representation. Not all","The name attribute of the resource associated with the status","be looked for in the namespace of the build unless","contains: email: description: - email of the source control user","- JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline build","entries in this list can only be removed. type: list","controller, with the controller field set to true. There cannot","to the API. cert_file: description: - Path to a certificate","description of the cause of the error. This field may","may reject unrecognized values. type: str kind: description: - Kind","In CamelCase. type: str message: description: - A human-readable description","from the key-value store until this reference is removed. Defaults","to an existing secret that you want to use in","- JenkinsfilePath is the optional path of the Jenkinsfile that","the object is non-nil, entries in this list can only","for a specific repo snapshot. This is optional. type: complex","- customStrategy holds the parameters to the Custom build strategy","to copy to the build directory. If the source path","not be modified further by any user. type: complex contains:","have not yet acted on this object. If nil or","type: str last_triggered_image_id: description: - lastTriggeredImageID is used internally by","object may still exist after this timestamp, until an administrator","contains: git: description: - Git contains information about git-based build","clients and . type: str self_link: description: - SelfLink is","when an archive is provided. Custom builds will receive this","\"default\" is the canonical representation. Not all objects are required","on this object. If nil or empty, this object has","builder container. type: list contains: name: description: - Name of","appropriate name automatically. Name is primarily intended for creation idempotence","an appropriate name automatically. Name is primarily intended for creation","not set this value. It is represented in RFC3339 form","- env contains additional environment variables you want to pass","is the path of the Dockerfile that will be used","allow a client to request the generation of an appropriate","of the underlying objects, returning a resource_version representing a consistent","str block_owner_deletion: description: - If true, AND if the owner","description: - Map of string keys and values that can","is an optional duration in seconds, counted from the time","and Docker builds, if binary.asFile is set the build will","Escaped references will never be expanded, regardless of whether the","representing a specific generation of the desired state. Populated by","description: - 'Status of the operation. One of: \"Success\" or","the specified contextDir. type: str source_strategy: description: - sourceStrategy holds","- imageChange contains parameters for an ImageChange type of trigger","the destination. type: str pull_secret: description: - pullSecret is a","file. contextDir may be used when an archive is provided.","Populated by the system. Read-only. type: str spec: description: -","optional: description: - Specify whether the Secret or it's key","type: complex contains: container_name: description: - 'Container name: required for","at the destination. type: str pull_secret: description: - pullSecret is","repository. type: str dockerfile: description: - dockerfile is the raw","why this operation is in the \"Failure\" status. If this","set of resources. Clients must treat these values as opaque","a string value representing the REST resource this object represents.","created from one build into a separate runtime image.' type:","str jenkinsfile_path: description: - JenkinsfilePath is the optional path of","all the input necessary to produce a new build, and","the responsible component that will remove the entry from the","of trigger type: complex contains: _from: description: - from is","compatibility cannot be preserved. type: str no_cache: description: - noCache","Populated by the system. Read-only. type: int initializers: description: -","type: str resource_version: description: - An opaque value that represents","currently supported.' type: complex contains: container_name: description: - 'Container name:","internal value, and may reject unrecognized values. type: str code:","would be used for setting\\ \\ up the authentication for","and space value for this object. It is typically generated","'runtimeImage is an optional image that is used to run","supported.' type: complex contains: container_name: description: - 'Container name: required","<reponame>cvicens/lab-knative #!/usr/bin/python # -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import","the description of a specific commit type: str type: description:","- author is the author of a specific commit type:","set to nil and the object is considered as initialized","to a secret to be used to pull the image","is managed by a controller, then an entry in this","from the requested resource Kind. type: str name: description: -","the registry. Each entry is an identifier for the responsible","but before the rest of your Dockerfile stanzas. The Dockerfile","description: - Name of the referent. type: str runtime_artifacts: description:","resolved, the reference in the input string will be unchanged.","or equal to \".\", in this case it just refers","object reference is to a container within a pod, this","build time. For the Source build strategy, these will be","the first array entry in \"items\"' type: str message: description:","image is committed, before it is pushed to a registry.","Path to a CA certificate used to authenticate with the","we default to \"Serial\". type: str service_account: description: - serviceAccount","within each name must be unique. An empty namespace is","been requested. Populated by the system when a graceful deletion","is no module object. All we can do is raise","used by the server, to generate a unique name ONLY","- args is a list of arguments that are provided","build, and the conditions when to trigger them. type: complex","execute in order before this object is visible. When the","'None' which means the final build image will be equivalent","resource and is not allowed to change on PUT operations.","in this pod). This syntax is chosen only to have","a pod, this would take on a value like: \"spec.containers{name}\"","must treat these values as opaque and passed unmodified back","image that is used to run an application without unneeded","valid secret key. type: str name: description: - Name of","Docker push to authentication enabled Docker Registry (or Docker Hub).","is equivalent to the \"default\" namespace, but \"default\" is the","is the name of the ServiceAccount to use to run","type: complex contains: api_version: description: - Version of the schema","build pod to always pull the images for the builder","description: - binary builds accept a binary as their input.","have buildable sources in directory other than root of repository.","Map of string keys and values that can be used","(where \"name\" refers to the name of the container that","a reference to a secret to be used to pull","a build config type: complex contains: last_version: description: - lastVersion","- Name of the referent. type: str secrets: description: -","build source type: complex contains: http_proxy: description: - httpProxy is","the system. Read-only. type: int initializers: description: - An initializer","an object instead of an entire object, this string should","An opaque value that represents the internal version of this","by the server, to generate a unique name ONLY IF","hook executed after the build output image is committed, before","type: description: - type of the build source, may be","C(True), and I(state) is C(present), an existing object will updated,","status: description: - status holds any relevant information about a","the conditions when to trigger them. type: complex contains: completion_deadline_seconds:","is a list of source and destination paths to copy","one ImageChangeTrigger with an empty From reference is allowed in","only for the build. type: list contains: destination_dir: description: -","persisted to storage and then deleted, ensuring that other clients","is a reference to a secret to be used to","but \"default\" is the canonical representation. Not all objects are","old failed builds to retain. If not specified, all failed","description: - secret is a reference to an existing secret","literal value of the label. type: str push_secret: description: -","image's default entrypoint. The arguments are placed immediately after the","\"\".' type: str value_from: description: - Source for the environment","build directory, where the Dockerfile is located, so users can","- 'Status of the operation. One of: \"Success\" or \"Failure\".'","it's key must be defined type: bool force_pull: description: -","be updated. type: str owner_references: description: - List of objects","if no container name is specified \"spec.containers[2]\" (container with index","if the Dockerfile cannot provide compatibility with the 'None' policy.","of build trigger type: str status: description: - status holds","nil and the object is considered as initialized and visible","environment variables; can only be set to true for GenericWebHook.","should retry (optionally after the time indicated in the Retry-After","specified, all failed builds are retained. type: int node_selector: description:","The structure of the source will depend on the type","repository to push to. In the case of an ImageStreamTag,","intended to define object attributes. type: dict src: description: -","The custom strategy receives this binary as standard input. This","it will either return 201 Created or 500 with Reason","ONLY IF the Name field has not been provided. If","specified and the generated name exists, the server will NOT","will be copied into the build directory, where the Dockerfile","be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is","at the destination. type: str runtime_image: description: - 'runtimeImage is","this field is used, the name returned to the client","defines the optional raw contents of a Jenkinsfile which defines","UTC. Populated by the system. Read-only. Null for lists. type:","pull the images for the builder or only pull if","JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if","Clients may not set this value. It is represented in","length. type: str value: description: - value defines the literal","available. type: str group: description: - The group attribute of","a matching I(name_) or I(type) are merged. To force the","pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.'","building the image. The default policy is 'None' which means","as `/webapp.war` for the builder. If left empty, the Docker","a controller which enforces some system invariant at object creation","same validation rules as the Name field, and may be","this object that can be used by clients to determine","encoded credentials. Supported auth methods\\ \\ are: ssh-privatekey.\" type: complex","a unique set of values. If a list contains a","field does not need to be set. type: complex contains:","field, the object will be persisted to storage and then","not been provided. If this field is used, the name","this timestamp, until an administrator or automated process can determine","- Name of the referent. type: str uid: description: -","Docker builds, this is the build context and an optional","may be set by external tools to store and retrieve","time in this field. Once set, this value may not","description: - PushSecret is the name of a Secret that","list. If the deletionTimestamp of the object is non-nil, entries","the referent. type: str field_path: description: - 'If referring to","is specified, the FROM may be modified based on your","is not empty. type: complex contains: config_map_key_ref: description: - Selects","contains a dictionary with a I(name) or I(type) attribute, a","the directory itself will not be created at the destination.","the container that triggered the event) or if no container","Specify whether the Secret or it's key must be defined","'Status of the operation. One of: \"Success\" or \"Failure\".' type:","Jenkinsfile that will be used to configure the pipeline relative","str source_secret: description: - \"sourceSecret is the name of a","of the operation. One of: \"Success\" or \"Failure\".' type: str","context: description: - The name of a context found in","optional image that is used to run an application without","def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as","key-value store until this reference is removed. Defaults to false.","helper. Logging info is written to KubeObjHelper.log default: false type:","is created, the system will populate this list with the","- Name of the referent. type: str source_secret: description: -","name ONLY IF the Name field has not been provided.","description: - Provide a username for connecting to the API.","with a I(name) or I(type) attribute, a strategic merge is","to either Command, Script or the Docker image's default entrypoint.","type: bool host: description: - Provide a URL for acessing","list contains: bitbucket: description: - BitbucketWebHook contains the parameters for","debug: description: - Enable debug output from the OpenShift helper.","by the system. Read-only. Value must be treated as opaque","whether the webhook can set environment variables; can only be","and configuration definition. Cannot be updated. type: str namespace: description:","to be set. type: complex contains: name: description: - Name","and their destinations that will be used only for the","empty there is no information available. type: str group: description:","referent. type: str to: description: - to defines an optional","when objects have changed. Value must be treated as opaque","the resulting image will be used in the FROM line","the parameters to the Custom build strategy type: complex contains:","- metadata for BuildConfigList. type: complex contains: resource_version: description: -","that can be used by clients to determine when objects","be used only for the build. type: list contains: destination_dir:","last_triggered_image_id: description: - lastTriggeredImageID is used internally by the ImageChangeController","Name of the environment variable. Must be a C_IDENTIFIER. type:","value: description: - 'Variable references $(VAR_NAME) are expanded using the","to the \"default\" namespace, but \"default\" is the canonical representation.","main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc:","when this object was created. It is not guaranteed to","image.' type: list contains: destination_dir: description: - destinationDir is the","a resource that may be set by external tools to","revision: description: - revision is the information from the source","ServiceAccount type: str source: description: - source describes the SCM","a list of secrets and their destinations that will be","may not contain slashes or be '..' or '.'. type:","one of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy:","paths: description: - paths is a list of source and","type: path ssl_ca_cert: description: - Path to a CA certificate","the secret type: complex contains: name: description: - Name of","description: - The key to select. type: str name: description:","to fields having multiple errors. Optional. Examples: \"name\" - the","requests.memory) are currently supported.' type: complex contains: container_name: description: -","by name) after the time in this field. Once set,","description: - String that identifies the server's internal version of","Name of the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts","container where the assemble script runs. Later, when the script","be unset or be set further into the future, although","optional raw contents of a Jenkinsfile which defines a Jenkins","int kind: description: - Kind is a string value representing","when objects have changed. May be used for optimistic concurrency,","prior to this time. For example, a user may request","will be included in the build pod type: list contains:","specifying \"webapp.war\" would place the provided binary as `/webapp.war` for","the Kubernetes API. key_file: description: - Path to a key","can be used by clients to determine when objects have","than the name passed. This value will also be combined","type: str value_from: description: - Source for the environment variable's","to \"1\" type: str resource: description: - 'Required: resource to","this object. If nil or empty, this object has been","OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList","name: description: - Name of the referent. type: str type:","type: str generation: description: - A sequence number representing a","a new build, and the conditions when to trigger them.","if the builder should pull the images from registry prior","of the application is still done in the builder image","a reader. type: str reason: description: - A machine-readable description","value of this field for those objects will be empty.","and after cleanup, remove the pod from the API. In","object will be persisted to storage and then deleted, ensuring","will be used only for the build. type: list contains:","used internally by the ImageChangeController to save last used image","a build. type: complex contains: custom_strategy: description: - customStrategy holds","arbitrary metadata. They are not queryable and should be preserved","is the list of domains for which the proxy should","container name is specified \"spec.containers[2]\" (container with index 2 in","- secret used to validate requests. type: str gitlab: description:","lists and updating dictionaries, with lists being merged into a","type: complex contains: str, str requests: description: - Requests describes","the status StatusReason. On some operations may differ from the","env contains additional environment variables you want to pass into","bool expose_docker_socket: description: - exposeDockerSocket will allow running Docker commands","treated as opaque by clients and . type: str self_link:","authentication for pulling the Docker images from the private Docker","patch operation results in merging lists and updating dictionaries, with","time. For the Source build strategy, these will be injected","and is not directly settable by a client. The resource","the OpenShift helper. Logging info is written to KubeObjHelper.log default:","empty From reference is allowed in a build configuration. type:","the list have been deleted, this object will be garbage","a secret to be used to pull the image from","hidden (in list/watch and get calls) from clients that haven't","referent. type: str optional: description: - Specify whether the ConfigMap","user type: str commit: description: - commit is the commit","container. type: list contains: name: description: - Name of the","name of a context found in the Kubernetes config file.","str uid: description: - UID is the unique in time","If specified, the time in seconds before the operation should","build trigger type: str status: description: - status holds any","description: - Specific resourceVersion to which this reference is made,","May include dot and postfix notation for nested attributes. Arrays","\"name\" - the field \"name\" on the current resource \"items[0].name\"","in the runtime image for use. Deprecated: This feature will","ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description:","resource or set of resources. Populated by the system. Read-only.","context dir. type: str git: description: - git contains optional","assumed to be an archive as described above. For Source","type: str resource: description: - 'Required: resource to select' type:","complex contains: str, str output: description: - output describes the","from the image are placed. type: str source_path: description: -","container that triggered the event) or if no container name","type: str run_policy: description: - RunPolicy describes how the new","type. type: complex contains: causes: description: - The Causes array","left empty, the Docker and Source build strategies assume this","the Kubernetes config file. debug: description: - Enable debug output","no information available. type: str group: description: - The group","Null for lists. type: complex contains: {} deletion_grace_period_seconds: description: -","from the registry. Each entry is an identifier for the","- destinationDir is the relative directory within the build directory","from. Must be a valid secret key. type: str name:","for an ImageChange type of trigger type: complex contains: _from:","a graceful deletion is requested by the user, and is","data associated with the reason. Each reason may define its","- If true, AND if the owner has the \"foregroundDeletion\"","- noProxy is the list of domains for which the","type: str kind: description: - Kind of the referent. type:","is set by the server when a graceful deletion is","pod gets scheduled in the system, that the build may","the Name field, and may be truncated by the length","equivalent to the \"default\" namespace, but \"default\" is the canonical","to inform about number of last triggered build. type: int","counted from the time when a build pod gets scheduled","file containing the YAML definition of the object. Mutually exclusive","number of last triggered build. type: int kind: description: -","finalizer, then the owner cannot be deleted from the key-value","will be used in the FROM line of the Dockerfile","description: - Path to a key file used to authenticate","the requested API version for the Build object serialized and","automated process can determine the resource is fully terminated. If","strategies assume this file is a zip, tar, or tar.gz","'.'. type: str context_dir: description: - contextDir specifies the sub-directory","- name of the source control user type: str message:","object, this string should contain a valid JSON/Go field access","list contains: destination_dir: description: - destinationDir is the directory where","type: str to: description: - to defines an optional location","of the build source, may be one of 'Source', 'Dockerfile',","is the type of build trigger type: str status: description:","resulting image. If there are multiple labels with the same","api_version: description: - API version of the referent. type: str","str, str cluster_name: description: - The name of the cluster","be created, patched, or deleted. When set to C(present), the","the post build hook, for example for running unit tests","builds are retained. type: int node_selector: description: - nodeSelector is","to the runtime image. sourcePath can be a file or","for setting\\ \\ up the authentication for cloning private repository.","configuration will be scheduled for execution. This is optional, if","metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains:","runtime image.' type: list contains: destination_dir: description: - destinationDir is","str value_from: description: - Source for the environment variable's value.","spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version","but simply warns if compatibility cannot be preserved. type: str","to select' type: str secret_key_ref: description: - Selects a key","the reason. Each reason may define its own extended details.","contains build arguments that will be resolved in the Dockerfile.","secret type: complex contains: name: description: - Name of the","true. type: bool pull_secret: description: - pullSecret is the name","type: list contains: api_version: description: - API version of the","- UID of the referent. type: str paths: description: -","the object is considered as initialized and visible to all","an archive is provided. Custom builds will receive this binary","resource_version representing a consistent version of the listed objects. version_added:","BuildConfig. type: complex contains: annotations: description: - Annotations is an","FROM, but before the rest of your Dockerfile stanzas. The","bool controller: description: - If true, this reference points to","will depend on the type of build to run type:","can be described). type: str kind: description: - Kind is","form and is in UTC. Populated by the system. Read-only.","is a reference to an existing secret that you want","final build image will be equivalent to an image created","to use in your build. type: complex contains: name: description:","type: str metadata: description: - metadata for BuildConfigList. type: complex","the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP,","provided. Custom builds will receive this binary as input on","str kind: description: - Kind is a string value representing","of the referent. type: str post_commit: description: - postCommit is","the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is used","up a Docker image repository to push to. In the","no_cache: description: - noCache if set to true indicates that","about a build config type: complex contains: last_version: description: -","secret used to validate requests. type: str gitlab: description: -","in UTC. Populated by the system. Read-only. Null for lists.","The Dockerfile source type may be used with other options","build may be active on a node before the system","than merged. default: false type: bool host: description: - Provide","field. Once set, this value may not be unset or","Cannot be updated. In CamelCase. type: str message: description: -","by clients to determine when objects have changed. Value must","describes if the controller should configure the build pod to","be deleted. This field is set by the server when","- sourceStrategy holds the parameters to the Source build strategy.","contains: causes: description: - The Causes array includes more details","operations. Clients may not set this value. It is represented","builds, this is the build context and an optional Dockerfile","be removed from the system. Only set when deletionTimestamp is","when updated It is optional. If no From is specified,","If a variable cannot be resolved, the reference in the","field to select in the specified API version. type: str","this build. type: complex contains: api_version: description: - API version","Docker Registry (or Docker Hub). type: complex contains: name: description:","can copy the needed artifacts in the runtime image for","validate requests. type: str github: description: - github contains the","metadata: description: - metadata for BuildConfigList. type: complex contains: resource_version:","on a node before the system actively tries to terminate","root of the specified contextDir. type: str source_strategy: description: -","the authentication method to be used and\\ \\ value is","to have buildable sources in directory other than root of","for execution. This is optional, if not specified we default","str cluster_name: description: - The name of the cluster which","description: - UID of the referent. type: str build_args: description:","control user type: str message: description: - message is the","will attempt to load the default configuration file from I(~/.kube/config.json).","will send a hard termination signal (SIGKILL) to the container","on the server. If this field is specified and the","artifacts in the runtime image for use. Deprecated: This feature","- Determines if an object should be created, patched, or","Dockerfile replaced in the context dir. type: str git: description:","env vars' type: str divisor: description: - Specifies the output","a BuildConfig. If no triggers are defined, a new build","is a controller which enforces some system invariant at object","not contain slashes or be '..' or '.'. type: str","data's key represent the authentication method to be used and\\","an existing secret that you want to use in your","the object has not been requested. Populated by the system","docker image should be pulled the resulting image will be","differ from the requested resource Kind. type: str name: description:","entry in this list will point to this controller, with","user type: str message: description: - message is the description","command to run. It may not be specified with Script.","name: description: - name of the source control user type:","str run_policy: description: - RunPolicy describes how the new build","copied into the build directory, where the Dockerfile is located,","one managing controller. type: list contains: api_version: description: - API","It may not be specified with Script. This might be","str runtime_image: description: - 'runtimeImage is an optional image that","Pending is a list of initializers that must execute in","pullSecret is the name of a Secret that would be","provided, the openshift client will attempt to load the default","type: str source: description: - source describes the SCM in","str secret_source: description: - secretSource is a reference to the","passed unmodified back to the server. Populated by the system.","description: - GenerateName is an optional prefix, used by the","will be set to nil and the object is considered","the status StatusReason (when there is a single name which","relevant information about a build config type: complex contains: last_version:","str source_path: description: - sourcePath is the absolute path of","version build_config_list: type: complex returned: when I(state) = C(present) contains:","custom_strategy: description: - customStrategy holds the parameters to the Custom","there is no module object. All we can do is","args: description: - args is a list of arguments that","str result: description: - If result is set with the","return a 409 - instead, it will either return 201","the internal version of this object that can be used","Jenkinsfile are both not specified, this defaults to Jenkinsfile in","objects. type: complex contains: str, str cluster_name: description: - The","a future release. Use ImageSource to copy binary artifacts created","kind: description: - Kind of the referent. type: str name:","to Jenkinsfile in the root of the specified contextDir. type:","that will trigger a build when updated It is optional.","secret_source: description: - secretSource is a reference to the secret","- Enable debug output from the OpenShift helper. Logging info","a piece of an object instead of an entire object,","unstructured key value map stored with a resource that may","should be retried. type: int uid: description: - UID of","the environment variable's value. Cannot be used if value is","run an application without unneeded dependencies installed. The building of","Jenkinsfile which defines a Jenkins pipeline build. type: str jenkinsfile_path:","used for setting\\ \\ up the authentication for cloning private","specified, this defaults to Jenkinsfile in the root of the","be a file or directory. destinationDir must be a directory.","Args. The `-i` flag is needed to support CentOS and","copy from the image. type: list contains: destination_dir: description: -","from the private Docker registries type: complex contains: name: description:","underlying objects, returning a resource_version representing a consistent version of","tar, or zip file depending on the strategy. For Docker","which can be described). type: str kind: description: - Kind","where the\\ \\ data's key represent the authentication method to","postCommit is a build hook executed after the build output","used to authenticate with the API. type: path context: description:","if value is not empty. type: complex contains: config_map_key_ref: description:","name: description: - Name of the referent. type: str optional:","uid: description: - UID of the referent. type: str paths:","description: - from is reference to an DockerImage, ImageStreamTag, or","example for running unit tests with `rake test`. If you","reference to a secret to be used to pull the","tools to store and retrieve arbitrary metadata. They are not","- Name of the referent. type: str type: description: -","in the input string will be unchanged. The $(VAR_NAME) syntax","a build when updated It is optional. If no From","and then deleted, ensuring that other clients can observe the","from which the docker image should be pulled type: complex","Docker commands (and build Docker images) from inside the Docker","Extended data associated with the reason. Each reason may define","variable cannot be resolved, the reference in the input string","defaults to Limits if that is explicitly specified, otherwise to","parameter values differ from the existing object's attributes, and deleted,","- Version of the schema the FieldPath is written in","list is used. type: list contains: name: description: - name","with a unique suffix. The provided value has the same","optional for env vars' type: str divisor: description: - Specifies","optional and the data returned is not guaranteed to conform","- Standard list metadata. type: complex contains: resource_version: description: -","JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline build strategy.","not set. type: int details: description: - Extended data associated","of 'ImageStreamTag' or 'DockerImage'. This value will be used to","resource_field_ref: description: - 'Selects a resource of the container: only","assume this file is a zip, tar, or tar.gz file","HTTP return code for this status, 0 if not set.","and not reachable by name) after the time in this","file. If not provided, and no other connection options are","type: complex contains: str, str name: description: - Name must","building images to reduce the final size or time spent","of the referent. type: str source_secret: description: - \"sourceSecret is","with the API. type: path context: description: - The name","referent. type: str scripts: description: - scripts is the location","lists, set the I(force) option to C(True). default: present choices:","termination signal (SIGKILL) to the container and after cleanup, remove","contains: limits: description: - Limits describes the maximum amount of","used, the name returned to the client will be different","the image from a registry If the image is pulled","build nodeselector values are ignored. type: complex contains: str, str","PATH. type: str resources: description: - resources computes resource requirements","Custom build strategy type: complex contains: _from: description: - from","409 - instead, it will either return 201 Created or","defaults to Jenkinsfile in the root of the specified contextDir.","source: description: - source describes the SCM in use. type:","syntax can be escaped with a double $$, ie: $$(VAR_NAME).","$$(VAR_NAME). Escaped references will never be expanded, regardless of whether","- Name of the environment variable. Must be a C_IDENTIFIER.","to the resulting image. If there are multiple labels with","and get calls) from clients that haven't explicitly asked to","reject unrecognized values. type: str code: description: - Suggested HTTP","is necessary to make `ruby`, `bundle` and other binaries available","Source build strategy, these will be injected into a container","str group: description: - The group attribute of the resource","from the secret should be available for the build time.","but does not override it. type: str status: description: -","type: str resources: description: - resources computes resource requirements to","description: - message is the description of a specific commit","calls) from clients that haven't explicitly asked to observe uninitialized","description: - secret used to validate requests. type: str generic:","this value. It is represented in RFC3339 form and is","description: - secretSource is a reference to the secret type:","an administrator or automated process can determine the resource is","tests with `rake test`. If you need control over the","no information available. A Reason clarifies an HTTP status code","Provide a username for connecting to the API. verify_ssl: description:","forces the Source build to do incremental builds if true.","and visible to all clients. type: list contains: name: description:","validation rules as the Name field, and may be truncated","description: - name of the process that is responsible for","domains for which the proxy should not be used type:","(contextDir). type: str env: description: - env contains additional environment","to run. It may not be specified with Script. This","0.3.3 ''' EXAMPLES = ''' ''' RETURN = ''' api_version:","- forcePull describes if the builder should pull the images","content of the directory will be copied, but the directory","in the time allotted, and the client should retry (optionally","as opaque and passed unmodified back to the server. They","this binary as standard input. This filename may not contain","should configure the build pod to always pull the images","complex contains: api_version: description: - APIVersion defines the versioned schema","reason. Each reason may define its own extended details. This","nodeselector values are ignored. type: complex contains: str, str output:","array entry in \"items\"' type: str message: description: - A","the object will be created, if it does not exist,","RunPolicy describes how the new build created from this build","unchanged. The $(VAR_NAME) syntax can be escaped with a double","your strategy environment will be added after the FROM, but","over https type: str no_proxy: description: - noProxy is the","sub-directory where the source code for the application exists. This","within the build directory where files copied from the image","clarifies an HTTP status code but does not override it.","execute the build. type: complex contains: limits: description: - Limits","the requested resource Kind. type: str name: description: - The","which to mount the secret type: str secret_source: description: -","that will be built. The structure of the source will","image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the system can","for executing the Docker push to authentication enabled Docker Registry","the image does not have `/bin/sh`, use Command and/or Args.","created, patched, or deleted. When set to C(present), the object","be shortened or the resource may be deleted prior to","within a namespace. Is required when creating resources, although some","\"items[0].name\" - the field \"name\" on the first array entry","\\ contains valid credentials for remote repository, where the\\ \\","be deleted (no longer visible from resource lists, and not","should contain a valid JSON/Go field access statement, such as","copy the needed artifacts in the runtime image for use.","specified with Command. Use Script when a shell script is","a single name which can be described). type: str retry_after_seconds:","for a particular resource or set of resources. Populated by","Value must be treated as opaque by clients and .","default: false type: bool host: description: - Provide a URL","deleted. This field is set by the server when a","that are provided to either Command, Script or the Docker","str message: description: - message is the description of a","the source that will be built. The structure of the","of whether the variable exists or not. Defaults to \"\".'","shortened or the resource may be deleted prior to this","a secret in the pod's namespace type: complex contains: key:","dockerfile_path: description: - dockerfilePath is the path of the Dockerfile","- Whether or not to verify the API server's SSL","label. It must have non-zero length. type: str value: description:","git repository over https type: str no_proxy: description: - noProxy","description: - If set to C(True), and I(state) is C(present),","If result is set with the Failure field, the object","description: - Name of the referent. type: str to: description:","used. type: list contains: name: description: - name defines the","Use Script when a shell script is appropriate to execute","in your build. type: complex contains: name: description: - Name","reject unrecognized values. type: str items: description: - items is","the referent. type: str docker_strategy: description: - dockerStrategy holds the","may appear more than once in an array of causes","in time and space value for this object. It is","does not exist, or patched, if parameter values differ from","time when a build pod gets scheduled in the system,","be set. type: complex contains: name: description: - Name of","has not been requested. Populated by the system when a","provided binary input should be considered a single file within","detailed causes. type: list contains: field: description: - 'The field","the referent. type: str uid: description: - UID of the","successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of old successful","API server's SSL certificates. type: bool requirements: - openshift ==","an HTTP status code but does not override it. type:","complex contains: custom_strategy: description: - customStrategy holds the parameters to","- Git contains information about git-based build source type: complex","requests. type: str image_change: description: - imageChange contains parameters for","creation_timestamp: description: - CreationTimestamp is a timestamp representing the server","the root of the context (contextDir). If both JenkinsfilePath &","requests to. Cannot be updated. In CamelCase. type: str metadata:","Servers should convert recognized schemas to the latest internal value,","in the Retry-After header). Applied only if Name is not","description: - spec holds all the input necessary to produce","to conform to any schema except that defined by the","into a container where the assemble script runs. Later, when","description: - resources computes resource requirements to execute the build.","key must be defined type: bool force_pull: description: - forcePull","configuration. type: complex contains: api_version: description: - API version of","str items: description: - items is a list of build","before the rest of your Dockerfile stanzas. The Dockerfile source","description: - postCommit is a build hook executed after the","amount of compute resources allowed. type: complex contains: str, str","of compute resources required. If Requests is omitted for a","uri points to the source that will be built. The","object. type: str result: description: - If result is set","be used to look up a Docker image repository to","are provided, the openshift client will attempt to load the","a Secret that would be used for setting up the","for creation idempotence and configuration definition. Cannot be updated. type:","the source control user type: str message: description: - message","run type: str images: description: - images describes a set","must be defined type: bool dockerfile_path: description: - dockerfilePath is","presented as-is to a reader. type: str reason: description: -","copy to the build directory. If the source path ends","of the referent. type: str uid: description: - UID of","metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description:","might be more convenient. type: list contains: str script: description:","setting\\ \\ up the authentication for cloning private repository. The","Name of the referent. type: str to: description: - to","empty map or a map with any values, default build","to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what","str metadata: description: - metadata for BuildConfig. type: complex contains:","description: - An initializer is a controller which enforces some","time allotted, and the client should retry (optionally after the","a URL for acessing the Kubernetes API. key_file: description: -","the unique in time and space value for this object.","description: - Pending is a list of initializers that must","uid: description: - UID of the referent. type: str scripts:","Value must be treated as opaque by clients and passed","- Provide a password for connecting to the API. Use","type: complex contains: causes: description: - The Causes array includes","Read-only. type: int initializers: description: - An initializer is a","field \"name\" on the first array entry in \"items\"' type:","description: - 'runtimeArtifacts specifies a list of source/destination pairs that","time at which this resource will be deleted. This field","directory inside the image to copy to the build directory.","all files injected will be truncated to zero length. For","service environment variables. If a variable cannot be resolved, the","type: list contains: destination_dir: description: - destinationDir is the relative","specified. type: str generation: description: - A sequence number representing","description: - Path to a certificate used to authenticate with","referring to a piece of an object instead of an","in the specified API version. type: str resource_field_ref: description: -","description: - The Causes array includes more details associated with","triggered the event) or if no container name is specified","secret used to validate requests. type: str image_change: description: -","the referent. type: str type: description: - type of build","owner cannot be deleted from the key-value store until this","file used to authenticate with the API. type: path kubeconfig:","by this build. The pod will be allowed to use","_from: description: - from is a reference to an ImageStreamTag","step, and will fail if the Dockerfile cannot provide compatibility","lists will be replaced, rather than merged. default: false type:","one in the list is used. type: list contains: name:","server on successful creation of a resource and is not","value of the label. type: str push_secret: description: - PushSecret","represents a list of secrets and their destinations that will","image_labels: description: - imageLabels define a list of labels that","bool force: description: - If set to C(True), and I(state)","StatusReason failure. Not all StatusReasons may provide detailed causes. type:","produce. type: complex contains: image_labels: description: - imageLabels define a","description: - secrets is a list of additional secrets that","- binary builds accept a binary as their input. The","context (contextDir). type: str env: description: - env contains additional","http type: str https_proxy: description: - httpsProxy is a proxy","partitions, this object may still exist after this timestamp, until","the suffix required to make the value unique on the","patched, or deleted. When set to C(present), the object will","that will be used to build the Docker image, relative","some resources may allow a client to request the generation","use a shell. In all other cases, using Script might","the exposed resources, defaults to \"1\" type: str resource: description:","name: description: - Name of the referent. type: str uid:","requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type:","source and destination paths to copy from the image. type:","multiple labels with the same name then the last one","the object reference is to a container within a pod,","images from registry prior to building. type: bool image_optimization_policy: description:","defined type: bool dockerfile_path: description: - dockerfilePath is the path","image does not have `/bin/sh`, use Command and/or Args. The","description: - Retrieve a list of build_configs. List operations provide","Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description: - Path","for GenericWebHook. type: bool secret: description: - secret used to","how to perform a build. type: complex contains: custom_strategy: description:","list of source and destination paths to copy from the","binary as `/webapp.war` for the builder. If left empty, the","repository. The secret\\ \\ contains valid credentials for remote repository,","module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a list","be a tar, gzipped tar, or zip file depending on","in the Ruby image, this is necessary to make `ruby`,","but, post build, you can copy the needed artifacts in","description: - contextDir specifies the sub-directory where the source code","module object. All we can do is raise the error.","is the raw contents of a Dockerfile which should be","- nodeSelector is a selector which must be true for","resources required. If Requests is omitted for a container, it","a set of images to be used to provide source","used if value is not empty. type: complex contains: config_map_key_ref:","when a build pod gets scheduled in the system, that","- If true, this reference points to the managing controller.","first array entry in \"items\"' type: str message: description: -","both not specified, this defaults to Jenkinsfile in the root","the parameters to the Source build strategy. type: complex contains:","not specified we default to \"Serial\". type: str service_account: description:","proxy should not be used type: str ref: description: -","image the Strategy should produce. type: complex contains: image_labels: description:","before this object is visible. When the last pending initializer","default entrypoint. The arguments are placed immediately after the command","this object. Populated by the system. Read-only. type: str uid:","lists. type: complex contains: {} deletion_grace_period_seconds: description: - Number of","If this object is managed by a controller, then an","If not set, graceful deletion of the object has not","UID of the resource. (when there is a single resource","the API. type: path state: description: - Determines if an","- the value of this field for those objects will","of the referent. type: str scripts: description: - scripts is","of an object.' type: str kind: description: - Kind of","volumes, optional for env vars' type: str divisor: description: -","select. type: str name: description: - Name of the referent.","type: complex contains: email: description: - email of the source","\\ data's key represent the authentication method to be used","Source and Docker builds, if binary.asFile is set the build","the webhook can set environment variables; can only be set","from the existing object's attributes, and deleted, if set to","that is responsible for initializing this object. type: str result:","are provided to either Command, Script or the Docker image's","up the authentication for executing the Docker push to authentication","will remove the entry from the list. If the deletionTimestamp","the list is used. type: list contains: name: description: -","push to authentication enabled Docker Registry (or Docker Hub). type:","this object. Populated by the system. Read-only. type: str '''","sending a graceful termination signal to the containers in the","controller which enforces some system invariant at object creation time.","the destination. type: str runtime_image: description: - 'runtimeImage is an","contains: str script: description: - script is a shell script","this object. It is typically generated by the server on","post build, you can copy the needed artifacts in the","replaced, rather than merged. default: false type: bool host: description:","if any. type: str uid: description: - UID of the","build to run type: str images: description: - images describes","description of why this operation is in the \"Failure\" status.","used to inform about number of last triggered build. type:","to nil and the object is considered as initialized and","image should be pulled type: complex contains: api_version: description: -","description: - to defines an optional location to push the","build will receive a directory with a single file. contextDir","to defines an optional location to push the output of","C(present), the object will be created, if it does not","Software Collections (SCL), in order to have the appropriate collections","empty. type: complex contains: config_map_key_ref: description: - Selects a key","the object belongs to. This is used to distinguish resources","an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image","scheduled for execution. This is optional, if not specified we","the build directory, where the Dockerfile is located, so users","API. type: path context: description: - The name of a","push to. In the case of an ImageStreamTag, the ImageStreamTag","E.g., in the Ruby image, this is necessary to make","this is necessary to make `ruby`, `bundle` and other binaries","the I(force) option to C(True). default: present choices: - present","The building of the application is still done in the","- SelfLink is a URL representing this object. Populated by","used to run an application without unneeded dependencies installed. The","any values, default build nodeselector values are ignored. type: complex","container, it defaults to Limits if that is explicitly specified,","30 seconds. The Kubelet will react by sending a graceful","controller: description: - If true, this reference points to the","request that a pod is deleted in 30 seconds. The","object, bypassing any modules parameters intended to define object attributes.","list can only be removed. type: list contains: str generate_name:","(SCL), in order to have the appropriate collections enabled in","scripts: description: - scripts is the location of Source scripts","for acessing the Kubernetes API. key_file: description: - Path to","explicitly specified, otherwise to an implementation-defined value. type: complex contains:","a graceful termination signal to the containers in the pod.","CreationTimestamp is a timestamp representing the server time when this","complex returned: when I(state) = C(present) contains: api_version: description: -","state: description: - Determines if an object should be created,","only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory)","it may be shortened or the resource may be deleted","is deleted in 30 seconds. The Kubelet will react by","with a resource that may be set by external tools","choices: - present - absent username: description: - Provide a","images from registry prior to building. type: bool incremental: description:","mountPath is the path at which to mount the secret","or 500 with Reason ServerTimeout indicating a unique name could","to pull the image from a registry If the image","multiple errors. Optional. Examples: \"name\" - the field \"name\" on","Docker image the Strategy should produce. type: complex contains: image_labels:","is committed, before it is pushed to a registry. type:","the Kubelet will send a hard termination signal (SIGKILL) to","treat these values as opaque and passed unmodified back to","key of the secret to select from. Must be a","a separate runtime image.' type: complex contains: api_version: description: -","certificate used to authenticate with the API. type: path context:","with the same name then the last one in the","describes the minimum amount of compute resources required. If Requests","have `/bin/sh`, or if you do not want to use","holds the parameters to the Custom build strategy type: complex","needs \"delete\" permission of the owner, otherwise 422 (Unprocessable Entity)","key must be defined type: bool expose_docker_socket: description: - exposeDockerSocket","of your Dockerfile stanzas. The Dockerfile source type may be","is explicitly specified, otherwise to an implementation-defined value. type: complex","policy 'SkipLayers' will avoid commiting new layers in between each","that triggered the event) or if no container name is","modified based on your strategy base image and additional ENV","type: complex contains: http_proxy: description: - httpProxy is a proxy","Suggested HTTP return code for this status, 0 if not","contains: resource_version: description: - String that identifies the server's internal","set in create or update request. type: str creation_timestamp: description:","it is empty, it may not be modified further by","Populated by the system when a graceful deletion is requested.","definition of the object. Mutually exclusive with I(resource_definition). type: path","contains: _from: description: - from is reference to an DockerImage,","the directory will be copied, but the directory itself will","requirements: - openshift == 0.3.3 ''' EXAMPLES = ''' '''","to build. type: str uri: description: - uri points to","- Pending is a list of initializers that must execute","I(name) or I(type) attribute, a strategic merge is performed, where","description: - Provide a password for connecting to the API.","type: list contains: str command: description: - command is the","relative to the root of the context (contextDir). type: str","There cannot be more than one managing controller. type: list","single file. contextDir may be used when an archive is","--no-cache=true flag type: bool pull_secret: description: - pullSecret is the","git - in those cases the Git repo will have","specific commit type: complex contains: email: description: - email of","referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is used internally","will avoid commiting new layers in between each image step,","suffix required to make the value unique on the server.","a resource_version representing a consistent version of the listed objects.","belongs to. This is used to distinguish resources with same","requests. type: str generic: description: - generic contains the parameters","type: bool incremental: description: - incremental flag forces the Source","name: description: - Name of the referent. type: str docker_strategy:","allowed to use secrets referenced by the ServiceAccount type: str","contains: _from: description: - from is a reference to an","- The group attribute of the resource associated with the","Source scripts type: str type: description: - type is the","haven't explicitly asked to observe uninitialized objects. When an object","error, as named by its JSON serialization. May include dot","description: - Name of the referent. type: str uid: description:","with I(resource_definition). type: path ssl_ca_cert: description: - Path to a","the API. type: path context: description: - The name of","(SIGKILL) to the container and after cleanup, remove the pod","resources: description: - resources computes resource requirements to execute the","pod created by this build. The pod will be allowed","update request. type: str creation_timestamp: description: - CreationTimestamp is a","will be added after the FROM, but before the rest","3339 date and time at which this resource will be","Populated by the system. Read-only. type: str reason: description: -","that can be used to organize and categorize (scope and","may set or modify this list. Once it is empty,","describes how the new build created from this build configuration","with the API. type: path state: description: - Determines if","I(type) are merged. To force the replacement of lists, set","will also be combined with a unique suffix. The provided","the build unless Namespace is specified. type: complex contains: api_version:","the minimum amount of compute resources required. If Requests is","separate runtime image.' type: complex contains: api_version: description: - API","in different clusters. This field is not set anywhere right","of the referent. type: str docker_strategy: description: - dockerStrategy holds","APIVersion defines the versioned schema of this representation of an","statement, such as desiredState.manifest.containers[2]. For example, if the object reference","piece of an object instead of an entire object, this","make the value unique on the server. If this field","complex contains: name: description: - Name of the referent. type:","buildArgs contains build arguments that will be resolved in the","to C(absent). A patch operation results in merging lists and","finalizers: description: - Must be empty before the object is","populate this list with the current set of initializers. Only","be run with `/bin/sh -ic`. It may not be specified","be copied into the build directory, where the Dockerfile is","cannot be deleted from the key-value store until this reference","- The name attribute of the resource associated with the","description: - UID of the referent. type: str scripts: description:","parameters to the Source build strategy. type: complex contains: _from:","only if Name is not specified. type: str generation: description:","bool force_pull: description: - forcePull describes if the controller should","distinguish resources with same name and namespace in different clusters.","the time in this field. Once set, this value may","description: - commit is the commit hash identifying a specific","- email of the source control user type: str name:","an object should be created, patched, or deleted. When set","objects are required to be scoped to a namespace -","the sub-directory where the source code for the application exists.","like: \"spec.containers{name}\" (where \"name\" refers to the name of the","resource associated with the status StatusReason. On some operations may","set of values. If a list contains a dictionary with","pending: description: - Pending is a list of initializers that","type: str metadata: description: - Standard list metadata. type: complex","failedBuildsHistoryLimit is the number of old failed builds to retain.","Secret or it's key must be defined type: bool jenkinsfile:","type: int kind: description: - Kind is a string value","installed. The building of the application is still done in","except OpenShiftAnsibleException as exc: # The helper failed to init,","opaque and passed unmodified back to the server. They may","in the list is used. type: list contains: name: description:","zero length. For the Docker build strategy, these will be","settable by a client. The resource is expected to be","object will be garbage collected. If this object is managed","exclusive with I(resource_definition). type: path ssl_ca_cert: description: - Path to","object. If ALL objects in the list have been deleted,","event) or if no container name is specified \"spec.containers[2]\" (container","is the information from the source for a specific repo","Populated by the system. Read-only. type: str self_link: description: -","the source will depend on the type of build to","to determine when objects have changed. May be used for","a URL representing this object. Populated by the system. Read-only.","a container within a pod, this would take on a","current resource \"items[0].name\" - the field \"name\" on the first","\"Failure\".' type: str labels: description: - Map of string keys","Name of the referent. type: str namespace: description: - Namespace","environment variables you want to pass into a build pipeline.","type: complex contains: image_labels: description: - imageLabels define a list","with any values, default build nodeselector values are ignored. type:","and apiserver is going to ignore it if set in","When an object is created, the system will populate this","with a matching I(name_) or I(type) are merged. To force","description: - Annotations is an unstructured key value map stored","description: - secret used to validate requests. type: str github:","objects in the list have been deleted, this object will","should pull the images from registry prior to building. type:","unmodified back to the server. Populated by the system. Read-only.","system, that the build may be active on a node","block_owner_deletion: description: - If true, AND if the owner has","to an empty map or a map with any values,","default build nodeselector values for the cluster. If set to","a client to request the generation of an appropriate name","images for the builder or only pull if it is","control user type: str commit: description: - commit is the","be executed with the --no-cache=true flag type: bool pull_secret: description:","updated. In CamelCase. type: str message: description: - A human-readable","A sequence number representing a specific generation of the desired","Must be a C_IDENTIFIER. type: str value: description: - 'Variable","a certificate used to authenticate with the API. type: path","- Namespace defines the space within each name must be","the proxy should not be used type: str ref: description:","The resource is expected to be deleted (no longer visible","Once it is empty, it may not be modified further","indicates that the provided binary input should be considered a","dictionaries, with lists being merged into a unique set of","their destinations that will be used only for the build.","- command is the command to run. It may not","canonical representation. Not all objects are required to be scoped","to be used to provide source for the build type:","description: - secret used to validate requests. type: str gitlab:","- String that identifies the server's internal version of this","control user type: str name: description: - name of the","description: - An opaque value that represents the internal version","a build pod gets scheduled in the system, that the","str secret_key_ref: description: - Selects a key of a secret","then the last one in the list is used. type:","bool kind: description: - Kind of the referent. type: str","items is a list of build configs type: list contains:","client. The resource is expected to be deleted (no longer","code: description: - Suggested HTTP return code for this status,","by the system. Read-only. type: int initializers: description: - An","If Requests is omitted for a container, it defaults to","unrecognized values. type: str items: description: - items is a","This allows to have buildable sources in directory other than","Standard list metadata. type: complex contains: resource_version: description: - String","it. type: str status: description: - 'Status of the operation.","truncated by the length of the suffix required to make","of lists, set the I(force) option to C(True). default: present","container_name: description: - 'Container name: required for volumes, optional for","field is a list of initializers that have not yet","''' RETURN = ''' api_version: type: string description: Requested API","same as 'SkipLayers' but simply warns if compatibility cannot be","are applied to the resulting image. If there are multiple","type: str name: description: - name of the source control","be resolved in the Dockerfile. See type: list contains: name:","with the reason. Each reason may define its own extended","the owner has the \"foregroundDeletion\" finalizer, then the owner cannot","- status holds any relevant information about a build config","the time allotted, and the client should retry (optionally after","the Retry-After header). Applied only if Name is not specified.","or \"Failure\".' type: str labels: description: - Map of string","different than the name passed. This value will also be","dictionary with a I(name) or I(type) attribute, a strategic merge","this list. Once it is empty, it may not be","is a selector which must be true for the build","this controller, with the controller field set to true. There","which the object belongs to. This is used to distinguish","server will NOT return a 409 - instead, it will","these values as opaque and passed unmodified back to the","str build_api_version: description: - buildAPIVersion is the requested API version","as input on STDIN. type: complex contains: as_file: description: -","exists. This allows to have buildable sources in directory other","images) from inside the Docker container. type: bool force_pull: description:","docker build must be executed with the --no-cache=true flag type:","the cause of the error. This field may be presented","build output image is committed, before it is pushed to","binary as standard input. This filename may not contain slashes","description: - Name of the referent. type: str docker_strategy: description:","type: str labels: description: - Map of string keys and","strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number","when deletionTimestamp is also set. May only be shortened. Read-only.","all successful builds are retained. type: int triggers: description: -","parameters for an ImageChange type of trigger type: complex contains:","resources, defaults to \"1\" type: str resource: description: - 'Required:","an identifier for the responsible component that will remove the","header). Applied only if Name is not specified. type: str","of build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is","is 'None' which means the final build image will be","enabled in the shell. E.g., in the Ruby image, this","nodeSelector is a selector which must be true for the","a pod is deleted in 30 seconds. The Kubelet will","of a context found in the Kubernetes config file. debug:","an ImageStreamTag, the ImageStreamTag will be looked for in the","performed, where individual elements with a matching I(name_) or I(type)","- Number of seconds allowed for this object to gracefully","2.3.0 author: OpenShift (@openshift) options: api_key: description: - Token used","- Token used to connect to the API. cert_file: description:","information about git build source type: complex contains: http_proxy: description:","connecting to the API. verify_ssl: description: - Whether or not","status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of","Provide a URL for acessing the Kubernetes API. key_file: description:","the image doesn't have `/bin/sh`, or if you do not","lists being merged into a unique set of values. If","an entry in this list will point to this controller,","build hook, for example for running unit tests with `rake","A human-readable description of the cause of the error. This","defaults to \"v1\". type: str field_path: description: - Path of","the server will NOT return a 409 - instead, it","false type: bool host: description: - Provide a URL for","description: - secret used to validate requests. type: str image_change:","the key-value store until this reference is removed. Defaults to","error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\"Module","should be pulled type: complex contains: api_version: description: - API","the deletion. type: complex contains: api_version: description: - APIVersion defines","str kind: description: - The kind attribute of the resource","in conjunction with I(username). resource_definition: description: - Provide the YAML","from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list","collected. If this object is managed by a controller, then","stanzas from your strategy environment will be added after the","the type of build trigger type: str status: description: -","build strategy. This strategy is in tech preview. type: complex","build created from this build configuration will be scheduled for","Cannot be used if value is not empty. type: complex","of secrets and their destinations that will be used only","signal (SIGKILL) to the container and after cleanup, remove the","the server when a graceful deletion is requested by the","build type: list contains: _from: description: - from is a","(@openshift) options: api_key: description: - Token used to connect to","- If set to C(True), and I(state) is C(present), an","- absent username: description: - Provide a username for connecting","external tools to store and retrieve arbitrary metadata. They are","str generic: description: - generic contains the parameters for a","not yet acted on this object. If nil or empty,","specifies a list of source/destination pairs that will be copied","you want to use in your build. type: complex contains:","resources. Populated by the system. Read-only. Value must be treated","deleted from the key-value store until this reference is removed.","vars' type: str divisor: description: - Specifies the output format","Not all StatusReasons may provide detailed causes. type: list contains:","set of images to be used to provide source for","httpProxy is a proxy used to reach the git repository","Reason ServerTimeout indicating a unique name could not be found","for example for running unit tests with `rake test`. If","'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy: description: -","without unneeded dependencies installed. The building of the application is","of initializers that have not yet acted on this object.","'v1') except OpenShiftAnsibleException as exc: # The helper failed to","complex contains: image_labels: description: - imageLabels define a list of","description: - name of the source control user type: str","key: description: - The key to select. type: str name:","STDIN. type: complex contains: as_file: description: - asFile indicates that","for remote repository, where the\\ \\ data's key represent the","This field is optional and the data returned is not","building. type: bool incremental: description: - incremental flag forces the","that other clients can observe the deletion. type: complex contains:","- Suggested HTTP return code for this status, 0 if","objects depended by this object. If ALL objects in the","name automatically. Name is primarily intended for creation idempotence and","no container name is specified \"spec.containers[2]\" (container with index 2","guaranteed to conform to any schema except that defined by","string keys and values that can be used to organize","enforces some system invariant at object creation time. This field","when a graceful deletion is requested. Read-only. type: complex contains:","to an implementation-defined value. type: complex contains: str, str revision:","contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in","pulled the resulting image will be used in the FROM","serialization. May include dot and postfix notation for nested attributes.","is no information available. type: str group: description: - The","name of the label. It must have non-zero length. type:","value will be used to look up a Docker image","Kind is a string value representing the REST resource this","spec holds all the input necessary to produce a new","to select from. Must be a valid secret key. type:","this list can only be removed. type: list contains: str","and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.'","apiserver is going to ignore it if set in create","dependencies installed. The building of the application is still done","- Name must be unique within a namespace. Is required","'Selects a resource of the container: only resources limits and","type: bool secret: description: - secret used to validate requests.","the builder or only pull if it is not present","_from: description: - from is a reference to an ImageStreamTag,","can only be set to true for GenericWebHook. type: bool","it is pushed to a registry. type: complex contains: args:","image are placed. type: str source_path: description: - sourcePath is","input. This filename may not contain slashes or be '..'","not to verify the API server's SSL certificates. type: bool","type: dict src: description: - Provide a path to a","are ignored. type: complex contains: str, str output: description: -","- secrets represents a list of secrets and their destinations","definition. Cannot be updated. type: str namespace: description: - Namespace","use. Deprecated: This feature will be removed in a future","the build. type: list contains: destination_dir: description: - destinationDir is","more than one managing controller. type: list contains: api_version: description:","be used to configure the pipeline relative to the root","Docker images from the private Docker registries type: complex contains:","ID for build type: str type: description: - type is","description: - status holds any relevant information about a build","of the referent. type: str build_args: description: - buildArgs contains","list of domains for which the proxy should not be","str reason: description: - A machine-readable description of the cause","to run type: str images: description: - images describes a","HTTP status code but does not override it. type: str","registry prior to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy","raise the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as","archive as described above. For Source and Docker builds, if","Name of the referent. type: str docker_strategy: description: - dockerStrategy","initializers that have not yet acted on this object. If","you do not want to use a shell. In all","values. type: str kind: description: - Kind is a string","metadata for BuildConfig. type: complex contains: annotations: description: - Annotations","strategy type: complex contains: _from: description: - from is reference","proxy used to reach the git repository over http type:","then an entry in this list will point to this","- Jenkinsfile defines the optional raw contents of a Jenkinsfile","equivalent to an image created by the Docker build API.","the resulting image. If there are multiple labels with the","the files from the secret should be available for the","image from a registry If the image is pulled from","image. If there are multiple labels with the same name","time spent building the image. The default policy is 'None'","build source, may be one of 'Source', 'Dockerfile', 'Binary', or","type: str result: description: - If result is set with","image, this is necessary to make `ruby`, `bundle` and other","a proxy used to reach the git repository over https","modify this list. Once it is empty, it may not","is needed to support CentOS and RHEL images that use","to store and retrieve arbitrary metadata. They are not queryable","if set in create or update request. type: str creation_timestamp:","expanded, regardless of whether the variable exists or not. Defaults","build pipeline. type: list contains: name: description: - Name of","is specified, the From image from the build strategy will","a build configuration. type: complex contains: api_version: description: - API","pulling the Docker images from the private Docker registries type:","the ServiceAccount type: str source: description: - source describes the","Defaults to \"\".' type: str value_from: description: - Source for","of build_configs. List operations provide a snapshot read of the","exist, or patched, if parameter values differ from the existing","read of the underlying objects, returning a resource_version representing a","from the OpenShift registry, this field does not need to","of the file or directory inside the image to copy","- from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage","The binary is generally assumed to be a tar, gzipped","command is the command to run. It may not be","not been requested. Populated by the system when a graceful","commit is the commit hash identifying a specific commit type:","itself will not be created at the destination. type: str","may be truncated by the length of the suffix required","builds, if binary.asFile is set the build will receive a","to building. type: bool incremental: description: - incremental flag forces","{} deletion_grace_period_seconds: description: - Number of seconds allowed for this","be unique within a namespace. Is required when creating resources,","not queryable and should be preserved when modifying objects. type:","list of arguments that are provided to either Command, Script","context_dir: description: - contextDir specifies the sub-directory where the source","be run. type: list contains: str command: description: - command","referent. type: str post_commit: description: - postCommit is a build","import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift","the path of the Dockerfile that will be used to","the file or directory inside the image to copy to","a CA certificate used to authenticate with the API. type:","type: str optional: description: - Specify whether the Secret or","objects. When an object is created, the system will populate","this is assumed to be an archive as described above.","to C(True), and I(state) is C(present), an existing object will","password: description: - Provide a password for connecting to the","scoped to a namespace - the value of this field","of the resource associated with the status StatusReason. On some","str resource_field_ref: description: - 'Selects a resource of the container:","source_strategy: description: - sourceStrategy holds the parameters to the Source","the custom builder type: str env: description: - env contains","client build creation. type: list contains: bitbucket: description: - BitbucketWebHook","this operation. type: str metadata: description: - Standard list metadata.","- Name of the referent. type: str jenkins_pipeline_strategy: description: -","is assumed to be an archive as described above. For","name defines the name of the label. It must have","by the system. Read-only. type: str uid: description: - UID","This field is set by the server when a graceful","the controller should configure the build pod to always pull","- Selects a key of a secret in the pod's","relative to the root of the context (contextDir). If both","does not have `/bin/sh`, use Command and/or Args. The `-i`","this is the build context and an optional Dockerfile may","own extended details. This field is optional and the data","the runtime image. sourcePath can be a file or directory.","applied to the resulting image. If there are multiple labels","pod is deleted in 30 seconds. The Kubelet will react","a list of source and destination paths to copy from","This is optional. type: complex contains: git: description: - Git","from the API. In the presence of network partitions, this","will be returned. type: bool controller: description: - If true,","env: description: - env contains additional environment variables you want","a user needs \"delete\" permission of the owner, otherwise 422","more than once in an array of causes due to","to terminate the build; value must be positive integer type:","to push the output of this build to. Kind must","must be defined type: bool force_pull: description: - forcePull describes","contains: {} deletion_grace_period_seconds: description: - Number of seconds allowed for","be empty. Must be a DNS_LABEL. Cannot be updated. type:","the build pod to fit on a node If nil,","the referent. type: str name: description: - Name of the","to the name of the container that triggered the event)","and extract it as the source. The custom strategy receives","to all clients. type: list contains: name: description: - name","\\ are: ssh-privatekey.\" type: complex contains: name: description: - Name","Read-only. type: complex contains: {} finalizers: description: - Must be","this value is empty there is no information available. type:","complex contains: author: description: - author is the author of","and namespace in different clusters. This field is not set","paths to copy from the image. type: list contains: destination_dir:","str metadata: description: - Standard list metadata. type: complex contains:","will be removed from the system. Only set when deletionTimestamp","build. type: int kind: description: - Kind is a string","str creation_timestamp: description: - CreationTimestamp is a timestamp representing the","some well-defined way of referencing a part of an object.'","If no triggers are defined, a new build can only","does not override it. type: str status: description: - 'Status","of compute resources allowed. type: complex contains: str, str requests:","of the referent. type: str secrets: description: - secrets is","the system. Read-only. Null for lists. type: complex contains: {}","the build source, may be one of 'Source', 'Dockerfile', 'Binary',","or 'Images' type: str run_policy: description: - RunPolicy describes how","value defines the literal value of the label. type: str","- The kind attribute of the resource associated with the","your strategy base image and additional ENV stanzas from your","clients. type: list contains: name: description: - name of the","the ImageStreamTag will be looked for in the namespace of","be allowed to use secrets referenced by the ServiceAccount type:","the context (contextDir). If both JenkinsfilePath & Jenkinsfile are both","made, if any. type: str uid: description: - UID of","- items is a list of build configs type: list","request. type: str creation_timestamp: description: - CreationTimestamp is a timestamp","- Specify whether the ConfigMap or it's key must be","system will populate this list with the current set of","contains: allow_env: description: - allowEnv determines whether the webhook can"],"string":"[\n \"finishes, all files injected will be truncated to zero length.\",\n \"is specified \\\"spec.containers[2]\\\" (container with index 2 in this pod).\",\n \"be set by external tools to store and retrieve arbitrary\",\n \"sourceStrategy holds the parameters to the Source build strategy. type:\",\n \"30 seconds, the Kubelet will send a hard termination signal\",\n \"into a separate runtime image.' type: complex contains: api_version: description:\",\n \"type: list contains: str generate_name: description: - GenerateName is an\",\n \"201 Created or 500 with Reason ServerTimeout indicating a unique\",\n \"- lastTriggeredImageID is used internally by the ImageChangeController to save\",\n \"state. Populated by the system. Read-only. type: int initializers: description:\",\n \"- Kind of the referent. type: str name: description: -\",\n \"Dockerfile cannot provide compatibility with the 'None' policy. An additional\",\n \"name of the container that triggered the event) or if\",\n \"author: OpenShift (@openshift) options: api_key: description: - Token used to\",\n \"Path of the field to select in the specified API\",\n \"dict src: description: - Provide a path to a file\",\n \"exist after this timestamp, until an administrator or automated process\",\n \"this time. For example, a user may request that a\",\n \"the namespace of the build unless Namespace is specified. type:\",\n \"For the Source build strategy, these will be injected into\",\n \"Read-only. type: str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list',\",\n \"can observe the deletion. type: complex contains: api_version: description: -\",\n \"and postfix notation for nested attributes. Arrays are zero-indexed. Fields\",\n \"type: list contains: name: description: - name defines the name\",\n \"type: str secret_source: description: - secretSource is a reference to\",\n \"the build context. For Source builds, this is assumed to\",\n \"referent. type: str resource_version: description: - Specific resourceVersion to which\",\n \"of the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies\",\n \"may be active on a node before the system actively\",\n \"the referent. type: str resource_version: description: - An opaque value\",\n \"type: list contains: _from: description: - from is a reference\",\n \"pipeline. type: list contains: name: description: - Name of the\",\n \"a selector which must be true for the build pod\",\n \"by the reason type. type: complex contains: causes: description: -\",\n \"be persisted to storage and then deleted, ensuring that other\",\n \"Name field, and may be truncated by the length of\",\n \"prior to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes\",\n \"the ConfigMap or it's key must be defined type: bool\",\n \"to true indicates that the docker build must be executed\",\n \"optional. type: complex contains: git: description: - Git contains information\",\n \"list of build configs type: list contains: api_version: description: -\",\n \"OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc)) if __name__ == '__main__':\",\n \"by the length of the suffix required to make the\",\n \"a double $$, ie: $$(VAR_NAME). Escaped references will never be\",\n \"the pod created by this build. The pod will be\",\n \"by the server when a graceful deletion is requested by\",\n \"responsible for initializing this object. type: str result: description: -\",\n \"all StatusReasons may provide detailed causes. type: list contains: field:\",\n \"with the API. type: path kubeconfig: description: - Path to\",\n \"string will be unchanged. The $(VAR_NAME) syntax can be escaped\",\n \"to the client will be different than the name passed.\",\n \"server. They may only be valid for a particular resource\",\n \"launched from a BuildConfig. If no triggers are defined, a\",\n \"present - absent username: description: - Provide a username for\",\n \"clients and passed unmodified back to the server. Populated by\",\n \"zero-indexed. Fields may appear more than once in an array\",\n \"if the owner has the \\\"foregroundDeletion\\\" finalizer, then the owner\",\n \"from the OpenShift helper. Logging info is written to KubeObjHelper.log\",\n \"the openshift client will attempt to load the default configuration\",\n \"the status StatusReason. type: str kind: description: - The kind\",\n \"is a reference to an ImageStreamTag that will trigger a\",\n \"image is pulled from the OpenShift registry, this field does\",\n \"not guaranteed to conform to any schema except that defined\",\n \"be used to organize and categorize (scope and select) objects.\",\n \"with Command. Use Script when a shell script is appropriate\",\n \"contextDir. type: str source_strategy: description: - sourceStrategy holds the parameters\",\n \"or be '..' or '.'. type: str context_dir: description: -\",\n \"UID of the referent. type: str build_api_version: description: - buildAPIVersion\",\n \"be created, if it does not exist, or patched, if\",\n \"secret: description: - secret used to validate requests. type: str\",\n \"on a node If nil, it can be overridden by\",\n \"a directory. destinationDir can also be empty or equal to\",\n \"a graceful deletion is requested. Read-only. type: complex contains: {}\",\n \"optimistic concurrency, change detection, and the watch operation on a\",\n \"to a piece of an object instead of an entire\",\n \"value must be positive integer type: int failed_builds_history_limit: description: -\",\n \"description: - script is a shell script to be run\",\n \"maximum amount of compute resources allowed. type: complex contains: str,\",\n \"value, and may reject unrecognized values. type: str code: description:\",\n \"is a build hook executed after the build output image\",\n \"(when there is a single resource which can be described).\",\n \"Dockerfile source type may be used with other options like\",\n \"defined, a new build can only occur as a result\",\n \"the API. Use in conjunction with I(username). resource_definition: description: -\",\n \"type: str reason: description: - A machine-readable description of why\",\n \"order before this object is visible. When the last pending\",\n \"- images describes a set of images to be used\",\n \"runtime image for use. Deprecated: This feature will be removed\",\n \"strategy is in tech preview. type: complex contains: env: description:\",\n \"unless Namespace is specified. type: complex contains: api_version: description: -\",\n \"image entrypoint, or if the image does not have `/bin/sh`,\",\n \"additional environment variables you want to pass into a builder\",\n \"minimum amount of compute resources required. If Requests is omitted\",\n \"description: - Whether or not to verify the API server's\",\n \"all objects are required to be scoped to a namespace\",\n \"this resource will be deleted. This field is set by\",\n \"the entry from the list. If the deletionTimestamp of the\",\n \"will be allowed to use secrets referenced by the ServiceAccount\",\n \"any innate Dockerfile replaced in the context dir. type: str\",\n \"from the build strategy will be used. Only one ImageChangeTrigger\",\n \"to accept type: str strategy: description: - strategy defines how\",\n \"the referent. type: str secrets: description: - secrets represents a\",\n \"receive this binary as input on STDIN. type: complex contains:\",\n \"username: description: - Provide a username for connecting to the\",\n \"should convert recognized schemas to the latest internal value, and\",\n \"after the time indicated in the Retry-After header). Applied only\",\n \"list contains a dictionary with a I(name) or I(type) attribute,\",\n \"terminate the build; value must be positive integer type: int\",\n \"resulting image will be used in the FROM line of\",\n \"- dockerfile is the raw contents of a Dockerfile which\",\n \"in those cases the Git repo will have any innate\",\n \"appropriate collections enabled in the shell. E.g., in the Ruby\",\n \"\\\\ up the authentication for cloning private repository. The secret\\\\\",\n \"set to an empty map or a map with any\",\n \"not provided, and no other connection options are provided, the\",\n \"is typically generated by the server on successful creation of\",\n \"- secrets is a list of additional secrets that will\",\n \"a valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For\",\n \"directory. destinationDir must be a directory. destinationDir can also be\",\n \"to reach the git repository over https type: str no_proxy:\",\n \"command to be run. type: list contains: str command: description:\",\n \"str status: description: - status holds any relevant information about\",\n \"container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu and\",\n \"be deleted from the key-value store until this reference is\",\n \"to have some well-defined way of referencing a part of\",\n \"as opaque by clients and . type: str self_link: description:\",\n \"for setting up the authentication for pulling the Docker images\",\n \"output image is committed, before it is pushed to a\",\n \"path ends in /. then the content of the directory\",\n \"a part of an object.' type: str kind: description: -\",\n \"description: - source describes the SCM in use. type: complex\",\n \"name which can be described). type: str retry_after_seconds: description: -\",\n \"the system will populate this list with the current set\",\n \"space within each name must be unique. An empty namespace\",\n \"scripts type: str type: description: - type is the kind\",\n \"an ImageChange type of trigger type: complex contains: _from: description:\",\n \"lists, and not reachable by name) after the time in\",\n \"registry If the image is pulled from the OpenShift registry,\",\n \"into the future, although it may be shortened or the\",\n \"resource_version: description: - String that identifies the server's internal version\",\n \"description: - APIVersion defines the versioned schema of this representation\",\n \"the images from registry prior to building. type: bool incremental:\",\n \"type: str github: description: - github contains the parameters for\",\n \"use. type: complex contains: binary: description: - binary builds accept\",\n \"a proxy used to reach the git repository over http\",\n \"presence of network partitions, this object may still exist after\",\n \"specific generation of the desired state. Populated by the system.\",\n \"escaped with a double $$, ie: $$(VAR_NAME). Escaped references will\",\n \"be copied from the builder to the runtime image. sourcePath\",\n \"- The name of the cluster which the object belongs\",\n \"arguments are placed immediately after the command to be run.\",\n \"used to build the Docker image, relative to the root\",\n \"the Git repo will have any innate Dockerfile replaced in\",\n \"email of the source control user type: str name: description:\",\n \"not exist, or patched, if parameter values differ from the\",\n \"of Source scripts type: str type: description: - type is\",\n \"from I(~/.kube/config.json). type: path password: description: - Provide a password\",\n \"to C(present), the object will be created, if it does\",\n \"defined type: bool field_ref: description: - 'Selects a field of\",\n \"destinations that will be used only for the build. type:\",\n \"may not be modified further by any user. type: complex\",\n \"type: str type: description: - type of build input to\",\n \"just refers to the root of WORKDIR. Deprecated: This feature\",\n \"that would be used for setting up the authentication for\",\n \"that the provided binary input should be considered a single\",\n \"root of WORKDIR. Deprecated: This feature will be removed in\",\n \"is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which\",\n \"suffix. The provided value has the same validation rules as\",\n \"selector which must be true for the build pod to\",\n \"uid: description: - UID is the unique in time and\",\n \"(optionally after the time indicated in the Retry-After header). Applied\",\n \"force_pull: description: - forcePull describes if the controller should configure\",\n \"the parameters to the Jenkins Pipeline build strategy. This strategy\",\n \"last triggered build. type: int kind: description: - Kind is\",\n \"the event) or if no container name is specified \\\"spec.containers[2]\\\"\",\n \"an optional prefix, used by the server, to generate a\",\n \"as the Name field, and may be truncated by the\",\n \"resource that has caused this error, as named by its\",\n \"is empty there is no information available. A Reason clarifies\",\n \"binary artifacts created from one build into a separate runtime\",\n \"description: - 'The field of the resource that has caused\",\n \"type: str source_secret: description: - \\\"sourceSecret is the name of\",\n \"creation idempotence and configuration definition. Cannot be updated. type: str\",\n \"back to the server. Populated by the system. Read-only. type:\",\n \"replication controllers and services. type: complex contains: str, str name:\",\n \"Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is RFC 3339\",\n \"type: str block_owner_deletion: description: - If true, AND if the\",\n \"the resource. (when there is a single resource which can\",\n \"type: str committer: description: - committer is the committer of\",\n \"an ImageStreamTag, ImageStreamImage, or DockerImage to copy source from. type:\",\n \"then deleted, ensuring that other clients can observe the deletion.\",\n \"automatically. Name is primarily intended for creation idempotence and configuration\",\n \"pod to fit on a node If nil, it can\",\n \"the root of WORKDIR. Deprecated: This feature will be removed\",\n \"forcePull describes if the controller should configure the build pod\",\n \"conform to any schema except that defined by the reason\",\n \"set to true for GenericWebHook. type: bool secret: description: -\",\n \"this value may not be unset or be set further\",\n \"must have non-zero length. type: str value: description: - value\",\n \"initializers that must execute in order before this object is\",\n \"an optional location to push the output of this build\",\n \"to an ImageStreamTag that will trigger a build when updated\",\n \"use secrets referenced by the ServiceAccount type: str source: description:\",\n \"build strategy will be used. Only one ImageChangeTrigger with an\",\n \"Name must be unique within a namespace. Is required when\",\n \"was created. It is not guaranteed to be set in\",\n \"stanzas. The Dockerfile source type may be used with other\",\n \"a zip, tar, or tar.gz file and extract it as\",\n \"make `ruby`, `bundle` and other binaries available in the PATH.\",\n \"used to organize and categorize (scope and select) objects. May\",\n \"of the object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert:\",\n \"clients can observe the deletion. type: complex contains: api_version: description:\",\n \"docker image should be pulled type: complex contains: api_version: description:\",\n \"or automated process can determine the resource is fully terminated.\",\n \"object is visible. When the last pending initializer is removed,\",\n \"type: complex contains: args: description: - args is a list\",\n \"Supported auth methods\\\\ \\\\ are: ssh-privatekey.\\\" type: complex contains: name:\",\n \"description: - triggers determine how new Builds can be launched\",\n \"Token used to connect to the API. cert_file: description: -\",\n \"methods\\\\ \\\\ are: ssh-privatekey.\\\" type: complex contains: name: description: -\",\n \"ImageChange type of trigger type: complex contains: _from: description: -\",\n \"- paths is a list of source and destination paths\",\n \"before it will be removed from the system. Only set\",\n \"as standard input. This filename may not contain slashes or\",\n \"Name is not specified. type: str generation: description: - A\",\n \"be true for the build pod to fit on a\",\n \"strategy. This strategy is in tech preview. type: complex contains:\",\n \"completely initialized. Otherwise, the object is considered uninitialized and is\",\n \"exposed resources, defaults to \\\"1\\\" type: str resource: description: -\",\n \"graceful deletion is requested. Read-only. type: complex contains: {} finalizers:\",\n \"Examples: \\\"name\\\" - the field \\\"name\\\" on the current resource\",\n \"type: path state: description: - Determines if an object should\",\n \"API. verify_ssl: description: - Whether or not to verify the\",\n \"used in the FROM line of the Dockerfile for this\",\n \"determine when objects have changed. May be used for optimistic\",\n \"type: path context: description: - The name of a context\",\n \"system actively tries to terminate the build; value must be\",\n \"run. It may not be specified with Script. This might\",\n \"reference is made, if any. type: str uid: description: -\",\n \"by clients and passed unmodified back to the server. Populated\",\n \"not be found in the time allotted, and the client\",\n \"bool dockerfile_path: description: - dockerfilePath is the path of the\",\n \"inside the Docker container. type: bool force_pull: description: - forcePull\",\n \"all other cases, using Script might be more convenient. type:\",\n \"str, str revision: description: - revision is the information from\",\n \"- UID of the resource. (when there is a single\",\n \"for BuildConfig. type: complex contains: annotations: description: - Annotations is\",\n \"on the current resource \\\"items[0].name\\\" - the field \\\"name\\\" on\",\n \"a reference to an ImageStreamTag, ImageStreamImage, or DockerImage to copy\",\n \"files copied from the image are placed. type: str source_path:\",\n \"key file used to authenticate with the API. type: path\",\n \"to configure the pipeline relative to the root of the\",\n \"value may not be unset or be set further into\",\n \"the API. In the presence of network partitions, this object\",\n \"description: - The group attribute of the resource associated with\",\n \"- buildArgs contains build arguments that will be resolved in\",\n \"str script: description: - script is a shell script to\",\n \"If not provided, and no other connection options are provided,\",\n \"is set, the initializers struct will be set to nil\",\n \"the Ruby image, this is necessary to make `ruby`, `bundle`\",\n \"description: - Token used to connect to the API. cert_file:\",\n \"str type: description: - type is the kind of build\",\n \"defines a Jenkins pipeline build. type: str jenkinsfile_path: description: -\",\n \"a container, it defaults to Limits if that is explicitly\",\n \"set, graceful deletion of the object has not been requested.\",\n \"execution. This is optional, if not specified we default to\",\n \"- httpProxy is a proxy used to reach the git\",\n \"config file. debug: description: - Enable debug output from the\",\n \"will be created, if it does not exist, or patched,\",\n \"that has caused this error, as named by its JSON\",\n \"for the Build object serialized and passed to the custom\",\n \"should be considered a single file within the build input.\",\n \"is the relative directory within the build directory where files\",\n \"description: - Standard list metadata. type: complex contains: resource_version: description:\",\n \"or if no container name is specified \\\"spec.containers[2]\\\" (container with\",\n \"- Specify whether the Secret or it's key must be\",\n \"the object will be persisted to storage and then deleted,\",\n \"is written to KubeObjHelper.log default: false type: bool force: description:\",\n \"will be copied, but the directory itself will not be\",\n \"root of the context (contextDir). If both JenkinsfilePath & Jenkinsfile\",\n \"object is considered as initialized and visible to all clients.\",\n \"str no_proxy: description: - noProxy is the list of domains\",\n \"always pull the images for the builder or only pull\",\n \"in the Kubernetes config file. debug: description: - Enable debug\",\n \"be created at the destination. type: str pull_secret: description: -\",\n \"an ImageStreamTag that will trigger a build when updated It\",\n \"strategic merge is performed, where individual elements with a matching\",\n \"an array of causes due to fields having multiple errors.\",\n \"a value like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers to the name\",\n \"complex contains: binary: description: - binary builds accept a binary\",\n \"until this reference is removed. Defaults to false. To set\",\n \"path at which to mount the secret type: str secret_source:\",\n \"going to ignore it if set in create or update\",\n \"credentials. Supported auth methods\\\\ \\\\ are: ssh-privatekey.\\\" type: complex contains:\",\n \"This strategy is in tech preview. type: complex contains: env:\",\n \"of why this operation is in the \\\"Failure\\\" status. If\",\n \"referent. type: str build_args: description: - buildArgs contains build arguments\",\n \"is the number of old failed builds to retain. If\",\n \"authentication for cloning private repository. The secret\\\\ \\\\ contains valid\",\n \"zip, tar, or tar.gz file and extract it as the\",\n \"path of the Jenkinsfile that will be used to configure\",\n \"use Command and/or Args. The `-i` flag is needed to\",\n \"merge is performed, where individual elements with a matching I(name_)\",\n \"depend on the type of build to run type: str\",\n \"of the error. If this value is empty there is\",\n \"of causes due to fields having multiple errors. Optional. Examples:\",\n \"- script is a shell script to be run with\",\n \"to be used and\\\\ \\\\ value is the base64 encoded\",\n \"from the source for a specific repo snapshot. This is\",\n \"referent. type: str type: description: - type of build input\",\n \"Must be a DNS_LABEL. Cannot be updated. type: str owner_references:\",\n \"the referent. type: str optional: description: - Specify whether the\",\n \"= OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The helper\",\n \"description: - Namespace defines the space within each name must\",\n \"dir. type: str git: description: - git contains optional information\",\n \"of build input to accept type: str strategy: description: -\",\n \"GitLab webhook type of trigger type: complex contains: allow_env: description:\",\n \"description: - 'Selects a resource of the container: only resources\",\n \"the provided binary as `/webapp.war` for the builder. If left\",\n \"experimental policy 'SkipLayers' will avoid commiting new layers in between\",\n \"str name: description: - Name must be unique within a\",\n \"or '.'. type: str context_dir: description: - contextDir specifies the\",\n \"str uri: description: - uri points to the source that\",\n \"double $$, ie: $$(VAR_NAME). Escaped references will never be expanded,\",\n \"complex contains: str, str cluster_name: description: - The name of\",\n \"to \\\"v1\\\". type: str field_path: description: - Path of the\",\n \"cases, using Script might be more convenient. type: list contains:\",\n \"Source for the environment variable's value. Cannot be used if\",\n \"description: - Must be empty before the object is deleted\",\n \"defines the space within each name must be unique. An\",\n \"to use to run the pod created by this build.\",\n \"further into the future, although it may be shortened or\",\n \"str resource_version: description: - Specific resourceVersion to which this reference\",\n \"description: - ref is the branch/tag/ref to build. type: str\",\n \"ignored. type: complex contains: str, str output: description: - output\",\n \"or not to verify the API server's SSL certificates. type:\",\n \"of this operation. type: str metadata: description: - Standard list\",\n \"path context: description: - The name of a context found\",\n \"imageOptimizationPolicy describes what optimizations the system can use when building\",\n \"the PATH. type: str resources: description: - resources computes resource\",\n \"type: str push_secret: description: - PushSecret is the name of\",\n \"to validate requests. type: str gitlab: description: - GitLabWebHook contains\",\n \"specified contextDir. type: str source_strategy: description: - sourceStrategy holds the\",\n \"the build will receive a directory with a single file.\",\n \"spec: description: - spec holds all the input necessary to\",\n \"a password for connecting to the API. Use in conjunction\",\n \"- Name of the referent. type: str to: description: -\",\n \"initializers. Only privileged users may set or modify this list.\",\n \"only occur as a result of an explicit client build\",\n \"be pulled type: complex contains: api_version: description: - API version\",\n \"str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to the\",\n \"these will be copied into the build directory, where the\",\n \"is requested. Read-only. type: complex contains: {} finalizers: description: -\",\n \"information from the source for a specific repo snapshot. This\",\n \"is a zip, tar, or tar.gz file and extract it\",\n \"not want to use a shell. In all other cases,\",\n \"identifying a specific commit type: str committer: description: - committer\",\n \"order to have the appropriate collections enabled in the shell.\",\n \"be truncated by the length of the suffix required to\",\n \"true, AND if the owner has the \\\"foregroundDeletion\\\" finalizer, then\",\n \"config_map_key_ref: description: - Selects a key of a ConfigMap. type:\",\n \"secret should be available for the build time. For the\",\n \"in /. then the content of the directory will be\",\n \"mount the secret type: str secret_source: description: - secretSource is\",\n \"- name of the source control user type: str commit:\",\n \"describes if the builder should pull the images from registry\",\n \"str github: description: - github contains the parameters for a\",\n \"JenkinsfilePath & Jenkinsfile are both not specified, this defaults to\",\n \"C(True). default: present choices: - present - absent username: description:\",\n \"Kubelet will react by sending a graceful termination signal to\",\n \"and . type: str self_link: description: - SelfLink is a\",\n \"this object to gracefully terminate before it will be removed\",\n \"may not be specified with Script. This might be needed\",\n \"controller. type: list contains: api_version: description: - API version of\",\n \"override it. type: str status: description: - 'Status of the\",\n \"status StatusReason (when there is a single name which can\",\n \"of images to be used to provide source for the\",\n \"deletion. type: complex contains: api_version: description: - APIVersion defines the\",\n \"optional. If no From is specified, the From image from\",\n \"to always pull the images for the builder or only\",\n \"contain slashes or be '..' or '.'. type: str context_dir:\",\n \"Only one ImageChangeTrigger with an empty From reference is allowed\",\n \"the resource is fully terminated. If not set, graceful deletion\",\n \"of the label. type: str push_secret: description: - PushSecret is\",\n \"StatusReason. On some operations may differ from the requested resource\",\n \"object instead of an entire object, this string should contain\",\n \"specified, otherwise to an implementation-defined value. type: complex contains: str,\",\n \"description: - allowEnv determines whether the webhook can set environment\",\n \"to an existing Kubernetes config file. If not provided, and\",\n \"the kind of build strategy. type: str successful_builds_history_limit: description: -\",\n \"module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc)) if __name__\",\n \"the initializers struct will be set to nil and the\",\n \"the relative directory within the build directory where files copied\",\n \"'Images' type: str run_policy: description: - RunPolicy describes how the\",\n \"operation. One of: \\\"Success\\\" or \\\"Failure\\\".' type: str labels: description:\",\n \"- uri points to the source that will be built.\",\n \"may not be specified with Command. Use Script when a\",\n \"that will be used to configure the pipeline relative to\",\n \"description: - lastTriggeredImageID is used internally by the ImageChangeController to\",\n \"unneeded dependencies installed. The building of the application is still\",\n \"the data returned is not guaranteed to conform to any\",\n \"The experimental policy 'SkipLayers' will avoid commiting new layers in\",\n \"int uid: description: - UID of the resource. (when there\",\n \"description of the cause of the error. If this value\",\n \"str owner_references: description: - List of objects depended by this\",\n \"should not be used type: str ref: description: - ref\",\n \"a resource and is not allowed to change on PUT\",\n \"to organize and categorize (scope and select) objects. May match\",\n \"the status of this operation. type: str metadata: description: -\",\n \"key must be defined type: bool dockerfile_path: description: - dockerfilePath\",\n \"the directory where the files from the secret should be\",\n \"list of initializers that have not yet acted on this\",\n \"In all other cases, using Script might be more convenient.\",\n \"and an optional Dockerfile may be specified to override any\",\n \"by the system. Read-only. type: str reason: description: - A\",\n \"points to the managing controller. type: bool kind: description: -\",\n \"appear more than once in an array of causes due\",\n \"str name: description: - name of the source control user\",\n \"image. The default policy is 'None' which means the final\",\n \"considered as initialized and visible to all clients. type: list\",\n \"complex contains: _from: description: - from is a reference to\",\n \"destinationDir must be a directory. destinationDir can also be empty\",\n \"str value: description: - 'Variable references $(VAR_NAME) are expanded using\",\n \"what optimizations the system can use when building images to\",\n \"the value unique on the server. If this field is\",\n \"not set, graceful deletion of the object has not been\",\n \"such as desiredState.manifest.containers[2]. For example, if the object reference is\",\n \"will be deleted. This field is set by the server\",\n \"\\\"v1\\\". type: str field_path: description: - Path of the field\",\n \"will receive this binary as input on STDIN. type: complex\",\n \"its JSON serialization. May include dot and postfix notation for\",\n \"is generally assumed to be a tar, gzipped tar, or\",\n \"Populated by the system. Read-only. type: str uid: description: -\",\n \"status holds any relevant information about a build config type:\",\n \"object has not been requested. Populated by the system when\",\n \"the build pod type: list contains: mount_path: description: - mountPath\",\n \"- output describes the Docker image the Strategy should produce.\",\n \"or empty, this object has been completely initialized. Otherwise, the\",\n \"have changed. Value must be treated as opaque by clients\",\n \"path to a file containing the YAML definition of the\",\n \"the image is pulled from the OpenShift registry, this field\",\n \"Limits describes the maximum amount of compute resources allowed. type:\",\n \"so there is no module object. All we can do\",\n \"commit: description: - commit is the commit hash identifying a\",\n \"int details: description: - Extended data associated with the reason.\",\n \"images to reduce the final size or time spent building\",\n \"the name of a Secret that would be used for\",\n \"the specified API version. type: str resource_field_ref: description: - 'Selects\",\n \"images from the private Docker registries type: complex contains: name:\",\n \"of the specified contextDir. type: str source_strategy: description: - sourceStrategy\",\n \"items: description: - items is a list of build configs\",\n \"time. For example, a user may request that a pod\",\n \"strategy. For Docker builds, this is the build context and\",\n \"retain. If not specified, all failed builds are retained. type:\",\n \"the referent. type: str resource_version: description: - Specific resourceVersion to\",\n \"is used. type: list contains: name: description: - name defines\",\n \"means the final build image will be equivalent to an\",\n \"and is in UTC. Populated by the system. Read-only. Null\",\n \"using the previous defined environment variables in the container and\",\n \"namespace type: complex contains: key: description: - The key of\",\n \"source. The custom strategy receives this binary as standard input.\",\n \"this value is empty there is no information available. A\",\n \"human-readable description of the status of this operation. type: str\",\n \"last used image ID for build type: str type: description:\",\n \"type: complex contains: as_file: description: - asFile indicates that the\",\n \"be specified to override any Dockerfile in the build context.\",\n \"a user may request that a pod is deleted in\",\n \"not override it. type: str status: description: - 'Status of\",\n \"listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description:\",\n \"are defined, a new build can only occur as a\",\n \"context. For Source builds, this is assumed to be an\",\n \"of replication controllers and services. type: complex contains: str, str\",\n \"set the build will receive a directory with a single\",\n \"until an administrator or automated process can determine the resource\",\n \"Dockerfile may be specified to override any Dockerfile in the\",\n \"will be scheduled for execution. This is optional, if not\",\n \"determine the resource is fully terminated. If not set, graceful\",\n \"more convenient. type: list contains: str script: description: - script\",\n \"to the root of the context (contextDir). type: str env:\",\n \"is the requested API version for the Build object serialized\",\n \"and requests.memory) are currently supported.' type: complex contains: container_name: description:\",\n \"If nil, it can be overridden by default build nodeselector\",\n \"representing this object. Populated by the system. Read-only. type: str\",\n \"arguments that are provided to either Command, Script or the\",\n \"root of the context (contextDir). type: str env: description: -\",\n \"the referent. type: str build_api_version: description: - buildAPIVersion is the\",\n \"build when updated It is optional. If no From is\",\n \"A patch operation results in merging lists and updating dictionaries,\",\n \"type: list contains: field: description: - 'The field of the\",\n \"the needed artifacts in the runtime image for use. Deprecated:\",\n \"the StatusReason failure. Not all StatusReasons may provide detailed causes.\",\n \"select) objects. May match selectors of replication controllers and services.\",\n \"the reason type. type: complex contains: causes: description: - The\",\n \"set of resources. Populated by the system. Read-only. Value must\",\n \"and destination paths to copy from the image. type: list\",\n \"over http type: str https_proxy: description: - httpsProxy is a\",\n \"future release. Use ImageSource to copy binary artifacts created from\",\n \"resource this object represents. Servers may infer this from the\",\n \"description: - Specifies the output format of the exposed resources,\",\n \"it defaults to Limits if that is explicitly specified, otherwise\",\n \"push_secret: description: - PushSecret is the name of a Secret\",\n \"The kind attribute of the resource associated with the status\",\n \"strategy, these will be injected into a container where the\",\n \"a strategic merge is performed, where individual elements with a\",\n \"run_policy: description: - RunPolicy describes how the new build created\",\n \"created at the destination. type: str pull_secret: description: - pullSecret\",\n \"This filename may not contain slashes or be '..' or\",\n \"contains: str command: description: - command is the command to\",\n \"type: str namespace: description: - Namespace of the referent. type:\",\n \"variables; can only be set to true for GenericWebHook. type:\",\n \"pullSecret is a reference to a secret to be used\",\n \"description: - Name of the referent. type: str jenkins_pipeline_strategy: description:\",\n \"'Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels,\",\n \"list contains: name: description: - name of the process that\",\n \"destination_dir: description: - destinationDir is the relative directory within the\",\n \"image ID for build type: str type: description: - type\",\n \"object to gracefully terminate before it will be removed from\",\n \"build pod to fit on a node If nil, it\",\n \"of an explicit client build creation. type: list contains: bitbucket:\",\n \"- pullSecret is a reference to a secret to be\",\n \"the \\\"Failure\\\" status. If this value is empty there is\",\n \"of the referent. type: str namespace: description: - Namespace of\",\n \"strategy will be used. Only one ImageChangeTrigger with an empty\",\n \"defines the versioned schema of this representation of an object.\",\n \"the author of a specific commit type: complex contains: email:\",\n \"reference is removed. Defaults to false. To set this field,\",\n \"COPY them during docker build. type: str secret: description: -\",\n \"complex contains: limits: description: - Limits describes the maximum amount\",\n \"is an unstructured key value map stored with a resource\",\n \"- Path to a certificate used to authenticate with the\",\n \"execute the post build hook, for example for running unit\",\n \"type: str value: description: - 'Variable references $(VAR_NAME) are expanded\",\n \"contains the parameters for a Bitbucket webhook type of trigger\",\n \"in this list will point to this controller, with the\",\n \"contains optional information about git build source type: complex contains:\",\n \"username for connecting to the API. verify_ssl: description: - Whether\",\n \"could not be found in the time allotted, and the\",\n \"of the exposed resources, defaults to \\\"1\\\" type: str resource:\",\n \"specified to override any Dockerfile in the build context. For\",\n \"created, if it does not exist, or patched, if parameter\",\n \"build. type: complex contains: api_version: description: - API version of\",\n \"name passed. This value will also be combined with a\",\n \"The key to select. type: str name: description: - Name\",\n \"the path at which to mount the secret type: str\",\n \"pull_secret: description: - pullSecret is a reference to a secret\",\n \"will point to this controller, with the controller field set\",\n \"of an ImageStreamTag, the ImageStreamTag will be looked for in\",\n \"a Jenkins pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath\",\n \"system when a graceful deletion is requested. Read-only. type: complex\",\n \"sources in directory other than root of repository. type: str\",\n \"Source build strategy. type: complex contains: _from: description: - from\",\n \"seconds allowed for this object to gracefully terminate before it\",\n \"parameters intended to define object attributes. type: dict src: description:\",\n \"a C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME)\",\n \"chosen only to have some well-defined way of referencing a\",\n \"API version build_config_list: type: complex returned: when I(state) = C(present)\",\n \"no failing result is set, the initializers struct will be\",\n \"copied from the builder to the runtime image. sourcePath can\",\n \"although it may be shortened or the resource may be\",\n \"description: - type of build input to accept type: str\",\n \"failed builds to retain. If not specified, all failed builds\",\n \"for connecting to the API. verify_ssl: description: - Whether or\",\n \"description: - type is the type of build trigger type:\",\n \"cannot be resolved, the reference in the input string will\",\n \"environment variables. If a variable cannot be resolved, the reference\",\n \"for BuildConfigList. type: complex contains: resource_version: description: - String that\",\n \"of values. If a list contains a dictionary with a\",\n \"\\\"Failure\\\" status. If this value is empty there is no\",\n \"will be unchanged. The $(VAR_NAME) syntax can be escaped with\",\n \"termination signal to the containers in the pod. After that\",\n \"the name of the container that triggered the event) or\",\n \"and select) objects. May match selectors of replication controllers and\",\n \"description: - Name of the environment variable. Must be a\",\n \"field of the resource that has caused this error, as\",\n \"type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of\",\n \"type: str no_cache: description: - noCache if set to true\",\n \"be described). type: str kind: description: - Kind is a\",\n \"type: str no_proxy: description: - noProxy is the list of\",\n \"build image will be equivalent to an image created by\",\n \"set with the Failure field, the object will be persisted\",\n \"Name of the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy\",\n \"the API server's SSL certificates. type: bool requirements: - openshift\",\n \"that will be included in the build pod type: list\",\n \"reason: description: - A machine-readable description of why this operation\",\n \"deleted, if set to C(absent). A patch operation results in\",\n \"be deleted prior to this time. For example, a user\",\n \"type: str uri: description: - uri points to the source\",\n \"of the source control user type: str commit: description: -\",\n \"description: - Path to a CA certificate used to authenticate\",\n \"server time when this object was created. It is not\",\n \"must be unique within a namespace. Is required when creating\",\n \"committer: description: - committer is the committer of a specific\",\n \"assemble script runs. Later, when the script finishes, all files\",\n \"required when creating resources, although some resources may allow a\",\n \"file from I(~/.kube/config.json). type: path password: description: - Provide a\",\n \"creation time. This field is a list of initializers that\",\n \"location to push the output of this build to. Kind\",\n \"build must be executed with the --no-cache=true flag type: bool\",\n \"node If nil, it can be overridden by default build\",\n \"OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The helper failed\",\n \"that have not yet acted on this object. If nil\",\n \"parameters for a GitHub webhook type of trigger type: complex\",\n \"input on STDIN. type: complex contains: as_file: description: - asFile\",\n \"as the source. The custom strategy receives this binary as\",\n \"- imageOptimizationPolicy describes what optimizations the system can use when\",\n \"the FROM may be modified based on your strategy base\",\n \"description: - UID of the referent. type: str resource_version: description:\",\n \"or tar.gz file and extract it as the source. The\",\n \"from your strategy environment will be added after the FROM,\",\n \"shell script is appropriate to execute the post build hook,\",\n \"build, you can copy the needed artifacts in the runtime\",\n \"Requested API version build_config_list: type: complex returned: when I(state) =\",\n \"type: str kind: description: - Kind is a string value\",\n \"repository over https type: str no_proxy: description: - noProxy is\",\n \"be scheduled for execution. This is optional, if not specified\",\n \"- GitLabWebHook contains the parameters for a GitLab webhook type\",\n \"type: str items: description: - items is a list of\",\n \"not directly settable by a client. The resource is expected\",\n \"status of this operation. type: str metadata: description: - Standard\",\n \"the build output image is committed, before it is pushed\",\n \"directory where files copied from the image are placed. type:\",\n \"in use. type: complex contains: binary: description: - binary builds\",\n \"and the client should retry (optionally after the time indicated\",\n \"concurrency, change detection, and the watch operation on a resource\",\n \"at which to mount the secret type: str secret_source: description:\",\n \"used to reach the git repository over https type: str\",\n \"strategy base image and additional ENV stanzas from your strategy\",\n \"parameters to the Jenkins Pipeline build strategy. This strategy is\",\n \"strategy. type: complex contains: _from: description: - from is reference\",\n \"successful builds are retained. type: int triggers: description: - triggers\",\n \"objects, returning a resource_version representing a consistent version of the\",\n \"Docker and Source build strategies assume this file is a\",\n \"and updating dictionaries, with lists being merged into a unique\",\n \"contains: as_file: description: - asFile indicates that the provided binary\",\n \"I(username). resource_definition: description: - Provide the YAML definition for the\",\n \"to zero length. For the Docker build strategy, these will\",\n \"description: - buildAPIVersion is the requested API version for the\",\n \"in the build context. For Source builds, this is assumed\",\n \"sourcePath can be a file or directory. destinationDir must be\",\n \"test`. If you need control over the image entrypoint, or\",\n \"a node If nil, it can be overridden by default\",\n \"type: str name: description: - The name attribute of the\",\n \"- Map of string keys and values that can be\",\n \"resources, although some resources may allow a client to request\",\n \"- 'runtimeImage is an optional image that is used to\",\n \"or COPY them during docker build. type: str secret: description:\",\n \"- openshift == 0.3.3 ''' EXAMPLES = ''' ''' RETURN\",\n \"https type: str no_proxy: description: - noProxy is the list\",\n \"of the referent. type: str build_api_version: description: - buildAPIVersion is\",\n \"user needs \\\"delete\\\" permission of the owner, otherwise 422 (Unprocessable\",\n \"values for the cluster. If set to an empty map\",\n \"be built. The structure of the source will depend on\",\n \"metadata for BuildConfigList. type: complex contains: resource_version: description: - String\",\n \"the FROM line of the Dockerfile for this build. type:\",\n \"associated with the status StatusReason. type: str kind: description: -\",\n \"\\\".\\\", in this case it just refers to the root\",\n \"str field_path: description: - 'If referring to a piece of\",\n \"is optional. If no From is specified, the From image\",\n \"type: bool kind: description: - Kind of the referent. type:\",\n \"commit type: str type: description: - type of the build\",\n \"kind attribute of the resource associated with the status StatusReason.\",\n \"be presented as-is to a reader. type: str reason: description:\",\n \"has the \\\"foregroundDeletion\\\" finalizer, then the owner cannot be deleted\",\n \"found in the time allotted, and the client should retry\",\n \"a username for connecting to the API. verify_ssl: description: -\",\n \"resource to select' type: str secret_key_ref: description: - Selects a\",\n \"value will also be combined with a unique suffix. The\",\n \"binary input should be considered a single file within the\",\n \"ignore it if set in create or update request. type:\",\n \"of build to run type: str images: description: - images\",\n \"builds if true. type: bool pull_secret: description: - pullSecret is\",\n \"a dictionary with a I(name) or I(type) attribute, a strategic\",\n \"the system when a graceful deletion is requested. Read-only. type:\",\n \"the input necessary to produce a new build, and the\",\n \"description: - Path of the field to select in the\",\n \"str value: description: - value defines the literal value of\",\n \"secret used to validate requests. type: str github: description: -\",\n \"of additional secrets that will be included in the build\",\n \"how new Builds can be launched from a BuildConfig. If\",\n \"to validate requests. type: str image_change: description: - imageChange contains\",\n \"of repository. type: str dockerfile: description: - dockerfile is the\",\n \"the builder image but, post build, you can copy the\",\n \"FROM may be modified based on your strategy base image\",\n \"Populated by the system. Read-only. Null for lists. type: complex\",\n \"the Docker build strategy. type: complex contains: _from: description: -\",\n \"For example, specifying \\\"webapp.war\\\" would place the provided binary as\",\n \"be built. When this option is specified, the FROM may\",\n \"If set to an empty map or a map with\",\n \"not be used type: str ref: description: - ref is\",\n \"kubeconfig: description: - Path to an existing Kubernetes config file.\",\n \"necessary to make `ruby`, `bundle` and other binaries available in\",\n \"is the kind of build strategy. type: str successful_builds_history_limit: description:\",\n \"- Path to a key file used to authenticate with\",\n \"a namespace. Is required when creating resources, although some resources\",\n \"not specified, all successful builds are retained. type: int triggers:\",\n \"complex contains: _from: description: - from is reference to an\",\n \"server's internal version of this object that can be used\",\n \"this would take on a value like: \\\"spec.containers{name}\\\" (where \\\"name\\\"\",\n \"- Provide the YAML definition for the object, bypassing any\",\n \"administrator or automated process can determine the resource is fully\",\n \"build_args: description: - buildArgs contains build arguments that will be\",\n \"build. type: complex contains: custom_strategy: description: - customStrategy holds the\",\n \"elements with a matching I(name_) or I(type) are merged. To\",\n \"description: - Suggested HTTP return code for this status, 0\",\n \"client will be different than the name passed. This value\",\n \"unique. An empty namespace is equivalent to the \\\"default\\\" namespace,\",\n \"of the ServiceAccount to use to run the pod created\",\n \"is the name of a Secret that would be used\",\n \"resource of the container: only resources limits and requests (limits.cpu,\",\n \"not empty. type: complex contains: config_map_key_ref: description: - Selects a\",\n \"the Dockerfile. See type: list contains: name: description: - Name\",\n \"for the application exists. This allows to have buildable sources\",\n \"description: - A machine-readable description of why this operation is\",\n \"API. key_file: description: - Path to a key file used\",\n \"have non-zero length. type: str value: description: - value defines\",\n \"resources computes resource requirements to execute the build. type: complex\",\n \"description: - github contains the parameters for a GitHub webhook\",\n \"specific commit type: str type: description: - type of the\",\n \"- GenerateName is an optional prefix, used by the server,\",\n \"referent. type: str build_api_version: description: - buildAPIVersion is the requested\",\n \"objects have changed. Value must be treated as opaque by\",\n \"'The field of the resource that has caused this error,\",\n \"message: description: - message is the description of a specific\",\n \"a timestamp representing the server time when this object was\",\n \"may be specified to override any Dockerfile in the build\",\n \"be defined type: bool jenkinsfile: description: - Jenkinsfile defines the\",\n \"to. Cannot be updated. In CamelCase. type: str metadata: description:\",\n \"you can copy the needed artifacts in the runtime image\",\n \"of the resource that has caused this error, as named\",\n \"provide source for the build type: list contains: _from: description:\",\n \"the same validation rules as the Name field, and may\",\n \"no_proxy: description: - noProxy is the list of domains for\",\n \"the final build image will be equivalent to an image\",\n \"to generate a unique name ONLY IF the Name field\",\n \"fully terminated. If not set, graceful deletion of the object\",\n \"type: list contains: mount_path: description: - mountPath is the path\",\n \"that the docker build must be executed with the --no-cache=true\",\n \"builds, this is assumed to be an archive as described\",\n \"attribute, a strategic merge is performed, where individual elements with\",\n \"code for the application exists. This allows to have buildable\",\n \"owner has the \\\"foregroundDeletion\\\" finalizer, then the owner cannot be\",\n \"- API version of the referent. type: str block_owner_deletion: description:\",\n \"key to select. type: str name: description: - Name of\",\n \"git build source type: complex contains: http_proxy: description: - httpProxy\",\n \"option to C(True). default: present choices: - present - absent\",\n \"environment variable. Must be a C_IDENTIFIER. type: str value: description:\",\n \"variables you want to pass into a build pipeline. type:\",\n \"#!/usr/bin/python # -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule,\",\n \"your build. type: complex contains: name: description: - Name of\",\n \"complex contains: str, str name: description: - Name must be\",\n \"signal to the containers in the pod. After that 30\",\n \"operations provide a snapshot read of the underlying objects, returning\",\n \"configure the build pod to always pull the images for\",\n \"'SkipLayersAndWarn' is the same as 'SkipLayers' but simply warns if\",\n \"One of: \\\"Success\\\" or \\\"Failure\\\".' type: str labels: description: -\",\n \"pod to always pull the images for the builder or\",\n \"for env vars' type: str divisor: description: - Specifies the\",\n \"ENV stanzas from your strategy environment will be added after\",\n \"operations. Populated by the system. Read-only. type: str spec: description:\",\n \"is no information available. A Reason clarifies an HTTP status\",\n \"The provided value has the same validation rules as the\",\n \"str type: description: - type is the type of build\",\n \"into a separate runtime image.' type: list contains: destination_dir: description:\",\n \"initializers struct will be set to nil and the object\",\n \"the number of old failed builds to retain. If not\",\n \"uid: description: - UID of the referent. type: str build_api_version:\",\n \"JSON serialization. May include dot and postfix notation for nested\",\n \"valid for a particular resource or set of resources. Populated\",\n \"after the FROM, but before the rest of your Dockerfile\",\n \"They may only be valid for a particular resource or\",\n \"the system. Read-only. type: str spec: description: - spec holds\",\n \"in this field. Once set, this value may not be\",\n \"not. Defaults to \\\"\\\".' type: str value_from: description: - Source\",\n \"status: description: - 'Status of the operation. One of: \\\"Success\\\"\",\n \"complex contains: args: description: - args is a list of\",\n \"the Build object serialized and passed to the custom builder\",\n \"updated. type: str owner_references: description: - List of objects depended\",\n \"set further into the future, although it may be shortened\",\n \"If the deletionTimestamp of the object is non-nil, entries in\",\n \"generated by the server on successful creation of a resource\",\n \"str images: description: - images describes a set of images\",\n \"layers in between each image step, and will fail if\",\n \"the cluster which the object belongs to. This is used\",\n \"set by the server when a graceful deletion is requested\",\n \"complex contains: http_proxy: description: - httpProxy is a proxy used\",\n \"key. type: str name: description: - Name of the referent.\",\n \"for the build type: list contains: _from: description: - from\",\n \"generally assumed to be a tar, gzipped tar, or zip\",\n \"be '..' or '.'. type: str context_dir: description: - contextDir\",\n \"yet acted on this object. If nil or empty, this\",\n \"convert recognized schemas to the latest internal value, and may\",\n \"exists or not. Defaults to \\\"\\\".' type: str value_from: description:\",\n \"and may reject unrecognized values. type: str kind: description: -\",\n \"other binaries available in the PATH. type: str resources: description:\",\n \"computes resource requirements to execute the build. type: complex contains:\",\n \"the client submits requests to. Cannot be updated. In CamelCase.\",\n \"changed. May be used for optimistic concurrency, change detection, and\",\n \"in the build pod type: list contains: mount_path: description: -\",\n \"of a Dockerfile which should be built. When this option\",\n \"default configuration file from I(~/.kube/config.json). type: path password: description: -\",\n \"description: - successfulBuildsHistoryLimit is the number of old successful builds\",\n \"human-readable description of the cause of the error. This field\",\n \"supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type:\",\n \"a GitLab webhook type of trigger type: complex contains: allow_env:\",\n \"type: complex contains: str, str cluster_name: description: - The name\",\n \"described above. For Source and Docker builds, if binary.asFile is\",\n \"about number of last triggered build. type: int kind: description:\",\n \"version of this object that can be used by clients\",\n \"variable. Must be a C_IDENTIFIER. type: str value: description: -\",\n \"non-nil, entries in this list can only be removed. type:\",\n \"jenkinsfile: description: - Jenkinsfile defines the optional raw contents of\",\n \"space value for this object. It is typically generated by\",\n \"is to a container within a pod, this would take\",\n \"single file within the build input. For example, specifying \\\"webapp.war\\\"\",\n \"to the container and after cleanup, remove the pod from\",\n \"type: complex contains: limits: description: - Limits describes the maximum\",\n \"trigger a build when updated It is optional. If no\",\n \"terminate before it will be removed from the system. Only\",\n \"Each entry is an identifier for the responsible component that\",\n \"str command: description: - command is the command to run.\",\n \"them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an\",\n \"This field is not set anywhere right now and apiserver\",\n \"'DockerImage'. This value will be used to look up a\",\n \"map with any values, default build nodeselector values are ignored.\",\n \"the current resource \\\"items[0].name\\\" - the field \\\"name\\\" on the\",\n \"Secret that would be used for setting up the authentication\",\n \"- DeletionTimestamp is RFC 3339 date and time at which\",\n \"image to copy to the build directory. If the source\",\n \"each image step, and will fail if the Dockerfile cannot\",\n \"this reference points to the managing controller. type: bool kind:\",\n \"needed to support CentOS and RHEL images that use Software\",\n \"type: str env: description: - env contains additional environment variables\",\n \"str docker_strategy: description: - dockerStrategy holds the parameters to the\",\n \"For Source builds, this is assumed to be an archive\",\n \"the authentication for pulling the Docker images from the private\",\n \"used to configure the pipeline relative to the root of\",\n \"type: str uid: description: - UID of the referent. type:\",\n \"be a C_IDENTIFIER. type: str value: description: - 'Variable references\",\n \"Must be a valid secret key. type: str name: description:\",\n \"holds the parameters to the Jenkins Pipeline build strategy. This\",\n \"After that 30 seconds, the Kubelet will send a hard\",\n \"to the server. Populated by the system. Read-only. type: str\",\n \"entry is an identifier for the responsible component that will\",\n \"want to use a shell. In all other cases, using\",\n \"A machine-readable description of why this operation is in the\",\n \"is used to distinguish resources with same name and namespace\",\n \"destination. type: str pull_secret: description: - pullSecret is a reference\",\n \"exposeDockerSocket will allow running Docker commands (and build Docker images)\",\n \"images describes a set of images to be used to\",\n \"list contains: name: description: - name defines the name of\",\n \"are required to be scoped to a namespace - the\",\n \"description: - If specified, the time in seconds before the\",\n \"description: - committer is the committer of a specific commit\",\n \"the Source build strategy. type: complex contains: _from: description: -\",\n \"by any user. type: complex contains: pending: description: - Pending\",\n \"I(resource_definition). type: path ssl_ca_cert: description: - Path to a CA\",\n \"type: complex contains: binary: description: - binary builds accept a\",\n \"list contains: destination_dir: description: - destinationDir is the relative directory\",\n \"schema except that defined by the reason type. type: complex\",\n \"users can ADD or COPY them during docker build. type:\",\n \"attributes, and deleted, if set to C(absent). A patch operation\",\n \"can set environment variables; can only be set to true\",\n \"and will fail if the Dockerfile cannot provide compatibility with\",\n \"must be treated as opaque by clients and passed unmodified\",\n \"to a key file used to authenticate with the API.\",\n \"- forcePull describes if the controller should configure the build\",\n \"there is a single resource which can be described). type:\",\n \"of the referent. type: str to: description: - to defines\",\n \"be modified further by any user. type: complex contains: pending:\",\n \"command: description: - command is the command to run. It\",\n \"structure of the source will depend on the type of\",\n \"on a resource or set of resources. Clients must treat\",\n \"the image entrypoint, or if the image does not have\",\n \"build configuration will be scheduled for execution. This is optional,\",\n \"having multiple errors. Optional. Examples: \\\"name\\\" - the field \\\"name\\\"\",\n \"contains: key: description: - The key to select. type: str\",\n \"binary as their input. The binary is generally assumed to\",\n \"description: - lastVersion is used to inform about number of\",\n \"empty there is no information available. A Reason clarifies an\",\n \"will fail if the Dockerfile cannot provide compatibility with the\",\n \"cloning private repository. The secret\\\\ \\\\ contains valid credentials for\",\n \"if binary.asFile is set the build will receive a directory\",\n \"receive a directory with a single file. contextDir may be\",\n \"the resource associated with the status StatusReason. type: str kind:\",\n \"be unique. An empty namespace is equivalent to the \\\"default\\\"\",\n \"part of an object.' type: str kind: description: - Kind\",\n \"used to reach the git repository over http type: str\",\n \"- type of the build source, may be one of\",\n \"network partitions, this object may still exist after this timestamp,\",\n \"in seconds before the operation should be retried. type: int\",\n \"str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of old\",\n \"generic: description: - generic contains the parameters for a Generic\",\n \"str, str output: description: - output describes the Docker image\",\n \"contains: container_name: description: - 'Container name: required for volumes, optional\",\n \"user type: str name: description: - name of the source\",\n \"the literal value of the label. type: str push_secret: description:\",\n \"to push to. In the case of an ImageStreamTag, the\",\n \"is written in terms of, defaults to \\\"v1\\\". type: str\",\n \"cluster_name: description: - The name of the cluster which the\",\n \"will be replaced, rather than merged. default: false type: bool\",\n \"in the namespace of the build unless Namespace is specified.\",\n \"will receive a directory with a single file. contextDir may\",\n \"needed if the image doesn't have `/bin/sh`, or if you\",\n \"simply warns if compatibility cannot be preserved. type: str no_cache:\",\n \"type: bool pull_secret: description: - pullSecret is the name of\",\n \"if set to C(absent). A patch operation results in merging\",\n \"description: - sourcePath is the absolute path of the file\",\n \"\\\"delete\\\" permission of the owner, otherwise 422 (Unprocessable Entity) will\",\n \"build into a separate runtime image.' type: list contains: destination_dir:\",\n \"the root of the context (contextDir). type: str env: description:\",\n \"strategy environment will be added after the FROM, but before\",\n \"opaque value that represents the internal version of this object\",\n \"operation is in the \\\"Failure\\\" status. If this value is\",\n \"be preserved. type: str no_cache: description: - noCache if set\",\n \"by the ServiceAccount type: str source: description: - source describes\",\n \"attribute of the resource associated with the status StatusReason (when\",\n \"options: api_key: description: - Token used to connect to the\",\n \"serviceAccount is the name of the ServiceAccount to use to\",\n \"are merged. To force the replacement of lists, set the\",\n \"status code but does not override it. type: str status:\",\n \"will be used. Only one ImageChangeTrigger with an empty From\",\n \"object is deleted from the registry. Each entry is an\",\n \"unique on the server. If this field is specified and\",\n \"are not queryable and should be preserved when modifying objects.\",\n \"the presence of network partitions, this object may still exist\",\n \"associated with the StatusReason failure. Not all StatusReasons may provide\",\n \"Requests describes the minimum amount of compute resources required. If\",\n \"managing controller. type: list contains: api_version: description: - API version\",\n \"snapshot read of the underlying objects, returning a resource_version representing\",\n \"the git repository over http type: str https_proxy: description: -\",\n \"requests to. Cannot be updated. In CamelCase. type: str message:\",\n \"reference points to the managing controller. type: bool kind: description:\",\n \"to run the pod created by this build. The pod\",\n \"setting up the authentication for pulling the Docker images from\",\n \"the previous defined environment variables in the container and any\",\n \"required to be scoped to a namespace - the value\",\n \"image.' type: complex contains: api_version: description: - API version of\",\n \"secrets and their destinations that will be used only for\",\n \"indicates that the docker build must be executed with the\",\n \"set in happens-before order across separate operations. Clients may not\",\n \"ConfigMap or it's key must be defined type: bool field_ref:\",\n \"to observe uninitialized objects. When an object is created, the\",\n \"push the output of this build to. Kind must be\",\n \"provide compatibility with the 'None' policy. An additional experimental policy\",\n \"'runtimeArtifacts specifies a list of source/destination pairs that will be\",\n \"would be used for setting up the authentication for executing\",\n \"field \\\"name\\\" on the current resource \\\"items[0].name\\\" - the field\",\n \"== 0.3.3 ''' EXAMPLES = ''' ''' RETURN = '''\",\n \"be active on a node before the system actively tries\",\n \"is the canonical representation. Not all objects are required to\",\n \"of this build to. Kind must be one of 'ImageStreamTag'\",\n \"is specified and the generated name exists, the server will\",\n \"- RunPolicy describes how the new build created from this\",\n \"\\\"Serial\\\". type: str service_account: description: - serviceAccount is the name\",\n \"then the content of the directory will be copied, but\",\n \"set or modify this list. Once it is empty, it\",\n \"triggers: description: - triggers determine how new Builds can be\",\n \"a binary as their input. The binary is generally assumed\",\n \"during docker build. type: str secret: description: - secret is\",\n \"use Software Collections (SCL), in order to have the appropriate\",\n \"of a Jenkinsfile which defines a Jenkins pipeline build. type:\",\n \"a Bitbucket webhook type of trigger type: complex contains: allow_env:\",\n \"from one build into a separate runtime image.' type: list\",\n \"an optional duration in seconds, counted from the time when\",\n \"nil, it can be overridden by default build nodeselector values\",\n \"used type: str ref: description: - ref is the branch/tag/ref\",\n \"of source/destination pairs that will be copied from the builder\",\n \"also be empty or equal to \\\".\\\", in this case\",\n \"replacement of lists, set the I(force) option to C(True). default:\",\n \"str pull_secret: description: - pullSecret is a reference to a\",\n \"custom builder type: str env: description: - env contains additional\",\n \"with the StatusReason failure. Not all StatusReasons may provide detailed\",\n \"postfix notation for nested attributes. Arrays are zero-indexed. Fields may\",\n \"the raw contents of a Dockerfile which should be built.\",\n \"type: bool requirements: - openshift == 0.3.3 ''' EXAMPLES =\",\n \"str code: description: - Suggested HTTP return code for this\",\n \"reference to an ImageStreamTag that will trigger a build when\",\n \"cannot provide compatibility with the 'None' policy. An additional experimental\",\n \"of trigger type: complex contains: allow_env: description: - allowEnv determines\",\n \"type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of\",\n \"with index 2 in this pod). This syntax is chosen\",\n \"name: description: - Name of the referent. type: str secrets:\",\n \"describes the maximum amount of compute resources allowed. type: complex\",\n \"support CentOS and RHEL images that use Software Collections (SCL),\",\n \"- UID of the referent. type: str build_args: description: -\",\n \"one build into a separate runtime image.' type: complex contains:\",\n \"is used to inform about number of last triggered build.\",\n \"deletion of the object has not been requested. Populated by\",\n \"description: - Name of the referent. type: str optional: description:\",\n \"configs type: list contains: api_version: description: - APIVersion defines the\",\n \"file or directory. destinationDir must be a directory. destinationDir can\",\n \"unique within a namespace. Is required when creating resources, although\",\n \"type: complex contains: last_version: description: - lastVersion is used to\",\n \"executing the Docker push to authentication enabled Docker Registry (or\",\n \"system. Read-only. type: str uid: description: - UID is the\",\n \"destinationDir is the directory where the files from the secret\",\n \"be used for setting up the authentication for executing the\",\n \"referent. type: str secrets: description: - secrets is a list\",\n \"conditions when to trigger them. type: complex contains: completion_deadline_seconds: description:\",\n \"UID of the referent. type: str post_commit: description: - postCommit\",\n \"this field. Once set, this value may not be unset\",\n \"Namespace of the referent. type: str resource_version: description: - Specific\",\n \"- value defines the literal value of the label. type:\",\n \"in create or update request. type: str creation_timestamp: description: -\",\n \"details: description: - Extended data associated with the reason. Each\",\n \"controller. type: bool kind: description: - Kind of the referent.\",\n \"- git contains optional information about git build source type:\",\n \"Specific resourceVersion to which this reference is made, if any.\",\n \"path password: description: - Provide a password for connecting to\",\n \"the parameters for a GitLab webhook type of trigger type:\",\n \"metadata: description: - Standard list metadata. type: complex contains: resource_version:\",\n \"the secret to select from. Must be a valid secret\",\n \"this field does not need to be set. type: complex\",\n \"set. type: complex contains: name: description: - Name of the\",\n \"referent. type: str optional: description: - Specify whether the Secret\",\n \"invariant at object creation time. This field is a list\",\n \"Enable debug output from the OpenShift helper. Logging info is\",\n \"environment variable's value. Cannot be used if value is not\",\n \"may be shortened or the resource may be deleted prior\",\n \"before the object is deleted from the registry. Each entry\",\n \"is C(present), an existing object will updated, and lists will\",\n \"- Path to a CA certificate used to authenticate with\",\n \"contains the parameters for a Generic webhook type of trigger\",\n \"commit type: complex contains: email: description: - email of the\",\n \"in the list have been deleted, this object will be\",\n \"to. Cannot be updated. In CamelCase. type: str message: description:\",\n \"ssh-privatekey.\\\" type: complex contains: name: description: - Name of the\",\n \"array of causes due to fields having multiple errors. Optional.\",\n \"rules as the Name field, and may be truncated by\",\n \"description: - Provide the YAML definition for the object, bypassing\",\n \"by clients to determine when objects have changed. May be\",\n \"relative directory within the build directory where files copied from\",\n \"with the status StatusReason. type: str kind: description: - The\",\n \"ConfigMap. type: complex contains: key: description: - The key to\",\n \"prior to building. type: bool incremental: description: - incremental flag\",\n \"a list of source/destination pairs that will be copied from\",\n \"pod, this would take on a value like: \\\"spec.containers{name}\\\" (where\",\n \"may infer this from the endpoint the client submits requests\",\n \"description: - imageChange contains parameters for an ImageChange type of\",\n \"the system. Read-only. type: str ''' def main(): try: module\",\n \"the source path ends in /. then the content of\",\n \"the \\\"foregroundDeletion\\\" finalizer, then the owner cannot be deleted from\",\n \"to execute the post build hook, for example for running\",\n \"type: str post_commit: description: - postCommit is a build hook\",\n \"used image ID for build type: str type: description: -\",\n \"where individual elements with a matching I(name_) or I(type) are\",\n \"of an object instead of an entire object, this string\",\n \"categorize (scope and select) objects. May match selectors of replication\",\n \"source control user type: str commit: description: - commit is\",\n \"set this value. It is represented in RFC3339 form and\",\n \"of the referent. type: str block_owner_deletion: description: - If true,\",\n \"old successful builds to retain. If not specified, all successful\",\n \"OpenShift helper. Logging info is written to KubeObjHelper.log default: false\",\n \"causes due to fields having multiple errors. Optional. Examples: \\\"name\\\"\",\n \"written to KubeObjHelper.log default: false type: bool force: description: -\",\n \"I(state) = C(present) contains: api_version: description: - APIVersion defines the\",\n \"object. Servers should convert recognized schemas to the latest internal\",\n \"by the system. Read-only. Null for lists. type: complex contains:\",\n \"new build can only occur as a result of an\",\n \"name: description: - Name of the referent. type: str source_secret:\",\n \"the referent. type: str secrets: description: - secrets is a\",\n \"not be specified with Script. This might be needed if\",\n \"set to true. There cannot be more than one managing\",\n \"int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old\",\n \"Secret or it's key must be defined type: bool force_pull:\",\n \"longer visible from resource lists, and not reachable by name)\",\n \"and the conditions when to trigger them. type: complex contains:\",\n \"of initializers. Only privileged users may set or modify this\",\n \"description: - Determines if an object should be created, patched,\",\n \"Name of the referent. type: str optional: description: - Specify\",\n \"map stored with a resource that may be set by\",\n \"instead of an entire object, this string should contain a\",\n \"ImageStreamTag, ImageStreamImage, or DockerImage to copy source from. type: complex\",\n \"Read-only. type: str reason: description: - A machine-readable description of\",\n \"all failed builds are retained. type: int node_selector: description: -\",\n \"the server, to generate a unique name ONLY IF the\",\n \"garbage collected. If this object is managed by a controller,\",\n \"httpsProxy is a proxy used to reach the git repository\",\n \"it may not be modified further by any user. type:\",\n \"for in the namespace of the build unless Namespace is\",\n \"src: description: - Provide a path to a file containing\",\n \"the branch/tag/ref to build. type: str uri: description: - uri\",\n \"group attribute of the resource associated with the status StatusReason.\",\n \"struct will be set to nil and the object is\",\n \"- BitbucketWebHook contains the parameters for a Bitbucket webhook type\",\n \"str secrets: description: - secrets represents a list of secrets\",\n \"from is a reference to an ImageStreamTag, ImageStreamImage, or DockerImage\",\n \"field has not been provided. If this field is used,\",\n \"to which this reference is made, if any. type: str\",\n \"a 409 - instead, it will either return 201 Created\",\n \"labels: description: - Map of string keys and values that\",\n \"They are not queryable and should be preserved when modifying\",\n \"array includes more details associated with the StatusReason failure. Not\",\n \"be set in happens-before order across separate operations. Clients may\",\n \"Script might be more convenient. type: list contains: str script:\",\n \"node_selector: description: - nodeSelector is a selector which must be\",\n \"source type may be used with other options like git\",\n \"str runtime_artifacts: description: - 'runtimeArtifacts specifies a list of source/destination\",\n \"build to do incremental builds if true. type: bool pull_secret:\",\n \"react by sending a graceful termination signal to the containers\",\n \"of the source control user type: str name: description: -\",\n \"Builds can be launched from a BuildConfig. If no triggers\",\n \"name could not be found in the time allotted, and\",\n \"complex contains: causes: description: - The Causes array includes more\",\n \"object. Populated by the system. Read-only. type: str uid: description:\",\n \"returned is not guaranteed to conform to any schema except\",\n \"a Docker image repository to push to. In the case\",\n \"- If result is set with the Failure field, the\",\n \"identifies the server's internal version of this object that can\",\n \"objects. May match selectors of replication controllers and services. type:\",\n \"terminated. If not set, graceful deletion of the object has\",\n \"script is appropriate to execute the post build hook, for\",\n \"merging lists and updating dictionaries, with lists being merged into\",\n \"is the optional path of the Jenkinsfile that will be\",\n \"doesn't have `/bin/sh`, or if you do not want to\",\n \"description: - UID of the resource. (when there is a\",\n \"is a reference to the secret type: complex contains: name:\",\n \"str no_cache: description: - noCache if set to true indicates\",\n \"of the desired state. Populated by the system. Read-only. type:\",\n \"type: list contains: name: description: - name of the process\",\n \"script is a shell script to be run with `/bin/sh\",\n \"For example, if the object reference is to a container\",\n \"can be escaped with a double $$, ie: $$(VAR_NAME). Escaped\",\n \"retry (optionally after the time indicated in the Retry-After header).\",\n \"entry from the list. If the deletionTimestamp of the object\",\n \"contains: name: description: - Name of the environment variable. Must\",\n \"anywhere right now and apiserver is going to ignore it\",\n \"the build directory where files copied from the image are\",\n \"- committer is the committer of a specific commit type:\",\n \"the existing object's attributes, and deleted, if set to C(absent).\",\n \"object represents. Servers may infer this from the endpoint the\",\n \"where the Dockerfile is located, so users can ADD or\",\n \"have been deleted, this object will be garbage collected. If\",\n \"unset or be set further into the future, although it\",\n \"api_version: type: string description: Requested API version build_config_list: type: complex\",\n \"example, if the object reference is to a container within\",\n \"field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName,\",\n \"application exists. This allows to have buildable sources in directory\",\n \"not be specified with Command. Use Script when a shell\",\n \"deletion_grace_period_seconds: description: - Number of seconds allowed for this object\",\n \"and retrieve arbitrary metadata. They are not queryable and should\",\n \"- the field \\\"name\\\" on the current resource \\\"items[0].name\\\" -\",\n \"is RFC 3339 date and time at which this resource\",\n \"status.podIP.' type: complex contains: api_version: description: - Version of the\",\n \"also be combined with a unique suffix. The provided value\",\n \"in the context dir. type: str git: description: - git\",\n \"- 'Variable references $(VAR_NAME) are expanded using the previous defined\",\n \"includes more details associated with the StatusReason failure. Not all\",\n \"non-zero length. type: str value: description: - value defines the\",\n \"description: - name defines the name of the label. It\",\n \"description: - revision is the information from the source for\",\n \"is removed, and no failing result is set, the initializers\",\n \"internal value, and may reject unrecognized values. type: str kind:\",\n \"watch operation on a resource or set of resources. Clients\",\n \"where the source code for the application exists. This allows\",\n \"-*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION\",\n \"not allowed to change on PUT operations. Populated by the\",\n \"successfulBuildsHistoryLimit is the number of old successful builds to retain.\",\n \"the time when a build pod gets scheduled in the\",\n \"be one of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str\",\n \"for build type: str type: description: - type is the\",\n \"to define object attributes. type: dict src: description: - Provide\",\n \"name: description: - Name of the referent. type: str jenkins_pipeline_strategy:\",\n \"context found in the Kubernetes config file. debug: description: -\",\n \"pulled from the OpenShift registry, this field does not need\",\n \"500 with Reason ServerTimeout indicating a unique name could not\",\n \"the time indicated in the Retry-After header). Applied only if\",\n \"compute resources allowed. type: complex contains: str, str requests: description:\",\n \"description: - The name of a context found in the\",\n \"time indicated in the Retry-After header). Applied only if Name\",\n \"Specify whether the ConfigMap or it's key must be defined\",\n \"strategy receives this binary as standard input. This filename may\",\n \"control over the image entrypoint, or if the image does\",\n \"description: - UID is the unique in time and space\",\n \"provide detailed causes. type: list contains: field: description: - 'The\",\n \"type: str message: description: - A human-readable description of the\",\n \"a list of additional secrets that will be included in\",\n \"optional information about git build source type: complex contains: http_proxy:\",\n \"to the root of WORKDIR. Deprecated: This feature will be\",\n \"false type: bool force: description: - If set to C(True),\",\n \"if true. type: bool pull_secret: description: - pullSecret is the\",\n \"removed from the system. Only set when deletionTimestamp is also\",\n \"of domains for which the proxy should not be used\",\n \"the server's internal version of this object that can be\",\n \"key_file: description: - Path to a key file used to\",\n \"system. Read-only. Value must be treated as opaque by clients\",\n \"determines whether the webhook can set environment variables; can only\",\n \"path kubeconfig: description: - Path to an existing Kubernetes config\",\n \"description: - asFile indicates that the provided binary input should\",\n \"you want to pass into a builder container. type: list\",\n \"the Custom build strategy type: complex contains: _from: description: -\",\n \"is a reference to an ImageStreamTag, ImageStreamImage, or DockerImage to\",\n \"ImageSource to copy binary artifacts created from one build into\",\n \"Not all objects are required to be scoped to a\",\n \"used to validate requests. type: str gitlab: description: - GitLabWebHook\",\n \"name: description: - name of the process that is responsible\",\n \"str, str name: description: - Name must be unique within\",\n \"a field of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations,\",\n \"OpenShift (@openshift) options: api_key: description: - Token used to connect\",\n \"is going to ignore it if set in create or\",\n \"metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version:\",\n \"to build the Docker image, relative to the root of\",\n \"will not be created at the destination. type: str runtime_image:\",\n \"- A human-readable description of the status of this operation.\",\n \"with same name and namespace in different clusters. This field\",\n \"should be preserved when modifying objects. type: complex contains: str,\",\n \"The $(VAR_NAME) syntax can be escaped with a double $$,\",\n \"- present - absent username: description: - Provide a username\",\n \"type: str value: description: - value defines the literal value\",\n \"should be available for the build time. For the Source\",\n \"unrecognized values. type: str code: description: - Suggested HTTP return\",\n \"secret in the pod's namespace type: complex contains: key: description:\",\n \"specified we default to \\\"Serial\\\". type: str service_account: description: -\",\n \"single resource which can be described). type: str kind: description:\",\n \"the client should retry (optionally after the time indicated in\",\n \"the Jenkinsfile that will be used to configure the pipeline\",\n \"ServerTimeout indicating a unique name could not be found in\",\n \"the field to select in the specified API version. type:\",\n \"Command and/or Args. The `-i` flag is needed to support\",\n \"not be created at the destination. type: str pull_secret: description:\",\n \"guaranteed to be set in happens-before order across separate operations.\",\n \"field access statement, such as desiredState.manifest.containers[2]. For example, if the\",\n \"if the controller should configure the build pod to always\",\n \"override any Dockerfile in the build context. For Source builds,\",\n \"label. type: str push_secret: description: - PushSecret is the name\",\n \"a shell script to be run with `/bin/sh -ic`. It\",\n \"if you do not want to use a shell. In\",\n \"them during docker build. type: str secret: description: - secret\",\n \"empty, it may not be modified further by any user.\",\n \"If both JenkinsfilePath & Jenkinsfile are both not specified, this\",\n \"str namespace: description: - Namespace of the referent. type: str\",\n \"parameters for a Generic webhook type of trigger type: complex\",\n \"to look up a Docker image repository to push to.\",\n \"provided to either Command, Script or the Docker image's default\",\n \"build configs type: list contains: api_version: description: - APIVersion defines\",\n \"this object. type: str result: description: - If result is\",\n \"node before the system actively tries to terminate the build;\",\n \"(in list/watch and get calls) from clients that haven't explicitly\",\n \"optional location to push the output of this build to.\",\n \"be used if value is not empty. type: complex contains:\",\n \"must be defined type: bool field_ref: description: - 'Selects a\",\n \"metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex\",\n \"- secret used to validate requests. type: str image_change: description:\",\n \"other options like git - in those cases the Git\",\n \"each name must be unique. An empty namespace is equivalent\",\n \"image step, and will fail if the Dockerfile cannot provide\",\n \"preserved. type: str no_cache: description: - noCache if set to\",\n \"pipeline relative to the root of the context (contextDir). If\",\n \"type: str owner_references: description: - List of objects depended by\",\n \"is specified. type: complex contains: api_version: description: - API version\",\n \"by its JSON serialization. May include dot and postfix notation\",\n \"resource_version: description: - Specific resourceVersion to which this reference is\",\n \"Path to a key file used to authenticate with the\",\n \"List operations provide a snapshot read of the underlying objects,\",\n \"a single file within the build input. For example, specifying\",\n \"that haven't explicitly asked to observe uninitialized objects. When an\",\n \"type: str generic: description: - generic contains the parameters for\",\n \"str image_change: description: - imageChange contains parameters for an ImageChange\",\n \"defined type: bool jenkinsfile: description: - Jenkinsfile defines the optional\",\n \"operation on a resource or set of resources. Clients must\",\n \"directory itself will not be created at the destination. type:\",\n \"depended by this object. If ALL objects in the list\",\n \"date and time at which this resource will be deleted.\",\n \"scheduled in the system, that the build may be active\",\n \"will be removed in a future release. Use ImageSource to\",\n \"is optional. type: complex contains: git: description: - Git contains\",\n \"from inside the Docker container. type: bool force_pull: description: -\",\n \"a node before the system actively tries to terminate the\",\n \"field_path: description: - 'If referring to a piece of an\",\n \"'Required: resource to select' type: str secret_key_ref: description: - Selects\",\n \"for this object to gracefully terminate before it will be\",\n \"bool secret: description: - secret used to validate requests. type:\",\n \"the list. If the deletionTimestamp of the object is non-nil,\",\n \"type: complex contains: annotations: description: - Annotations is an unstructured\",\n \"namespace - the value of this field for those objects\",\n \"type: complex returned: when I(state) = C(present) contains: api_version: description:\",\n \"location of Source scripts type: str type: description: - type\",\n \"a resource of the container: only resources limits and requests\",\n \"define object attributes. type: dict src: description: - Provide a\",\n \"Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc))\",\n \"referent. type: str uid: description: - UID of the referent.\",\n \"the resource that has caused this error, as named by\",\n \"be returned. type: bool controller: description: - If true, this\",\n \"of the environment variable. Must be a C_IDENTIFIER. type: str\",\n \"the deletionTimestamp of the object is non-nil, entries in this\",\n \"contains: field: description: - 'The field of the resource that\",\n \"gitlab: description: - GitLabWebHook contains the parameters for a GitLab\",\n \"str build_args: description: - buildArgs contains build arguments that will\",\n \"API. Use in conjunction with I(username). resource_definition: description: - Provide\",\n \"connection options are provided, the openshift client will attempt to\",\n \"an implementation-defined value. type: complex contains: str, str revision: description:\",\n \"source from. type: complex contains: api_version: description: - API version\",\n \"directory with a single file. contextDir may be used when\",\n \"description: Requested API version build_config_list: type: complex returned: when I(state)\",\n \"is raise the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException\",\n \"& Jenkinsfile are both not specified, this defaults to Jenkinsfile\",\n \"entry in \\\"items\\\"' type: str message: description: - A human-readable\",\n \"In the case of an ImageStreamTag, the ImageStreamTag will be\",\n \"value, and may reject unrecognized values. type: str kind: description:\",\n \"which must be true for the build pod to fit\",\n \"source_secret: description: - \\\"sourceSecret is the name of a Secret\",\n \"builds accept a binary as their input. The binary is\",\n \"will updated, and lists will be replaced, rather than merged.\",\n \"define its own extended details. This field is optional and\",\n \"in a build configuration. type: complex contains: api_version: description: -\",\n \"cause of the error. If this value is empty there\",\n \"operation results in merging lists and updating dictionaries, with lists\",\n \"value_from: description: - Source for the environment variable's value. Cannot\",\n \"type: str images: description: - images describes a set of\",\n \"secret used to validate requests. type: str generic: description: -\",\n \"to an image created by the Docker build API. The\",\n \"be removed. type: list contains: str generate_name: description: - GenerateName\",\n \"This might be needed if the image doesn't have `/bin/sh`,\",\n \"list of initializers that must execute in order before this\",\n \"image for use. Deprecated: This feature will be removed in\",\n \"need control over the image entrypoint, or if the image\",\n \"version for the Build object serialized and passed to the\",\n \"type: complex contains: key: description: - The key to select.\",\n \"type: str secrets: description: - secrets is a list of\",\n \"graceful deletion is requested by the user, and is not\",\n \"UID of the referent. type: str resource_version: description: - An\",\n \"it can be overridden by default build nodeselector values for\",\n \"Provide a path to a file containing the YAML definition\",\n \"contains: author: description: - author is the author of a\",\n \"than one managing controller. type: list contains: api_version: description: -\",\n \"Determines if an object should be created, patched, or deleted.\",\n \"description: - Name of the referent. type: str source_secret: description:\",\n \"description: - nodeSelector is a selector which must be true\",\n \"as 'SkipLayers' but simply warns if compatibility cannot be preserved.\",\n \"be used. Only one ImageChangeTrigger with an empty From reference\",\n \"used to authenticate with the API. type: path kubeconfig: description:\",\n \"user, and is not directly settable by a client. The\",\n \"of the referent. type: str field_path: description: - 'If referring\",\n \"type: str resource_field_ref: description: - 'Selects a resource of the\",\n \"the container and any service environment variables. If a variable\",\n \"be needed if the image doesn't have `/bin/sh`, or if\",\n \"that is explicitly specified, otherwise to an implementation-defined value. type:\",\n \"build strategy type: complex contains: _from: description: - from is\",\n \"internal version of this object that can be used by\",\n \"owner_references: description: - List of objects depended by this object.\",\n \"description: - output describes the Docker image the Strategy should\",\n \"name: description: - Name of the referent. type: str to:\",\n \"when a shell script is appropriate to execute the post\",\n \"StatusReason. type: str kind: description: - The kind attribute of\",\n \"be treated as opaque by clients and . type: str\",\n \"from. type: complex contains: api_version: description: - API version of\",\n \"the Source build strategy, these will be injected into a\",\n \"required for volumes, optional for env vars' type: str divisor:\",\n \"by default build nodeselector values for the cluster. If set\",\n \"the process that is responsible for initializing this object. type:\",\n \"in directory other than root of repository. type: str dockerfile:\",\n \"and is hidden (in list/watch and get calls) from clients\",\n \"''' api_version: type: string description: Requested API version build_config_list: type:\",\n \"updated It is optional. If no From is specified, the\",\n \"code but does not override it. type: str status: description:\",\n \"seconds before the operation should be retried. type: int uid:\",\n \"description: - Source for the environment variable's value. Cannot be\",\n \"is represented in RFC3339 form and is in UTC. Populated\",\n \"for cloning private repository. The secret\\\\ \\\\ contains valid credentials\",\n \"to the Jenkins Pipeline build strategy. This strategy is in\",\n \"the error. If this value is empty there is no\",\n \"- CreationTimestamp is a timestamp representing the server time when\",\n \"private Docker registries type: complex contains: name: description: - Name\",\n \"Name of the referent. type: str uid: description: - UID\",\n \"type: str message: description: - message is the description of\",\n \"'SkipLayers' but simply warns if compatibility cannot be preserved. type:\",\n \"to. Kind must be one of 'ImageStreamTag' or 'DockerImage'. This\",\n \"tar, gzipped tar, or zip file depending on the strategy.\",\n \"that you want to use in your build. type: complex\",\n \"Git contains information about git-based build source type: complex contains:\",\n \"recognized schemas to the latest internal value, and may reject\",\n \"of the referent. type: str env: description: - env contains\",\n \"(when there is a single name which can be described).\",\n \"output from the OpenShift helper. Logging info is written to\",\n \"environment variables in the container and any service environment variables.\",\n \"- Specifies the output format of the exposed resources, defaults\",\n \"value: description: - value defines the literal value of the\",\n \"SSL certificates. type: bool requirements: - openshift == 0.3.3 '''\",\n \"true for the build pod to fit on a node\",\n \"completionDeadlineSeconds is an optional duration in seconds, counted from the\",\n \"destination. type: str runtime_image: description: - 'runtimeImage is an optional\",\n \"GenerateName is an optional prefix, used by the server, to\",\n \"existing object's attributes, and deleted, if set to C(absent). A\",\n \"of the secret to select from. Must be a valid\",\n \"The group attribute of the resource associated with the status\",\n \"submits requests to. Cannot be updated. In CamelCase. type: str\",\n \"- UID of the referent. type: str last_triggered_image_id: description: -\",\n \"If there are multiple labels with the same name then\",\n \"if Name is not specified. type: str generation: description: -\",\n \"The key of the secret to select from. Must be\",\n \"for volumes, optional for env vars' type: str divisor: description:\",\n \"type of trigger type: complex contains: allow_env: description: - allowEnv\",\n \"the case of an ImageStreamTag, the ImageStreamTag will be looked\",\n \"Deprecated: This feature will be removed in a future release.\",\n \"the Dockerfile for this build. type: complex contains: api_version: description:\",\n \"the build context and an optional Dockerfile may be specified\",\n \"creation of a resource and is not allowed to change\",\n \"over the image entrypoint, or if the image does not\",\n \"failed builds are retained. type: int node_selector: description: - nodeSelector\",\n \"have the appropriate collections enabled in the shell. E.g., in\",\n \"type: str self_link: description: - SelfLink is a URL representing\",\n \"- 'runtimeArtifacts specifies a list of source/destination pairs that will\",\n \"of the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is\",\n \"description: - Number of seconds allowed for this object to\",\n \"for which the proxy should not be used type: str\",\n \"resource may be deleted prior to this time. For example,\",\n \"type: path password: description: - Provide a password for connecting\",\n \"to make `ruby`, `bundle` and other binaries available in the\",\n \"references $(VAR_NAME) are expanded using the previous defined environment variables\",\n \"description: - type is the kind of build strategy. type:\",\n \"updated. In CamelCase. type: str metadata: description: - metadata for\",\n \"referent. type: str namespace: description: - Namespace of the referent.\",\n \"the final size or time spent building the image. The\",\n \"has caused this error, as named by its JSON serialization.\",\n \"the SCM in use. type: complex contains: binary: description: -\",\n \"result is set, the initializers struct will be set to\",\n \"git-based build source type: complex contains: author: description: - author\",\n \"caused this error, as named by its JSON serialization. May\",\n \"description: - Path to an existing Kubernetes config file. If\",\n \"is the author of a specific commit type: complex contains:\",\n \"returned to the client will be different than the name\",\n \"GitHub webhook type of trigger type: complex contains: allow_env: description:\",\n \"it is not present locally type: bool pull_secret: description: -\",\n \"ServiceAccount to use to run the pod created by this\",\n \"may not be unset or be set further into the\",\n \"pass into a build pipeline. type: list contains: name: description:\",\n \"a list of initializers that have not yet acted on\",\n \"creating resources, although some resources may allow a client to\",\n \"type: complex contains: _from: description: - from is reference to\",\n \"type: str ref: description: - ref is the branch/tag/ref to\",\n \"ImageChangeController to save last used image ID for build type:\",\n \"timestamp representing the server time when this object was created.\",\n \"or set of resources. Populated by the system. Read-only. Value\",\n \"where the files from the secret should be available for\",\n \"in the FROM line of the Dockerfile for this build.\",\n \"immediately after the command to be run. type: list contains:\",\n \"version of the listed objects. version_added: 2.3.0 author: OpenShift (@openshift)\",\n \"SCM in use. type: complex contains: binary: description: - binary\",\n \"creation. type: list contains: bitbucket: description: - BitbucketWebHook contains the\",\n \"In CamelCase. type: str metadata: description: - metadata for BuildConfigList.\",\n \"deletion is requested. Read-only. type: complex contains: {} finalizers: description:\",\n \"reader. type: str reason: description: - A machine-readable description of\",\n \"to the Source build strategy. type: complex contains: _from: description:\",\n \"a Generic webhook type of trigger type: complex contains: allow_env:\",\n \"- Limits describes the maximum amount of compute resources allowed.\",\n \"source type: complex contains: http_proxy: description: - httpProxy is a\",\n \"the base64 encoded credentials. Supported auth methods\\\\ \\\\ are: ssh-privatekey.\\\"\",\n \"description: - Provide a URL for acessing the Kubernetes API.\",\n \"based on your strategy base image and additional ENV stanzas\",\n \"is the committer of a specific commit type: complex contains:\",\n \"containers in the pod. After that 30 seconds, the Kubelet\",\n \"of last triggered build. type: int kind: description: - Kind\",\n \"secrets: description: - secrets is a list of additional secrets\",\n \"to the secret type: complex contains: name: description: - Name\",\n \"as opaque by clients and passed unmodified back to the\",\n \"and services. type: complex contains: str, str name: description: -\",\n \"- UID of the referent. type: str scripts: description: -\",\n \"to authenticate with the API. type: path kubeconfig: description: -\",\n \"to run an application without unneeded dependencies installed. The building\",\n \"uid: description: - UID of the referent. type: str build_args:\",\n \"registry prior to building. type: bool incremental: description: - incremental\",\n \"the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the\",\n \"type: description: - type is the kind of build strategy.\",\n \"int deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date and\",\n \"the image. type: list contains: destination_dir: description: - destinationDir is\",\n \"api_version: description: - Version of the schema the FieldPath is\",\n \"client to request the generation of an appropriate name automatically.\",\n \"placed immediately after the command to be run. type: list\",\n \"runtime_artifacts: description: - 'runtimeArtifacts specifies a list of source/destination pairs\",\n \"the authentication for cloning private repository. The secret\\\\ \\\\ contains\",\n \"not specified, all failed builds are retained. type: int node_selector:\",\n \"password for connecting to the API. Use in conjunction with\",\n \"Each reason may define its own extended details. This field\",\n \"triggered build. type: int kind: description: - Kind is a\",\n \"- Kind is a string value representing the REST resource\",\n \"- Extended data associated with the reason. Each reason may\",\n \"visible from resource lists, and not reachable by name) after\",\n \"parameters for a GitLab webhook type of trigger type: complex\",\n \"updated. type: str namespace: description: - Namespace defines the space\",\n \"individual elements with a matching I(name_) or I(type) are merged.\",\n \"of the referent. type: str name: description: - Name of\",\n \"builder should pull the images from registry prior to building.\",\n \"object's attributes, and deleted, if set to C(absent). A patch\",\n \"is not guaranteed to conform to any schema except that\",\n \"allowed to change on PUT operations. Populated by the system.\",\n \"complex contains: git: description: - Git contains information about git-based\",\n \"name is specified \\\"spec.containers[2]\\\" (container with index 2 in this\",\n \"the application is still done in the builder image but,\",\n \"BuildConfig. If no triggers are defined, a new build can\",\n \"to a container within a pod, this would take on\",\n \"set the I(force) option to C(True). default: present choices: -\",\n \"size or time spent building the image. The default policy\",\n \"str committer: description: - committer is the committer of a\",\n \"PushSecret is the name of a Secret that would be\",\n \"set to C(True), and I(state) is C(present), an existing object\",\n \"imageLabels define a list of labels that are applied to\",\n \"to true for GenericWebHook. type: bool secret: description: - secret\",\n \"= ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve\",\n \"list of labels that are applied to the resulting image.\",\n \"On some operations may differ from the requested resource Kind.\",\n \"and categorize (scope and select) objects. May match selectors of\",\n \"not reachable by name) after the time in this field.\",\n \"can only occur as a result of an explicit client\",\n \"be a valid secret key. type: str name: description: -\",\n \"force: description: - If set to C(True), and I(state) is\",\n \"type: list contains: bitbucket: description: - BitbucketWebHook contains the parameters\",\n \"of old failed builds to retain. If not specified, all\",\n \"the value of this field for those objects will be\",\n \"as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc)) if __name__ == '__main__': main()\",\n \"integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number\",\n \"this operation is in the \\\"Failure\\\" status. If this value\",\n \"object that can be used by clients to determine when\",\n \"be injected into a container where the assemble script runs.\",\n \"a key of a ConfigMap. type: complex contains: key: description:\",\n \"deletionTimestamp is also set. May only be shortened. Read-only. type:\",\n \"from a BuildConfig. If no triggers are defined, a new\",\n \"description: - httpProxy is a proxy used to reach the\",\n \"line of the Dockerfile for this build. type: complex contains:\",\n \"str name: description: - Name of the referent. type: str\",\n \"description: - git contains optional information about git build source\",\n \"Namespace is specified. type: complex contains: api_version: description: - API\",\n \"build configuration. type: complex contains: api_version: description: - API version\",\n \"visible. When the last pending initializer is removed, and no\",\n \"for nested attributes. Arrays are zero-indexed. Fields may appear more\",\n \"description: - noCache if set to true indicates that the\",\n \"description: - Selects a key of a ConfigMap. type: complex\",\n \"to copy binary artifacts created from one build into a\",\n \"type of trigger type: complex contains: _from: description: - from\",\n \"(container with index 2 in this pod). This syntax is\",\n \"example, specifying \\\"webapp.war\\\" would place the provided binary as `/webapp.war`\",\n \"by a client. The resource is expected to be deleted\",\n \"secret: description: - secret is a reference to an existing\",\n \"existing object will updated, and lists will be replaced, rather\",\n \"build. type: str secret: description: - secret is a reference\",\n \"to \\\".\\\", in this case it just refers to the\",\n \"str type: description: - type of build input to accept\",\n \"this reference is made, if any. type: str uid: description:\",\n \"as desiredState.manifest.containers[2]. For example, if the object reference is to\",\n \"created, the system will populate this list with the current\",\n \"be scoped to a namespace - the value of this\",\n \"the watch operation on a resource or set of resources.\",\n \"to the server. They may only be valid for a\",\n \"to false. To set this field, a user needs \\\"delete\\\"\",\n \"executed with the --no-cache=true flag type: bool pull_secret: description: -\",\n \"and the data returned is not guaranteed to conform to\",\n \"the system, that the build may be active on a\",\n \"This value will also be combined with a unique suffix.\",\n \"namespace, but \\\"default\\\" is the canonical representation. Not all objects\",\n \"merged into a unique set of values. If a list\",\n \"contains: str, str name: description: - Name must be unique\",\n \"the parameters for a Bitbucket webhook type of trigger type:\",\n \"rather than merged. default: false type: bool host: description: -\",\n \"used for setting up the authentication for executing the Docker\",\n \"of the cause of the error. This field may be\",\n \"contains: env: description: - env contains additional environment variables you\",\n \"do is raise the error. raise Exception(exc.message) try: module.execute_module() except\",\n \"expose_docker_socket: description: - exposeDockerSocket will allow running Docker commands (and\",\n \"ref is the branch/tag/ref to build. type: str uri: description:\",\n \"terms of, defaults to \\\"v1\\\". type: str field_path: description: -\",\n \"FROM line of the Dockerfile for this build. type: complex\",\n \"for optimistic concurrency, change detection, and the watch operation on\",\n \"and no other connection options are provided, the openshift client\",\n \"If true, AND if the owner has the \\\"foregroundDeletion\\\" finalizer,\",\n \"description: - List of objects depended by this object. If\",\n \"on the first array entry in \\\"items\\\"' type: str message:\",\n \"owner, otherwise 422 (Unprocessable Entity) will be returned. type: bool\",\n \"used to distinguish resources with same name and namespace in\",\n \"prefix, used by the server, to generate a unique name\",\n \"referent. type: str source_secret: description: - \\\"sourceSecret is the name\",\n \"If not specified, all failed builds are retained. type: int\",\n \"type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a list of\",\n \"Dockerfile for this build. type: complex contains: api_version: description: -\",\n \"include dot and postfix notation for nested attributes. Arrays are\",\n \"type: str context_dir: description: - contextDir specifies the sub-directory where\",\n \"try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: #\",\n \"for lists. type: complex contains: {} deletion_grace_period_seconds: description: - Number\",\n \"to an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker\",\n \"the From image from the build strategy will be used.\",\n \"secrets referenced by the ServiceAccount type: str source: description: -\",\n \"the same name then the last one in the list\",\n \"determine when objects have changed. Value must be treated as\",\n \"may allow a client to request the generation of an\",\n \"server, to generate a unique name ONLY IF the Name\",\n \"complex contains: api_version: description: - Version of the schema the\",\n \"it's key must be defined type: bool jenkinsfile: description: -\",\n \"ADD or COPY them during docker build. type: str secret:\",\n \"a specific commit type: str type: description: - type of\",\n \"causes: description: - The Causes array includes more details associated\",\n \"AND if the owner has the \\\"foregroundDeletion\\\" finalizer, then the\",\n \"(scope and select) objects. May match selectors of replication controllers\",\n \"build; value must be positive integer type: int failed_builds_history_limit: description:\",\n \"- name defines the name of the label. It must\",\n \"description: - args is a list of arguments that are\",\n \"Dockerfile in the build context. For Source builds, this is\",\n \"be updated. type: str namespace: description: - Namespace defines the\",\n \"a client. The resource is expected to be deleted (no\",\n \"Source build to do incremental builds if true. type: bool\",\n \"new build created from this build configuration will be scheduled\",\n \"contains the parameters for a GitLab webhook type of trigger\",\n \"type: description: - type of build input to accept type:\",\n \"customStrategy holds the parameters to the Custom build strategy type:\",\n \"is omitted for a container, it defaults to Limits if\",\n \"at which this resource will be deleted. This field is\",\n \"the system. Read-only. type: str uid: description: - UID is\",\n \"will react by sending a graceful termination signal to the\",\n \"that will be used only for the build. type: list\",\n \"to an ImageStreamTag, ImageStreamImage, or DockerImage to copy source from.\",\n \"if set to true indicates that the docker build must\",\n \"those objects will be empty. Must be a DNS_LABEL. Cannot\",\n \"available for the build time. For the Source build strategy,\",\n \"a build pipeline. type: list contains: name: description: - Name\",\n \"JenkinsfilePath is the optional path of the Jenkinsfile that will\",\n \"on STDIN. type: complex contains: as_file: description: - asFile indicates\",\n \"key value map stored with a resource that may be\",\n \"str requests: description: - Requests describes the minimum amount of\",\n \"object was created. It is not guaranteed to be set\",\n \"the versioned schema of this representation of an object. Servers\",\n \"with the status StatusReason (when there is a single name\",\n \"the docker image should be pulled the resulting image will\",\n \"api_version: description: - APIVersion defines the versioned schema of this\",\n \"should produce. type: complex contains: image_labels: description: - imageLabels define\",\n \"set this field, a user needs \\\"delete\\\" permission of the\",\n \"for setting up the authentication for executing the Docker push\",\n \"when creating resources, although some resources may allow a client\",\n \"str metadata: description: - metadata for BuildConfigList. type: complex contains:\",\n \"value has the same validation rules as the Name field,\",\n \"whether the Secret or it's key must be defined type:\",\n \"(Unprocessable Entity) will be returned. type: bool controller: description: -\",\n \"in the system, that the build may be active on\",\n \"set to true indicates that the docker build must be\",\n \"sourcePath is the absolute path of the file or directory\",\n \"ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of\",\n \"files injected will be truncated to zero length. For the\",\n \"assumed to be a tar, gzipped tar, or zip file\",\n \"validate requests. type: str gitlab: description: - GitLabWebHook contains the\",\n \"a namespace - the value of this field for those\",\n \"allowEnv determines whether the webhook can set environment variables; can\",\n \"Jenkins pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath is\",\n \"feature will be removed in a future release. Use ImageSource\",\n \"secrets represents a list of secrets and their destinations that\",\n \"description: - GitLabWebHook contains the parameters for a GitLab webhook\",\n \"description: - from is a reference to an ImageStreamTag that\",\n \"value, and may reject unrecognized values. type: str items: description:\",\n \"For the Docker build strategy, these will be copied into\",\n \"an optional Dockerfile may be specified to override any Dockerfile\",\n \"- resources computes resource requirements to execute the build. type:\",\n \"convenient. type: list contains: str script: description: - script is\",\n \"of the label. It must have non-zero length. type: str\",\n \"in this list can only be removed. type: list contains:\",\n \"From reference is allowed in a build configuration. type: complex\",\n \"empty before the object is deleted from the registry. Each\",\n \"source describes the SCM in use. type: complex contains: binary:\",\n \"It is optional. If no From is specified, the From\",\n \"container and any service environment variables. If a variable cannot\",\n \"number of old failed builds to retain. If not specified,\",\n \"config file. If not provided, and no other connection options\",\n \"is hidden (in list/watch and get calls) from clients that\",\n \"Whether or not to verify the API server's SSL certificates.\",\n \"retain. If not specified, all successful builds are retained. type:\",\n \"variable exists or not. Defaults to \\\"\\\".' type: str value_from:\",\n \"resource that may be set by external tools to store\",\n \"change detection, and the watch operation on a resource or\",\n \"this reference is removed. Defaults to false. To set this\",\n \"May be used for optimistic concurrency, change detection, and the\",\n \"metadata. type: complex contains: resource_version: description: - String that identifies\",\n \"must be one of 'ImageStreamTag' or 'DockerImage'. This value will\",\n \"objects will be empty. Must be a DNS_LABEL. Cannot be\",\n \"in the Dockerfile. See type: list contains: name: description: -\",\n \"how the new build created from this build configuration will\",\n \"config type: complex contains: last_version: description: - lastVersion is used\",\n \"git contains optional information about git build source type: complex\",\n \"a Jenkinsfile which defines a Jenkins pipeline build. type: str\",\n \"certificate used to authenticate with the API. type: path state:\",\n \"or 'DockerImage'. This value will be used to look up\",\n \"\\\"name\\\" on the current resource \\\"items[0].name\\\" - the field \\\"name\\\"\",\n \"files from the secret should be available for the build\",\n \"Servers may infer this from the endpoint the client submits\",\n \"is not guaranteed to be set in happens-before order across\",\n \"trigger type: complex contains: allow_env: description: - allowEnv determines whether\",\n \"a reference to an ImageStreamTag that will trigger a build\",\n \"an existing object will updated, and lists will be replaced,\",\n \"only be shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp\",\n \"created from this build configuration will be scheduled for execution.\",\n \"and any service environment variables. If a variable cannot be\",\n \"Name of the referent. type: str secrets: description: - secrets\",\n \"- secret used to validate requests. type: str generic: description:\",\n \"= C(present) contains: api_version: description: - APIVersion defines the versioned\",\n \"DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: -\",\n \"build to. Kind must be one of 'ImageStreamTag' or 'DockerImage'.\",\n \"version. type: str resource_field_ref: description: - 'Selects a resource of\",\n \"namespace. Is required when creating resources, although some resources may\",\n \"is removed. Defaults to false. To set this field, a\",\n \"- UID of the referent. type: str post_commit: description: -\",\n \"RFC3339 form and is in UTC. Populated by the system.\",\n \"- 'Container name: required for volumes, optional for env vars'\",\n \"typically generated by the server on successful creation of a\",\n \"str commit: description: - commit is the commit hash identifying\",\n \"a key file used to authenticate with the API. type:\",\n \"an optional image that is used to run an application\",\n \"number representing a specific generation of the desired state. Populated\",\n \"shell. E.g., in the Ruby image, this is necessary to\",\n \"selectors of replication controllers and services. type: complex contains: str,\",\n \"to a file containing the YAML definition of the object.\",\n \"description: - email of the source control user type: str\",\n \"source code for the application exists. This allows to have\",\n \"the referent. type: str env: description: - env contains additional\",\n \"a variable cannot be resolved, the reference in the input\",\n \"that represents the internal version of this object that can\",\n \"build type: str type: description: - type is the type\",\n \"unique set of values. If a list contains a dictionary\",\n \"fields having multiple errors. Optional. Examples: \\\"name\\\" - the field\",\n \"name of the process that is responsible for initializing this\",\n \"present choices: - present - absent username: description: - Provide\",\n \"specific repo snapshot. This is optional. type: complex contains: git:\",\n \"a shell. In all other cases, using Script might be\",\n \"described). type: str kind: description: - Kind is a string\",\n \"str, str requests: description: - Requests describes the minimum amount\",\n \"value for this object. It is typically generated by the\",\n \"allowed. type: complex contains: str, str requests: description: - Requests\",\n \"or zip file depending on the strategy. For Docker builds,\",\n \"or it's key must be defined type: bool force_pull: description:\",\n \"build. type: complex contains: limits: description: - Limits describes the\",\n \"YAML definition of the object. Mutually exclusive with I(resource_definition). type:\",\n \"description: - dockerStrategy holds the parameters to the Docker build\",\n \"requests. type: str gitlab: description: - GitLabWebHook contains the parameters\",\n \"not be created at the destination. type: str runtime_image: description:\",\n \"For Source and Docker builds, if binary.asFile is set the\",\n \"Docker image's default entrypoint. The arguments are placed immediately after\",\n \"- exposeDockerSocket will allow running Docker commands (and build Docker\",\n \"be overridden by default build nodeselector values for the cluster.\",\n \"the FieldPath is written in terms of, defaults to \\\"v1\\\".\",\n \"primarily intended for creation idempotence and configuration definition. Cannot be\",\n \"a separate runtime image.' type: list contains: destination_dir: description: -\",\n \"for a container, it defaults to Limits if that is\",\n \"If this field is specified and the generated name exists,\",\n \"implementation-defined value. type: complex contains: str, str revision: description: -\",\n \"contains: config_map_key_ref: description: - Selects a key of a ConfigMap.\",\n \"Is required when creating resources, although some resources may allow\",\n \"overridden by default build nodeselector values for the cluster. If\",\n \"build. type: str uri: description: - uri points to the\",\n \"(contextDir). If both JenkinsfilePath & Jenkinsfile are both not specified,\",\n \"controller, then an entry in this list will point to\",\n \"the image. The default policy is 'None' which means the\",\n \"defined type: bool force_pull: description: - forcePull describes if the\",\n \"within the build input. For example, specifying \\\"webapp.war\\\" would place\",\n \"Cannot be updated. In CamelCase. type: str metadata: description: -\",\n \"DNS_LABEL. Cannot be updated. type: str owner_references: description: - List\",\n \"to a registry. type: complex contains: args: description: - args\",\n \"new build, and the conditions when to trigger them. type:\",\n \"it does not exist, or patched, if parameter values differ\",\n \"type: str kind: description: - The kind attribute of the\",\n \"by the user, and is not directly settable by a\",\n \"resource requirements to execute the build. type: complex contains: limits:\",\n \"description: - A human-readable description of the cause of the\",\n \"OpenShiftAnsibleException as exc: # The helper failed to init, so\",\n \"contains valid credentials for remote repository, where the\\\\ \\\\ data's\",\n \"or if the image does not have `/bin/sh`, use Command\",\n \"version of the referent. type: str field_path: description: - 'If\",\n \"system. Read-only. type: str spec: description: - spec holds all\",\n \"pushed to a registry. type: complex contains: args: description: -\",\n \"list of secrets and their destinations that will be used\",\n \"right now and apiserver is going to ignore it if\",\n \"type: bool jenkinsfile: description: - Jenkinsfile defines the optional raw\",\n \"key of a ConfigMap. type: complex contains: key: description: -\",\n \"false. To set this field, a user needs \\\"delete\\\" permission\",\n \"is optional and the data returned is not guaranteed to\",\n \"type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the\",\n \"to reduce the final size or time spent building the\",\n \"list contains: name: description: - Name of the environment variable.\",\n \"type: complex contains: git: description: - Git contains information about\",\n \"C(absent). A patch operation results in merging lists and updating\",\n \"When this option is specified, the FROM may be modified\",\n \"returned: when I(state) = C(present) contains: api_version: description: - APIVersion\",\n \"merged. To force the replacement of lists, set the I(force)\",\n \"- The name of a context found in the Kubernetes\",\n \"visible to all clients. type: list contains: name: description: -\",\n \"available in the PATH. type: str resources: description: - resources\",\n \"or I(type) are merged. To force the replacement of lists,\",\n \"information available. A Reason clarifies an HTTP status code but\",\n \"author of a specific commit type: complex contains: email: description:\",\n \"Kind must be one of 'ImageStreamTag' or 'DockerImage'. This value\",\n \"type: str field_path: description: - 'If referring to a piece\",\n \"resource: description: - 'Required: resource to select' type: str secret_key_ref:\",\n \"RETURN = ''' api_version: type: string description: Requested API version\",\n \"the error. This field may be presented as-is to a\",\n \"value is not empty. type: complex contains: config_map_key_ref: description: -\",\n \"valid JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example,\",\n \"used and\\\\ \\\\ value is the base64 encoded credentials. Supported\",\n \"contains: destination_dir: description: - destinationDir is the directory where the\",\n \"auth methods\\\\ \\\\ are: ssh-privatekey.\\\" type: complex contains: name: description:\",\n \"set to C(present), the object will be created, if it\",\n \"optional path of the Jenkinsfile that will be used to\",\n \"be preserved when modifying objects. type: complex contains: str, str\",\n \"context and an optional Dockerfile may be specified to override\",\n \"injected will be truncated to zero length. For the Docker\",\n \"intended for creation idempotence and configuration definition. Cannot be updated.\",\n \"defaults to \\\"1\\\" type: str resource: description: - 'Required: resource\",\n \"and passed to the custom builder type: str env: description:\",\n \"be pulled the resulting image will be used in the\",\n \"Registry (or Docker Hub). type: complex contains: name: description: -\",\n \"str message: description: - A human-readable description of the status\",\n \"registry. type: complex contains: args: description: - args is a\",\n \"- serviceAccount is the name of the ServiceAccount to use\",\n \"absolute path of the file or directory inside the image\",\n \"secret_key_ref: description: - Selects a key of a secret in\",\n \"= ''' api_version: type: string description: Requested API version build_config_list:\",\n \"imageChange contains parameters for an ImageChange type of trigger type:\",\n \"has not been provided. If this field is used, the\",\n \"is also set. May only be shortened. Read-only. type: int\",\n \"resource is fully terminated. If not set, graceful deletion of\",\n \"resource. (when there is a single resource which can be\",\n \"- The key to select. type: str name: description: -\",\n \"of the error. This field may be presented as-is to\",\n \"to retain. If not specified, all successful builds are retained.\",\n \"object.' type: str kind: description: - Kind of the referent.\",\n \". type: str self_link: description: - SelfLink is a URL\",\n \"to the API. Use in conjunction with I(username). resource_definition: description:\",\n \"helper failed to init, so there is no module object.\",\n \"contents of a Dockerfile which should be built. When this\",\n \"I(force) option to C(True). default: present choices: - present -\",\n \"be added after the FROM, but before the rest of\",\n \"\\\"spec.containers[2]\\\" (container with index 2 in this pod). This syntax\",\n \"value that represents the internal version of this object that\",\n \"image. sourcePath can be a file or directory. destinationDir must\",\n \"is used to run an application without unneeded dependencies installed.\",\n \"\\\"name\\\" on the first array entry in \\\"items\\\"' type: str\",\n \"pull the image from a registry If the image is\",\n \"seconds. The Kubelet will react by sending a graceful termination\",\n \"- An initializer is a controller which enforces some system\",\n \"dockerfile is the raw contents of a Dockerfile which should\",\n \"exists, the server will NOT return a 409 - instead,\",\n \"with the 'None' policy. An additional experimental policy 'SkipLayersAndWarn' is\",\n \"parameters to the Docker build strategy. type: complex contains: _from:\",\n \"value. type: complex contains: str, str revision: description: - revision\",\n \"description: - A human-readable description of the status of this\",\n \"can be overridden by default build nodeselector values for the\",\n \"authentication method to be used and\\\\ \\\\ value is the\",\n \"of the object has not been requested. Populated by the\",\n \"send a hard termination signal (SIGKILL) to the container and\",\n \"pod from the API. In the presence of network partitions,\",\n \"type: complex contains: resource_version: description: - String that identifies the\",\n \"An initializer is a controller which enforces some system invariant\",\n \"Optional. Examples: \\\"name\\\" - the field \\\"name\\\" on the current\",\n \"this case it just refers to the root of WORKDIR.\",\n \"the owner, otherwise 422 (Unprocessable Entity) will be returned. type:\",\n \"the optional raw contents of a Jenkinsfile which defines a\",\n \"requests.cpu and requests.memory) are currently supported.' type: complex contains: container_name:\",\n \"before the operation should be retried. type: int uid: description:\",\n \"spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: -\",\n \"description: - buildArgs contains build arguments that will be resolved\",\n \"# -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException\",\n \"contains: last_version: description: - lastVersion is used to inform about\",\n \"list of additional secrets that will be included in the\",\n \"the resource may be deleted prior to this time. For\",\n \"are retained. type: int node_selector: description: - nodeSelector is a\",\n \"pairs that will be copied from the builder to the\",\n \"for this build. type: complex contains: api_version: description: - API\",\n \"description: - \\\"sourceSecret is the name of a Secret that\",\n \"in 30 seconds. The Kubelet will react by sending a\",\n \"gets scheduled in the system, that the build may be\",\n \"may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' type:\",\n \"version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description: - Token\",\n \"path of the Dockerfile that will be used to build\",\n \"kind of build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit\",\n \"directory will be copied, but the directory itself will not\",\n \"is non-nil, entries in this list can only be removed.\",\n \"merged. default: false type: bool host: description: - Provide a\",\n \"description: - from is a reference to an ImageStreamTag, ImageStreamImage,\",\n \"Version of the schema the FieldPath is written in terms\",\n \"is requested by the user, and is not directly settable\",\n \"some operations may differ from the requested resource Kind. type:\",\n \"set to C(absent). A patch operation results in merging lists\",\n \"an empty map or a map with any values, default\",\n \"422 (Unprocessable Entity) will be returned. type: bool controller: description:\",\n \"tar, or tar.gz file and extract it as the source.\",\n \"of a ConfigMap. type: complex contains: key: description: - The\",\n \"of a resource and is not allowed to change on\",\n \"pull if it is not present locally type: bool pull_secret:\",\n \"or be set further into the future, although it may\",\n \"be expanded, regardless of whether the variable exists or not.\",\n \"info is written to KubeObjHelper.log default: false type: bool force:\",\n \"to a CA certificate used to authenticate with the API.\",\n \"description: - 'Container name: required for volumes, optional for env\",\n \"to validate requests. type: str github: description: - github contains\",\n \"field is specified and the generated name exists, the server\",\n \"are currently supported.' type: complex contains: container_name: description: - 'Container\",\n \"str resource: description: - 'Required: resource to select' type: str\",\n \"In CamelCase. type: str metadata: description: - metadata for BuildConfig.\",\n \"application without unneeded dependencies installed. The building of the application\",\n \"pending initializer is removed, and no failing result is set,\",\n \"source_path: description: - sourcePath is the absolute path of the\",\n \"scripts is the location of Source scripts type: str type:\",\n \"registry. Each entry is an identifier for the responsible component\",\n \"system. Read-only. Null for lists. type: complex contains: {} deletion_grace_period_seconds:\",\n \"a file or directory. destinationDir must be a directory. destinationDir\",\n \"expected to be deleted (no longer visible from resource lists,\",\n \"Path to an existing Kubernetes config file. If not provided,\",\n \"example, a user may request that a pod is deleted\",\n \"jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to the Jenkins\",\n \"github: description: - github contains the parameters for a GitHub\",\n \"additional secrets that will be included in the build pod\",\n \"describes what optimizations the system can use when building images\",\n \"receives this binary as standard input. This filename may not\",\n \"system. Only set when deletionTimestamp is also set. May only\",\n \"returned. type: bool controller: description: - If true, this reference\",\n \"'Container name: required for volumes, optional for env vars' type:\",\n \"must be defined type: bool jenkinsfile: description: - Jenkinsfile defines\",\n \"str ref: description: - ref is the branch/tag/ref to build.\",\n \"from the endpoint the client submits requests to. Cannot be\",\n \"registry, this field does not need to be set. type:\",\n \"Kubelet will send a hard termination signal (SIGKILL) to the\",\n \"'If referring to a piece of an object instead of\",\n \"true. There cannot be more than one managing controller. type:\",\n \"select in the specified API version. type: str resource_field_ref: description:\",\n \"machine-readable description of the cause of the error. If this\",\n \"the Docker build API. The experimental policy 'SkipLayers' will avoid\",\n \"API version for the Build object serialized and passed to\",\n \"- from is a reference to an ImageStreamTag, ImageStreamImage, or\",\n \"be created at the destination. type: str runtime_image: description: -\",\n \"after cleanup, remove the pod from the API. In the\",\n \"the docker build must be executed with the --no-cache=true flag\",\n \"- to defines an optional location to push the output\",\n \"is a list of initializers that must execute in order\",\n \"empty. Must be a DNS_LABEL. Cannot be updated. type: str\",\n \"produce a new build, and the conditions when to trigger\",\n \"type: complex contains: custom_strategy: description: - customStrategy holds the parameters\",\n \"may define its own extended details. This field is optional\",\n \"dockerfilePath is the path of the Dockerfile that will be\",\n \"additional environment variables you want to pass into a build\",\n \"future, although it may be shortened or the resource may\",\n \"filename may not contain slashes or be '..' or '.'.\",\n \"be used to build the Docker image, relative to the\",\n \"input necessary to produce a new build, and the conditions\",\n \"refers to the name of the container that triggered the\",\n \"by the server on successful creation of a resource and\",\n \"save last used image ID for build type: str type:\",\n \"the build may be active on a node before the\",\n \"Fields may appear more than once in an array of\",\n \"system. Read-only. type: str self_link: description: - SelfLink is a\",\n \"values, default build nodeselector values are ignored. type: complex contains:\",\n \"description: - 'Required: resource to select' type: str secret_key_ref: description:\",\n \"format of the exposed resources, defaults to \\\"1\\\" type: str\",\n \"contains: http_proxy: description: - httpProxy is a proxy used to\",\n \"cleanup, remove the pod from the API. In the presence\",\n \"is a proxy used to reach the git repository over\",\n \"removed. Defaults to false. To set this field, a user\",\n \"consistent version of the listed objects. version_added: 2.3.0 author: OpenShift\",\n \"cluster which the object belongs to. This is used to\",\n \"is a timestamp representing the server time when this object\",\n \"occur as a result of an explicit client build creation.\",\n \"group: description: - The group attribute of the resource associated\",\n \"the container and after cleanup, remove the pod from the\",\n \"This feature will be removed in a future release. Use\",\n \"that use Software Collections (SCL), in order to have the\",\n \"empty namespace is equivalent to the \\\"default\\\" namespace, but \\\"default\\\"\",\n \"any modules parameters intended to define object attributes. type: dict\",\n \"The name of the cluster which the object belongs to.\",\n \"- triggers determine how new Builds can be launched from\",\n \"if compatibility cannot be preserved. type: str no_cache: description: -\",\n \"annotations: description: - Annotations is an unstructured key value map\",\n \"for the build. type: list contains: destination_dir: description: - destinationDir\",\n \"str output: description: - output describes the Docker image the\",\n \"str push_secret: description: - PushSecret is the name of a\",\n \"resources. Clients must treat these values as opaque and passed\",\n \"default to \\\"Serial\\\". type: str service_account: description: - serviceAccount is\",\n \"description: - 'If referring to a piece of an object\",\n \"which can be described). type: str retry_after_seconds: description: - If\",\n \"namespace is equivalent to the \\\"default\\\" namespace, but \\\"default\\\" is\",\n \"successful creation of a resource and is not allowed to\",\n \"to execute the build. type: complex contains: limits: description: -\",\n \"return code for this status, 0 if not set. type:\",\n \"Use in conjunction with I(username). resource_definition: description: - Provide the\",\n \"request the generation of an appropriate name automatically. Name is\",\n \"object is managed by a controller, then an entry in\",\n \"store and retrieve arbitrary metadata. They are not queryable and\",\n \"int triggers: description: - triggers determine how new Builds can\",\n \"deleted, this object will be garbage collected. If this object\",\n \"be used and\\\\ \\\\ value is the base64 encoded credentials.\",\n \"this object is managed by a controller, then an entry\",\n \"- mountPath is the path at which to mount the\",\n \"be garbage collected. If this object is managed by a\",\n \"will populate this list with the current set of initializers.\",\n \"defines how to perform a build. type: complex contains: custom_strategy:\",\n \"bool host: description: - Provide a URL for acessing the\",\n \"new Builds can be launched from a BuildConfig. If no\",\n \"raw contents of a Dockerfile which should be built. When\",\n \"registries type: complex contains: name: description: - Name of the\",\n \"KubeObjHelper.log default: false type: bool force: description: - If set\",\n \"will be resolved in the Dockerfile. See type: list contains:\",\n \"will allow running Docker commands (and build Docker images) from\",\n \"defined by the reason type. type: complex contains: causes: description:\",\n \"When set to C(present), the object will be created, if\",\n \"The name of a context found in the Kubernetes config\",\n \"created by this build. The pod will be allowed to\",\n \"type is the type of build trigger type: str status:\",\n \"does not need to be set. type: complex contains: name:\",\n \"a DNS_LABEL. Cannot be updated. type: str owner_references: description: -\",\n \"object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description: -\",\n \"contains: annotations: description: - Annotations is an unstructured key value\",\n \"set, the initializers struct will be set to nil and\",\n \"name exists, the server will NOT return a 409 -\",\n \"be treated as opaque by clients and passed unmodified back\",\n \"deleted, ensuring that other clients can observe the deletion. type:\",\n \"containing the YAML definition of the object. Mutually exclusive with\",\n \"that defined by the reason type. type: complex contains: causes:\",\n \"ImageStreamTag, the ImageStreamTag will be looked for in the namespace\",\n \"The Kubelet will react by sending a graceful termination signal\",\n \"this build configuration will be scheduled for execution. This is\",\n \"the command to run. It may not be specified with\",\n \"indicated in the Retry-After header). Applied only if Name is\",\n \"name: description: - Name of the environment variable. Must be\",\n \"are: ssh-privatekey.\\\" type: complex contains: name: description: - Name of\",\n \"- type is the kind of build strategy. type: str\",\n \"when modifying objects. type: complex contains: str, str cluster_name: description:\",\n \"str scripts: description: - scripts is the location of Source\",\n \"complex contains: annotations: description: - Annotations is an unstructured key\",\n \"secret to select from. Must be a valid secret key.\",\n \"must be defined type: bool expose_docker_socket: description: - exposeDockerSocket will\",\n \"acessing the Kubernetes API. key_file: description: - Path to a\",\n \"referent. type: str field_path: description: - 'If referring to a\",\n \"you need control over the image entrypoint, or if the\",\n \"into the build directory, where the Dockerfile is located, so\",\n \"is a single name which can be described). type: str\",\n \"Namespace defines the space within each name must be unique.\",\n \"done in the builder image but, post build, you can\",\n \"from the list. If the deletionTimestamp of the object is\",\n \"pod. After that 30 seconds, the Kubelet will send a\",\n \"may be modified based on your strategy base image and\",\n \"generation of the desired state. Populated by the system. Read-only.\",\n \"description: - SelfLink is a URL representing this object. Populated\",\n \"a particular resource or set of resources. Populated by the\",\n \"is made, if any. type: str uid: description: - UID\",\n \"to be run. type: list contains: str command: description: -\",\n \"then the owner cannot be deleted from the key-value store\",\n \"name: description: - The name attribute of the resource associated\",\n \"shell. In all other cases, using Script might be more\",\n \"webhook can set environment variables; can only be set to\",\n \"removed in a future release. Use ImageSource to copy binary\",\n \"Applied only if Name is not specified. type: str generation:\",\n \"`bundle` and other binaries available in the PATH. type: str\",\n \"or the Docker image's default entrypoint. The arguments are placed\",\n \"base64 encoded credentials. Supported auth methods\\\\ \\\\ are: ssh-privatekey.\\\" type:\",\n \"this error, as named by its JSON serialization. May include\",\n \"The secret\\\\ \\\\ contains valid credentials for remote repository, where\",\n \"description: - Limits describes the maximum amount of compute resources\",\n \"validate requests. type: str generic: description: - generic contains the\",\n \"considered a single file within the build input. For example,\",\n \"reference to an ImageStreamTag, ImageStreamImage, or DockerImage to copy source\",\n \"details. This field is optional and the data returned is\",\n \"0 if not set. type: int details: description: - Extended\",\n \"This field may be presented as-is to a reader. type:\",\n \"int initializers: description: - An initializer is a controller which\",\n \"commiting new layers in between each image step, and will\",\n \"Arrays are zero-indexed. Fields may appear more than once in\",\n \"optional, if not specified we default to \\\"Serial\\\". type: str\",\n \"description: - Name must be unique within a namespace. Is\",\n \"there is a single name which can be described). type:\",\n \"is in tech preview. type: complex contains: env: description: -\",\n \"description: - Requests describes the minimum amount of compute resources\",\n \"for the builder. If left empty, the Docker and Source\",\n \"The pod will be allowed to use secrets referenced by\",\n \"to be scoped to a namespace - the value of\",\n \"description: - JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline\",\n \"I(type) attribute, a strategic merge is performed, where individual elements\",\n \"binaries available in the PATH. type: str resources: description: -\",\n \"patched, if parameter values differ from the existing object's attributes,\",\n \"field may be presented as-is to a reader. type: str\",\n \"metadata: description: - metadata for BuildConfig. type: complex contains: annotations:\",\n \"build. type: str jenkinsfile_path: description: - JenkinsfilePath is the optional\",\n \"- metadata for BuildConfig. type: complex contains: annotations: description: -\",\n \"hash identifying a specific commit type: str committer: description: -\",\n \"resource will be deleted. This field is set by the\",\n \"empty, this object has been completely initialized. Otherwise, the object\",\n \"openshift == 0.3.3 ''' EXAMPLES = ''' ''' RETURN =\",\n \"name: description: - Name must be unique within a namespace.\",\n \"with an empty From reference is allowed in a build\",\n \"field, and may be truncated by the length of the\",\n \"ends in /. then the content of the directory will\",\n \"description: - paths is a list of source and destination\",\n \"built. The structure of the source will depend on the\",\n \"are expanded using the previous defined environment variables in the\",\n \"description: - dockerfilePath is the path of the Dockerfile that\",\n \"name of a Secret that would be used for setting\\\\\",\n \"true, this reference points to the managing controller. type: bool\",\n \"is set the build will receive a directory with a\",\n \"the build input. For example, specifying \\\"webapp.war\\\" would place the\",\n \"take on a value like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers to\",\n \"by sending a graceful termination signal to the containers in\",\n \"create or update request. type: str creation_timestamp: description: - CreationTimestamp\",\n \"in the container and any service environment variables. If a\",\n \"in order to have the appropriate collections enabled in the\",\n \"be a DNS_LABEL. Cannot be updated. type: str owner_references: description:\",\n \"with lists being merged into a unique set of values.\",\n \"- Must be empty before the object is deleted from\",\n \"than root of repository. type: str dockerfile: description: - dockerfile\",\n \"timestamp, until an administrator or automated process can determine the\",\n \"clusters. This field is not set anywhere right now and\",\n \"debug output from the OpenShift helper. Logging info is written\",\n \"necessary to produce a new build, and the conditions when\",\n \"a list of labels that are applied to the resulting\",\n \"unique name could not be found in the time allotted,\",\n \"Entity) will be returned. type: bool controller: description: - If\",\n \"description: - completionDeadlineSeconds is an optional duration in seconds, counted\",\n \"modifying objects. type: complex contains: str, str cluster_name: description: -\",\n \"type of the build source, may be one of 'Source',\",\n \"when to trigger them. type: complex contains: completion_deadline_seconds: description: -\",\n \"initialized and visible to all clients. type: list contains: name:\",\n \"str to: description: - to defines an optional location to\",\n \"running Docker commands (and build Docker images) from inside the\",\n \"str last_triggered_image_id: description: - lastTriggeredImageID is used internally by the\",\n \"openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a list of\",\n \"must be unique. An empty namespace is equivalent to the\",\n \"copy source from. type: complex contains: api_version: description: - API\",\n \"incremental: description: - incremental flag forces the Source build to\",\n \"Number of seconds allowed for this object to gracefully terminate\",\n \"build Docker images) from inside the Docker container. type: bool\",\n \"the user, and is not directly settable by a client.\",\n \"of the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName,\",\n \"Created or 500 with Reason ServerTimeout indicating a unique name\",\n \"- name of the process that is responsible for initializing\",\n \"be specified with Command. Use Script when a shell script\",\n \"separate runtime image.' type: list contains: destination_dir: description: - destinationDir\",\n \"the name passed. This value will also be combined with\",\n \"error. If this value is empty there is no information\",\n \"the resource associated with the status StatusReason (when there is\",\n \"type: str retry_after_seconds: description: - If specified, the time in\",\n \"be escaped with a double $$, ie: $$(VAR_NAME). Escaped references\",\n \"API. In the presence of network partitions, this object may\",\n \"ensuring that other clients can observe the deletion. type: complex\",\n \"C(present), an existing object will updated, and lists will be\",\n \"type: str namespace: description: - Namespace defines the space within\",\n \"List of objects depended by this object. If ALL objects\",\n \"type: str build_args: description: - buildArgs contains build arguments that\",\n \"accept type: str strategy: description: - strategy defines how to\",\n \"for this status, 0 if not set. type: int details:\",\n \"or it's key must be defined type: bool dockerfile_path: description:\",\n \"image should be pulled the resulting image will be used\",\n \"is not allowed to change on PUT operations. Populated by\",\n \"resource lists, and not reachable by name) after the time\",\n \"key must be defined type: bool jenkinsfile: description: - Jenkinsfile\",\n \"description: - customStrategy holds the parameters to the Custom build\",\n \"to make the value unique on the server. If this\",\n \"initializer is removed, and no failing result is set, the\",\n \"information available. type: str group: description: - The group attribute\",\n \"pass into a builder container. type: list contains: name: description:\",\n \"is empty there is no information available. type: str group:\",\n \"and may reject unrecognized values. type: str items: description: -\",\n \"required to make the value unique on the server. If\",\n \"is the same as 'SkipLayers' but simply warns if compatibility\",\n \"description: - UID of the referent. type: str last_triggered_image_id: description:\",\n \"only to have some well-defined way of referencing a part\",\n \"- An opaque value that represents the internal version of\",\n \"of the referent. type: str resource_version: description: - An opaque\",\n \"of the resource associated with the status StatusReason. type: str\",\n \"of the cluster which the object belongs to. This is\",\n \"path ssl_ca_cert: description: - Path to a CA certificate used\",\n \"the rest of your Dockerfile stanzas. The Dockerfile source type\",\n \"be updated. In CamelCase. type: str metadata: description: - metadata\",\n \"UID of the referent. type: str env: description: - env\",\n \"the label. It must have non-zero length. type: str value:\",\n \"context (contextDir). If both JenkinsfilePath & Jenkinsfile are both not\",\n \"- PushSecret is the name of a Secret that would\",\n \"Once set, this value may not be unset or be\",\n \"is used internally by the ImageChangeController to save last used\",\n \"If left empty, the Docker and Source build strategies assume\",\n \"not guaranteed to be set in happens-before order across separate\",\n \"strategy: description: - strategy defines how to perform a build.\",\n \"- 'If referring to a piece of an object instead\",\n \"Otherwise, the object is considered uninitialized and is hidden (in\",\n \"may not set this value. It is represented in RFC3339\",\n \"BuildConfigList description: - Retrieve a list of build_configs. List operations\",\n \"field is optional and the data returned is not guaranteed\",\n \"retrieve arbitrary metadata. They are not queryable and should be\",\n \"From image from the build strategy will be used. Only\",\n \"description: - API version of the referent. type: str field_path:\",\n \"Reason clarifies an HTTP status code but does not override\",\n \"noCache if set to true indicates that the docker build\",\n \"description: - strategy defines how to perform a build. type:\",\n \"unique suffix. The provided value has the same validation rules\",\n \"only be set to true for GenericWebHook. type: bool secret:\",\n \"field is used, the name returned to the client will\",\n \"an object. Servers should convert recognized schemas to the latest\",\n \"- secret used to validate requests. type: str github: description:\",\n \"- allowEnv determines whether the webhook can set environment variables;\",\n \"be included in the build pod type: list contains: mount_path:\",\n \"removed. type: list contains: str generate_name: description: - GenerateName is\",\n \"str resources: description: - resources computes resource requirements to execute\",\n \"of the status of this operation. type: str metadata: description:\",\n \"openshift client will attempt to load the default configuration file\",\n \"commit type: str committer: description: - committer is the committer\",\n \"Name of the referent. type: str source_secret: description: - \\\"sourceSecret\",\n \"appropriate to execute the post build hook, for example for\",\n \"The `-i` flag is needed to support CentOS and RHEL\",\n \"(no longer visible from resource lists, and not reachable by\",\n \"use to run the pod created by this build. The\",\n \"resource Kind. type: str name: description: - The name attribute\",\n \"which defines a Jenkins pipeline build. type: str jenkinsfile_path: description:\",\n \"`rake test`. If you need control over the image entrypoint,\",\n \"object is considered uninitialized and is hidden (in list/watch and\",\n \"name of a Secret that would be used for setting\",\n \"raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\",\",\n \"PUT operations. Populated by the system. Read-only. type: str spec:\",\n \"to this time. For example, a user may request that\",\n \"This is optional, if not specified we default to \\\"Serial\\\".\",\n \"a list of initializers that must execute in order before\",\n \"to ignore it if set in create or update request.\",\n \"- Requests describes the minimum amount of compute resources required.\",\n \"the system. Read-only. type: str self_link: description: - SelfLink is\",\n \"the pod. After that 30 seconds, the Kubelet will send\",\n \"retained. type: int triggers: description: - triggers determine how new\",\n \"a result of an explicit client build creation. type: list\",\n \"the build time. For the Source build strategy, these will\",\n \"endpoint the client submits requests to. Cannot be updated. In\",\n \"preserved when modifying objects. type: complex contains: str, str cluster_name:\",\n \"nil or empty, this object has been completely initialized. Otherwise,\",\n \"Collections (SCL), in order to have the appropriate collections enabled\",\n \"will be empty. Must be a DNS_LABEL. Cannot be updated.\",\n \"the new build created from this build configuration will be\",\n \"set. May only be shortened. Read-only. type: int deletion_timestamp: description:\",\n \"''' EXAMPLES = ''' ''' RETURN = ''' api_version: type:\",\n \"git repository over http type: str https_proxy: description: - httpsProxy\",\n \"\\\"items\\\"' type: str message: description: - A human-readable description of\",\n \"the server. They may only be valid for a particular\",\n \"It is typically generated by the server on successful creation\",\n \"referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters\",\n \"build input to accept type: str strategy: description: - strategy\",\n \"the application exists. This allows to have buildable sources in\",\n \"environment will be added after the FROM, but before the\",\n \"type: int details: description: - Extended data associated with the\",\n \"object. If nil or empty, this object has been completely\",\n \"a hard termination signal (SIGKILL) to the container and after\",\n \"with Reason ServerTimeout indicating a unique name could not be\",\n \"after the command to be run. type: list contains: str\",\n \"with `rake test`. If you need control over the image\",\n \"type: str git: description: - git contains optional information about\",\n \"inside the image to copy to the build directory. If\",\n \"name of the source control user type: str message: description:\",\n \"shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is RFC\",\n \"initializing this object. type: str result: description: - If result\",\n \"instead, it will either return 201 Created or 500 with\",\n \"may only be valid for a particular resource or set\",\n \"remote repository, where the\\\\ \\\\ data's key represent the authentication\",\n \"one build into a separate runtime image.' type: list contains:\",\n \"UID of the referent. type: str build_args: description: - buildArgs\",\n \"avoid commiting new layers in between each image step, and\",\n \"on the type of build to run type: str images:\",\n \"description: - items is a list of build configs type:\",\n \"may be used with other options like git - in\",\n \"it if set in create or update request. type: str\",\n \"type of build to run type: str images: description: -\",\n \"as_file: description: - asFile indicates that the provided binary input\",\n \"compatibility with the 'None' policy. An additional experimental policy 'SkipLayersAndWarn'\",\n \"of resources. Clients must treat these values as opaque and\",\n \"source that will be built. The structure of the source\",\n \"file and extract it as the source. The custom strategy\",\n \"the cluster. If set to an empty map or a\",\n \"attribute of the resource associated with the status StatusReason. On\",\n \"for the responsible component that will remove the entry from\",\n \"of the field to select in the specified API version.\",\n \"the builder should pull the images from registry prior to\",\n \"if not specified we default to \\\"Serial\\\". type: str service_account:\",\n \"requested by the user, and is not directly settable by\",\n \"file. debug: description: - Enable debug output from the OpenShift\",\n \"{} finalizers: description: - Must be empty before the object\",\n \"a snapshot read of the underlying objects, returning a resource_version\",\n \"list contains: field: description: - 'The field of the resource\",\n \"controller field set to true. There cannot be more than\",\n \"we can do is raise the error. raise Exception(exc.message) try:\",\n \"resource associated with the status StatusReason (when there is a\",\n \"otherwise to an implementation-defined value. type: complex contains: str, str\",\n \"of the source will depend on the type of build\",\n \"str resource_version: description: - An opaque value that represents the\",\n \"be used for optimistic concurrency, change detection, and the watch\",\n \"with Script. This might be needed if the image doesn't\",\n \"type: bool controller: description: - If true, this reference points\",\n \"in the pod. After that 30 seconds, the Kubelet will\",\n \"list contains: api_version: description: - API version of the referent.\",\n \"holds all the input necessary to produce a new build,\",\n \"further by any user. type: complex contains: pending: description: -\",\n \"int node_selector: description: - nodeSelector is a selector which must\",\n \"representation of an object. Servers should convert recognized schemas to\",\n \"- type is the type of build trigger type: str\",\n \"- message is the description of a specific commit type:\",\n \"`-i` flag is needed to support CentOS and RHEL images\",\n \"is the unique in time and space value for this\",\n \"default: false type: bool force: description: - If set to\",\n \"is considered uninitialized and is hidden (in list/watch and get\",\n \"the object is deleted from the registry. Each entry is\",\n \"- contextDir specifies the sub-directory where the source code for\",\n \"be combined with a unique suffix. The provided value has\",\n \"from a registry If the image is pulled from the\",\n \"from the system. Only set when deletionTimestamp is also set.\",\n \"the command to be run. type: list contains: str command:\",\n \"other than root of repository. type: str dockerfile: description: -\",\n \"is an optional prefix, used by the server, to generate\",\n \"reference in the input string will be unchanged. The $(VAR_NAME)\",\n \"the referent. type: str build_args: description: - buildArgs contains build\",\n \"the future, although it may be shortened or the resource\",\n \"will be built. The structure of the source will depend\",\n \"representing the server time when this object was created. It\",\n \"it's key must be defined type: bool dockerfile_path: description: -\",\n \"complex contains: str, str requests: description: - Requests describes the\",\n \"in the \\\"Failure\\\" status. If this value is empty there\",\n \"description: - secrets represents a list of secrets and their\",\n \"for those objects will be empty. Must be a DNS_LABEL.\",\n \"- Provide a URL for acessing the Kubernetes API. key_file:\",\n \"- A human-readable description of the cause of the error.\",\n \"are placed immediately after the command to be run. type:\",\n \"to Limits if that is explicitly specified, otherwise to an\",\n \"the pod's namespace type: complex contains: key: description: - The\",\n \"set, this value may not be unset or be set\",\n \"replaced in the context dir. type: str git: description: -\",\n \"is the base64 encoded credentials. Supported auth methods\\\\ \\\\ are:\",\n \"reference is allowed in a build configuration. type: complex contains:\",\n \"This value will be used to look up a Docker\",\n \"generate_name: description: - GenerateName is an optional prefix, used by\",\n \"when I(state) = C(present) contains: api_version: description: - APIVersion defines\",\n \"requests: description: - Requests describes the minimum amount of compute\",\n \"strategy defines how to perform a build. type: complex contains:\",\n \"the referent. type: str block_owner_deletion: description: - If true, AND\",\n \"do incremental builds if true. type: bool pull_secret: description: -\",\n \"Provide a password for connecting to the API. Use in\",\n \"in between each image step, and will fail if the\",\n \"to gracefully terminate before it will be removed from the\",\n \"of a Secret that would be used for setting up\",\n \"single name which can be described). type: str retry_after_seconds: description:\",\n \"the Docker container. type: bool force_pull: description: - forcePull describes\",\n \"for the cluster. If set to an empty map or\",\n \"_from: description: - from is reference to an DockerImage, ImageStreamTag,\",\n \"Docker build strategy. type: complex contains: _from: description: - from\",\n \"both JenkinsfilePath & Jenkinsfile are both not specified, this defaults\",\n \"must be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit\",\n \"might be needed if the image doesn't have `/bin/sh`, or\",\n \"the reference in the input string will be unchanged. The\",\n \"of the directory will be copied, but the directory itself\",\n \"be different than the name passed. This value will also\",\n \"deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date and time\",\n \"type may be used with other options like git -\",\n \"source for the build type: list contains: _from: description: -\",\n \"inform about number of last triggered build. type: int kind:\",\n \"bool force_pull: description: - forcePull describes if the builder should\",\n \"contains: name: description: - Name of the referent. type: str\",\n \"repository, where the\\\\ \\\\ data's key represent the authentication method\",\n \"contains: str generate_name: description: - GenerateName is an optional prefix,\",\n \"will be used to look up a Docker image repository\",\n \"for the environment variable's value. Cannot be used if value\",\n \"Read-only. type: str uid: description: - UID is the unique\",\n \"is the directory where the files from the secret should\",\n \"name of the cluster which the object belongs to. This\",\n \"description: - The kind attribute of the resource associated with\",\n \"to the API. verify_ssl: description: - Whether or not to\",\n \"build_configs. List operations provide a snapshot read of the underlying\",\n \"path of the file or directory inside the image to\",\n \"used when an archive is provided. Custom builds will receive\",\n \"reference to an existing secret that you want to use\",\n \"From is specified, the From image from the build strategy\",\n \"- Name of the referent. type: str namespace: description: -\",\n \"of a specific commit type: complex contains: email: description: -\",\n \"but the directory itself will not be created at the\",\n \"to \\\"Serial\\\". type: str service_account: description: - serviceAccount is the\",\n \"OpenShift BuildConfigList description: - Retrieve a list of build_configs. List\",\n \"now and apiserver is going to ignore it if set\",\n \"It is not guaranteed to be set in happens-before order\",\n \"runtime_image: description: - 'runtimeImage is an optional image that is\",\n \"will be different than the name passed. This value will\",\n \"entire object, this string should contain a valid JSON/Go field\",\n \"idempotence and configuration definition. Cannot be updated. type: str namespace:\",\n \"If this value is empty there is no information available.\",\n \"Populated by the system. Read-only. type: str ''' def main():\",\n \"string value representing the REST resource this object represents. Servers\",\n \"or update request. type: str creation_timestamp: description: - CreationTimestamp is\",\n \"the source code for the application exists. This allows to\",\n \"reference is to a container within a pod, this would\",\n \"image will be used in the FROM line of the\",\n \"values differ from the existing object's attributes, and deleted, if\",\n \"the Docker build strategy, these will be copied into the\",\n \"type: str type: description: - type of the build source,\",\n \"UID of the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID\",\n \"used by clients to determine when objects have changed. Value\",\n \"secret to be used to pull the image from a\",\n \"innate Dockerfile replaced in the context dir. type: str git:\",\n \"an archive as described above. For Source and Docker builds,\",\n \"- Path of the field to select in the specified\",\n \"webhook type of trigger type: complex contains: allow_env: description: -\",\n \"separate operations. Clients may not set this value. It is\",\n \"like git - in those cases the Git repo will\",\n \"- If specified, the time in seconds before the operation\",\n \"build directory where files copied from the image are placed.\",\n \"value. Cannot be used if value is not empty. type:\",\n \"description: - Jenkinsfile defines the optional raw contents of a\",\n \"`ruby`, `bundle` and other binaries available in the PATH. type:\",\n \"nested attributes. Arrays are zero-indexed. Fields may appear more than\",\n \"paths is a list of source and destination paths to\",\n \"for a GitLab webhook type of trigger type: complex contains:\",\n \"is chosen only to have some well-defined way of referencing\",\n \"If you need control over the image entrypoint, or if\",\n \"be used for setting up the authentication for pulling the\",\n \"type: bool expose_docker_socket: description: - exposeDockerSocket will allow running Docker\",\n \"to do incremental builds if true. type: bool pull_secret: description:\",\n \"this representation of an object. Servers should convert recognized schemas\",\n \"except that defined by the reason type. type: complex contains:\",\n \"allowed for this object to gracefully terminate before it will\",\n \"images: description: - images describes a set of images to\",\n \"server when a graceful deletion is requested by the user,\",\n \"api_key: description: - Token used to connect to the API.\",\n \"of network partitions, this object may still exist after this\",\n \"to support CentOS and RHEL images that use Software Collections\",\n \"is deleted from the registry. Each entry is an identifier\",\n \"of an entire object, this string should contain a valid\",\n \"is set with the Failure field, the object will be\",\n \"configuration file from I(~/.kube/config.json). type: path password: description: - Provide\",\n \"shell script to be run with `/bin/sh -ic`. It may\",\n \"contains: api_version: description: - Version of the schema the FieldPath\",\n \"fit on a node If nil, it can be overridden\",\n \"are both not specified, this defaults to Jenkinsfile in the\",\n \"optional duration in seconds, counted from the time when a\",\n \"to be a tar, gzipped tar, or zip file depending\",\n \"to determine when objects have changed. Value must be treated\",\n \"a map with any values, default build nodeselector values are\",\n \"where files copied from the image are placed. type: str\",\n \"authentication enabled Docker Registry (or Docker Hub). type: complex contains:\",\n \"\\\"foregroundDeletion\\\" finalizer, then the owner cannot be deleted from the\",\n \"base image and additional ENV stanzas from your strategy environment\",\n \"this string should contain a valid JSON/Go field access statement,\",\n \"details associated with the StatusReason failure. Not all StatusReasons may\",\n \"description: - 'Variable references $(VAR_NAME) are expanded using the previous\",\n \"with the Failure field, the object will be persisted to\",\n \"type: bool force: description: - If set to C(True), and\",\n \"unique name ONLY IF the Name field has not been\",\n \"would take on a value like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers\",\n \"contains: str, str requests: description: - Requests describes the minimum\",\n \"author: description: - author is the author of a specific\",\n \"a specific generation of the desired state. Populated by the\",\n \"build_config_list: type: complex returned: when I(state) = C(present) contains: api_version:\",\n \"will have any innate Dockerfile replaced in the context dir.\",\n \"a new build can only occur as a result of\",\n \"metadata. They are not queryable and should be preserved when\",\n \"be available for the build time. For the Source build\",\n \"- Retrieve a list of build_configs. List operations provide a\",\n \"a container where the assemble script runs. Later, when the\",\n \"list contains: str script: description: - script is a shell\",\n \"input to accept type: str strategy: description: - strategy defines\",\n \"resolved in the Dockerfile. See type: list contains: name: description:\",\n \"str gitlab: description: - GitLabWebHook contains the parameters for a\",\n \"also set. May only be shortened. Read-only. type: int deletion_timestamp:\",\n \"authenticate with the API. type: path context: description: - The\",\n \"build input. For example, specifying \\\"webapp.war\\\" would place the provided\",\n \"provided value has the same validation rules as the Name\",\n \"system. Read-only. type: str ''' def main(): try: module =\",\n \"type: complex contains: str, str output: description: - output describes\",\n \"description: - JenkinsfilePath is the optional path of the Jenkinsfile\",\n \"collections enabled in the shell. E.g., in the Ruby image,\",\n \"trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is\",\n \"last pending initializer is removed, and no failing result is\",\n \"reason may define its own extended details. This field is\",\n \"- 'Selects a resource of the container: only resources limits\",\n \"the number of old successful builds to retain. If not\",\n \"- Provide a username for connecting to the API. verify_ssl:\",\n \"description: - Name of the referent. type: str secrets: description:\",\n \"will be truncated to zero length. For the Docker build\",\n \"enabled Docker Registry (or Docker Hub). type: complex contains: name:\",\n \"the private Docker registries type: complex contains: name: description: -\",\n \"and the generated name exists, the server will NOT return\",\n \"which means the final build image will be equivalent to\",\n \"the appropriate collections enabled in the shell. E.g., in the\",\n \"specified, the time in seconds before the operation should be\",\n \"type: bool field_ref: description: - 'Selects a field of the\",\n \"is a list of initializers that have not yet acted\",\n \"is an optional image that is used to run an\",\n \"type: str strategy: description: - strategy defines how to perform\",\n \"of the container: only resources limits and requests (limits.cpu, limits.memory,\",\n \"where the assemble script runs. Later, when the script finishes,\",\n \"- Path to an existing Kubernetes config file. If not\",\n \"images to be used to provide source for the build\",\n \"any service environment variables. If a variable cannot be resolved,\",\n \"- Annotations is an unstructured key value map stored with\",\n \"pod type: list contains: mount_path: description: - mountPath is the\",\n \"post build hook, for example for running unit tests with\",\n \"values that can be used to organize and categorize (scope\",\n \"kind: description: - Kind is a string value representing the\",\n \"container. type: bool force_pull: description: - forcePull describes if the\",\n \"from registry prior to building. type: bool incremental: description: -\",\n \"ImageStreamImage from which the docker image should be pulled type:\",\n \"utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = '''\",\n \"has the same validation rules as the Name field, and\",\n \"Name field has not been provided. If this field is\",\n \"the root of the specified contextDir. type: str source_strategy: description:\",\n \"Read-only. type: str spec: description: - spec holds all the\",\n \"Kind. type: str name: description: - The name attribute of\",\n \"slashes or be '..' or '.'. type: str context_dir: description:\",\n \"contextDir specifies the sub-directory where the source code for the\",\n \"to the containers in the pod. After that 30 seconds,\",\n \"from this build configuration will be scheduled for execution. This\",\n \"namespace: description: - Namespace defines the space within each name\",\n \"information about git-based build source type: complex contains: author: description:\",\n \"build. type: complex contains: name: description: - Name of the\",\n \"list with the current set of initializers. Only privileged users\",\n \"Specifies the output format of the exposed resources, defaults to\",\n \"from resource lists, and not reachable by name) after the\",\n \"and passed unmodified back to the server. Populated by the\",\n \"\\\\ value is the base64 encoded credentials. Supported auth methods\\\\\",\n \"is optional, if not specified we default to \\\"Serial\\\". type:\",\n \"the provided binary input should be considered a single file\",\n \"the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a\",\n \"well-defined way of referencing a part of an object.' type:\",\n \"description: - uri points to the source that will be\",\n \"for the builder or only pull if it is not\",\n \"the Jenkins Pipeline build strategy. This strategy is in tech\",\n \"added after the FROM, but before the rest of your\",\n \"keys and values that can be used to organize and\",\n \"after this timestamp, until an administrator or automated process can\",\n \"if the object reference is to a container within a\",\n \"Kubernetes API. key_file: description: - Path to a key file\",\n \"will be used to configure the pipeline relative to the\",\n \"the 'None' policy. An additional experimental policy 'SkipLayersAndWarn' is the\",\n \"amount of compute resources required. If Requests is omitted for\",\n \"the build; value must be positive integer type: int failed_builds_history_limit:\",\n \"used for optimistic concurrency, change detection, and the watch operation\",\n \"- generic contains the parameters for a Generic webhook type\",\n \"conjunction with I(username). resource_definition: description: - Provide the YAML definition\",\n \"deleted. When set to C(present), the object will be created,\",\n \"type: str commit: description: - commit is the commit hash\",\n \"of arguments that are provided to either Command, Script or\",\n \"not need to be set. type: complex contains: name: description:\",\n \"Kind of the referent. type: str name: description: - Name\",\n \"the pipeline relative to the root of the context (contextDir).\",\n \"between each image step, and will fail if the Dockerfile\",\n \"the referent. type: str namespace: description: - Namespace of the\",\n \"build context. For Source builds, this is assumed to be\",\n \"cases the Git repo will have any innate Dockerfile replaced\",\n \"bool pull_secret: description: - pullSecret is the name of a\",\n \"by the system. Read-only. type: str ''' def main(): try:\",\n \"some system invariant at object creation time. This field is\",\n \"hook, for example for running unit tests with `rake test`.\",\n \"- UID is the unique in time and space value\",\n \"latest internal value, and may reject unrecognized values. type: str\",\n \"client will attempt to load the default configuration file from\",\n \"type: str spec: description: - spec holds all the input\",\n \"to retain. If not specified, all failed builds are retained.\",\n \"str context_dir: description: - contextDir specifies the sub-directory where the\",\n \"setting up the authentication for executing the Docker push to\",\n \"from registry prior to building. type: bool image_optimization_policy: description: -\",\n \"truncated to zero length. For the Docker build strategy, these\",\n \"want to use in your build. type: complex contains: name:\",\n \"branch/tag/ref to build. type: str uri: description: - uri points\",\n \"Secret that would be used for setting\\\\ \\\\ up the\",\n \"the underlying objects, returning a resource_version representing a consistent version\",\n \"Jenkinsfile defines the optional raw contents of a Jenkinsfile which\",\n \"to mount the secret type: str secret_source: description: - secretSource\",\n \"from is a reference to an ImageStreamTag that will trigger\",\n \"determine how new Builds can be launched from a BuildConfig.\",\n \"be copied, but the directory itself will not be created\",\n \"be considered a single file within the build input. For\",\n \"- \\\"sourceSecret is the name of a Secret that would\",\n \"type: str divisor: description: - Specifies the output format of\",\n \"type: complex contains: str, str revision: description: - revision is\",\n \"is a list of additional secrets that will be included\",\n \"deleted in 30 seconds. The Kubelet will react by sending\",\n \"runtime image.' type: complex contains: api_version: description: - API version\",\n \"matching I(name_) or I(type) are merged. To force the replacement\",\n \"deleted from the registry. Each entry is an identifier for\",\n \"name: description: - name defines the name of the label.\",\n \"It may not be specified with Command. Use Script when\",\n \"and no failing result is set, the initializers struct will\",\n \"place the provided binary as `/webapp.war` for the builder. If\",\n \"resource_version: description: - An opaque value that represents the internal\",\n \"referent. type: str secrets: description: - secrets represents a list\",\n \"of the resource associated with the status StatusReason (when there\",\n \"Requests is omitted for a container, it defaults to Limits\",\n \"output: description: - output describes the Docker image the Strategy\",\n \"- postCommit is a build hook executed after the build\",\n \"to any schema except that defined by the reason type.\",\n \"or ImageStreamImage from which the docker image should be pulled\",\n \"type: bool force_pull: description: - forcePull describes if the builder\",\n \"the name returned to the client will be different than\",\n \"explicitly asked to observe uninitialized objects. When an object is\",\n \"directory. destinationDir can also be empty or equal to \\\".\\\",\",\n \"a shell script is appropriate to execute the post build\",\n \"complex contains: str, str revision: description: - revision is the\",\n \"\\\"spec.containers{name}\\\" (where \\\"name\\\" refers to the name of the container\",\n \"to select in the specified API version. type: str resource_field_ref:\",\n \"directory, where the Dockerfile is located, so users can ADD\",\n \"Command. Use Script when a shell script is appropriate to\",\n \"SelfLink is a URL representing this object. Populated by the\",\n \"is the number of old successful builds to retain. If\",\n \"contain a valid JSON/Go field access statement, such as desiredState.manifest.containers[2].\",\n \"this pod). This syntax is chosen only to have some\",\n \"name of the source control user type: str commit: description:\",\n \"run. type: list contains: str command: description: - command is\",\n \"specific commit type: str committer: description: - committer is the\",\n \"deletion is requested by the user, and is not directly\",\n \"that the build may be active on a node before\",\n \"this from the endpoint the client submits requests to. Cannot\",\n \"contains: key: description: - The key of the secret to\",\n \"the information from the source for a specific repo snapshot.\",\n \"and\\\\ \\\\ value is the base64 encoded credentials. Supported auth\",\n \"specified API version. type: str resource_field_ref: description: - 'Selects a\",\n \"Later, when the script finishes, all files injected will be\",\n \"Custom builds will receive this binary as input on STDIN.\",\n \"should be pulled the resulting image will be used in\",\n \"source type: complex contains: author: description: - author is the\",\n \"may still exist after this timestamp, until an administrator or\",\n \"trigger type: str status: description: - status holds any relevant\",\n \"description: - env contains additional environment variables you want to\",\n \"UID of the referent. type: str paths: description: - paths\",\n \"for the build pod to fit on a node If\",\n \"storage and then deleted, ensuring that other clients can observe\",\n \"This syntax is chosen only to have some well-defined way\",\n \"to this controller, with the controller field set to true.\",\n \"file within the build input. For example, specifying \\\"webapp.war\\\" would\",\n \"would be used for setting up the authentication for pulling\",\n \"image_change: description: - imageChange contains parameters for an ImageChange type\",\n \"a unique suffix. The provided value has the same validation\",\n \"the label. type: str push_secret: description: - PushSecret is the\",\n \"specified, the FROM may be modified based on your strategy\",\n \"to provide source for the build type: list contains: _from:\",\n \"fail if the Dockerfile cannot provide compatibility with the 'None'\",\n \"true for GenericWebHook. type: bool secret: description: - secret used\",\n \"result of an explicit client build creation. type: list contains:\",\n \"versioned schema of this representation of an object. Servers should\",\n \"description: - Kind is a string value representing the REST\",\n \"It is represented in RFC3339 form and is in UTC.\",\n \"extract it as the source. The custom strategy receives this\",\n \"that may be set by external tools to store and\",\n \"exc: # The helper failed to init, so there is\",\n \"an object is created, the system will populate this list\",\n \"in RFC3339 form and is in UTC. Populated by the\",\n \"not be unset or be set further into the future,\",\n \"initializers: description: - An initializer is a controller which enforces\",\n \"field set to true. There cannot be more than one\",\n \"incremental builds if true. type: bool pull_secret: description: - pullSecret\",\n \"name and namespace in different clusters. This field is not\",\n \"A machine-readable description of the cause of the error. If\",\n \"defined environment variables in the container and any service environment\",\n \"type: str paths: description: - paths is a list of\",\n \"particular resource or set of resources. Populated by the system.\",\n \"to authenticate with the API. type: path context: description: -\",\n \"type: complex contains: api_version: description: - APIVersion defines the versioned\",\n \"the REST resource this object represents. Servers may infer this\",\n \"to request the generation of an appropriate name automatically. Name\",\n \"description: - 'Selects a field of the pod: supports metadata.name,\",\n \"clients that haven't explicitly asked to observe uninitialized objects. When\",\n \"contains: pending: description: - Pending is a list of initializers\",\n \"for pulling the Docker images from the private Docker registries\",\n \"provided binary as `/webapp.war` for the builder. If left empty,\",\n \"- completionDeadlineSeconds is an optional duration in seconds, counted from\",\n \"binary as input on STDIN. type: complex contains: as_file: description:\",\n \"a list of build_configs. List operations provide a snapshot read\",\n \"that will remove the entry from the list. If the\",\n \"be used to provide source for the build type: list\",\n \"complex contains: container_name: description: - 'Container name: required for volumes,\",\n \"Only privileged users may set or modify this list. Once\",\n \"from the image. type: list contains: destination_dir: description: - destinationDir\",\n \"the object. Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description:\",\n \"use when building images to reduce the final size or\",\n \"differ from the existing object's attributes, and deleted, if set\",\n \"the system can use when building images to reduce the\",\n \"user may request that a pod is deleted in 30\",\n \"may reject unrecognized values. type: str code: description: - Suggested\",\n \"contains: api_version: description: - API version of the referent. type:\",\n \"there is no information available. A Reason clarifies an HTTP\",\n \"value is empty there is no information available. A Reason\",\n \"description: - dockerfile is the raw contents of a Dockerfile\",\n \"combined with a unique suffix. The provided value has the\",\n \"will either return 201 Created or 500 with Reason ServerTimeout\",\n \"the authentication for executing the Docker push to authentication enabled\",\n \"can determine the resource is fully terminated. If not set,\",\n \"describes the SCM in use. type: complex contains: binary: description:\",\n \"object attributes. type: dict src: description: - Provide a path\",\n \"refers to the root of WORKDIR. Deprecated: This feature will\",\n \"configure the pipeline relative to the root of the context\",\n \"archive is provided. Custom builds will receive this binary as\",\n \"object should be created, patched, or deleted. When set to\",\n \"referencing a part of an object.' type: str kind: description:\",\n \"path state: description: - Determines if an object should be\",\n \"be described). type: str retry_after_seconds: description: - If specified, the\",\n \"their input. The binary is generally assumed to be a\",\n \"type: str source_path: description: - sourcePath is the absolute path\",\n \"''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException\",\n \"get calls) from clients that haven't explicitly asked to observe\",\n \"executed after the build output image is committed, before it\",\n \"to pass into a build pipeline. type: list contains: name:\",\n \"- ref is the branch/tag/ref to build. type: str uri:\",\n \"the Docker image's default entrypoint. The arguments are placed immediately\",\n \"due to fields having multiple errors. Optional. Examples: \\\"name\\\" -\",\n \"than once in an array of causes due to fields\",\n \"list of build_configs. List operations provide a snapshot read of\",\n \"no other connection options are provided, the openshift client will\",\n \"a unique name could not be found in the time\",\n \"the Strategy should produce. type: complex contains: image_labels: description: -\",\n \"the images from registry prior to building. type: bool image_optimization_policy:\",\n \"component that will remove the entry from the list. If\",\n \"it's key must be defined type: bool field_ref: description: -\",\n \"'None' policy. An additional experimental policy 'SkipLayersAndWarn' is the same\",\n \"type: str type: description: - type is the type of\",\n \"option is specified, the FROM may be modified based on\",\n \"clients to determine when objects have changed. May be used\",\n \"a ConfigMap. type: complex contains: key: description: - The key\",\n \"type: int uid: description: - UID of the resource. (when\",\n \"select from. Must be a valid secret key. type: str\",\n \"allotted, and the client should retry (optionally after the time\",\n \"of the Jenkinsfile that will be used to configure the\",\n \"name) after the time in this field. Once set, this\",\n \"to. In the case of an ImageStreamTag, the ImageStreamTag will\",\n \"to authentication enabled Docker Registry (or Docker Hub). type: complex\",\n \"of, defaults to \\\"v1\\\". type: str field_path: description: - Path\",\n \"or I(type) attribute, a strategic merge is performed, where individual\",\n \"have changed. May be used for optimistic concurrency, change detection,\",\n \"a build hook executed after the build output image is\",\n \"description: - A machine-readable description of the cause of the\",\n \"and other binaries available in the PATH. type: str resources:\",\n \"used to connect to the API. cert_file: description: - Path\",\n \"only be removed. type: list contains: str generate_name: description: -\",\n \"pull the images from registry prior to building. type: bool\",\n \"once in an array of causes due to fields having\",\n \"the system. Only set when deletionTimestamp is also set. May\",\n \"the space within each name must be unique. An empty\",\n \"an empty From reference is allowed in a build configuration.\",\n \"field is not set anywhere right now and apiserver is\",\n \"- Name of the referent. type: str runtime_artifacts: description: -\",\n \"be one of 'ImageStreamTag' or 'DockerImage'. This value will be\",\n \"required. If Requests is omitted for a container, it defaults\",\n \"contains: custom_strategy: description: - customStrategy holds the parameters to the\",\n \"to copy source from. type: complex contains: api_version: description: -\",\n \"is visible. When the last pending initializer is removed, and\",\n \"process can determine the resource is fully terminated. If not\",\n \"parameters to the Custom build strategy type: complex contains: _from:\",\n \"if not set. type: int details: description: - Extended data\",\n \"schema the FieldPath is written in terms of, defaults to\",\n \"- secretSource is a reference to the secret type: complex\",\n \"the committer of a specific commit type: complex contains: email:\",\n \"namespace in different clusters. This field is not set anywhere\",\n \"type: complex contains: allow_env: description: - allowEnv determines whether the\",\n \"complex contains: as_file: description: - asFile indicates that the provided\",\n \"Read-only. Value must be treated as opaque by clients and\",\n \"considered uninitialized and is hidden (in list/watch and get calls)\",\n \"reference to the secret type: complex contains: name: description: -\",\n \"machine-readable description of why this operation is in the \\\"Failure\\\"\",\n \"Ruby image, this is necessary to make `ruby`, `bundle` and\",\n \"to true. There cannot be more than one managing controller.\",\n \"''' ''' RETURN = ''' api_version: type: string description: Requested\",\n \"this build to. Kind must be one of 'ImageStreamTag' or\",\n \"should be created, patched, or deleted. When set to C(present),\",\n \"to the Custom build strategy type: complex contains: _from: description:\",\n \"up the authentication for pulling the Docker images from the\",\n \"divisor: description: - Specifies the output format of the exposed\",\n \"key: description: - The key of the secret to select\",\n \"name attribute of the resource associated with the status StatusReason\",\n \"set when deletionTimestamp is also set. May only be shortened.\",\n \"limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently\",\n \"the server. Populated by the system. Read-only. type: str self_link:\",\n \"description: - Extended data associated with the reason. Each reason\",\n \"of referencing a part of an object.' type: str kind:\",\n \"different clusters. This field is not set anywhere right now\",\n \"to. This is used to distinguish resources with same name\",\n \"uid: description: - UID of the referent. type: str resource_version:\",\n \"input string will be unchanged. The $(VAR_NAME) syntax can be\",\n \"with `/bin/sh -ic`. It may not be specified with Command.\",\n \"self_link: description: - SelfLink is a URL representing this object.\",\n \"time when this object was created. It is not guaranteed\",\n \"complex contains: {} finalizers: description: - Must be empty before\",\n \"so users can ADD or COPY them during docker build.\",\n \"type: str uid: description: - UID is the unique in\",\n \"message is the description of a specific commit type: str\",\n \"type: int node_selector: description: - nodeSelector is a selector which\",\n \"URL for acessing the Kubernetes API. key_file: description: - Path\",\n \"referent. type: str paths: description: - paths is a list\",\n \"- Name of the referent. type: str docker_strategy: description: -\",\n \"being merged into a unique set of values. If a\",\n \"must be treated as opaque by clients and . type:\",\n \"error. This field may be presented as-is to a reader.\",\n \"for the object, bypassing any modules parameters intended to define\",\n \"type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional\",\n \"result: description: - If result is set with the Failure\",\n \"REST resource this object represents. Servers may infer this from\",\n \"reference to an DockerImage, ImageStreamTag, or ImageStreamImage from which the\",\n \"and may be truncated by the length of the suffix\",\n \"available. A Reason clarifies an HTTP status code but does\",\n \"length. For the Docker build strategy, these will be copied\",\n \"tech preview. type: complex contains: env: description: - env contains\",\n \"str divisor: description: - Specifies the output format of the\",\n \"queryable and should be preserved when modifying objects. type: complex\",\n \"a registry. type: complex contains: args: description: - args is\",\n \"the managing controller. type: bool kind: description: - Kind of\",\n \"str generate_name: description: - GenerateName is an optional prefix, used\",\n \"be valid for a particular resource or set of resources.\",\n \"- The Causes array includes more details associated with the\",\n \"- 'Required: resource to select' type: str secret_key_ref: description: -\",\n \"bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the system\",\n \"of the referent. type: str secrets: description: - secrets represents\",\n \"been deleted, this object will be garbage collected. If this\",\n \"store until this reference is removed. Defaults to false. To\",\n \"GenericWebHook. type: bool secret: description: - secret used to validate\",\n \"description: - metadata for BuildConfigList. type: complex contains: resource_version: description:\",\n \"the object, bypassing any modules parameters intended to define object\",\n \"a I(name) or I(type) attribute, a strategic merge is performed,\",\n \"to validate requests. type: str generic: description: - generic contains\",\n \"description: - CreationTimestamp is a timestamp representing the server time\",\n \"can use when building images to reduce the final size\",\n \"It must have non-zero length. type: str value: description: -\",\n \"complex contains: last_version: description: - lastVersion is used to inform\",\n \"to the Docker build strategy. type: complex contains: _from: description:\",\n \"to C(True). default: present choices: - present - absent username:\",\n \"set of initializers. Only privileged users may set or modify\",\n \"object serialized and passed to the custom builder type: str\",\n \"the build strategy will be used. Only one ImageChangeTrigger with\",\n \"and the object is considered as initialized and visible to\",\n \"the Secret or it's key must be defined type: bool\",\n \"description: - Specify whether the Secret or it's key must\",\n \"and may reject unrecognized values. type: str code: description: -\",\n \"version of the referent. type: str block_owner_deletion: description: - If\",\n \"complex contains: config_map_key_ref: description: - Selects a key of a\",\n \"Read-only. Null for lists. type: complex contains: {} deletion_grace_period_seconds: description:\",\n \"complex contains: email: description: - email of the source control\",\n \"description: - noProxy is the list of domains for which\",\n \"an existing Kubernetes config file. If not provided, and no\",\n \"to verify the API server's SSL certificates. type: bool requirements:\",\n \"Failure field, the object will be persisted to storage and\",\n \"cause of the error. This field may be presented as-is\",\n \"provided. If this field is used, the name returned to\",\n \"I(~/.kube/config.json). type: path password: description: - Provide a password for\",\n \"that 30 seconds, the Kubelet will send a hard termination\",\n \"key must be defined type: bool field_ref: description: - 'Selects\",\n \"contains additional environment variables you want to pass into a\",\n \"stored with a resource that may be set by external\",\n \"look up a Docker image repository to push to. In\",\n \"the operation should be retried. type: int uid: description: -\",\n \"ImageStreamTag, or ImageStreamImage from which the docker image should be\",\n \"API version. type: str resource_field_ref: description: - 'Selects a resource\",\n \"is fully terminated. If not set, graceful deletion of the\",\n \"changed. Value must be treated as opaque by clients and\",\n \"clients to determine when objects have changed. Value must be\",\n \"bool requirements: - openshift == 0.3.3 ''' EXAMPLES = '''\",\n \"source for a specific repo snapshot. This is optional. type:\",\n \"URL representing this object. Populated by the system. Read-only. type:\",\n \"will be persisted to storage and then deleted, ensuring that\",\n \"described). type: str retry_after_seconds: description: - If specified, the time\",\n \"builds will receive this binary as input on STDIN. type:\",\n \"of the referent. type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds\",\n \"referenced by the ServiceAccount type: str source: description: - source\",\n \"created. It is not guaranteed to be set in happens-before\",\n \"or deleted. When set to C(present), the object will be\",\n \"can only be removed. type: list contains: str generate_name: description:\",\n \"cannot be more than one managing controller. type: list contains:\",\n \"build config type: complex contains: last_version: description: - lastVersion is\",\n \"the Docker and Source build strategies assume this file is\",\n \"up the authentication for cloning private repository. The secret\\\\ \\\\\",\n \"named by its JSON serialization. May include dot and postfix\",\n \"code for this status, 0 if not set. type: int\",\n \"description: - DeletionTimestamp is RFC 3339 date and time at\",\n \"committer is the committer of a specific commit type: complex\",\n \"by the Docker build API. The experimental policy 'SkipLayers' will\",\n \"defines an optional location to push the output of this\",\n \"type: str secret: description: - secret is a reference to\",\n \"to use a shell. In all other cases, using Script\",\n \"list contains: str generate_name: description: - GenerateName is an optional\",\n \"name must be unique. An empty namespace is equivalent to\",\n \"the runtime image for use. Deprecated: This feature will be\",\n \"objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key: description: -\",\n \"provided, and no other connection options are provided, the openshift\",\n \"perform a build. type: complex contains: custom_strategy: description: - customStrategy\",\n \"a key of a secret in the pod's namespace type:\",\n \"to storage and then deleted, ensuring that other clients can\",\n \"`/bin/sh -ic`. It may not be specified with Command. Use\",\n \"description: - destinationDir is the directory where the files from\",\n \"is not specified. type: str generation: description: - A sequence\",\n \"lastTriggeredImageID is used internally by the ImageChangeController to save last\",\n \"definition for the object, bypassing any modules parameters intended to\",\n \"script: description: - script is a shell script to be\",\n \"dockerfile: description: - dockerfile is the raw contents of a\",\n \"'Variable references $(VAR_NAME) are expanded using the previous defined environment\",\n \"build pod gets scheduled in the system, that the build\",\n \"status. If this value is empty there is no information\",\n \"resource or set of resources. Clients must treat these values\",\n \"set. type: int details: description: - Extended data associated with\",\n \"for a Bitbucket webhook type of trigger type: complex contains:\",\n \"- lastVersion is used to inform about number of last\",\n \"contains: image_labels: description: - imageLabels define a list of labels\",\n \"new layers in between each image step, and will fail\",\n \"result is set with the Failure field, the object will\",\n \"by the ImageChangeController to save last used image ID for\",\n \"description: - images describes a set of images to be\",\n \"\\\"name\\\" refers to the name of the container that triggered\",\n \"CamelCase. type: str metadata: description: - metadata for BuildConfig. type:\",\n \"contents of a Jenkinsfile which defines a Jenkins pipeline build.\",\n \"be modified based on your strategy base image and additional\",\n \"to the source that will be built. The structure of\",\n \"is the absolute path of the file or directory inside\",\n \"uri: description: - uri points to the source that will\",\n \"of the container that triggered the event) or if no\",\n \"Docker build strategy, these will be copied into the build\",\n \"directory within the build directory where files copied from the\",\n \"ImageStreamImage from which the docker image should be pulled the\",\n \"the system. Read-only. Value must be treated as opaque by\",\n \"description: - If true, AND if the owner has the\",\n \"time in seconds before the operation should be retried. type:\",\n \"allow running Docker commands (and build Docker images) from inside\",\n \"Clients must treat these values as opaque and passed unmodified\",\n \"copy binary artifacts created from one build into a separate\",\n \"from clients that haven't explicitly asked to observe uninitialized objects.\",\n \"OpenShift registry, this field does not need to be set.\",\n \"at object creation time. This field is a list of\",\n \"of resources. Populated by the system. Read-only. Value must be\",\n \"message: description: - A human-readable description of the status of\",\n \"The arguments are placed immediately after the command to be\",\n \"description: - Enable debug output from the OpenShift helper. Logging\",\n \"Generic webhook type of trigger type: complex contains: allow_env: description:\",\n \"will never be expanded, regardless of whether the variable exists\",\n \"- from is a reference to an ImageStreamTag that will\",\n \"- The key of the secret to select from. Must\",\n \"have any innate Dockerfile replaced in the context dir. type:\",\n \"trigger type: complex contains: _from: description: - from is a\",\n \"schemas to the latest internal value, and may reject unrecognized\",\n \"authentication for executing the Docker push to authentication enabled Docker\",\n \"and RHEL images that use Software Collections (SCL), in order\",\n \"for the build time. For the Source build strategy, these\",\n \"str https_proxy: description: - httpsProxy is a proxy used to\",\n \"a file containing the YAML definition of the object. Mutually\",\n \"represents. Servers may infer this from the endpoint the client\",\n \"contains: name: description: - name defines the name of the\",\n \"time. This field is a list of initializers that have\",\n \"may be used when an archive is provided. Custom builds\",\n \"list contains: _from: description: - from is a reference to\",\n \"resource associated with the status StatusReason. type: str kind: description:\",\n \"will be garbage collected. If this object is managed by\",\n \"destinationDir can also be empty or equal to \\\".\\\", in\",\n \"and should be preserved when modifying objects. type: complex contains:\",\n \"Docker images) from inside the Docker container. type: bool force_pull:\",\n \"the source. The custom strategy receives this binary as standard\",\n \"and deleted, if set to C(absent). A patch operation results\",\n \"opaque by clients and passed unmodified back to the server.\",\n \"the variable exists or not. Defaults to \\\"\\\".' type: str\",\n \"parameters for a Bitbucket webhook type of trigger type: complex\",\n \"artifacts created from one build into a separate runtime image.'\",\n \"the name of the label. It must have non-zero length.\",\n \"notation for nested attributes. Arrays are zero-indexed. Fields may appear\",\n \"type: str runtime_image: description: - 'runtimeImage is an optional image\",\n \"- UID of the referent. type: str env: description: -\",\n \"type: complex contains: pending: description: - Pending is a list\",\n \"tar.gz file and extract it as the source. The custom\",\n \"Selects a key of a secret in the pod's namespace\",\n \"input should be considered a single file within the build\",\n \"type: str resource_version: description: - Specific resourceVersion to which this\",\n \"this binary as input on STDIN. type: complex contains: as_file:\",\n \"CentOS and RHEL images that use Software Collections (SCL), in\",\n \"associated with the status StatusReason. On some operations may differ\",\n \"is a shell script to be run with `/bin/sh -ic`.\",\n \"privileged users may set or modify this list. Once it\",\n \"type: list contains: str script: description: - script is a\",\n \"valid credentials for remote repository, where the\\\\ \\\\ data's key\",\n \"across separate operations. Clients may not set this value. It\",\n \"modified further by any user. type: complex contains: pending: description:\",\n \"is the description of a specific commit type: str type:\",\n \"uid: description: - UID of the referent. type: str post_commit:\",\n \"within a pod, this would take on a value like:\",\n \"coding: utf-8 -*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION =\",\n \"Selects a key of a ConfigMap. type: complex contains: key:\",\n \"If nil or empty, this object has been completely initialized.\",\n \"build into a separate runtime image.' type: complex contains: api_version:\",\n \"the Docker images from the private Docker registries type: complex\",\n \"build unless Namespace is specified. type: complex contains: api_version: description:\",\n \"- sourcePath is the absolute path of the file or\",\n \"failed to init, so there is no module object. All\",\n \"CA certificate used to authenticate with the API. type: path\",\n \"list have been deleted, this object will be garbage collected.\",\n \"run with `/bin/sh -ic`. It may not be specified with\",\n \"str env: description: - env contains additional environment variables you\",\n \"http_proxy: description: - httpProxy is a proxy used to reach\",\n \"Secret or it's key must be defined type: bool dockerfile_path:\",\n \"I(name_) or I(type) are merged. To force the replacement of\",\n \"of this representation of an object. Servers should convert recognized\",\n \"API. The experimental policy 'SkipLayers' will avoid commiting new layers\",\n \"ImageStreamTag will be looked for in the namespace of the\",\n \"on the strategy. For Docker builds, this is the build\",\n \"by a controller, then an entry in this list will\",\n \"in the root of the specified contextDir. type: str source_strategy:\",\n \"description of a specific commit type: str type: description: -\",\n \"the name of the ServiceAccount to use to run the\",\n \"observe uninitialized objects. When an object is created, the system\",\n \"must be a directory. destinationDir can also be empty or\",\n \"users may set or modify this list. Once it is\",\n \"resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are\",\n \"be an archive as described above. For Source and Docker\",\n \"Retrieve a list of build_configs. List operations provide a snapshot\",\n \"still done in the builder image but, post build, you\",\n \"been provided. If this field is used, the name returned\",\n \"str git: description: - git contains optional information about git\",\n \"found in the Kubernetes config file. debug: description: - Enable\",\n \"str post_commit: description: - postCommit is a build hook executed\",\n \"Cannot be updated. type: str namespace: description: - Namespace defines\",\n \"description: - RunPolicy describes how the new build created from\",\n \"list contains: str command: description: - command is the command\",\n \"the content of the directory will be copied, but the\",\n \"to have the appropriate collections enabled in the shell. E.g.,\",\n \"A Reason clarifies an HTTP status code but does not\",\n \"description: - Name of the referent. type: str type: description:\",\n \"triggers determine how new Builds can be launched from a\",\n \"description: - metadata for BuildConfig. type: complex contains: annotations: description:\",\n \"API. type: path kubeconfig: description: - Path to an existing\",\n \"the system. Read-only. type: str reason: description: - A machine-readable\",\n \"Docker registries type: complex contains: name: description: - Name of\",\n \"connecting to the API. Use in conjunction with I(username). resource_definition:\",\n \"description: - Namespace of the referent. type: str resource_version: description:\",\n \"GitLabWebHook contains the parameters for a GitLab webhook type of\",\n \"RFC 3339 date and time at which this resource will\",\n \"be used to pull the image from a registry If\",\n \"be launched from a BuildConfig. If no triggers are defined,\",\n \"the server. If this field is specified and the generated\",\n \"See type: list contains: name: description: - Name of the\",\n \"container and after cleanup, remove the pod from the API.\",\n \"by the system. Read-only. type: str spec: description: - spec\",\n \"used to authenticate with the API. type: path state: description:\",\n \"force_pull: description: - forcePull describes if the builder should pull\",\n \"the Docker push to authentication enabled Docker Registry (or Docker\",\n \"system can use when building images to reduce the final\",\n \"(limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex\",\n \"of this object that can be used by clients to\",\n \"container within a pod, this would take on a value\",\n \"authenticate with the API. type: path state: description: - Determines\",\n \"image will be equivalent to an image created by the\",\n \"is appropriate to execute the post build hook, for example\",\n \"description: - command is the command to run. It may\",\n \"a Dockerfile which should be built. When this option is\",\n \"this list with the current set of initializers. Only privileged\",\n \"source will depend on the type of build to run\",\n \"May match selectors of replication controllers and services. type: complex\",\n \"To set this field, a user needs \\\"delete\\\" permission of\",\n \"description: - serviceAccount is the name of the ServiceAccount to\",\n \"to be an archive as described above. For Source and\",\n \"complex contains: allow_env: description: - allowEnv determines whether the webhook\",\n \"the client will be different than the name passed. This\",\n \"description: - Specify whether the ConfigMap or it's key must\",\n \"of the schema the FieldPath is written in terms of,\",\n \"placed. type: str source_path: description: - sourcePath is the absolute\",\n \"can be a file or directory. destinationDir must be a\",\n \"secrets is a list of additional secrets that will be\",\n \"Pipeline build strategy. This strategy is in tech preview. type:\",\n \"referent. type: str runtime_artifacts: description: - 'runtimeArtifacts specifies a list\",\n \"positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is the\",\n \"to save last used image ID for build type: str\",\n \"retained. type: int node_selector: description: - nodeSelector is a selector\",\n \"forcePull describes if the builder should pull the images from\",\n \"is not directly settable by a client. The resource is\",\n \"any user. type: complex contains: pending: description: - Pending is\",\n \"secret type: str secret_source: description: - secretSource is a reference\",\n \"access statement, such as desiredState.manifest.containers[2]. For example, if the object\",\n \"in the shell. E.g., in the Ruby image, this is\",\n \"of build configs type: list contains: api_version: description: - APIVersion\",\n \"is the location of Source scripts type: str type: description:\",\n \"the parameters for a GitHub webhook type of trigger type:\",\n \"as exc: # The helper failed to init, so there\",\n \"output of this build to. Kind must be one of\",\n \"and values that can be used to organize and categorize\",\n \"spent building the image. The default policy is 'None' which\",\n \"kind: description: - The kind attribute of the resource associated\",\n \"lastVersion is used to inform about number of last triggered\",\n \"description: - incremental flag forces the Source build to do\",\n \"type is the kind of build strategy. type: str successful_builds_history_limit:\",\n \"the generation of an appropriate name automatically. Name is primarily\",\n \"causes. type: list contains: field: description: - 'The field of\",\n \"resources with same name and namespace in different clusters. This\",\n \"Must be empty before the object is deleted from the\",\n \"will be looked for in the namespace of the build\",\n \"in the pod's namespace type: complex contains: key: description: -\",\n \"present locally type: bool pull_secret: description: - pullSecret is the\",\n \"API. cert_file: description: - Path to a certificate used to\",\n \"host: description: - Provide a URL for acessing the Kubernetes\",\n \"value is the base64 encoded credentials. Supported auth methods\\\\ \\\\\",\n \"Limits if that is explicitly specified, otherwise to an implementation-defined\",\n \"type: description: - type is the type of build trigger\",\n \"the schema the FieldPath is written in terms of, defaults\",\n \"controllers and services. type: complex contains: str, str name: description:\",\n \"- List of objects depended by this object. If ALL\",\n \"Causes array includes more details associated with the StatusReason failure.\",\n \"the input string will be unchanged. The $(VAR_NAME) syntax can\",\n \"images that use Software Collections (SCL), in order to have\",\n \"- A sequence number representing a specific generation of the\",\n \"of an object. Servers should convert recognized schemas to the\",\n \"resource_definition: description: - Provide the YAML definition for the object,\",\n \"may request that a pod is deleted in 30 seconds.\",\n \"- destinationDir is the directory where the files from the\",\n \"the last one in the list is used. type: list\",\n \"bypassing any modules parameters intended to define object attributes. type:\",\n \"desiredState.manifest.containers[2]. For example, if the object reference is to a\",\n \"representing the REST resource this object represents. Servers may infer\",\n \"short_description: OpenShift BuildConfigList description: - Retrieve a list of build_configs.\",\n \"contains the parameters for a GitHub webhook type of trigger\",\n \"str optional: description: - Specify whether the Secret or it's\",\n \"Script or the Docker image's default entrypoint. The arguments are\",\n \"created by the Docker build API. The experimental policy 'SkipLayers'\",\n \"type: int initializers: description: - An initializer is a controller\",\n \"Dockerfile that will be used to build the Docker image,\",\n \"complex contains: {} deletion_grace_period_seconds: description: - Number of seconds allowed\",\n \"Logging info is written to KubeObjHelper.log default: false type: bool\",\n \"be defined type: bool force_pull: description: - forcePull describes if\",\n \"builder image but, post build, you can copy the needed\",\n \"This is used to distinguish resources with same name and\",\n \"it's key must be defined type: bool expose_docker_socket: description: -\",\n \"which should be built. When this option is specified, the\",\n \"of a Secret that would be used for setting\\\\ \\\\\",\n \"want to pass into a builder container. type: list contains:\",\n \"image. type: list contains: destination_dir: description: - destinationDir is the\",\n \"the pod from the API. In the presence of network\",\n \"this list will point to this controller, with the controller\",\n \"the --no-cache=true flag type: bool pull_secret: description: - pullSecret is\",\n \"on PUT operations. Populated by the system. Read-only. type: str\",\n \"source path ends in /. then the content of the\",\n \"successful builds to retain. If not specified, all successful builds\",\n \"for a Generic webhook type of trigger type: complex contains:\",\n \"The default policy is 'None' which means the final build\",\n \"file or directory inside the image to copy to the\",\n \"a reference to the secret type: complex contains: name: description:\",\n \"the ImageChangeController to save last used image ID for build\",\n \"will not be created at the destination. type: str pull_secret:\",\n \"Docker image, relative to the root of the context (contextDir).\",\n \"omitted for a container, it defaults to Limits if that\",\n \"- Selects a key of a ConfigMap. type: complex contains:\",\n \"of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy: description:\",\n \"empty or equal to \\\".\\\", in this case it just\",\n \"object is non-nil, entries in this list can only be\",\n \"description: - pullSecret is the name of a Secret that\",\n \"Use ImageSource to copy binary artifacts created from one build\",\n \"the listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options: api_key:\",\n \"- APIVersion defines the versioned schema of this representation of\",\n \"the FROM, but before the rest of your Dockerfile stanzas.\",\n \"the environment variable. Must be a C_IDENTIFIER. type: str value:\",\n \"the time in seconds before the operation should be retried.\",\n \"repo snapshot. This is optional. type: complex contains: git: description:\",\n \"type: str group: description: - The group attribute of the\",\n \"status StatusReason. On some operations may differ from the requested\",\n \"to perform a build. type: complex contains: custom_strategy: description: -\",\n \"contains: {} finalizers: description: - Must be empty before the\",\n \"of this field for those objects will be empty. Must\",\n \"the system actively tries to terminate the build; value must\",\n \"is expected to be deleted (no longer visible from resource\",\n \"for running unit tests with `rake test`. If you need\",\n \"as-is to a reader. type: str reason: description: - A\",\n \"Name is primarily intended for creation idempotence and configuration definition.\",\n \"otherwise 422 (Unprocessable Entity) will be returned. type: bool controller:\",\n \"the owner cannot be deleted from the key-value store until\",\n \"returning a resource_version representing a consistent version of the listed\",\n \"the OpenShift registry, this field does not need to be\",\n \"after the build output image is committed, before it is\",\n \"docker_strategy: description: - dockerStrategy holds the parameters to the Docker\",\n \"requested resource Kind. type: str name: description: - The name\",\n \"deleted (no longer visible from resource lists, and not reachable\",\n \"the object is considered uninitialized and is hidden (in list/watch\",\n \"needed artifacts in the runtime image for use. Deprecated: This\",\n \"- UID of the referent. type: str resource_version: description: -\",\n \"not specified. type: str generation: description: - A sequence number\",\n \"of the Dockerfile for this build. type: complex contains: api_version:\",\n \"written in terms of, defaults to \\\"v1\\\". type: str field_path:\",\n \"credentials for remote repository, where the\\\\ \\\\ data's key represent\",\n \"this object. Populated by the system. Read-only. type: str reason:\",\n \"failed_builds_history_limit: description: - failedBuildsHistoryLimit is the number of old failed\",\n \"ssl_ca_cert: description: - Path to a CA certificate used to\",\n \"seconds, the Kubelet will send a hard termination signal (SIGKILL)\",\n \"a single file. contextDir may be used when an archive\",\n \"name then the last one in the list is used.\",\n \"status, 0 if not set. type: int details: description: -\",\n \"destinationDir is the relative directory within the build directory where\",\n \"the script finishes, all files injected will be truncated to\",\n \"same name then the last one in the list is\",\n \"the source for a specific repo snapshot. This is optional.\",\n \"type: str secret_key_ref: description: - Selects a key of a\",\n \"graceful termination signal to the containers in the pod. After\",\n \"Hub). type: complex contains: name: description: - Name of the\",\n \"a registry If the image is pulled from the OpenShift\",\n \"a resource or set of resources. Clients must treat these\",\n \"specified, all successful builds are retained. type: int triggers: description:\",\n \"Source build strategies assume this file is a zip, tar,\",\n \"in the builder image but, post build, you can copy\",\n \"is the command to run. It may not be specified\",\n \"jenkinsfile_path: description: - JenkinsfilePath is the optional path of the\",\n \"in happens-before order across separate operations. Clients may not set\",\n \"the build type: list contains: _from: description: - from is\",\n \"copied from the image are placed. type: str source_path: description:\",\n \"and the watch operation on a resource or set of\",\n \"build nodeselector values for the cluster. If set to an\",\n \"are placed. type: str source_path: description: - sourcePath is the\",\n \"reason: description: - A machine-readable description of the cause of\",\n \"running unit tests with `rake test`. If you need control\",\n \"connect to the API. cert_file: description: - Path to a\",\n \"must be true for the build pod to fit on\",\n \"incremental flag forces the Source build to do incremental builds\",\n \"there are multiple labels with the same name then the\",\n \"when a graceful deletion is requested by the user, and\",\n \"retry_after_seconds: description: - If specified, the time in seconds before\",\n \"the build. type: complex contains: limits: description: - Limits describes\",\n \"the container: only resources limits and requests (limits.cpu, limits.memory, requests.cpu\",\n \"values. If a list contains a dictionary with a I(name)\",\n \"object has been completely initialized. Otherwise, the object is considered\",\n \"of the context (contextDir). type: str env: description: - env\",\n \"values. type: str items: description: - items is a list\",\n \"is provided. Custom builds will receive this binary as input\",\n \"type: str service_account: description: - serviceAccount is the name of\",\n \"the context dir. type: str git: description: - git contains\",\n \"or modify this list. Once it is empty, it may\",\n \"str spec: description: - spec holds all the input necessary\",\n \"used to validate requests. type: str github: description: - github\",\n \"initializer is a controller which enforces some system invariant at\",\n \"file is a zip, tar, or tar.gz file and extract\",\n \"resourceVersion to which this reference is made, if any. type:\",\n \"str type: description: - type of the build source, may\",\n \"equal to \\\".\\\", in this case it just refers to\",\n \"whether the ConfigMap or it's key must be defined type:\",\n \"String that identifies the server's internal version of this object\",\n \"or only pull if it is not present locally type:\",\n \"to be deleted (no longer visible from resource lists, and\",\n \"the YAML definition for the object, bypassing any modules parameters\",\n \"list contains: mount_path: description: - mountPath is the path at\",\n \"- type of build input to accept type: str strategy:\",\n \"description: - If result is set with the Failure field,\",\n \"output format of the exposed resources, defaults to \\\"1\\\" type:\",\n \"to the managing controller. type: bool kind: description: - Kind\",\n \"the containers in the pod. After that 30 seconds, the\",\n \"key represent the authentication method to be used and\\\\ \\\\\",\n \"describes the Docker image the Strategy should produce. type: complex\",\n \"the referent. type: str scripts: description: - scripts is the\",\n \"an application without unneeded dependencies installed. The building of the\",\n \"- instead, it will either return 201 Created or 500\",\n \"the Docker image the Strategy should produce. type: complex contains:\",\n \"referent. type: str resource_version: description: - An opaque value that\",\n \"the Dockerfile is located, so users can ADD or COPY\",\n \"a context found in the Kubernetes config file. debug: description:\",\n \"managing controller. type: bool kind: description: - Kind of the\",\n \"be used when an archive is provided. Custom builds will\",\n \"a valid secret key. type: str name: description: - Name\",\n \"to reach the git repository over http type: str https_proxy:\",\n \"will be used to build the Docker image, relative to\",\n \"description: - type of the build source, may be one\",\n \"args is a list of arguments that are provided to\",\n \"all clients. type: list contains: name: description: - name of\",\n \"to trigger them. type: complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds\",\n \"that are applied to the resulting image. If there are\",\n \"points to the source that will be built. The structure\",\n \"the latest internal value, and may reject unrecognized values. type:\",\n \"If true, this reference points to the managing controller. type:\",\n \"type: str code: description: - Suggested HTTP return code for\",\n \"set by external tools to store and retrieve arbitrary metadata.\",\n \"ImageStreamTag that will trigger a build when updated It is\",\n \"describes a set of images to be used to provide\",\n \"holds the parameters to the Docker build strategy. type: complex\",\n \"build. The pod will be allowed to use secrets referenced\",\n \"In the presence of network partitions, this object may still\",\n \"on your strategy base image and additional ENV stanzas from\",\n \"type: str field_path: description: - Path of the field to\",\n \"of the resource. (when there is a single resource which\",\n \"Jenkins Pipeline build strategy. This strategy is in tech preview.\",\n \"resources may allow a client to request the generation of\",\n \"type: path kubeconfig: description: - Path to an existing Kubernetes\",\n \"regardless of whether the variable exists or not. Defaults to\",\n \"Script. This might be needed if the image doesn't have\",\n \"before the system actively tries to terminate the build; value\",\n \"binary builds accept a binary as their input. The binary\",\n \"of the context (contextDir). If both JenkinsfilePath & Jenkinsfile are\",\n \"is a URL representing this object. Populated by the system.\",\n \"type: string description: Requested API version build_config_list: type: complex returned:\",\n \"be set further into the future, although it may be\",\n \"secret that you want to use in your build. type:\",\n \"by the system when a graceful deletion is requested. Read-only.\",\n \"type: str secrets: description: - secrets represents a list of\",\n \"that will be resolved in the Dockerfile. See type: list\",\n \"the docker image should be pulled type: complex contains: api_version:\",\n \"= ''' ''' RETURN = ''' api_version: type: string description:\",\n \"image and additional ENV stanzas from your strategy environment will\",\n \"image repository to push to. In the case of an\",\n \"of the referent. type: str type: description: - type of\",\n \"build API. The experimental policy 'SkipLayers' will avoid commiting new\",\n \"with the --no-cache=true flag type: bool pull_secret: description: - pullSecret\",\n \"you want to pass into a build pipeline. type: list\",\n \"may be presented as-is to a reader. type: str reason:\",\n \"complex contains: key: description: - The key of the secret\",\n \"opaque by clients and . type: str self_link: description: -\",\n \"type: str docker_strategy: description: - dockerStrategy holds the parameters to\",\n \"description: - 'runtimeImage is an optional image that is used\",\n \"of objects depended by this object. If ALL objects in\",\n \"contains: mount_path: description: - mountPath is the path at which\",\n \"dot and postfix notation for nested attributes. Arrays are zero-indexed.\",\n \"a list of build configs type: list contains: api_version: description:\",\n \"is a single resource which can be described). type: str\",\n \"contains: args: description: - args is a list of arguments\",\n \"policy is 'None' which means the final build image will\",\n \"the operation. One of: \\\"Success\\\" or \\\"Failure\\\".' type: str labels:\",\n \"locally type: bool pull_secret: description: - pullSecret is the name\",\n \"client submits requests to. Cannot be updated. In CamelCase. type:\",\n \"process that is responsible for initializing this object. type: str\",\n \"\\\"1\\\" type: str resource: description: - 'Required: resource to select'\",\n \"type: complex contains: key: description: - The key of the\",\n \"description: - pullSecret is a reference to a secret to\",\n \"it just refers to the root of WORKDIR. Deprecated: This\",\n \"the default configuration file from I(~/.kube/config.json). type: path password: description:\",\n \"permission of the owner, otherwise 422 (Unprocessable Entity) will be\",\n \"description: - exposeDockerSocket will allow running Docker commands (and build\",\n \"a Secret that would be used for setting\\\\ \\\\ up\",\n \"allow_env: description: - allowEnv determines whether the webhook can set\",\n \"generated name exists, the server will NOT return a 409\",\n \"Cannot be updated. type: str owner_references: description: - List of\",\n \"match selectors of replication controllers and services. type: complex contains:\",\n \"is located, so users can ADD or COPY them during\",\n \"the optional path of the Jenkinsfile that will be used\",\n \"message: description: - A human-readable description of the cause of\",\n \"be removed in a future release. Use ImageSource to copy\",\n \"be empty before the object is deleted from the registry.\",\n \"str dockerfile: description: - dockerfile is the raw contents of\",\n \"way of referencing a part of an object.' type: str\",\n \"requested. Populated by the system when a graceful deletion is\",\n \"name: description: - Name of the referent. type: str runtime_artifacts:\",\n \"name of the ServiceAccount to use to run the pod\",\n \"load the default configuration file from I(~/.kube/config.json). type: path password:\",\n \"holds any relevant information about a build config type: complex\",\n \"file depending on the strategy. For Docker builds, this is\",\n \"image from the build strategy will be used. Only one\",\n \"committed, before it is pushed to a registry. type: complex\",\n \"str uid: description: - UID of the referent. type: str\",\n \"asFile indicates that the provided binary input should be considered\",\n \"previous defined environment variables in the container and any service\",\n \"- strategy defines how to perform a build. type: complex\",\n \"module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc: # The\",\n \"\\\"Success\\\" or \\\"Failure\\\".' type: str labels: description: - Map of\",\n \"representation. Not all objects are required to be scoped to\",\n \"proxy used to reach the git repository over https type:\",\n \"type: list contains: name: description: - Name of the environment\",\n \"contains: str, str cluster_name: description: - The name of the\",\n \"Command, Script or the Docker image's default entrypoint. The arguments\",\n \"contains information about git-based build source type: complex contains: author:\",\n \"be used by clients to determine when objects have changed.\",\n \"str self_link: description: - SelfLink is a URL representing this\",\n \"$$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless\",\n \"observe the deletion. type: complex contains: api_version: description: - APIVersion\",\n \"of the object is non-nil, entries in this list can\",\n \"For Docker builds, this is the build context and an\",\n \"to init, so there is no module object. All we\",\n \"and passed unmodified back to the server. They may only\",\n \"type: str gitlab: description: - GitLabWebHook contains the parameters for\",\n \"be defined type: bool dockerfile_path: description: - dockerfilePath is the\",\n \"optimizations the system can use when building images to reduce\",\n \"values. type: str code: description: - Suggested HTTP return code\",\n \"the generated name exists, the server will NOT return a\",\n \"Build object serialized and passed to the custom builder type:\",\n \"namespace of the build unless Namespace is specified. type: complex\",\n \"may provide detailed causes. type: list contains: field: description: -\",\n \"str status: description: - 'Status of the operation. One of:\",\n \"nodeselector values for the cluster. If set to an empty\",\n \"return 201 Created or 500 with Reason ServerTimeout indicating a\",\n \"strategy, these will be copied into the build directory, where\",\n \"be set to true for GenericWebHook. type: bool secret: description:\",\n \"passed unmodified back to the server. They may only be\",\n \"YAML definition for the object, bypassing any modules parameters intended\",\n \"by this object. If ALL objects in the list have\",\n \"in a future release. Use ImageSource to copy binary artifacts\",\n \"Secret or it's key must be defined type: bool expose_docker_socket:\",\n \"$(VAR_NAME) syntax can be escaped with a double $$, ie:\",\n \"used to validate requests. type: str generic: description: - generic\",\n \"defined type: bool expose_docker_socket: description: - exposeDockerSocket will allow running\",\n \"type: complex contains: author: description: - author is the author\",\n \"type: str scripts: description: - scripts is the location of\",\n \"to: description: - to defines an optional location to push\",\n \"the Source build to do incremental builds if true. type:\",\n \"the secret should be available for the build time. For\",\n \"type: str pull_secret: description: - pullSecret is a reference to\",\n \"description: - UID of the referent. type: str paths: description:\",\n \"DockerImage to copy source from. type: complex contains: api_version: description:\",\n \"specified with Script. This might be needed if the image\",\n \"the\\\\ \\\\ data's key represent the authentication method to be\",\n \"any. type: str uid: description: - UID of the referent.\",\n \"str strategy: description: - strategy defines how to perform a\",\n \"run the pod created by this build. The pod will\",\n \"API version of the referent. type: str block_owner_deletion: description: -\",\n \"of the cause of the error. If this value is\",\n \"represents the internal version of this object that can be\",\n \"the builder to the runtime image. sourcePath can be a\",\n \"the server time when this object was created. It is\",\n \"cluster. If set to an empty map or a map\",\n \"extended details. This field is optional and the data returned\",\n \"OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description:\",\n \"be used with other options like git - in those\",\n \"# The helper failed to init, so there is no\",\n \"client should retry (optionally after the time indicated in the\",\n \"https_proxy: description: - httpsProxy is a proxy used to reach\",\n \"pod's namespace type: complex contains: key: description: - The key\",\n \"this object has been completely initialized. Otherwise, the object is\",\n \"author is the author of a specific commit type: complex\",\n \"image doesn't have `/bin/sh`, or if you do not want\",\n \"str reason: description: - A machine-readable description of why this\",\n \"copied, but the directory itself will not be created at\",\n \"the absolute path of the file or directory inside the\",\n \"if it is not present locally type: bool pull_secret: description:\",\n \"contains: name: description: - name of the process that is\",\n \"should be built. When this option is specified, the FROM\",\n \"2 in this pod). This syntax is chosen only to\",\n \"case it just refers to the root of WORKDIR. Deprecated:\",\n \"referent. type: str docker_strategy: description: - dockerStrategy holds the parameters\",\n \"type: str https_proxy: description: - httpsProxy is a proxy used\",\n \"the server on successful creation of a resource and is\",\n \"contains: binary: description: - binary builds accept a binary as\",\n \"it will be removed from the system. Only set when\",\n \"script finishes, all files injected will be truncated to zero\",\n \"- Name of the referent. type: str optional: description: -\",\n \"contextDir may be used when an archive is provided. Custom\",\n \"- failedBuildsHistoryLimit is the number of old failed builds to\",\n \"or it's key must be defined type: bool jenkinsfile: description:\",\n \"complex contains: resource_version: description: - String that identifies the server's\",\n \"custom strategy receives this binary as standard input. This filename\",\n \"is a list of build configs type: list contains: api_version:\",\n \"map or a map with any values, default build nodeselector\",\n \"last one in the list is used. type: list contains:\",\n \"is in the \\\"Failure\\\" status. If this value is empty\",\n \"An empty namespace is equivalent to the \\\"default\\\" namespace, but\",\n \"preview. type: complex contains: env: description: - env contains additional\",\n \"ImageChangeTrigger with an empty From reference is allowed in a\",\n \"description: - Kind of the referent. type: str name: description:\",\n \"build creation. type: list contains: bitbucket: description: - BitbucketWebHook contains\",\n \"is the path at which to mount the secret type:\",\n \"hard termination signal (SIGKILL) to the container and after cleanup,\",\n \"post_commit: description: - postCommit is a build hook executed after\",\n \"operation should be retried. type: int uid: description: - UID\",\n \"is a string value representing the REST resource this object\",\n \"serialized and passed to the custom builder type: str env:\",\n \"responsible component that will remove the entry from the list.\",\n \"in seconds, counted from the time when a build pod\",\n \"For example, a user may request that a pod is\",\n \"- dockerfilePath is the path of the Dockerfile that will\",\n \"build hook executed after the build output image is committed,\",\n \"commands (and build Docker images) from inside the Docker container.\",\n \"those cases the Git repo will have any innate Dockerfile\",\n \"be equivalent to an image created by the Docker build\",\n \"used to pull the image from a registry If the\",\n \"field_ref: description: - 'Selects a field of the pod: supports\",\n \"object will be created, if it does not exist, or\",\n \"`/webapp.war` for the builder. If left empty, the Docker and\",\n \"builds to retain. If not specified, all failed builds are\",\n \"it as the source. The custom strategy receives this binary\",\n \"actively tries to terminate the build; value must be positive\",\n \"str namespace: description: - Namespace defines the space within each\",\n \"rest of your Dockerfile stanzas. The Dockerfile source type may\",\n \"optional prefix, used by the server, to generate a unique\",\n \"passed to the custom builder type: str env: description: -\",\n \"string description: Requested API version build_config_list: type: complex returned: when\",\n \"of the referent. type: str optional: description: - Specify whether\",\n \"updated, and lists will be replaced, rather than merged. default:\",\n \"service_account: description: - serviceAccount is the name of the ServiceAccount\",\n \"limits.memory, requests.cpu and requests.memory) are currently supported.' type: complex contains:\",\n \"the builder. If left empty, the Docker and Source build\",\n \"can also be empty or equal to \\\".\\\", in this\",\n \"Docker container. type: bool force_pull: description: - forcePull describes if\",\n \"never be expanded, regardless of whether the variable exists or\",\n \"associated with the status StatusReason (when there is a single\",\n \"type: int deletion_timestamp: description: - DeletionTimestamp is RFC 3339 date\",\n \"requested API version for the Build object serialized and passed\",\n \"defines the literal value of the label. type: str push_secret:\",\n \"this field, a user needs \\\"delete\\\" permission of the owner,\",\n \"key of a secret in the pod's namespace type: complex\",\n \"Name of the referent. type: str type: description: - type\",\n \"type: complex contains: env: description: - env contains additional environment\",\n \"as described above. For Source and Docker builds, if binary.asFile\",\n \"to distinguish resources with same name and namespace in different\",\n \"type: str status: description: - 'Status of the operation. One\",\n \"into a unique set of values. If a list contains\",\n \"use in your build. type: complex contains: name: description: -\",\n \"contains: bitbucket: description: - BitbucketWebHook contains the parameters for a\",\n \"provide a snapshot read of the underlying objects, returning a\",\n \"its own extended details. This field is optional and the\",\n \"the same as 'SkipLayers' but simply warns if compatibility cannot\",\n \"the list of domains for which the proxy should not\",\n \"to the root of the context (contextDir). If both JenkinsfilePath\",\n \"referent. type: str env: description: - env contains additional environment\",\n \"- Specific resourceVersion to which this reference is made, if\",\n \"the Docker image, relative to the root of the context\",\n \"- spec holds all the input necessary to produce a\",\n \"type: complex contains: {} deletion_grace_period_seconds: description: - Number of seconds\",\n \"is the branch/tag/ref to build. type: str uri: description: -\",\n \"to authenticate with the API. type: path state: description: -\",\n \"variable's value. Cannot be used if value is not empty.\",\n \"If not specified, all successful builds are retained. type: int\",\n \"change on PUT operations. Populated by the system. Read-only. type:\",\n \"RHEL images that use Software Collections (SCL), in order to\",\n \"flag is needed to support CentOS and RHEL images that\",\n \"an object.' type: str kind: description: - Kind of the\",\n \"secretSource is a reference to the secret type: complex contains:\",\n \"description: - The name attribute of the resource associated with\",\n \"object. Populated by the system. Read-only. type: str ''' def\",\n \"binary: description: - binary builds accept a binary as their\",\n \"the cause of the error. If this value is empty\",\n \"The Causes array includes more details associated with the StatusReason\",\n \"pipeline build. type: str jenkinsfile_path: description: - JenkinsfilePath is the\",\n \"type of build input to accept type: str strategy: description:\",\n \"from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage from\",\n \"has been completely initialized. Otherwise, the object is considered uninitialized\",\n \"the output format of the exposed resources, defaults to \\\"1\\\"\",\n \"the controller field set to true. There cannot be more\",\n \"description: - Version of the schema the FieldPath is written\",\n \"build_api_version: description: - buildAPIVersion is the requested API version for\",\n \"contains: destination_dir: description: - destinationDir is the relative directory within\",\n \"is performed, where individual elements with a matching I(name_) or\",\n \"list metadata. type: complex contains: resource_version: description: - String that\",\n \"type: complex contains: _from: description: - from is a reference\",\n \"be defined type: bool field_ref: description: - 'Selects a field\",\n \"WORKDIR. Deprecated: This feature will be removed in a future\",\n \"CamelCase. type: str message: description: - A human-readable description of\",\n \"failure. Not all StatusReasons may provide detailed causes. type: list\",\n \"is primarily intended for creation idempotence and configuration definition. Cannot\",\n \"to a namespace - the value of this field for\",\n \"destination_dir: description: - destinationDir is the directory where the files\",\n \"or patched, if parameter values differ from the existing object's\",\n \"flag forces the Source build to do incremental builds if\",\n \"root of repository. type: str dockerfile: description: - dockerfile is\",\n \"init, so there is no module object. All we can\",\n \"information about a build config type: complex contains: last_version: description:\",\n \"for this object. It is typically generated by the server\",\n \"revision is the information from the source for a specific\",\n \"of the listed objects. version_added: 2.3.0 author: OpenShift (@openshift) options:\",\n \"pod will be allowed to use secrets referenced by the\",\n \"optional: description: - Specify whether the ConfigMap or it's key\",\n \"C_IDENTIFIER. type: str value: description: - 'Variable references $(VAR_NAME) are\",\n \"a GitHub webhook type of trigger type: complex contains: allow_env:\",\n \"the source control user type: str name: description: - name\",\n \"pull_secret: description: - pullSecret is the name of a Secret\",\n \"- scripts is the location of Source scripts type: str\",\n \"list will point to this controller, with the controller field\",\n \"is still done in the builder image but, post build,\",\n \"the ServiceAccount to use to run the pod created by\",\n \"warns if compatibility cannot be preserved. type: str no_cache: description:\",\n \"references will never be expanded, regardless of whether the variable\",\n \"`/bin/sh`, use Command and/or Args. The `-i` flag is needed\",\n \"bitbucket: description: - BitbucketWebHook contains the parameters for a Bitbucket\",\n \"an entire object, this string should contain a valid JSON/Go\",\n \"the API. verify_ssl: description: - Whether or not to verify\",\n \"and Source build strategies assume this file is a zip,\",\n \"can be used to organize and categorize (scope and select)\",\n \"str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except\",\n \"associated with the reason. Each reason may define its own\",\n \"will be copied from the builder to the runtime image.\",\n \"bool jenkinsfile: description: - Jenkinsfile defines the optional raw contents\",\n \"detection, and the watch operation on a resource or set\",\n \"Path to a certificate used to authenticate with the API.\",\n \"or set of resources. Clients must treat these values as\",\n \"- 'The field of the resource that has caused this\",\n \"/. then the content of the directory will be copied,\",\n \"destination paths to copy from the image. type: list contains:\",\n \"\\\"webapp.war\\\" would place the provided binary as `/webapp.war` for the\",\n \"the secret type: str secret_source: description: - secretSource is a\",\n \"which the docker image should be pulled the resulting image\",\n \"type: str optional: description: - Specify whether the ConfigMap or\",\n \"the referent. type: str source_secret: description: - \\\"sourceSecret is the\",\n \"type: str ''' def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1')\",\n \"script runs. Later, when the script finishes, all files injected\",\n \"the resource associated with the status StatusReason. On some operations\",\n \"a specific repo snapshot. This is optional. type: complex contains:\",\n \"raw contents of a Jenkinsfile which defines a Jenkins pipeline\",\n \"failing result is set, the initializers struct will be set\",\n \"build strategy, these will be injected into a container where\",\n \"values as opaque and passed unmodified back to the server.\",\n \"binary.asFile is set the build will receive a directory with\",\n \"server. If this field is specified and the generated name\",\n \"repo will have any innate Dockerfile replaced in the context\",\n \"in this case it just refers to the root of\",\n \"may reject unrecognized values. type: str items: description: - items\",\n \"be found in the time allotted, and the client should\",\n \"that a pod is deleted in 30 seconds. The Kubelet\",\n \"except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc)) if __name__ ==\",\n \"unique in time and space value for this object. It\",\n \"about git-based build source type: complex contains: author: description: -\",\n \"namespace: description: - Namespace of the referent. type: str resource_version:\",\n \"empty, the Docker and Source build strategies assume this file\",\n \"description: - mountPath is the path at which to mount\",\n \"labels with the same name then the last one in\",\n \"build strategies assume this file is a zip, tar, or\",\n \"Read-only. type: str self_link: description: - SelfLink is a URL\",\n \"or time spent building the image. The default policy is\",\n \"must execute in order before this object is visible. When\",\n \"type: int triggers: description: - triggers determine how new Builds\",\n \"uninitialized objects. When an object is created, the system will\",\n \"binary is generally assumed to be a tar, gzipped tar,\",\n \"can be launched from a BuildConfig. If no triggers are\",\n \"the Name field has not been provided. If this field\",\n \"description: - author is the author of a specific commit\",\n \"buildAPIVersion is the requested API version for the Build object\",\n \"value representing the REST resource this object represents. Servers may\",\n \"'SkipLayers' will avoid commiting new layers in between each image\",\n \"set anywhere right now and apiserver is going to ignore\",\n \"of WORKDIR. Deprecated: This feature will be removed in a\",\n \"by clients and . type: str self_link: description: - SelfLink\",\n \"to copy from the image. type: list contains: destination_dir: description:\",\n \"the parameters for a Generic webhook type of trigger type:\",\n \"into a builder container. type: list contains: name: description: -\",\n \"of old successful builds to retain. If not specified, all\",\n \"reach the git repository over http type: str https_proxy: description:\",\n \"API. type: path state: description: - Determines if an object\",\n \"of the suffix required to make the value unique on\",\n \"of the Dockerfile that will be used to build the\",\n \"graceful deletion of the object has not been requested. Populated\",\n \"a specific commit type: str committer: description: - committer is\",\n \"or it's key must be defined type: bool expose_docker_socket: description:\",\n \"of source and destination paths to copy from the image.\",\n \"acted on this object. If nil or empty, this object\",\n \"to connect to the API. cert_file: description: - Path to\",\n \"to a reader. type: str reason: description: - A machine-readable\",\n \"Kubernetes config file. debug: description: - Enable debug output from\",\n \"if that is explicitly specified, otherwise to an implementation-defined value.\",\n \"injected into a container where the assemble script runs. Later,\",\n \"builder to the runtime image. sourcePath can be a file\",\n \"of the build unless Namespace is specified. type: complex contains:\",\n \"value. It is represented in RFC3339 form and is in\",\n \"- Namespace of the referent. type: str resource_version: description: -\",\n \"the field \\\"name\\\" on the current resource \\\"items[0].name\\\" - the\",\n \"generate a unique name ONLY IF the Name field has\",\n \"and is not allowed to change on PUT operations. Populated\",\n \"Dockerfile stanzas. The Dockerfile source type may be used with\",\n \"compute resources required. If Requests is omitted for a container,\",\n \"which this resource will be deleted. This field is set\",\n \"be defined type: bool expose_docker_socket: description: - exposeDockerSocket will allow\",\n \"source/destination pairs that will be copied from the builder to\",\n \"type: str source_strategy: description: - sourceStrategy holds the parameters to\",\n \"description: - forcePull describes if the builder should pull the\",\n \"gracefully terminate before it will be removed from the system.\",\n \"If the image is pulled from the OpenShift registry, this\",\n \"Strategy should produce. type: complex contains: image_labels: description: - imageLabels\",\n \"Bitbucket webhook type of trigger type: complex contains: allow_env: description:\",\n \"reach the git repository over https type: str no_proxy: description:\",\n \"will NOT return a 409 - instead, it will either\",\n \"requirements to execute the build. type: complex contains: limits: description:\",\n \"- commit is the commit hash identifying a specific commit\",\n \"of the referent. type: str paths: description: - paths is\",\n \"seconds, counted from the time when a build pod gets\",\n \"be used type: str ref: description: - ref is the\",\n \"builder. If left empty, the Docker and Source build strategies\",\n \"the output of this build to. Kind must be one\",\n \"script to be run with `/bin/sh -ic`. It may not\",\n \"- the field \\\"name\\\" on the first array entry in\",\n \"no triggers are defined, a new build can only occur\",\n \"noProxy is the list of domains for which the proxy\",\n \"policy. An additional experimental policy 'SkipLayersAndWarn' is the same as\",\n \"type: complex contains: config_map_key_ref: description: - Selects a key of\",\n \"your Dockerfile stanzas. The Dockerfile source type may be used\",\n \"would place the provided binary as `/webapp.war` for the builder.\",\n \"accept a binary as their input. The binary is generally\",\n \"build arguments that will be resolved in the Dockerfile. See\",\n \"object is created, the system will populate this list with\",\n \"type: str reason: description: - A machine-readable description of the\",\n \"asked to observe uninitialized objects. When an object is created,\",\n \"commit hash identifying a specific commit type: str committer: description:\",\n \"source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images'\",\n \"- in those cases the Git repo will have any\",\n \"by the system. Read-only. type: str self_link: description: - SelfLink\",\n \"the current set of initializers. Only privileged users may set\",\n \"will trigger a build when updated It is optional. If\",\n \"or if you do not want to use a shell.\",\n \"for initializing this object. type: str result: description: - If\",\n \"or not. Defaults to \\\"\\\".' type: str value_from: description: -\",\n \"description: - The key of the secret to select from.\",\n \"If no From is specified, the From image from the\",\n \"input. For example, specifying \\\"webapp.war\\\" would place the provided binary\",\n \"mount_path: description: - mountPath is the path at which to\",\n \"define a list of labels that are applied to the\",\n \"image but, post build, you can copy the needed artifacts\",\n \"by external tools to store and retrieve arbitrary metadata. They\",\n \"- revision is the information from the source for a\",\n \"contains parameters for an ImageChange type of trigger type: complex\",\n \"image created by the Docker build API. The experimental policy\",\n \"the replacement of lists, set the I(force) option to C(True).\",\n \"type: complex contains: api_version: description: - API version of the\",\n \"flag type: bool pull_secret: description: - pullSecret is the name\",\n \"in the PATH. type: str resources: description: - resources computes\",\n \"method to be used and\\\\ \\\\ value is the base64\",\n \"be a directory. destinationDir can also be empty or equal\",\n \"tries to terminate the build; value must be positive integer\",\n \"length of the suffix required to make the value unique\",\n \"UID of the referent. type: str scripts: description: - scripts\",\n \"list/watch and get calls) from clients that haven't explicitly asked\",\n \"a specific commit type: complex contains: email: description: - email\",\n \"requested. Read-only. type: complex contains: {} finalizers: description: - Must\",\n \"want to pass into a build pipeline. type: list contains:\",\n \"description: - UID of the referent. type: str post_commit: description:\",\n \"str revision: description: - revision is the information from the\",\n \"the Dockerfile that will be used to build the Docker\",\n \"with the current set of initializers. Only privileged users may\",\n \"source control user type: str message: description: - message is\",\n \"to the build directory. If the source path ends in\",\n \"with I(username). resource_definition: description: - Provide the YAML definition for\",\n \"str message: description: - A human-readable description of the cause\",\n \"results in merging lists and updating dictionaries, with lists being\",\n \"is in UTC. Populated by the system. Read-only. Null for\",\n \"pod). This syntax is chosen only to have some well-defined\",\n \"other connection options are provided, the openshift client will attempt\",\n \"object. Populated by the system. Read-only. type: str reason: description:\",\n \"represented in RFC3339 form and is in UTC. Populated by\",\n \"to the custom builder type: str env: description: - env\",\n \"more details associated with the StatusReason failure. Not all StatusReasons\",\n \"a controller, then an entry in this list will point\",\n \"IF the Name field has not been provided. If this\",\n \"build directory. If the source path ends in /. then\",\n \"bool field_ref: description: - 'Selects a field of the pod:\",\n \"be used in the FROM line of the Dockerfile for\",\n \"will be equivalent to an image created by the Docker\",\n \"certificates. type: bool requirements: - openshift == 0.3.3 ''' EXAMPLES\",\n \"description: - Git contains information about git-based build source type:\",\n \"services. type: complex contains: str, str name: description: - Name\",\n \"I(state) is C(present), an existing object will updated, and lists\",\n \"of the owner, otherwise 422 (Unprocessable Entity) will be returned.\",\n \"description: - Name of the referent. type: str namespace: description:\",\n \"default policy is 'None' which means the final build image\",\n \"attempt to load the default configuration file from I(~/.kube/config.json). type:\",\n \"type: str metadata: description: - metadata for BuildConfig. type: complex\",\n \"building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations\",\n \"resource \\\"items[0].name\\\" - the field \\\"name\\\" on the first array\",\n \"attribute of the resource associated with the status StatusReason. type:\",\n \"release. Use ImageSource to copy binary artifacts created from one\",\n \"builder type: str env: description: - env contains additional environment\",\n \"the API. cert_file: description: - Path to a certificate used\",\n \"reason type. type: complex contains: causes: description: - The Causes\",\n \"in an array of causes due to fields having multiple\",\n \"be truncated to zero length. For the Docker build strategy,\",\n \"attributes. type: dict src: description: - Provide a path to\",\n \"updating dictionaries, with lists being merged into a unique set\",\n \"number of old successful builds to retain. If not specified,\",\n \"to KubeObjHelper.log default: false type: bool force: description: - If\",\n \"the build directory. If the source path ends in /.\",\n \"description: - The name of the cluster which the object\",\n \"this option is specified, the FROM may be modified based\",\n \"- pullSecret is the name of a Secret that would\",\n \"snapshot. This is optional. type: complex contains: git: description: -\",\n \"of the process that is responsible for initializing this object.\",\n \"internal value, and may reject unrecognized values. type: str items:\",\n \"description of the status of this operation. type: str metadata:\",\n \"the referent. type: str post_commit: description: - postCommit is a\",\n \"a consistent version of the listed objects. version_added: 2.3.0 author:\",\n \"BuildConfigList. type: complex contains: resource_version: description: - String that identifies\",\n \"of an appropriate name automatically. Name is primarily intended for\",\n \"description: - A sequence number representing a specific generation of\",\n \"variables in the container and any service environment variables. If\",\n \"if it does not exist, or patched, if parameter values\",\n \"entrypoint, or if the image does not have `/bin/sh`, use\",\n \"type: list contains: destination_dir: description: - destinationDir is the directory\",\n \"source control user type: str name: description: - name of\",\n \"value like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers to the name of\",\n \"optional Dockerfile may be specified to override any Dockerfile in\",\n \"pulled type: complex contains: api_version: description: - API version of\",\n \"desired state. Populated by the system. Read-only. type: int initializers:\",\n \"sequence number representing a specific generation of the desired state.\",\n \"An additional experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers'\",\n \"If the source path ends in /. then the content\",\n \"the image are placed. type: str source_path: description: - sourcePath\",\n \"system invariant at object creation time. This field is a\",\n \"str optional: description: - Specify whether the ConfigMap or it's\",\n \"unit tests with `rake test`. If you need control over\",\n \"directory. If the source path ends in /. then the\",\n \"reduce the final size or time spent building the image.\",\n \"Git repo will have any innate Dockerfile replaced in the\",\n \"these will be injected into a container where the assemble\",\n \"happens-before order across separate operations. Clients may not set this\",\n \"field, a user needs \\\"delete\\\" permission of the owner, otherwise\",\n \"the field \\\"name\\\" on the first array entry in \\\"items\\\"'\",\n \"requests. type: str github: description: - github contains the parameters\",\n \"the location of Source scripts type: str type: description: -\",\n \"default: present choices: - present - absent username: description: -\",\n \"limits: description: - Limits describes the maximum amount of compute\",\n \"can be described). type: str retry_after_seconds: description: - If specified,\",\n \"of labels that are applied to the resulting image. If\",\n \"as their input. The binary is generally assumed to be\",\n \"from the builder to the runtime image. sourcePath can be\",\n \"\\\"sourceSecret is the name of a Secret that would be\",\n \"contains: str, str revision: description: - revision is the information\",\n \"description: - imageLabels define a list of labels that are\",\n \"builder or only pull if it is not present locally\",\n \"into a build pipeline. type: list contains: name: description: -\",\n \"this object was created. It is not guaranteed to be\",\n \"description: - value defines the literal value of the label.\",\n \"resource which can be described). type: str kind: description: -\",\n \"To force the replacement of lists, set the I(force) option\",\n \"resource is expected to be deleted (no longer visible from\",\n \"system. Read-only. type: int initializers: description: - An initializer is\",\n \"- asFile indicates that the provided binary input should be\",\n \"data returned is not guaranteed to conform to any schema\",\n \"object. All we can do is raise the error. raise\",\n \"build context and an optional Dockerfile may be specified to\",\n \"operations may differ from the requested resource Kind. type: str\",\n \"DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image should\",\n \"complex contains: env: description: - env contains additional environment variables\",\n \"which the proxy should not be used type: str ref:\",\n \"indicating a unique name could not be found in the\",\n \"str labels: description: - Map of string keys and values\",\n \"'..' or '.'. type: str context_dir: description: - contextDir specifies\",\n \"point to this controller, with the controller field set to\",\n \"a tar, gzipped tar, or zip file depending on the\",\n \"options like git - in those cases the Git repo\",\n \"a single resource which can be described). type: str kind:\",\n \"is the commit hash identifying a specific commit type: str\",\n \"list contains: api_version: description: - APIVersion defines the versioned schema\",\n \"allows to have buildable sources in directory other than root\",\n \"on successful creation of a resource and is not allowed\",\n \"depending on the strategy. For Docker builds, this is the\",\n \"after the time in this field. Once set, this value\",\n \"if parameter values differ from the existing object's attributes, and\",\n \"represent the authentication method to be used and\\\\ \\\\ value\",\n \"used to provide source for the build type: list contains:\",\n \"directory where the files from the secret should be available\",\n \"is allowed in a build configuration. type: complex contains: api_version:\",\n \"to produce a new build, and the conditions when to\",\n \"type: complex contains: {} finalizers: description: - Must be empty\",\n \"docker build. type: str secret: description: - secret is a\",\n \"of a secret in the pod's namespace type: complex contains:\",\n \"either Command, Script or the Docker image's default entrypoint. The\",\n \"have `/bin/sh`, use Command and/or Args. The `-i` flag is\",\n \"one of 'ImageStreamTag' or 'DockerImage'. This value will be used\",\n \"and additional ENV stanzas from your strategy environment will be\",\n \"only pull if it is not present locally type: bool\",\n \"organize and categorize (scope and select) objects. May match selectors\",\n \"to be set in happens-before order across separate operations. Clients\",\n \"used by clients to determine when objects have changed. May\",\n \"is empty, it may not be modified further by any\",\n \"to the latest internal value, and may reject unrecognized values.\",\n \"with a single file. contextDir may be used when an\",\n \"absent username: description: - Provide a username for connecting to\",\n \"Annotations is an unstructured key value map stored with a\",\n \"for a GitHub webhook type of trigger type: complex contains:\",\n \"other clients can observe the deletion. type: complex contains: api_version:\",\n \"labels that are applied to the resulting image. If there\",\n \"additional experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but\",\n \"the endpoint the client submits requests to. Cannot be updated.\",\n \"description: - Provide a path to a file containing the\",\n \"the canonical representation. Not all objects are required to be\",\n \"of a specific commit type: str type: description: - type\",\n \"if the image doesn't have `/bin/sh`, or if you do\",\n \"type: complex contains: name: description: - Name of the referent.\",\n \"type: str creation_timestamp: description: - CreationTimestamp is a timestamp representing\",\n \"been completely initialized. Otherwise, the object is considered uninitialized and\",\n \"str secret: description: - secret is a reference to an\",\n \"'Binary', or 'Images' type: str run_policy: description: - RunPolicy describes\",\n \"any Dockerfile in the build context. For Source builds, this\",\n \"a reference to an existing secret that you want to\",\n \"other cases, using Script might be more convenient. type: list\",\n \"value is empty there is no information available. type: str\",\n \"directly settable by a client. The resource is expected to\",\n \"str source_strategy: description: - sourceStrategy holds the parameters to the\",\n \"str generation: description: - A sequence number representing a specific\",\n \"bool incremental: description: - incremental flag forces the Source build\",\n \"referent. type: str name: description: - Name of the referent.\",\n \"type: list contains: api_version: description: - APIVersion defines the versioned\",\n \"ALL objects in the list have been deleted, this object\",\n \"type: str build_api_version: description: - buildAPIVersion is the requested API\",\n \"Docker image repository to push to. In the case of\",\n \"either return 201 Created or 500 with Reason ServerTimeout indicating\",\n \"the referent. type: str paths: description: - paths is a\",\n \"type: str image_change: description: - imageChange contains parameters for an\",\n \"is considered as initialized and visible to all clients. type:\",\n \"reject unrecognized values. type: str kind: description: - Kind is\",\n \"complex contains: key: description: - The key to select. type:\",\n \"that identifies the server's internal version of this object that\",\n \"index 2 in this pod). This syntax is chosen only\",\n \"this object is visible. When the last pending initializer is\",\n \"dockerStrategy holds the parameters to the Docker build strategy. type:\",\n \"description: - sourceStrategy holds the parameters to the Source build\",\n \"used to look up a Docker image repository to push\",\n \"existing Kubernetes config file. If not provided, and no other\",\n \"secret key. type: str name: description: - Name of the\",\n \"C(present) contains: api_version: description: - APIVersion defines the versioned schema\",\n \"is responsible for initializing this object. type: str result: description:\",\n \"a list contains a dictionary with a I(name) or I(type)\",\n \"Populated by the system. Read-only. Value must be treated as\",\n \"is used, the name returned to the client will be\",\n \"or a map with any values, default build nodeselector values\",\n \"referent. type: str block_owner_deletion: description: - If true, AND if\",\n \"Docker Hub). type: complex contains: name: description: - Name of\",\n \"name: required for volumes, optional for env vars' type: str\",\n \"to use secrets referenced by the ServiceAccount type: str source:\",\n \"representing a consistent version of the listed objects. version_added: 2.3.0\",\n \"complex contains: api_version: description: - API version of the referent.\",\n \"to be run with `/bin/sh -ic`. It may not be\",\n \"- successfulBuildsHistoryLimit is the number of old successful builds to\",\n \"are retained. type: int triggers: description: - triggers determine how\",\n \"this object will be garbage collected. If this object is\",\n \"description: - UID of the referent. type: str build_api_version: description:\",\n \"to override any Dockerfile in the build context. For Source\",\n \"be resolved, the reference in the input string will be\",\n \"the assemble script runs. Later, when the script finishes, all\",\n \"used for setting up the authentication for pulling the Docker\",\n \"or directory. destinationDir must be a directory. destinationDir can also\",\n \"builds are retained. type: int triggers: description: - triggers determine\",\n \"server. Populated by the system. Read-only. type: str self_link: description:\",\n \"and time at which this resource will be deleted. This\",\n \"ImageStreamImage, or DockerImage to copy source from. type: complex contains:\",\n \"options are provided, the openshift client will attempt to load\",\n \"Docker builds, if binary.asFile is set the build will receive\",\n \"with the status StatusReason. On some operations may differ from\",\n \"with the controller field set to true. There cannot be\",\n \"the length of the suffix required to make the value\",\n \"All we can do is raise the error. raise Exception(exc.message)\",\n \"reachable by name) after the time in this field. Once\",\n \"the type of build to run type: str images: description:\",\n \"using Script might be more convenient. type: list contains: str\",\n \"be empty or equal to \\\".\\\", in this case it\",\n \"name returned to the client will be different than the\",\n \"an unstructured key value map stored with a resource that\",\n \"zip file depending on the strategy. For Docker builds, this\",\n \"in order before this object is visible. When the last\",\n \"arguments that will be resolved in the Dockerfile. See type:\",\n \"this object represents. Servers may infer this from the endpoint\",\n \"complex contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration\",\n \"- 'Selects a field of the pod: supports metadata.name, metadata.namespace,\",\n \"string should contain a valid JSON/Go field access statement, such\",\n \"str kind: description: - Kind of the referent. type: str\",\n \"from the time when a build pod gets scheduled in\",\n \"identifier for the responsible component that will remove the entry\",\n \"object belongs to. This is used to distinguish resources with\",\n \"object. It is typically generated by the server on successful\",\n \"type: str jenkins_pipeline_strategy: description: - JenkinsPipelineStrategy holds the parameters to\",\n \"description: - UID of the referent. type: str env: description:\",\n \"in tech preview. type: complex contains: env: description: - env\",\n \"type: bool dockerfile_path: description: - dockerfilePath is the path of\",\n \"try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module failed!\\\", error=str(exc)) if\",\n \"build strategy, these will be copied into the build directory,\",\n \"build strategy. type: complex contains: _from: description: - from is\",\n \"- API version of the referent. type: str field_path: description:\",\n \"BitbucketWebHook contains the parameters for a Bitbucket webhook type of\",\n \"complex contains: pending: description: - Pending is a list of\",\n \"committer of a specific commit type: complex contains: email: description:\",\n \"internally by the ImageChangeController to save last used image ID\",\n \"secret\\\\ \\\\ contains valid credentials for remote repository, where the\\\\\",\n \"- A machine-readable description of the cause of the error.\",\n \"can ADD or COPY them during docker build. type: str\",\n \"May only be shortened. Read-only. type: int deletion_timestamp: description: -\",\n \"the desired state. Populated by the system. Read-only. type: int\",\n \"type: str jenkinsfile_path: description: - JenkinsfilePath is the optional path\",\n \"secrets that will be included in the build pod type:\",\n \"same name and namespace in different clusters. This field is\",\n \"the \\\"default\\\" namespace, but \\\"default\\\" is the canonical representation. Not\",\n \"which enforces some system invariant at object creation time. This\",\n \"do not want to use a shell. In all other\",\n \"description: - scripts is the location of Source scripts type:\",\n \"field for those objects will be empty. Must be a\",\n \"can do is raise the error. raise Exception(exc.message) try: module.execute_module()\",\n \"unmodified back to the server. They may only be valid\",\n \"git: description: - Git contains information about git-based build source\",\n \"If set to C(True), and I(state) is C(present), an existing\",\n \"errors. Optional. Examples: \\\"name\\\" - the field \\\"name\\\" on the\",\n \"set environment variables; can only be set to true for\",\n \"be shortened. Read-only. type: int deletion_timestamp: description: - DeletionTimestamp is\",\n \"- github contains the parameters for a GitHub webhook type\",\n \"cert_file: description: - Path to a certificate used to authenticate\",\n \"configuration definition. Cannot be updated. type: str namespace: description: -\",\n \"If this field is used, the name returned to the\",\n \"gzipped tar, or zip file depending on the strategy. For\",\n \"If a list contains a dictionary with a I(name) or\",\n \"current set of initializers. Only privileged users may set or\",\n \"secret is a reference to an existing secret that you\",\n \"still exist after this timestamp, until an administrator or automated\",\n \"is not present locally type: bool pull_secret: description: - pullSecret\",\n \"type: str status: description: - status holds any relevant information\",\n \"application is still done in the builder image but, post\",\n \"for use. Deprecated: This feature will be removed in a\",\n \"Dockerfile is located, so users can ADD or COPY them\",\n \"this field is specified and the generated name exists, the\",\n \"an image created by the Docker build API. The experimental\",\n \"final size or time spent building the image. The default\",\n \"holds the parameters to the Source build strategy. type: complex\",\n \"no module object. All we can do is raise the\",\n \"for connecting to the API. Use in conjunction with I(username).\",\n \"contains: api_version: description: - APIVersion defines the versioned schema of\",\n \"although some resources may allow a client to request the\",\n \"active on a node before the system actively tries to\",\n \"Only set when deletionTimestamp is also set. May only be\",\n \"name: description: - Name of the referent. type: str namespace:\",\n \"order across separate operations. Clients may not set this value.\",\n \"(or Docker Hub). type: complex contains: name: description: - Name\",\n \"be retried. type: int uid: description: - UID of the\",\n \"and I(state) is C(present), an existing object will updated, and\",\n \"specified \\\"spec.containers[2]\\\" (container with index 2 in this pod). This\",\n \"be replaced, rather than merged. default: false type: bool host:\",\n \"or the resource may be deleted prior to this time.\",\n \"this field for those objects will be empty. Must be\",\n \"is a list of arguments that are provided to either\",\n \"of the source control user type: str message: description: -\",\n \"Jenkinsfile in the root of the specified contextDir. type: str\",\n \"whether the variable exists or not. Defaults to \\\"\\\".' type:\",\n \"- incremental flag forces the Source build to do incremental\",\n \"or DockerImage to copy source from. type: complex contains: api_version:\",\n \"to be used to pull the image from a registry\",\n \"field: description: - 'The field of the resource that has\",\n \"Retry-After header). Applied only if Name is not specified. type:\",\n \"or it's key must be defined type: bool field_ref: description:\",\n \"before it is pushed to a registry. type: complex contains:\",\n \"build. type: list contains: destination_dir: description: - destinationDir is the\",\n \"duration in seconds, counted from the time when a build\",\n \"no From is specified, the From image from the build\",\n \"NOT return a 409 - instead, it will either return\",\n \"- buildAPIVersion is the requested API version for the Build\",\n \"a path to a file containing the YAML definition of\",\n \"be specified with Script. This might be needed if the\",\n \"this build. The pod will be allowed to use secrets\",\n \"select' type: str secret_key_ref: description: - Selects a key of\",\n \"Source builds, this is assumed to be an archive as\",\n \"true indicates that the docker build must be executed with\",\n \"to fit on a node If nil, it can be\",\n \"infer this from the endpoint the client submits requests to.\",\n \"only be valid for a particular resource or set of\",\n \"directory other than root of repository. type: str dockerfile: description:\",\n \"resources allowed. type: complex contains: str, str requests: description: -\",\n \"variables. If a variable cannot be resolved, the reference in\",\n \"The helper failed to init, so there is no module\",\n \"type: str name: description: - Name of the referent. type:\",\n \"runs. Later, when the script finishes, all files injected will\",\n \"as a result of an explicit client build creation. type:\",\n \"is the build context and an optional Dockerfile may be\",\n \"UID is the unique in time and space value for\",\n \"from one build into a separate runtime image.' type: complex\",\n \"last_version: description: - lastVersion is used to inform about number\",\n \"when building images to reduce the final size or time\",\n \"unrecognized values. type: str kind: description: - Kind is a\",\n \"of the referent. type: str resource_version: description: - Specific resourceVersion\",\n \"- Provide a path to a file containing the YAML\",\n \"be unchanged. The $(VAR_NAME) syntax can be escaped with a\",\n \"will be injected into a container where the assemble script\",\n \"the git repository over https type: str no_proxy: description: -\",\n \"to load the default configuration file from I(~/.kube/config.json). type: path\",\n \"be more than one managing controller. type: list contains: api_version:\",\n \"output describes the Docker image the Strategy should produce. type:\",\n \"removed, and no failing result is set, the initializers struct\",\n \"objects have changed. May be used for optimistic concurrency, change\",\n \"Docker build API. The experimental policy 'SkipLayers' will avoid commiting\",\n \"used to validate requests. type: str image_change: description: - imageChange\",\n \"Script when a shell script is appropriate to execute the\",\n \"not have `/bin/sh`, use Command and/or Args. The `-i` flag\",\n \"authenticate with the API. type: path kubeconfig: description: - Path\",\n \"of: \\\"Success\\\" or \\\"Failure\\\".' type: str labels: description: - Map\",\n \"policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but simply warns\",\n \"need to be set. type: complex contains: name: description: -\",\n \"defines the name of the label. It must have non-zero\",\n \"specified, the From image from the build strategy will be\",\n \"the last pending initializer is removed, and no failing result\",\n \"input. The binary is generally assumed to be a tar,\",\n \"- source describes the SCM in use. type: complex contains:\",\n \"a list of arguments that are provided to either Command,\",\n \"description: - API version of the referent. type: str block_owner_deletion:\",\n \"syntax is chosen only to have some well-defined way of\",\n \"the parameters to the Docker build strategy. type: complex contains:\",\n \"- noCache if set to true indicates that the docker\",\n \"on a value like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers to the\",\n \"the maximum amount of compute resources allowed. type: complex contains:\",\n \"- dockerStrategy holds the parameters to the Docker build strategy.\",\n \"with other options like git - in those cases the\",\n \"DeletionTimestamp is RFC 3339 date and time at which this\",\n \"be updated. In CamelCase. type: str message: description: - A\",\n \"completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in seconds,\",\n \"that must execute in order before this object is visible.\",\n \"built. When this option is specified, the FROM may be\",\n \"the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc:\",\n \"server's SSL certificates. type: bool requirements: - openshift == 0.3.3\",\n \"- A machine-readable description of why this operation is in\",\n \"of seconds allowed for this object to gracefully terminate before\",\n \"any schema except that defined by the reason type. type:\",\n \"list of source/destination pairs that will be copied from the\",\n \"generation of an appropriate name automatically. Name is primarily intended\",\n \"builds to retain. If not specified, all successful builds are\",\n \"above. For Source and Docker builds, if binary.asFile is set\",\n \"the YAML definition of the object. Mutually exclusive with I(resource_definition).\",\n \"list. Once it is empty, it may not be modified\",\n \"- imageLabels define a list of labels that are applied\",\n \"Dockerfile. See type: list contains: name: description: - Name of\",\n \"deletionTimestamp of the object is non-nil, entries in this list\",\n \"system. Read-only. type: str reason: description: - A machine-readable description\",\n \"or directory inside the image to copy to the build\",\n \"not set anywhere right now and apiserver is going to\",\n \"may differ from the requested resource Kind. type: str name:\",\n \"included in the build pod type: list contains: mount_path: description:\",\n \"field is set by the server when a graceful deletion\",\n \"email: description: - email of the source control user type:\",\n \"be more convenient. type: list contains: str script: description: -\",\n \"of initializers that must execute in order before this object\",\n \"retried. type: int uid: description: - UID of the resource.\",\n \"have some well-defined way of referencing a part of an\",\n \"if an object should be created, patched, or deleted. When\",\n \"''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a\",\n \"the strategy. For Docker builds, this is the build context\",\n \"description: - If true, this reference points to the managing\",\n \"which the docker image should be pulled type: complex contains:\",\n \"specified. type: complex contains: api_version: description: - API version of\",\n \"type: str dockerfile: description: - dockerfile is the raw contents\",\n \"any relevant information about a build config type: complex contains:\",\n \"FieldPath is written in terms of, defaults to \\\"v1\\\". type:\",\n \"allowed in a build configuration. type: complex contains: api_version: description:\",\n \"type of build trigger type: str status: description: - status\",\n \"uninitialized and is hidden (in list/watch and get calls) from\",\n \"str name: description: - The name attribute of the resource\",\n \"is not set anywhere right now and apiserver is going\",\n \"in merging lists and updating dictionaries, with lists being merged\",\n \"deleted prior to this time. For example, a user may\",\n \"github contains the parameters for a GitHub webhook type of\",\n \"description: - BitbucketWebHook contains the parameters for a Bitbucket webhook\",\n \"that is used to run an application without unneeded dependencies\",\n \"generation: description: - A sequence number representing a specific generation\",\n \"build source type: complex contains: author: description: - author is\",\n \"additional ENV stanzas from your strategy environment will be added\",\n \"be used for setting\\\\ \\\\ up the authentication for cloning\",\n \"generic contains the parameters for a Generic webhook type of\",\n \"description: - Selects a key of a secret in the\",\n \"- UID of the referent. type: str build_api_version: description: -\",\n \"verify the API server's SSL certificates. type: bool requirements: -\",\n \"value unique on the server. If this field is specified\",\n \"- secret is a reference to an existing secret that\",\n \"managed by a controller, then an entry in this list\",\n \"initialized. Otherwise, the object is considered uninitialized and is hidden\",\n \"This field is a list of initializers that have not\",\n \"located, so users can ADD or COPY them during docker\",\n \"str field_path: description: - Path of the field to select\",\n \"runtime image. sourcePath can be a file or directory. destinationDir\",\n \"description: - generic contains the parameters for a Generic webhook\",\n \"used with other options like git - in those cases\",\n \"operation. type: str metadata: description: - Standard list metadata. type:\",\n \"created at the destination. type: str runtime_image: description: - 'runtimeImage\",\n \"description: - imageOptimizationPolicy describes what optimizations the system can use\",\n \"the context (contextDir). type: str env: description: - env contains\",\n \"the commit hash identifying a specific commit type: str committer:\",\n \"controller should configure the build pod to always pull the\",\n \"explicit client build creation. type: list contains: bitbucket: description: -\",\n \"a unique name ONLY IF the Name field has not\",\n \"$(VAR_NAME) are expanded using the previous defined environment variables in\",\n \"that would be used for setting\\\\ \\\\ up the authentication\",\n \"image, relative to the root of the context (contextDir). type:\",\n \"treated as opaque by clients and passed unmodified back to\",\n \"the source control user type: str commit: description: - commit\",\n \"back to the server. They may only be valid for\",\n \"-*- from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module:\",\n \"the shell. E.g., in the Ruby image, this is necessary\",\n \"uid: description: - UID of the referent. type: str env:\",\n \"remove the pod from the API. In the presence of\",\n \"uid: description: - UID of the resource. (when there is\",\n \"time and space value for this object. It is typically\",\n \"specifies the sub-directory where the source code for the application\",\n \"Defaults to false. To set this field, a user needs\",\n \"to select. type: str name: description: - Name of the\",\n \"this status, 0 if not set. type: int details: description:\",\n \"description: - failedBuildsHistoryLimit is the number of old failed builds\",\n \"(and build Docker images) from inside the Docker container. type:\",\n \"When the last pending initializer is removed, and no failing\",\n \"building of the application is still done in the builder\",\n \"uid: description: - UID of the referent. type: str last_triggered_image_id:\",\n \"to change on PUT operations. Populated by the system. Read-only.\",\n \"this object. If ALL objects in the list have been\",\n \"build pod type: list contains: mount_path: description: - mountPath is\",\n \"str retry_after_seconds: description: - If specified, the time in seconds\",\n \"cannot be preserved. type: str no_cache: description: - noCache if\",\n \"validate requests. type: str image_change: description: - imageChange contains parameters\",\n \"in terms of, defaults to \\\"v1\\\". type: str field_path: description:\",\n \"standard input. This filename may not contain slashes or be\",\n \"build the Docker image, relative to the root of the\",\n \"may be deleted prior to this time. For example, a\",\n \"to a certificate used to authenticate with the API. type:\",\n \"of string keys and values that can be used to\",\n \"field_path: description: - Path of the field to select in\",\n \"modules parameters intended to define object attributes. type: dict src:\",\n \"Provide the YAML definition for the object, bypassing any modules\",\n \"triggers are defined, a new build can only occur as\",\n \"value map stored with a resource that may be set\",\n \"looked for in the namespace of the build unless Namespace\",\n \"as named by its JSON serialization. May include dot and\",\n \"in \\\"items\\\"' type: str message: description: - A human-readable description\",\n \"user. type: complex contains: pending: description: - Pending is a\",\n \"which this reference is made, if any. type: str uid:\",\n \"ref: description: - ref is the branch/tag/ref to build. type:\",\n \"repository over http type: str https_proxy: description: - httpsProxy is\",\n \"the Failure field, the object will be persisted to storage\",\n \"- httpsProxy is a proxy used to reach the git\",\n \"str service_account: description: - serviceAccount is the name of the\",\n \"the build pod to always pull the images for the\",\n \"default build nodeselector values are ignored. type: complex contains: str,\",\n \"case of an ImageStreamTag, the ImageStreamTag will be looked for\",\n \"this defaults to Jenkinsfile in the root of the specified\",\n \"this object may still exist after this timestamp, until an\",\n \"existing secret that you want to use in your build.\",\n \"buildable sources in directory other than root of repository. type:\",\n \"a builder container. type: list contains: name: description: - Name\",\n \"git: description: - git contains optional information about git build\",\n \"StatusReason (when there is a single name which can be\",\n \"not present locally type: bool pull_secret: description: - pullSecret is\",\n \"about git build source type: complex contains: http_proxy: description: -\",\n \"the Dockerfile cannot provide compatibility with the 'None' policy. An\",\n \"an explicit client build creation. type: list contains: bitbucket: description:\",\n \"as initialized and visible to all clients. type: list contains:\",\n \"str source: description: - source describes the SCM in use.\",\n \"description: - httpsProxy is a proxy used to reach the\",\n \"environment variables you want to pass into a builder container.\",\n \"to pass into a builder container. type: list contains: name:\",\n \"from which the docker image should be pulled the resulting\",\n \"EXAMPLES = ''' ''' RETURN = ''' api_version: type: string\",\n \"build can only occur as a result of an explicit\",\n \"if the image does not have `/bin/sh`, use Command and/or\",\n \"the referent. type: str to: description: - to defines an\",\n \"StatusReasons may provide detailed causes. type: list contains: field: description:\",\n \"build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the\",\n \"CamelCase. type: str metadata: description: - metadata for BuildConfigList. type:\",\n \"used. Only one ImageChangeTrigger with an empty From reference is\",\n \"is pushed to a registry. type: complex contains: args: description:\",\n \"be set to nil and the object is considered as\",\n \"object creation time. This field is a list of initializers\",\n \"used only for the build. type: list contains: destination_dir: description:\",\n \"status StatusReason. type: str kind: description: - The kind attribute\",\n \"force the replacement of lists, set the I(force) option to\",\n \"Dockerfile which should be built. When this option is specified,\",\n \"to \\\"\\\".' type: str value_from: description: - Source for the\",\n \"are zero-indexed. Fields may appear more than once in an\",\n \"type: bool force_pull: description: - forcePull describes if the controller\",\n \"values are ignored. type: complex contains: str, str output: description:\",\n \"API version of the referent. type: str field_path: description: -\",\n \"Kubernetes config file. If not provided, and no other connection\",\n \"there is no information available. type: str group: description: -\",\n \"a directory with a single file. contextDir may be used\",\n \"secrets: description: - secrets represents a list of secrets and\",\n \"is an identifier for the responsible component that will remove\",\n \"are multiple labels with the same name then the last\",\n \"entrypoint. The arguments are placed immediately after the command to\",\n \"If ALL objects in the list have been deleted, this\",\n \"and/or Args. The `-i` flag is needed to support CentOS\",\n \"the image to copy to the build directory. If the\",\n \"and lists will be replaced, rather than merged. default: false\",\n \"variables you want to pass into a builder container. type:\",\n \"type: str type: description: - type is the kind of\",\n \"expanded using the previous defined environment variables in the container\",\n \"'ImageStreamTag' or 'DockerImage'. This value will be used to look\",\n \"that will be copied from the builder to the runtime\",\n \"- Source for the environment variable's value. Cannot be used\",\n \"the API. type: path kubeconfig: description: - Path to an\",\n \"verify_ssl: description: - Whether or not to verify the API\",\n \"private repository. The secret\\\\ \\\\ contains valid credentials for remote\",\n \"is pulled from the OpenShift registry, this field does not\",\n \"schema of this representation of an object. Servers should convert\",\n \"str paths: description: - paths is a list of source\",\n \"experimental policy 'SkipLayersAndWarn' is the same as 'SkipLayers' but simply\",\n \"contains: str, str output: description: - output describes the Docker\",\n \"object will updated, and lists will be replaced, rather than\",\n \"the images for the builder or only pull if it\",\n \"-ic`. It may not be specified with Command. Use Script\",\n \"str secrets: description: - secrets is a list of additional\",\n \"attributes. Arrays are zero-indexed. Fields may appear more than once\",\n \"must be executed with the --no-cache=true flag type: bool pull_secret:\",\n \"not specified, this defaults to Jenkinsfile in the root of\",\n \"description: - forcePull describes if the controller should configure the\",\n \"this file is a zip, tar, or tar.gz file and\",\n \"A human-readable description of the status of this operation. type:\",\n \"'Dockerfile', 'Binary', or 'Images' type: str run_policy: description: - RunPolicy\",\n \"when the script finishes, all files injected will be truncated\",\n \"passed. This value will also be combined with a unique\",\n \"description: - destinationDir is the relative directory within the build\",\n \"with a double $$, ie: $$(VAR_NAME). Escaped references will never\",\n \"`/bin/sh`, or if you do not want to use a\",\n \"remove the entry from the list. If the deletionTimestamp of\",\n \"\\\"default\\\" namespace, but \\\"default\\\" is the canonical representation. Not all\",\n \"The name attribute of the resource associated with the status\",\n \"be looked for in the namespace of the build unless\",\n \"contains: email: description: - email of the source control user\",\n \"- JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline build\",\n \"entries in this list can only be removed. type: list\",\n \"controller, with the controller field set to true. There cannot\",\n \"to the API. cert_file: description: - Path to a certificate\",\n \"description of the cause of the error. This field may\",\n \"may reject unrecognized values. type: str kind: description: - Kind\",\n \"In CamelCase. type: str message: description: - A human-readable description\",\n \"from the key-value store until this reference is removed. Defaults\",\n \"to an existing secret that you want to use in\",\n \"- JenkinsfilePath is the optional path of the Jenkinsfile that\",\n \"the object is non-nil, entries in this list can only\",\n \"for a specific repo snapshot. This is optional. type: complex\",\n \"- customStrategy holds the parameters to the Custom build strategy\",\n \"to copy to the build directory. If the source path\",\n \"not be modified further by any user. type: complex contains:\",\n \"have not yet acted on this object. If nil or\",\n \"type: str last_triggered_image_id: description: - lastTriggeredImageID is used internally by\",\n \"object may still exist after this timestamp, until an administrator\",\n \"contains: git: description: - Git contains information about git-based build\",\n \"clients and . type: str self_link: description: - SelfLink is\",\n \"when an archive is provided. Custom builds will receive this\",\n \"\\\"default\\\" is the canonical representation. Not all objects are required\",\n \"on this object. If nil or empty, this object has\",\n \"builder container. type: list contains: name: description: - Name of\",\n \"appropriate name automatically. Name is primarily intended for creation idempotence\",\n \"an appropriate name automatically. Name is primarily intended for creation\",\n \"not set this value. It is represented in RFC3339 form\",\n \"- env contains additional environment variables you want to pass\",\n \"is the path of the Dockerfile that will be used\",\n \"allow a client to request the generation of an appropriate\",\n \"of the underlying objects, returning a resource_version representing a consistent\",\n \"str block_owner_deletion: description: - If true, AND if the owner\",\n \"description: - Map of string keys and values that can\",\n \"is an optional duration in seconds, counted from the time\",\n \"and Docker builds, if binary.asFile is set the build will\",\n \"Escaped references will never be expanded, regardless of whether the\",\n \"representing a specific generation of the desired state. Populated by\",\n \"description: - 'Status of the operation. One of: \\\"Success\\\" or\",\n \"the specified contextDir. type: str source_strategy: description: - sourceStrategy holds\",\n \"- imageChange contains parameters for an ImageChange type of trigger\",\n \"the destination. type: str pull_secret: description: - pullSecret is a\",\n \"file. contextDir may be used when an archive is provided.\",\n \"Populated by the system. Read-only. type: str spec: description: -\",\n \"optional: description: - Specify whether the Secret or it's key\",\n \"type: complex contains: container_name: description: - 'Container name: required for\",\n \"at the destination. type: str pull_secret: description: - pullSecret is\",\n \"repository. type: str dockerfile: description: - dockerfile is the raw\",\n \"why this operation is in the \\\"Failure\\\" status. If this\",\n \"set of resources. Clients must treat these values as opaque\",\n \"a string value representing the REST resource this object represents.\",\n \"created from one build into a separate runtime image.' type:\",\n \"str jenkinsfile_path: description: - JenkinsfilePath is the optional path of\",\n \"all the input necessary to produce a new build, and\",\n \"the responsible component that will remove the entry from the\",\n \"of trigger type: complex contains: _from: description: - from is\",\n \"compatibility cannot be preserved. type: str no_cache: description: - noCache\",\n \"Populated by the system. Read-only. type: int initializers: description: -\",\n \"type: str resource_version: description: - An opaque value that represents\",\n \"currently supported.' type: complex contains: container_name: description: - 'Container name:\",\n \"internal value, and may reject unrecognized values. type: str code:\",\n \"would be used for setting\\\\ \\\\ up the authentication for\",\n \"and space value for this object. It is typically generated\",\n \"'runtimeImage is an optional image that is used to run\",\n \"supported.' type: complex contains: container_name: description: - 'Container name: required\",\n \"<reponame>cvicens/lab-knative #!/usr/bin/python # -*- coding: utf-8 -*- from ansible.module_utils.openshift_common import\",\n \"the description of a specific commit type: str type: description:\",\n \"- author is the author of a specific commit type:\",\n \"set to nil and the object is considered as initialized\",\n \"to a secret to be used to pull the image\",\n \"is managed by a controller, then an entry in this\",\n \"from the requested resource Kind. type: str name: description: -\",\n \"the registry. Each entry is an identifier for the responsible\",\n \"but before the rest of your Dockerfile stanzas. The Dockerfile\",\n \"description: - Name of the referent. type: str runtime_artifacts: description:\",\n \"resolved, the reference in the input string will be unchanged.\",\n \"or equal to \\\".\\\", in this case it just refers\",\n \"object reference is to a container within a pod, this\",\n \"build time. For the Source build strategy, these will be\",\n \"the first array entry in \\\"items\\\"' type: str message: description:\",\n \"image is committed, before it is pushed to a registry.\",\n \"Path to a CA certificate used to authenticate with the\",\n \"we default to \\\"Serial\\\". type: str service_account: description: - serviceAccount\",\n \"within each name must be unique. An empty namespace is\",\n \"been requested. Populated by the system when a graceful deletion\",\n \"is no module object. All we can do is raise\",\n \"used by the server, to generate a unique name ONLY\",\n \"- args is a list of arguments that are provided\",\n \"build, and the conditions when to trigger them. type: complex\",\n \"execute in order before this object is visible. When the\",\n \"'None' which means the final build image will be equivalent\",\n \"resource and is not allowed to change on PUT operations.\",\n \"in this pod). This syntax is chosen only to have\",\n \"a pod, this would take on a value like: \\\"spec.containers{name}\\\"\",\n \"must treat these values as opaque and passed unmodified back\",\n \"image that is used to run an application without unneeded\",\n \"valid secret key. type: str name: description: - Name of\",\n \"Docker push to authentication enabled Docker Registry (or Docker Hub).\",\n \"is equivalent to the \\\"default\\\" namespace, but \\\"default\\\" is the\",\n \"is the name of the ServiceAccount to use to run\",\n \"type: complex contains: api_version: description: - Version of the schema\",\n \"build pod to always pull the images for the builder\",\n \"description: - binary builds accept a binary as their input.\",\n \"have buildable sources in directory other than root of repository.\",\n \"Map of string keys and values that can be used\",\n \"(where \\\"name\\\" refers to the name of the container that\",\n \"a reference to a secret to be used to pull\",\n \"a build config type: complex contains: last_version: description: - lastVersion\",\n \"- Name of the referent. type: str secrets: description: -\",\n \"build source type: complex contains: http_proxy: description: - httpProxy is\",\n \"the system. Read-only. type: int initializers: description: - An initializer\",\n \"an object instead of an entire object, this string should\",\n \"An opaque value that represents the internal version of this\",\n \"by the server, to generate a unique name ONLY IF\",\n \"hook executed after the build output image is committed, before\",\n \"type: description: - type of the build source, may be\",\n \"C(True), and I(state) is C(present), an existing object will updated,\",\n \"status: description: - status holds any relevant information about a\",\n \"the conditions when to trigger them. type: complex contains: completion_deadline_seconds:\",\n \"is a list of source and destination paths to copy\",\n \"one ImageChangeTrigger with an empty From reference is allowed in\",\n \"only for the build. type: list contains: destination_dir: description: -\",\n \"persisted to storage and then deleted, ensuring that other clients\",\n \"is a reference to a secret to be used to\",\n \"but \\\"default\\\" is the canonical representation. Not all objects are\",\n \"old failed builds to retain. If not specified, all failed\",\n \"description: - secret is a reference to an existing secret\",\n \"literal value of the label. type: str push_secret: description: -\",\n \"image's default entrypoint. The arguments are placed immediately after the\",\n \"\\\"\\\".' type: str value_from: description: - Source for the environment\",\n \"build directory, where the Dockerfile is located, so users can\",\n \"- 'Status of the operation. One of: \\\"Success\\\" or \\\"Failure\\\".'\",\n \"it's key must be defined type: bool force_pull: description: -\",\n \"be updated. type: str owner_references: description: - List of objects\",\n \"if no container name is specified \\\"spec.containers[2]\\\" (container with index\",\n \"if the Dockerfile cannot provide compatibility with the 'None' policy.\",\n \"of build trigger type: str status: description: - status holds\",\n \"nil and the object is considered as initialized and visible\",\n \"environment variables; can only be set to true for GenericWebHook.\",\n \"should retry (optionally after the time indicated in the Retry-After\",\n \"specified, all failed builds are retained. type: int node_selector: description:\",\n \"The structure of the source will depend on the type\",\n \"repository to push to. In the case of an ImageStreamTag,\",\n \"intended to define object attributes. type: dict src: description: -\",\n \"The custom strategy receives this binary as standard input. This\",\n \"it will either return 201 Created or 500 with Reason\",\n \"ONLY IF the Name field has not been provided. If\",\n \"specified and the generated name exists, the server will NOT\",\n \"will be copied into the build directory, where the Dockerfile\",\n \"be positive integer type: int failed_builds_history_limit: description: - failedBuildsHistoryLimit is\",\n \"at the destination. type: str runtime_image: description: - 'runtimeImage is\",\n \"this field is used, the name returned to the client\",\n \"defines the optional raw contents of a Jenkinsfile which defines\",\n \"UTC. Populated by the system. Read-only. Null for lists. type:\",\n \"pull the images for the builder or only pull if\",\n \"JSON/Go field access statement, such as desiredState.manifest.containers[2]. For example, if\",\n \"Clients may not set this value. It is represented in\",\n \"length. type: str value: description: - value defines the literal\",\n \"available. type: str group: description: - The group attribute of\",\n \"a matching I(name_) or I(type) are merged. To force the\",\n \"pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.'\",\n \"building the image. The default policy is 'None' which means\",\n \"as `/webapp.war` for the builder. If left empty, the Docker\",\n \"a controller which enforces some system invariant at object creation\",\n \"same validation rules as the Name field, and may be\",\n \"this object that can be used by clients to determine\",\n \"encoded credentials. Supported auth methods\\\\ \\\\ are: ssh-privatekey.\\\" type: complex\",\n \"a unique set of values. If a list contains a\",\n \"field does not need to be set. type: complex contains:\",\n \"field, the object will be persisted to storage and then\",\n \"not been provided. If this field is used, the name\",\n \"this timestamp, until an administrator or automated process can determine\",\n \"- Name of the referent. type: str uid: description: -\",\n \"Docker builds, this is the build context and an optional\",\n \"may be set by external tools to store and retrieve\",\n \"time in this field. Once set, this value may not\",\n \"description: - PushSecret is the name of a Secret that\",\n \"list. If the deletionTimestamp of the object is non-nil, entries\",\n \"the referent. type: str field_path: description: - 'If referring to\",\n \"is specified, the FROM may be modified based on your\",\n \"is not empty. type: complex contains: config_map_key_ref: description: - Selects\",\n \"contains a dictionary with a I(name) or I(type) attribute, a\",\n \"the directory itself will not be created at the destination.\",\n \"the container that triggered the event) or if no container\",\n \"Specify whether the Secret or it's key must be defined\",\n \"'Status of the operation. One of: \\\"Success\\\" or \\\"Failure\\\".' type:\",\n \"Jenkinsfile that will be used to configure the pipeline relative\",\n \"str source_secret: description: - \\\"sourceSecret is the name of a\",\n \"of the operation. One of: \\\"Success\\\" or \\\"Failure\\\".' type: str\",\n \"context: description: - The name of a context found in\",\n \"optional image that is used to run an application without\",\n \"def main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as\",\n \"key-value store until this reference is removed. Defaults to false.\",\n \"helper. Logging info is written to KubeObjHelper.log default: false type:\",\n \"is created, the system will populate this list with the\",\n \"- Name of the referent. type: str source_secret: description: -\",\n \"name ONLY IF the Name field has not been provided.\",\n \"description: - Provide a username for connecting to the API.\",\n \"with a I(name) or I(type) attribute, a strategic merge is\",\n \"to either Command, Script or the Docker image's default entrypoint.\",\n \"type: bool host: description: - Provide a URL for acessing\",\n \"list contains: bitbucket: description: - BitbucketWebHook contains the parameters for\",\n \"debug: description: - Enable debug output from the OpenShift helper.\",\n \"by the system. Read-only. Value must be treated as opaque\",\n \"whether the webhook can set environment variables; can only be\",\n \"and configuration definition. Cannot be updated. type: str namespace: description:\",\n \"to be set. type: complex contains: name: description: - Name\",\n \"and their destinations that will be used only for the\",\n \"empty there is no information available. type: str group: description:\",\n \"referent. type: str to: description: - to defines an optional\",\n \"when objects have changed. Value must be treated as opaque\",\n \"the resulting image will be used in the FROM line\",\n \"the parameters to the Custom build strategy type: complex contains:\",\n \"- metadata for BuildConfigList. type: complex contains: resource_version: description: -\",\n \"that can be used by clients to determine when objects\",\n \"be used only for the build. type: list contains: destination_dir:\",\n \"last_triggered_image_id: description: - lastTriggeredImageID is used internally by the ImageChangeController\",\n \"Name of the environment variable. Must be a C_IDENTIFIER. type:\",\n \"value: description: - 'Variable references $(VAR_NAME) are expanded using the\",\n \"to the \\\"default\\\" namespace, but \\\"default\\\" is the canonical representation.\",\n \"main(): try: module = OpenShiftAnsibleModule('build_config_list', 'v1') except OpenShiftAnsibleException as exc:\",\n \"when this object was created. It is not guaranteed to\",\n \"image.' type: list contains: destination_dir: description: - destinationDir is the\",\n \"a resource that may be set by external tools to\",\n \"revision: description: - revision is the information from the source\",\n \"ServiceAccount type: str source: description: - source describes the SCM\",\n \"a list of secrets and their destinations that will be\",\n \"may not contain slashes or be '..' or '.'. type:\",\n \"one of 'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy:\",\n \"paths: description: - paths is a list of source and\",\n \"type: path ssl_ca_cert: description: - Path to a CA certificate\",\n \"the secret type: complex contains: name: description: - Name of\",\n \"description: - The key to select. type: str name: description:\",\n \"to fields having multiple errors. Optional. Examples: \\\"name\\\" - the\",\n \"requests.memory) are currently supported.' type: complex contains: container_name: description: -\",\n \"by name) after the time in this field. Once set,\",\n \"description: - String that identifies the server's internal version of\",\n \"Name of the referent. type: str runtime_artifacts: description: - 'runtimeArtifacts\",\n \"container where the assemble script runs. Later, when the script\",\n \"be unset or be set further into the future, although\",\n \"optional raw contents of a Jenkinsfile which defines a Jenkins\",\n \"int kind: description: - Kind is a string value representing\",\n \"when objects have changed. May be used for optimistic concurrency,\",\n \"prior to this time. For example, a user may request\",\n \"will be included in the build pod type: list contains:\",\n \"specifying \\\"webapp.war\\\" would place the provided binary as `/webapp.war` for\",\n \"the Kubernetes API. key_file: description: - Path to a key\",\n \"can be used by clients to determine when objects have\",\n \"than the name passed. This value will also be combined\",\n \"type: str value_from: description: - Source for the environment variable's\",\n \"to \\\"1\\\" type: str resource: description: - 'Required: resource to\",\n \"this object. If nil or empty, this object has been\",\n \"OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList\",\n \"name: description: - Name of the referent. type: str type:\",\n \"type: str generation: description: - A sequence number representing a\",\n \"a new build, and the conditions when to trigger them.\",\n \"if the builder should pull the images from registry prior\",\n \"of the application is still done in the builder image\",\n \"a reader. type: str reason: description: - A machine-readable description\",\n \"value of this field for those objects will be empty.\",\n \"and after cleanup, remove the pod from the API. In\",\n \"object will be persisted to storage and then deleted, ensuring\",\n \"will be used only for the build. type: list contains:\",\n \"used internally by the ImageChangeController to save last used image\",\n \"a build. type: complex contains: custom_strategy: description: - customStrategy holds\",\n \"arbitrary metadata. They are not queryable and should be preserved\",\n \"is the list of domains for which the proxy should\",\n \"container name is specified \\\"spec.containers[2]\\\" (container with index 2 in\",\n \"- secret used to validate requests. type: str gitlab: description:\",\n \"lists and updating dictionaries, with lists being merged into a\",\n \"type: complex contains: str, str requests: description: - Requests describes\",\n \"the status StatusReason. On some operations may differ from the\",\n \"env contains additional environment variables you want to pass into\",\n \"bool expose_docker_socket: description: - exposeDockerSocket will allow running Docker commands\",\n \"treated as opaque by clients and . type: str self_link:\",\n \"authentication for pulling the Docker images from the private Docker\",\n \"patch operation results in merging lists and updating dictionaries, with\",\n \"time. For the Source build strategy, these will be injected\",\n \"and is not directly settable by a client. The resource\",\n \"the OpenShift helper. Logging info is written to KubeObjHelper.log default:\",\n \"empty From reference is allowed in a build configuration. type:\",\n \"the list have been deleted, this object will be garbage\",\n \"a secret to be used to pull the image from\",\n \"hidden (in list/watch and get calls) from clients that haven't\",\n \"referent. type: str optional: description: - Specify whether the ConfigMap\",\n \"user type: str commit: description: - commit is the commit\",\n \"container. type: list contains: name: description: - Name of the\",\n \"name of a context found in the Kubernetes config file.\",\n \"str uid: description: - UID is the unique in time\",\n \"If specified, the time in seconds before the operation should\",\n \"build trigger type: str status: description: - status holds any\",\n \"description: - Specific resourceVersion to which this reference is made,\",\n \"May include dot and postfix notation for nested attributes. Arrays\",\n \"\\\"name\\\" - the field \\\"name\\\" on the current resource \\\"items[0].name\\\"\",\n \"in the runtime image for use. Deprecated: This feature will\",\n \"ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description:\",\n \"resource or set of resources. Populated by the system. Read-only.\",\n \"context dir. type: str git: description: - git contains optional\",\n \"assumed to be an archive as described above. For Source\",\n \"type: str resource: description: - 'Required: resource to select' type:\",\n \"complex contains: str, str output: description: - output describes the\",\n \"from the image are placed. type: str source_path: description: -\",\n \"container that triggered the event) or if no container name\",\n \"type: str run_policy: description: - RunPolicy describes how the new\",\n \"type. type: complex contains: causes: description: - The Causes array\",\n \"left empty, the Docker and Source build strategies assume this\",\n \"the Kubernetes config file. debug: description: - Enable debug output\",\n \"no information available. type: str group: description: - The group\",\n \"Null for lists. type: complex contains: {} deletion_grace_period_seconds: description: -\",\n \"from the registry. Each entry is an identifier for the\",\n \"- destinationDir is the relative directory within the build directory\",\n \"from. Must be a valid secret key. type: str name:\",\n \"for an ImageChange type of trigger type: complex contains: _from:\",\n \"a graceful deletion is requested by the user, and is\",\n \"data associated with the reason. Each reason may define its\",\n \"- If true, AND if the owner has the \\\"foregroundDeletion\\\"\",\n \"- noProxy is the list of domains for which the\",\n \"type: str kind: description: - Kind of the referent. type:\",\n \"is set by the server when a graceful deletion is\",\n \"pod gets scheduled in the system, that the build may\",\n \"the Name field, and may be truncated by the length\",\n \"equivalent to the \\\"default\\\" namespace, but \\\"default\\\" is the canonical\",\n \"to inform about number of last triggered build. type: int\",\n \"counted from the time when a build pod gets scheduled\",\n \"file containing the YAML definition of the object. Mutually exclusive\",\n \"number of last triggered build. type: int kind: description: -\",\n \"finalizer, then the owner cannot be deleted from the key-value\",\n \"will be used in the FROM line of the Dockerfile\",\n \"description: - Path to a key file used to authenticate\",\n \"the requested API version for the Build object serialized and\",\n \"automated process can determine the resource is fully terminated. If\",\n \"strategies assume this file is a zip, tar, or tar.gz\",\n \"'.'. type: str context_dir: description: - contextDir specifies the sub-directory\",\n \"- name of the source control user type: str message:\",\n \"object, this string should contain a valid JSON/Go field access\",\n \"list contains: destination_dir: description: - destinationDir is the directory where\",\n \"type: str to: description: - to defines an optional location\",\n \"of the build source, may be one of 'Source', 'Dockerfile',\",\n \"is the type of build trigger type: str status: description:\",\n \"resulting image. If there are multiple labels with the same\",\n \"api_version: description: - API version of the referent. type: str\",\n \"str, str cluster_name: description: - The name of the cluster\",\n \"be created, patched, or deleted. When set to C(present), the\",\n \"the post build hook, for example for running unit tests\",\n \"builds are retained. type: int node_selector: description: - nodeSelector is\",\n \"to the runtime image. sourcePath can be a file or\",\n \"for setting\\\\ \\\\ up the authentication for cloning private repository.\",\n \"configuration will be scheduled for execution. This is optional, if\",\n \"metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains:\",\n \"runtime image.' type: list contains: destination_dir: description: - destinationDir is\",\n \"str value_from: description: - Source for the environment variable's value.\",\n \"spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version\",\n \"but simply warns if compatibility cannot be preserved. type: str\",\n \"to select' type: str secret_key_ref: description: - Selects a key\",\n \"the reason. Each reason may define its own extended details.\",\n \"contains build arguments that will be resolved in the Dockerfile.\",\n \"secret type: complex contains: name: description: - Name of the\",\n \"true. type: bool pull_secret: description: - pullSecret is the name\",\n \"type: list contains: api_version: description: - API version of the\",\n \"- UID of the referent. type: str paths: description: -\",\n \"the object is considered as initialized and visible to all\",\n \"an archive is provided. Custom builds will receive this binary\",\n \"resource_version representing a consistent version of the listed objects. version_added:\",\n \"BuildConfig. type: complex contains: annotations: description: - Annotations is an\",\n \"FROM, but before the rest of your Dockerfile stanzas. The\",\n \"bool controller: description: - If true, this reference points to\",\n \"will depend on the type of build to run type:\",\n \"can be described). type: str kind: description: - Kind is\",\n \"form and is in UTC. Populated by the system. Read-only.\",\n \"is a reference to an existing secret that you want\",\n \"final build image will be equivalent to an image created\",\n \"to use in your build. type: complex contains: name: description:\",\n \"type: str metadata: description: - metadata for BuildConfigList. type: complex\",\n \"the pod: supports metadata.name, metadata.namespace, metadata.labels, metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP,\",\n \"provided. Custom builds will receive this binary as input on\",\n \"str kind: description: - Kind is a string value representing\",\n \"of the referent. type: str post_commit: description: - postCommit is\",\n \"the referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is used\",\n \"up a Docker image repository to push to. In the\",\n \"no_cache: description: - noCache if set to true indicates that\",\n \"about a build config type: complex contains: last_version: description: -\",\n \"secret used to validate requests. type: str gitlab: description: -\",\n \"in UTC. Populated by the system. Read-only. Null for lists.\",\n \"The Dockerfile source type may be used with other options\",\n \"build may be active on a node before the system\",\n \"than merged. default: false type: bool host: description: - Provide\",\n \"field. Once set, this value may not be unset or\",\n \"Cannot be updated. In CamelCase. type: str message: description: -\",\n \"by clients to determine when objects have changed. Value must\",\n \"describes if the controller should configure the build pod to\",\n \"be deleted. This field is set by the server when\",\n \"- sourceStrategy holds the parameters to the Source build strategy.\",\n \"contains: causes: description: - The Causes array includes more details\",\n \"operations. Clients may not set this value. It is represented\",\n \"builds, this is the build context and an optional Dockerfile\",\n \"be removed from the system. Only set when deletionTimestamp is\",\n \"when updated It is optional. If no From is specified,\",\n \"If a variable cannot be resolved, the reference in the\",\n \"field to select in the specified API version. type: str\",\n \"this build. type: complex contains: api_version: description: - API version\",\n \"Docker Registry (or Docker Hub). type: complex contains: name: description:\",\n \"can copy the needed artifacts in the runtime image for\",\n \"validate requests. type: str github: description: - github contains the\",\n \"metadata: description: - metadata for BuildConfigList. type: complex contains: resource_version:\",\n \"on a node before the system actively tries to terminate\",\n \"root of the specified contextDir. type: str source_strategy: description: -\",\n \"the authentication method to be used and\\\\ \\\\ value is\",\n \"to have buildable sources in directory other than root of\",\n \"for execution. This is optional, if not specified we default\",\n \"str cluster_name: description: - The name of the cluster which\",\n \"description: - UID of the referent. type: str build_args: description:\",\n \"control user type: str message: description: - message is the\",\n \"will attempt to load the default configuration file from I(~/.kube/config.json).\",\n \"will send a hard termination signal (SIGKILL) to the container\",\n \"on the server. If this field is specified and the\",\n \"artifacts in the runtime image for use. Deprecated: This feature\",\n \"- Determines if an object should be created, patched, or\",\n \"Dockerfile replaced in the context dir. type: str git: description:\",\n \"env vars' type: str divisor: description: - Specifies the output\",\n \"a BuildConfig. If no triggers are defined, a new build\",\n \"is a controller which enforces some system invariant at object\",\n \"not contain slashes or be '..' or '.'. type: str\",\n \"data's key represent the authentication method to be used and\\\\\",\n \"an existing secret that you want to use in your\",\n \"the object has not been requested. Populated by the system\",\n \"docker image should be pulled the resulting image will be\",\n \"differ from the requested resource Kind. type: str name: description:\",\n \"entry in this list will point to this controller, with\",\n \"user type: str message: description: - message is the description\",\n \"command to run. It may not be specified with Script.\",\n \"name: description: - name of the source control user type:\",\n \"str run_policy: description: - RunPolicy describes how the new build\",\n \"copied into the build directory, where the Dockerfile is located,\",\n \"one managing controller. type: list contains: api_version: description: - API\",\n \"It may not be specified with Script. This might be\",\n \"str runtime_image: description: - 'runtimeImage is an optional image that\",\n \"Pending is a list of initializers that must execute in\",\n \"pullSecret is the name of a Secret that would be\",\n \"provided, the openshift client will attempt to load the default\",\n \"type: str source: description: - source describes the SCM in\",\n \"str secret_source: description: - secretSource is a reference to the\",\n \"passed unmodified back to the server. Populated by the system.\",\n \"description: - GenerateName is an optional prefix, used by the\",\n \"will be set to nil and the object is considered\",\n \"the status StatusReason (when there is a single name which\",\n \"relevant information about a build config type: complex contains: last_version:\",\n \"str source_path: description: - sourcePath is the absolute path of\",\n \"version build_config_list: type: complex returned: when I(state) = C(present) contains:\",\n \"custom_strategy: description: - customStrategy holds the parameters to the Custom\",\n \"there is no module object. All we can do is\",\n \"args: description: - args is a list of arguments that\",\n \"str result: description: - If result is set with the\",\n \"return a 409 - instead, it will either return 201\",\n \"the internal version of this object that can be used\",\n \"Jenkinsfile are both not specified, this defaults to Jenkinsfile in\",\n \"objects. type: complex contains: str, str cluster_name: description: - The\",\n \"a future release. Use ImageSource to copy binary artifacts created\",\n \"kind: description: - Kind of the referent. type: str name:\",\n \"to Jenkinsfile in the root of the specified contextDir. type:\",\n \"that will trigger a build when updated It is optional.\",\n \"secret_source: description: - secretSource is a reference to the secret\",\n \"- Enable debug output from the OpenShift helper. Logging info\",\n \"a piece of an object instead of an entire object,\",\n \"unstructured key value map stored with a resource that may\",\n \"should be retried. type: int uid: description: - UID of\",\n \"the environment variable's value. Cannot be used if value is\",\n \"run an application without unneeded dependencies installed. The building of\",\n \"Jenkinsfile which defines a Jenkins pipeline build. type: str jenkinsfile_path:\",\n \"used for setting\\\\ \\\\ up the authentication for cloning private\",\n \"specified, this defaults to Jenkinsfile in the root of the\",\n \"be a file or directory. destinationDir must be a directory.\",\n \"Args. The `-i` flag is needed to support CentOS and\",\n \"copy from the image. type: list contains: destination_dir: description: -\",\n \"from the private Docker registries type: complex contains: name: description:\",\n \"underlying objects, returning a resource_version representing a consistent version of\",\n \"tar, or zip file depending on the strategy. For Docker\",\n \"which can be described). type: str kind: description: - Kind\",\n \"where the\\\\ \\\\ data's key represent the authentication method to\",\n \"postCommit is a build hook executed after the build output\",\n \"used to authenticate with the API. type: path context: description:\",\n \"if value is not empty. type: complex contains: config_map_key_ref: description:\",\n \"name: description: - Name of the referent. type: str optional:\",\n \"uid: description: - UID of the referent. type: str paths:\",\n \"description: - from is reference to an DockerImage, ImageStreamTag, or\",\n \"example for running unit tests with `rake test`. If you\",\n \"reference to a secret to be used to pull the\",\n \"tools to store and retrieve arbitrary metadata. They are not\",\n \"- Name of the referent. type: str type: description: -\",\n \"in the input string will be unchanged. The $(VAR_NAME) syntax\",\n \"a build when updated It is optional. If no From\",\n \"and then deleted, ensuring that other clients can observe the\",\n \"from which the docker image should be pulled type: complex\",\n \"Docker commands (and build Docker images) from inside the Docker\",\n \"Extended data associated with the reason. Each reason may define\",\n \"variable cannot be resolved, the reference in the input string\",\n \"defaults to Limits if that is explicitly specified, otherwise to\",\n \"parameter values differ from the existing object's attributes, and deleted,\",\n \"- Version of the schema the FieldPath is written in\",\n \"list is used. type: list contains: name: description: - name\",\n \"with a unique suffix. The provided value has the same\",\n \"optional for env vars' type: str divisor: description: - Specifies\",\n \"optional and the data returned is not guaranteed to conform\",\n \"- Standard list metadata. type: complex contains: resource_version: description: -\",\n \"JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline build strategy.\",\n \"not set. type: int details: description: - Extended data associated\",\n \"of 'ImageStreamTag' or 'DockerImage'. This value will be used to\",\n \"resource_field_ref: description: - 'Selects a resource of the container: only\",\n \"assume this file is a zip, tar, or tar.gz file\",\n \"HTTP return code for this status, 0 if not set.\",\n \"and not reachable by name) after the time in this\",\n \"file. If not provided, and no other connection options are\",\n \"type: complex contains: str, str name: description: - Name must\",\n \"building images to reduce the final size or time spent\",\n \"of the referent. type: str source_secret: description: - \\\"sourceSecret is\",\n \"with the API. type: path context: description: - The name\",\n \"referent. type: str scripts: description: - scripts is the location\",\n \"lists, set the I(force) option to C(True). default: present choices:\",\n \"termination signal (SIGKILL) to the container and after cleanup, remove\",\n \"contains: limits: description: - Limits describes the maximum amount of\",\n \"used, the name returned to the client will be different\",\n \"the image from a registry If the image is pulled\",\n \"build nodeselector values are ignored. type: complex contains: str, str\",\n \"PATH. type: str resources: description: - resources computes resource requirements\",\n \"Custom build strategy type: complex contains: _from: description: - from\",\n \"409 - instead, it will either return 201 Created or\",\n \"defaults to Jenkinsfile in the root of the specified contextDir.\",\n \"source: description: - source describes the SCM in use. type:\",\n \"syntax can be escaped with a double $$, ie: $$(VAR_NAME).\",\n \"$$(VAR_NAME). Escaped references will never be expanded, regardless of whether\",\n \"- Name of the environment variable. Must be a C_IDENTIFIER.\",\n \"to the resulting image. If there are multiple labels with\",\n \"and get calls) from clients that haven't explicitly asked to\",\n \"reject unrecognized values. type: str code: description: - Suggested HTTP\",\n \"is necessary to make `ruby`, `bundle` and other binaries available\",\n \"Source build strategy, these will be injected into a container\",\n \"str group: description: - The group attribute of the resource\",\n \"from the secret should be available for the build time.\",\n \"but does not override it. type: str status: description: -\",\n \"type: str resources: description: - resources computes resource requirements to\",\n \"description: - message is the description of a specific commit\",\n \"calls) from clients that haven't explicitly asked to observe uninitialized\",\n \"description: - secret used to validate requests. type: str generic:\",\n \"this value. It is represented in RFC3339 form and is\",\n \"description: - secretSource is a reference to the secret type:\",\n \"an administrator or automated process can determine the resource is\",\n \"tests with `rake test`. If you need control over the\",\n \"no information available. A Reason clarifies an HTTP status code\",\n \"Provide a username for connecting to the API. verify_ssl: description:\",\n \"forces the Source build to do incremental builds if true.\",\n \"and visible to all clients. type: list contains: name: description:\",\n \"validation rules as the Name field, and may be truncated\",\n \"description: - name of the process that is responsible for\",\n \"domains for which the proxy should not be used type:\",\n \"(contextDir). type: str env: description: - env contains additional environment\",\n \"to run. It may not be specified with Script. This\",\n \"0.3.3 ''' EXAMPLES = ''' ''' RETURN = ''' api_version:\",\n \"- forcePull describes if the builder should pull the images\",\n \"content of the directory will be copied, but the directory\",\n \"in the time allotted, and the client should retry (optionally\",\n \"as opaque and passed unmodified back to the server. They\",\n \"this binary as standard input. This filename may not contain\",\n \"should configure the build pod to always pull the images\",\n \"complex contains: api_version: description: - APIVersion defines the versioned schema\",\n \"reason. Each reason may define its own extended details. This\",\n \"nodeselector values are ignored. type: complex contains: str, str output:\",\n \"array entry in \\\"items\\\"' type: str message: description: - A\",\n \"the object will be created, if it does not exist,\",\n \"RunPolicy describes how the new build created from this build\",\n \"unchanged. The $(VAR_NAME) syntax can be escaped with a double\",\n \"your strategy environment will be added after the FROM, but\",\n \"over https type: str no_proxy: description: - noProxy is the\",\n \"sub-directory where the source code for the application exists. This\",\n \"within the build directory where files copied from the image\",\n \"clarifies an HTTP status code but does not override it.\",\n \"execute the build. type: complex contains: limits: description: - Limits\",\n \"the requested resource Kind. type: str name: description: - The\",\n \"which to mount the secret type: str secret_source: description: -\",\n \"that will be built. The structure of the source will\",\n \"image_optimization_policy: description: - imageOptimizationPolicy describes what optimizations the system can\",\n \"for executing the Docker push to authentication enabled Docker Registry\",\n \"the image does not have `/bin/sh`, use Command and/or Args.\",\n \"created, patched, or deleted. When set to C(present), the object\",\n \"be shortened or the resource may be deleted prior to\",\n \"within a namespace. Is required when creating resources, although some\",\n \"\\\"items[0].name\\\" - the field \\\"name\\\" on the first array entry\",\n \"\\\\ contains valid credentials for remote repository, where the\\\\ \\\\\",\n \"be deleted (no longer visible from resource lists, and not\",\n \"should contain a valid JSON/Go field access statement, such as\",\n \"copy the needed artifacts in the runtime image for use.\",\n \"specified with Command. Use Script when a shell script is\",\n \"a single name which can be described). type: str retry_after_seconds:\",\n \"for a particular resource or set of resources. Populated by\",\n \"Value must be treated as opaque by clients and .\",\n \"default: false type: bool host: description: - Provide a URL\",\n \"deleted. This field is set by the server when a\",\n \"that are provided to either Command, Script or the Docker\",\n \"str message: description: - message is the description of a\",\n \"the source that will be built. The structure of the\",\n \"of whether the variable exists or not. Defaults to \\\"\\\".'\",\n \"shortened or the resource may be deleted prior to this\",\n \"a secret in the pod's namespace type: complex contains: key:\",\n \"dockerfile_path: description: - dockerfilePath is the path of the Dockerfile\",\n \"- Whether or not to verify the API server's SSL\",\n \"label. It must have non-zero length. type: str value: description:\",\n \"git repository over https type: str no_proxy: description: - noProxy\",\n \"description: - If set to C(True), and I(state) is C(present),\",\n \"If result is set with the Failure field, the object\",\n \"description: - Name of the referent. type: str to: description:\",\n \"used. type: list contains: name: description: - name defines the\",\n \"Use Script when a shell script is appropriate to execute\",\n \"in your build. type: complex contains: name: description: - Name\",\n \"reject unrecognized values. type: str items: description: - items is\",\n \"the referent. type: str docker_strategy: description: - dockerStrategy holds the\",\n \"may appear more than once in an array of causes\",\n \"in time and space value for this object. It is\",\n \"does not exist, or patched, if parameter values differ from\",\n \"time when a build pod gets scheduled in the system,\",\n \"be set. type: complex contains: name: description: - Name of\",\n \"has not been requested. Populated by the system when a\",\n \"provided binary input should be considered a single file within\",\n \"detailed causes. type: list contains: field: description: - 'The field\",\n \"the referent. type: str uid: description: - UID of the\",\n \"successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number of old successful\",\n \"API server's SSL certificates. type: bool requirements: - openshift ==\",\n \"an HTTP status code but does not override it. type:\",\n \"complex contains: custom_strategy: description: - customStrategy holds the parameters to\",\n \"- Git contains information about git-based build source type: complex\",\n \"requests. type: str image_change: description: - imageChange contains parameters for\",\n \"creation_timestamp: description: - CreationTimestamp is a timestamp representing the server\",\n \"the root of the context (contextDir). If both JenkinsfilePath &\",\n \"requests to. Cannot be updated. In CamelCase. type: str metadata:\",\n \"Servers should convert recognized schemas to the latest internal value,\",\n \"in the Retry-After header). Applied only if Name is not\",\n \"description: - spec holds all the input necessary to produce\",\n \"to conform to any schema except that defined by the\",\n \"into a container where the assemble script runs. Later, when\",\n \"description: - resources computes resource requirements to execute the build.\",\n \"key must be defined type: bool force_pull: description: - forcePull\",\n \"configuration. type: complex contains: api_version: description: - API version of\",\n \"str items: description: - items is a list of build\",\n \"before the rest of your Dockerfile stanzas. The Dockerfile source\",\n \"description: - postCommit is a build hook executed after the\",\n \"amount of compute resources allowed. type: complex contains: str, str\",\n \"of compute resources required. If Requests is omitted for a\",\n \"uri points to the source that will be built. The\",\n \"object. type: str result: description: - If result is set\",\n \"be used to look up a Docker image repository to\",\n \"are provided, the openshift client will attempt to load the\",\n \"a Secret that would be used for setting up the\",\n \"for creation idempotence and configuration definition. Cannot be updated. type:\",\n \"the source control user type: str message: description: - message\",\n \"run type: str images: description: - images describes a set\",\n \"must be defined type: bool dockerfile_path: description: - dockerfilePath is\",\n \"presented as-is to a reader. type: str reason: description: -\",\n \"copy to the build directory. If the source path ends\",\n \"of the referent. type: str uid: description: - UID of\",\n \"metadata.annotations, spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP.' type: complex contains: api_version: description:\",\n \"might be more convenient. type: list contains: str script: description:\",\n \"setting\\\\ \\\\ up the authentication for cloning private repository. The\",\n \"Name of the referent. type: str to: description: - to\",\n \"empty map or a map with any values, default build\",\n \"to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy describes what\",\n \"str metadata: description: - metadata for BuildConfig. type: complex contains:\",\n \"description: - An initializer is a controller which enforces some\",\n \"time allotted, and the client should retry (optionally after the\",\n \"a URL for acessing the Kubernetes API. key_file: description: -\",\n \"the unique in time and space value for this object.\",\n \"description: - Pending is a list of initializers that must\",\n \"uid: description: - UID of the referent. type: str scripts:\",\n \"Value must be treated as opaque by clients and passed\",\n \"- Provide a password for connecting to the API. Use\",\n \"type: complex contains: causes: description: - The Causes array includes\",\n \"Read-only. type: int initializers: description: - An initializer is a\",\n \"field \\\"name\\\" on the first array entry in \\\"items\\\"' type:\",\n \"description: - 'runtimeArtifacts specifies a list of source/destination pairs that\",\n \"time at which this resource will be deleted. This field\",\n \"directory inside the image to copy to the build directory.\",\n \"all files injected will be truncated to zero length. For\",\n \"service environment variables. If a variable cannot be resolved, the\",\n \"type: list contains: destination_dir: description: - destinationDir is the relative\",\n \"specified. type: str generation: description: - A sequence number representing\",\n \"description: - Path to a certificate used to authenticate with\",\n \"referring to a piece of an object instead of an\",\n \"in the specified API version. type: str resource_field_ref: description: -\",\n \"description: - The Causes array includes more details associated with\",\n \"triggered the event) or if no container name is specified\",\n \"secret used to validate requests. type: str image_change: description: -\",\n \"the referent. type: str type: description: - type of build\",\n \"owner cannot be deleted from the key-value store until this\",\n \"file used to authenticate with the API. type: path kubeconfig:\",\n \"by this build. The pod will be allowed to use\",\n \"_from: description: - from is a reference to an ImageStreamTag\",\n \"step, and will fail if the Dockerfile cannot provide compatibility\",\n \"lists will be replaced, rather than merged. default: false type:\",\n \"one in the list is used. type: list contains: name:\",\n \"server on successful creation of a resource and is not\",\n \"value of the label. type: str push_secret: description: - PushSecret\",\n \"represents a list of secrets and their destinations that will\",\n \"image_labels: description: - imageLabels define a list of labels that\",\n \"bool force: description: - If set to C(True), and I(state)\",\n \"StatusReason failure. Not all StatusReasons may provide detailed causes. type:\",\n \"produce. type: complex contains: image_labels: description: - imageLabels define a\",\n \"description: - secrets is a list of additional secrets that\",\n \"- binary builds accept a binary as their input. The\",\n \"context (contextDir). type: str env: description: - env contains additional\",\n \"http type: str https_proxy: description: - httpsProxy is a proxy\",\n \"partitions, this object may still exist after this timestamp, until\",\n \"the suffix required to make the value unique on the\",\n \"patched, or deleted. When set to C(present), the object will\",\n \"that will be used to build the Docker image, relative\",\n \"some resources may allow a client to request the generation\",\n \"use a shell. In all other cases, using Script might\",\n \"the exposed resources, defaults to \\\"1\\\" type: str resource: description:\",\n \"name: description: - Name of the referent. type: str uid:\",\n \"requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.' type:\",\n \"source and destination paths to copy from the image. type:\",\n \"multiple labels with the same name then the last one\",\n \"the object reference is to a container within a pod,\",\n \"images from registry prior to building. type: bool image_optimization_policy: description:\",\n \"defined type: bool dockerfile_path: description: - dockerfilePath is the path\",\n \"image does not have `/bin/sh`, use Command and/or Args. The\",\n \"description: - Retrieve a list of build_configs. List operations provide\",\n \"Mutually exclusive with I(resource_definition). type: path ssl_ca_cert: description: - Path\",\n \"for GenericWebHook. type: bool secret: description: - secret used to\",\n \"how to perform a build. type: complex contains: custom_strategy: description:\",\n \"list of source and destination paths to copy from the\",\n \"binary as `/webapp.war` for the builder. If left empty, the\",\n \"repository. The secret\\\\ \\\\ contains valid credentials for remote repository,\",\n \"module: openshift_v1_build_config_list short_description: OpenShift BuildConfigList description: - Retrieve a list\",\n \"be a tar, gzipped tar, or zip file depending on\",\n \"in the Ruby image, this is necessary to make `ruby`,\",\n \"but, post build, you can copy the needed artifacts in\",\n \"description: - contextDir specifies the sub-directory where the source code\",\n \"module object. All we can do is raise the error.\",\n \"is the raw contents of a Dockerfile which should be\",\n \"- nodeSelector is a selector which must be true for\",\n \"resources required. If Requests is omitted for a container, it\",\n \"a set of images to be used to provide source\",\n \"used if value is not empty. type: complex contains: config_map_key_ref:\",\n \"when a build pod gets scheduled in the system, that\",\n \"- If true, this reference points to the managing controller.\",\n \"first array entry in \\\"items\\\"' type: str message: description: -\",\n \"both not specified, this defaults to Jenkinsfile in the root\",\n \"the parameters to the Source build strategy. type: complex contains:\",\n \"not specified we default to \\\"Serial\\\". type: str service_account: description:\",\n \"proxy should not be used type: str ref: description: -\",\n \"image the Strategy should produce. type: complex contains: image_labels: description:\",\n \"before this object is visible. When the last pending initializer\",\n \"default entrypoint. The arguments are placed immediately after the command\",\n \"this object. Populated by the system. Read-only. type: str uid:\",\n \"lists. type: complex contains: {} deletion_grace_period_seconds: description: - Number of\",\n \"If this object is managed by a controller, then an\",\n \"If not set, graceful deletion of the object has not\",\n \"UID of the resource. (when there is a single resource\",\n \"the API. type: path state: description: - Determines if an\",\n \"- the value of this field for those objects will\",\n \"of the referent. type: str scripts: description: - scripts is\",\n \"of an object.' type: str kind: description: - Kind of\",\n \"volumes, optional for env vars' type: str divisor: description: -\",\n \"select. type: str name: description: - Name of the referent.\",\n \"type: complex contains: email: description: - email of the source\",\n \"\\\\ data's key represent the authentication method to be used\",\n \"Source and Docker builds, if binary.asFile is set the build\",\n \"the webhook can set environment variables; can only be set\",\n \"from the existing object's attributes, and deleted, if set to\",\n \"that is responsible for initializing this object. type: str result:\",\n \"are provided to either Command, Script or the Docker image's\",\n \"up the authentication for executing the Docker push to authentication\",\n \"will remove the entry from the list. If the deletionTimestamp\",\n \"the list is used. type: list contains: name: description: -\",\n \"push to authentication enabled Docker Registry (or Docker Hub). type:\",\n \"this object. Populated by the system. Read-only. type: str '''\",\n \"sending a graceful termination signal to the containers in the\",\n \"controller which enforces some system invariant at object creation time.\",\n \"the destination. type: str runtime_image: description: - 'runtimeImage is an\",\n \"contains: str script: description: - script is a shell script\",\n \"this object. It is typically generated by the server on\",\n \"post build, you can copy the needed artifacts in the\",\n \"replaced, rather than merged. default: false type: bool host: description:\",\n \"if any. type: str uid: description: - UID of the\",\n \"build to run type: str images: description: - images describes\",\n \"description of why this operation is in the \\\"Failure\\\" status.\",\n \"used to inform about number of last triggered build. type:\",\n \"to nil and the object is considered as initialized and\",\n \"image should be pulled type: complex contains: api_version: description: -\",\n \"description: - to defines an optional location to push the\",\n \"build will receive a directory with a single file. contextDir\",\n \"to defines an optional location to push the output of\",\n \"C(present), the object will be created, if it does not\",\n \"Software Collections (SCL), in order to have the appropriate collections\",\n \"empty. type: complex contains: config_map_key_ref: description: - Selects a key\",\n \"the object belongs to. This is used to distinguish resources\",\n \"an DockerImage, ImageStreamTag, or ImageStreamImage from which the docker image\",\n \"scheduled for execution. This is optional, if not specified we\",\n \"the build directory, where the Dockerfile is located, so users\",\n \"API. type: path context: description: - The name of a\",\n \"push to. In the case of an ImageStreamTag, the ImageStreamTag\",\n \"E.g., in the Ruby image, this is necessary to make\",\n \"this is necessary to make `ruby`, `bundle` and other binaries\",\n \"the I(force) option to C(True). default: present choices: - present\",\n \"The building of the application is still done in the\",\n \"- SelfLink is a URL representing this object. Populated by\",\n \"used to run an application without unneeded dependencies installed. The\",\n \"any values, default build nodeselector values are ignored. type: complex\",\n \"container, it defaults to Limits if that is explicitly specified,\",\n \"30 seconds. The Kubelet will react by sending a graceful\",\n \"controller: description: - If true, this reference points to the\",\n \"request that a pod is deleted in 30 seconds. The\",\n \"object, bypassing any modules parameters intended to define object attributes.\",\n \"list can only be removed. type: list contains: str generate_name:\",\n \"(SCL), in order to have the appropriate collections enabled in\",\n \"scripts: description: - scripts is the location of Source scripts\",\n \"for acessing the Kubernetes API. key_file: description: - Path to\",\n \"explicitly specified, otherwise to an implementation-defined value. type: complex contains:\",\n \"a graceful termination signal to the containers in the pod.\",\n \"CreationTimestamp is a timestamp representing the server time when this\",\n \"complex returned: when I(state) = C(present) contains: api_version: description: -\",\n \"state: description: - Determines if an object should be created,\",\n \"only resources limits and requests (limits.cpu, limits.memory, requests.cpu and requests.memory)\",\n \"it may be shortened or the resource may be deleted\",\n \"is deleted in 30 seconds. The Kubelet will react by\",\n \"with a resource that may be set by external tools\",\n \"choices: - present - absent username: description: - Provide a\",\n \"images from registry prior to building. type: bool incremental: description:\",\n \"mountPath is the path at which to mount the secret\",\n \"or 500 with Reason ServerTimeout indicating a unique name could\",\n \"to pull the image from a registry If the image\",\n \"multiple errors. Optional. Examples: \\\"name\\\" - the field \\\"name\\\" on\",\n \"Docker image the Strategy should produce. type: complex contains: image_labels:\",\n \"is committed, before it is pushed to a registry. type:\",\n \"the Kubelet will send a hard termination signal (SIGKILL) to\",\n \"treat these values as opaque and passed unmodified back to\",\n \"key of the secret to select from. Must be a\",\n \"a separate runtime image.' type: complex contains: api_version: description: -\",\n \"certificate used to authenticate with the API. type: path context:\",\n \"with the same name then the last one in the\",\n \"describes the minimum amount of compute resources required. If Requests\",\n \"have `/bin/sh`, or if you do not want to use\",\n \"holds the parameters to the Custom build strategy type: complex\",\n \"needs \\\"delete\\\" permission of the owner, otherwise 422 (Unprocessable Entity)\",\n \"key must be defined type: bool expose_docker_socket: description: - exposeDockerSocket\",\n \"of your Dockerfile stanzas. The Dockerfile source type may be\",\n \"is explicitly specified, otherwise to an implementation-defined value. type: complex\",\n \"policy 'SkipLayers' will avoid commiting new layers in between each\",\n \"that triggered the event) or if no container name is\",\n \"modified based on your strategy base image and additional ENV\",\n \"type: complex contains: http_proxy: description: - httpProxy is a proxy\",\n \"Suggested HTTP return code for this status, 0 if not\",\n \"contains: resource_version: description: - String that identifies the server's internal\",\n \"set in create or update request. type: str creation_timestamp: description:\",\n \"it is empty, it may not be modified further by\",\n \"Populated by the system when a graceful deletion is requested.\",\n \"definition of the object. Mutually exclusive with I(resource_definition). type: path\",\n \"contains: _from: description: - from is reference to an DockerImage,\",\n \"the directory will be copied, but the directory itself will\",\n \"requirements: - openshift == 0.3.3 ''' EXAMPLES = ''' '''\",\n \"to build. type: str uri: description: - uri points to\",\n \"- Pending is a list of initializers that must execute\",\n \"I(name) or I(type) attribute, a strategic merge is performed, where\",\n \"description: - Provide a password for connecting to the API.\",\n \"type: list contains: str command: description: - command is the\",\n \"relative to the root of the context (contextDir). type: str\",\n \"There cannot be more than one managing controller. type: list\",\n \"single file. contextDir may be used when an archive is\",\n \"--no-cache=true flag type: bool pull_secret: description: - pullSecret is the\",\n \"git - in those cases the Git repo will have\",\n \"specific commit type: complex contains: email: description: - email of\",\n \"referent. type: str last_triggered_image_id: description: - lastTriggeredImageID is used internally\",\n \"will avoid commiting new layers in between each image step,\",\n \"suffix required to make the value unique on the server.\",\n \"a resource_version representing a consistent version of the listed objects.\",\n \"belongs to. This is used to distinguish resources with same\",\n \"requests. type: str generic: description: - generic contains the parameters\",\n \"type: bool incremental: description: - incremental flag forces the Source\",\n \"name: description: - Name of the referent. type: str docker_strategy:\",\n \"allowed to use secrets referenced by the ServiceAccount type: str\",\n \"contains: _from: description: - from is a reference to an\",\n \"- The group attribute of the resource associated with the\",\n \"Source scripts type: str type: description: - type is the\",\n \"haven't explicitly asked to observe uninitialized objects. When an object\",\n \"error, as named by its JSON serialization. May include dot\",\n \"description: - Name of the referent. type: str uid: description:\",\n \"with I(resource_definition). type: path ssl_ca_cert: description: - Path to a\",\n \"the API. type: path context: description: - The name of\",\n \"(SIGKILL) to the container and after cleanup, remove the pod\",\n \"resources: description: - resources computes resource requirements to execute the\",\n \"pod created by this build. The pod will be allowed\",\n \"update request. type: str creation_timestamp: description: - CreationTimestamp is a\",\n \"will be added after the FROM, but before the rest\",\n \"3339 date and time at which this resource will be\",\n \"Populated by the system. Read-only. type: str reason: description: -\",\n \"that can be used to organize and categorize (scope and\",\n \"may set or modify this list. Once it is empty,\",\n \"describes how the new build created from this build configuration\",\n \"with the API. type: path state: description: - Determines if\",\n \"I(type) are merged. To force the replacement of lists, set\",\n \"will also be combined with a unique suffix. The provided\",\n \"the build unless Namespace is specified. type: complex contains: api_version:\",\n \"the minimum amount of compute resources required. If Requests is\",\n \"separate runtime image.' type: complex contains: api_version: description: - API\",\n \"in different clusters. This field is not set anywhere right\",\n \"of the referent. type: str docker_strategy: description: - dockerStrategy holds\",\n \"APIVersion defines the versioned schema of this representation of an\",\n \"statement, such as desiredState.manifest.containers[2]. For example, if the object reference\",\n \"piece of an object instead of an entire object, this\",\n \"make the value unique on the server. If this field\",\n \"complex contains: name: description: - Name of the referent. type:\",\n \"buildArgs contains build arguments that will be resolved in the\",\n \"to C(absent). A patch operation results in merging lists and\",\n \"finalizers: description: - Must be empty before the object is\",\n \"populate this list with the current set of initializers. Only\",\n \"be run with `/bin/sh -ic`. It may not be specified\",\n \"be copied into the build directory, where the Dockerfile is\",\n \"cannot be deleted from the key-value store until this reference\",\n \"- The name attribute of the resource associated with the\",\n \"description: - UID of the referent. type: str scripts: description:\",\n \"parameters to the Source build strategy. type: complex contains: _from:\",\n \"only if Name is not specified. type: str generation: description:\",\n \"bool force_pull: description: - forcePull describes if the controller should\",\n \"distinguish resources with same name and namespace in different clusters.\",\n \"the time in this field. Once set, this value may\",\n \"description: - commit is the commit hash identifying a specific\",\n \"- email of the source control user type: str name:\",\n \"an object should be created, patched, or deleted. When set\",\n \"objects are required to be scoped to a namespace -\",\n \"the sub-directory where the source code for the application exists.\",\n \"like: \\\"spec.containers{name}\\\" (where \\\"name\\\" refers to the name of the\",\n \"resource associated with the status StatusReason. On some operations may\",\n \"set of values. If a list contains a dictionary with\",\n \"pending: description: - Pending is a list of initializers that\",\n \"type: str metadata: description: - Standard list metadata. type: complex\",\n \"failedBuildsHistoryLimit is the number of old failed builds to retain.\",\n \"Secret or it's key must be defined type: bool jenkinsfile:\",\n \"type: int kind: description: - Kind is a string value\",\n \"installed. The building of the application is still done in\",\n \"except OpenShiftAnsibleException as exc: # The helper failed to init,\",\n \"opaque and passed unmodified back to the server. They may\",\n \"in the list is used. type: list contains: name: description:\",\n \"zero length. For the Docker build strategy, these will be\",\n \"settable by a client. The resource is expected to be\",\n \"object will be garbage collected. If this object is managed\",\n \"exclusive with I(resource_definition). type: path ssl_ca_cert: description: - Path to\",\n \"object. If ALL objects in the list have been deleted,\",\n \"event) or if no container name is specified \\\"spec.containers[2]\\\" (container\",\n \"is the information from the source for a specific repo\",\n \"Populated by the system. Read-only. type: str self_link: description: -\",\n \"the source will depend on the type of build to\",\n \"to determine when objects have changed. May be used for\",\n \"a URL representing this object. Populated by the system. Read-only.\",\n \"a container within a pod, this would take on a\",\n \"current resource \\\"items[0].name\\\" - the field \\\"name\\\" on the first\",\n \"\\\"Failure\\\".' type: str labels: description: - Map of string keys\",\n \"Name of the referent. type: str namespace: description: - Namespace\",\n \"environment variables you want to pass into a build pipeline.\",\n \"type: complex contains: image_labels: description: - imageLabels define a list\",\n \"with any values, default build nodeselector values are ignored. type:\",\n \"and apiserver is going to ignore it if set in\",\n \"When an object is created, the system will populate this\",\n \"with a matching I(name_) or I(type) are merged. To force\",\n \"description: - Annotations is an unstructured key value map stored\",\n \"description: - secret used to validate requests. type: str github:\",\n \"objects in the list have been deleted, this object will\",\n \"should pull the images from registry prior to building. type:\",\n \"unmodified back to the server. Populated by the system. Read-only.\",\n \"system, that the build may be active on a node\",\n \"block_owner_deletion: description: - If true, AND if the owner has\",\n \"to an empty map or a map with any values,\",\n \"default build nodeselector values for the cluster. If set to\",\n \"a client to request the generation of an appropriate name\",\n \"images for the builder or only pull if it is\",\n \"control user type: str commit: description: - commit is the\",\n \"be executed with the --no-cache=true flag type: bool pull_secret: description:\",\n \"updated. In CamelCase. type: str message: description: - A human-readable\",\n \"A sequence number representing a specific generation of the desired\",\n \"Must be a C_IDENTIFIER. type: str value: description: - 'Variable\",\n \"a certificate used to authenticate with the API. type: path\",\n \"- Namespace defines the space within each name must be\",\n \"the proxy should not be used type: str ref: description:\",\n \"The resource is expected to be deleted (no longer visible\",\n \"Once it is empty, it may not be modified further\",\n \"indicates that the provided binary input should be considered a\",\n \"dictionaries, with lists being merged into a unique set of\",\n \"their destinations that will be used only for the build.\",\n \"- command is the command to run. It may not\",\n \"canonical representation. Not all objects are required to be scoped\",\n \"to be used to provide source for the build type:\",\n \"description: - secret used to validate requests. type: str gitlab:\",\n \"- String that identifies the server's internal version of this\",\n \"control user type: str name: description: - name of the\",\n \"description: - An opaque value that represents the internal version\",\n \"a build pod gets scheduled in the system, that the\",\n \"str secret_key_ref: description: - Selects a key of a secret\",\n \"then the last one in the list is used. type:\",\n \"bool kind: description: - Kind of the referent. type: str\",\n \"items is a list of build configs type: list contains:\",\n \"client. The resource is expected to be deleted (no longer\",\n \"code: description: - Suggested HTTP return code for this status,\",\n \"by the system. Read-only. type: int initializers: description: - An\",\n \"If Requests is omitted for a container, it defaults to\",\n \"unrecognized values. type: str items: description: - items is a\",\n \"This allows to have buildable sources in directory other than\",\n \"Standard list metadata. type: complex contains: resource_version: description: - String\",\n \"it. type: str status: description: - 'Status of the operation.\",\n \"truncated by the length of the suffix required to make\",\n \"of lists, set the I(force) option to C(True). default: present\",\n \"container_name: description: - 'Container name: required for volumes, optional for\",\n \"field is a list of initializers that have not yet\",\n \"''' RETURN = ''' api_version: type: string description: Requested API\",\n \"same as 'SkipLayers' but simply warns if compatibility cannot be\",\n \"are applied to the resulting image. If there are multiple\",\n \"type: str name: description: - name of the source control\",\n \"be resolved in the Dockerfile. See type: list contains: name:\",\n \"with the reason. Each reason may define its own extended\",\n \"the owner has the \\\"foregroundDeletion\\\" finalizer, then the owner cannot\",\n \"- status holds any relevant information about a build config\",\n \"the time allotted, and the client should retry (optionally after\",\n \"the Retry-After header). Applied only if Name is not specified.\",\n \"or \\\"Failure\\\".' type: str labels: description: - Map of string\",\n \"different than the name passed. This value will also be\",\n \"dictionary with a I(name) or I(type) attribute, a strategic merge\",\n \"this list. Once it is empty, it may not be\",\n \"is a selector which must be true for the build\",\n \"this controller, with the controller field set to true. There\",\n \"which the object belongs to. This is used to distinguish\",\n \"server will NOT return a 409 - instead, it will\",\n \"these values as opaque and passed unmodified back to the\",\n \"str build_api_version: description: - buildAPIVersion is the requested API version\",\n \"as input on STDIN. type: complex contains: as_file: description: -\",\n \"exists. This allows to have buildable sources in directory other\",\n \"images) from inside the Docker container. type: bool force_pull: description:\",\n \"docker build must be executed with the --no-cache=true flag type:\",\n \"the cause of the error. This field may be presented\",\n \"build output image is committed, before it is pushed to\",\n \"binary as standard input. This filename may not contain slashes\",\n \"description: - Name of the referent. type: str docker_strategy: description:\",\n \"type: str labels: description: - Map of string keys and\",\n \"strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is the number\",\n \"when deletionTimestamp is also set. May only be shortened. Read-only.\",\n \"all successful builds are retained. type: int triggers: description: -\",\n \"parameters for an ImageChange type of trigger type: complex contains:\",\n \"resources, defaults to \\\"1\\\" type: str resource: description: - 'Required:\",\n \"an identifier for the responsible component that will remove the\",\n \"header). Applied only if Name is not specified. type: str\",\n \"of build strategy. type: str successful_builds_history_limit: description: - successfulBuildsHistoryLimit is\",\n \"is 'None' which means the final build image will be\",\n \"enabled in the shell. E.g., in the Ruby image, this\",\n \"nodeSelector is a selector which must be true for the\",\n \"a pod is deleted in 30 seconds. The Kubelet will\",\n \"of a context found in the Kubernetes config file. debug:\",\n \"an ImageStreamTag, the ImageStreamTag will be looked for in the\",\n \"performed, where individual elements with a matching I(name_) or I(type)\",\n \"- Number of seconds allowed for this object to gracefully\",\n \"2.3.0 author: OpenShift (@openshift) options: api_key: description: - Token used\",\n \"- Token used to connect to the API. cert_file: description:\",\n \"information about git build source type: complex contains: http_proxy: description:\",\n \"connecting to the API. verify_ssl: description: - Whether or not\",\n \"status.hostIP, status.podIP.' type: complex contains: api_version: description: - Version of\",\n \"Provide a URL for acessing the Kubernetes API. key_file: description:\",\n \"the image doesn't have `/bin/sh`, or if you do not\",\n \"lists being merged into a unique set of values. If\",\n \"an entry in this list will point to this controller,\",\n \"build hook, for example for running unit tests with `rake\",\n \"A human-readable description of the cause of the error. This\",\n \"defaults to \\\"v1\\\". type: str field_path: description: - Path of\",\n \"the server will NOT return a 409 - instead, it\",\n \"false type: bool host: description: - Provide a URL for\",\n \"description: - secret used to validate requests. type: str image_change:\",\n \"the key-value store until this reference is removed. Defaults to\",\n \"error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as exc: module.fail_json(msg=\\\"Module\",\n \"should be pulled type: complex contains: api_version: description: - API\",\n \"the deletion. type: complex contains: api_version: description: - APIVersion defines\",\n \"str kind: description: - The kind attribute of the resource\",\n \"in conjunction with I(username). resource_definition: description: - Provide the YAML\",\n \"from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list\",\n \"collected. If this object is managed by a controller, then\",\n \"stanzas from your strategy environment will be added after the\",\n \"the type of build trigger type: str status: description: -\",\n \"build strategy. This strategy is in tech preview. type: complex\",\n \"build created from this build configuration will be scheduled for\",\n \"Cannot be used if value is not empty. type: complex\",\n \"of secrets and their destinations that will be used only\",\n \"signal (SIGKILL) to the container and after cleanup, remove the\",\n \"the server when a graceful deletion is requested by the\",\n \"build type: list contains: _from: description: - from is a\",\n \"(@openshift) options: api_key: description: - Token used to connect to\",\n \"- If set to C(True), and I(state) is C(present), an\",\n \"- absent username: description: - Provide a username for connecting\",\n \"external tools to store and retrieve arbitrary metadata. They are\",\n \"str generic: description: - generic contains the parameters for a\",\n \"not yet acted on this object. If nil or empty,\",\n \"specifies a list of source/destination pairs that will be copied\",\n \"you want to use in your build. type: complex contains:\",\n \"resources. Populated by the system. Read-only. Value must be treated\",\n \"deleted from the key-value store until this reference is removed.\",\n \"vars' type: str divisor: description: - Specifies the output format\",\n \"Not all StatusReasons may provide detailed causes. type: list contains:\",\n \"set of images to be used to provide source for\",\n \"httpProxy is a proxy used to reach the git repository\",\n \"Reason ServerTimeout indicating a unique name could not be found\",\n \"for example for running unit tests with `rake test`. If\",\n \"'Source', 'Dockerfile', 'Binary', or 'Images' type: str run_policy: description: -\",\n \"without unneeded dependencies installed. The building of the application is\",\n \"of initializers that have not yet acted on this object.\",\n \"'v1') except OpenShiftAnsibleException as exc: # The helper failed to\",\n \"complex contains: image_labels: description: - imageLabels define a list of\",\n \"description: - name of the source control user type: str\",\n \"key: description: - The key to select. type: str name:\",\n \"STDIN. type: complex contains: as_file: description: - asFile indicates that\",\n \"for remote repository, where the\\\\ \\\\ data's key represent the\",\n \"This field is optional and the data returned is not\",\n \"building. type: bool incremental: description: - incremental flag forces the\",\n \"that other clients can observe the deletion. type: complex contains:\",\n \"- Suggested HTTP return code for this status, 0 if\",\n \"objects depended by this object. If ALL objects in the\",\n \"name automatically. Name is primarily intended for creation idempotence and\",\n \"no container name is specified \\\"spec.containers[2]\\\" (container with index 2\",\n \"guaranteed to conform to any schema except that defined by\",\n \"string keys and values that can be used to organize\",\n \"enforces some system invariant at object creation time. This field\",\n \"when a graceful deletion is requested. Read-only. type: complex contains:\",\n \"to an implementation-defined value. type: complex contains: str, str revision:\",\n \"contains: completion_deadline_seconds: description: - completionDeadlineSeconds is an optional duration in\",\n \"pulled the resulting image will be used in the FROM\",\n \"serialization. May include dot and postfix notation for nested attributes.\",\n \"is no information available. type: str group: description: - The\",\n \"name of the label. It must have non-zero length. type:\",\n \"value will be used to look up a Docker image\",\n \"Kind is a string value representing the REST resource this\",\n \"spec holds all the input necessary to produce a new\",\n \"to select from. Must be a valid secret key. type:\",\n \"this list can only be removed. type: list contains: str\",\n \"and requests (limits.cpu, limits.memory, requests.cpu and requests.memory) are currently supported.'\",\n \"apiserver is going to ignore it if set in create\",\n \"dependencies installed. The building of the application is still done\",\n \"- Name must be unique within a namespace. Is required\",\n \"'Selects a resource of the container: only resources limits and\",\n \"type: bool secret: description: - secret used to validate requests.\",\n \"the builder or only pull if it is not present\",\n \"_from: description: - from is a reference to an ImageStreamTag,\",\n \"can only be set to true for GenericWebHook. type: bool\",\n \"it is pushed to a registry. type: complex contains: args:\",\n \"image are placed. type: str source_path: description: - sourcePath is\",\n \"input. This filename may not contain slashes or be '..'\",\n \"not to verify the API server's SSL certificates. type: bool\",\n \"type: dict src: description: - Provide a path to a\",\n \"are ignored. type: complex contains: str, str output: description: -\",\n \"- secrets represents a list of secrets and their destinations\",\n \"definition. Cannot be updated. type: str namespace: description: - Namespace\",\n \"use. Deprecated: This feature will be removed in a future\",\n \"the build. type: list contains: destination_dir: description: - destinationDir is\",\n \"more than one managing controller. type: list contains: api_version: description:\",\n \"be used to configure the pipeline relative to the root\",\n \"Docker images from the private Docker registries type: complex contains:\",\n \"ID for build type: str type: description: - type is\",\n \"description: - status holds any relevant information about a build\",\n \"of the referent. type: str build_args: description: - buildArgs contains\",\n \"list of domains for which the proxy should not be\",\n \"str reason: description: - A machine-readable description of the cause\",\n \"to run type: str images: description: - images describes a\",\n \"HTTP status code but does not override it. type: str\",\n \"registry prior to building. type: bool image_optimization_policy: description: - imageOptimizationPolicy\",\n \"raise the error. raise Exception(exc.message) try: module.execute_module() except OpenShiftAnsibleException as\",\n \"archive as described above. For Source and Docker builds, if\",\n \"Name of the referent. type: str docker_strategy: description: - dockerStrategy\",\n \"initializers that have not yet acted on this object. If\",\n \"you do not want to use a shell. In all\",\n \"values. type: str kind: description: - Kind is a string\",\n \"metadata for BuildConfig. type: complex contains: annotations: description: - Annotations\",\n \"strategy type: complex contains: _from: description: - from is reference\",\n \"proxy used to reach the git repository over http type:\",\n \"then an entry in this list will point to this\",\n \"- Jenkinsfile defines the optional raw contents of a Jenkinsfile\",\n \"equivalent to an image created by the Docker build API.\",\n \"the resulting image. If there are multiple labels with the\",\n \"the files from the secret should be available for the\",\n \"image from a registry If the image is pulled from\",\n \"image. If there are multiple labels with the same name\",\n \"time spent building the image. The default policy is 'None'\",\n \"build source, may be one of 'Source', 'Dockerfile', 'Binary', or\",\n \"type: str result: description: - If result is set with\",\n \"image, this is necessary to make `ruby`, `bundle` and other\",\n \"a proxy used to reach the git repository over https\",\n \"modify this list. Once it is empty, it may not\",\n \"is needed to support CentOS and RHEL images that use\",\n \"to store and retrieve arbitrary metadata. They are not queryable\",\n \"if set in create or update request. type: str creation_timestamp:\",\n \"expanded, regardless of whether the variable exists or not. Defaults\",\n \"build pipeline. type: list contains: name: description: - Name of\",\n \"is specified, the From image from the build strategy will\",\n \"a build configuration. type: complex contains: api_version: description: - API\",\n \"pulling the Docker images from the private Docker registries type:\",\n \"the ServiceAccount type: str source: description: - source describes the\",\n \"Defaults to \\\"\\\".' type: str value_from: description: - Source for\",\n \"of build_configs. List operations provide a snapshot read of the\",\n \"exist, or patched, if parameter values differ from the existing\",\n \"read of the underlying objects, returning a resource_version representing a\",\n \"from the OpenShift registry, this field does not need to\",\n \"of the file or directory inside the image to copy\",\n \"- from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage\",\n \"The binary is generally assumed to be a tar, gzipped\",\n \"command is the command to run. It may not be\",\n \"not been requested. Populated by the system when a graceful\",\n \"commit is the commit hash identifying a specific commit type:\",\n \"itself will not be created at the destination. type: str\",\n \"may be truncated by the length of the suffix required\",\n \"builds, if binary.asFile is set the build will receive a\",\n \"to building. type: bool incremental: description: - incremental flag forces\",\n \"{} deletion_grace_period_seconds: description: - Number of seconds allowed for this\",\n \"be unique within a namespace. Is required when creating resources,\",\n \"not queryable and should be preserved when modifying objects. type:\",\n \"list of arguments that are provided to either Command, Script\",\n \"context_dir: description: - contextDir specifies the sub-directory where the source\",\n \"be run. type: list contains: str command: description: - command\",\n \"referent. type: str post_commit: description: - postCommit is a build\",\n \"import OpenShiftAnsibleModule, OpenShiftAnsibleException DOCUMENTATION = ''' module: openshift_v1_build_config_list short_description: OpenShift\",\n \"the path of the Dockerfile that will be used to\",\n \"the file or directory inside the image to copy to\",\n \"a CA certificate used to authenticate with the API. type:\",\n \"type: str optional: description: - Specify whether the Secret or\",\n \"objects. When an object is created, the system will populate\",\n \"this is assumed to be an archive as described above.\",\n \"to C(True), and I(state) is C(present), an existing object will\",\n \"password: description: - Provide a password for connecting to the\",\n \"scoped to a namespace - the value of this field\",\n \"of the resource associated with the status StatusReason. On some\",\n \"str resource_field_ref: description: - 'Selects a resource of the container:\",\n \"source_strategy: description: - sourceStrategy holds the parameters to the Source\",\n \"the custom builder type: str env: description: - env contains\",\n \"client build creation. type: list contains: bitbucket: description: - BitbucketWebHook\",\n \"this operation. type: str metadata: description: - Standard list metadata.\",\n \"- Name of the referent. type: str jenkins_pipeline_strategy: description: -\",\n \"is assumed to be an archive as described above. For\",\n \"name defines the name of the label. It must have\",\n \"by the system. Read-only. type: str uid: description: - UID\",\n \"This field is set by the server when a graceful\",\n \"the controller should configure the build pod to always pull\",\n \"- Selects a key of a secret in the pod's\",\n \"relative to the root of the context (contextDir). If both\",\n \"does not have `/bin/sh`, use Command and/or Args. The `-i`\",\n \"this is the build context and an optional Dockerfile may\",\n \"own extended details. This field is optional and the data\",\n \"the runtime image. sourcePath can be a file or directory.\",\n \"applied to the resulting image. If there are multiple labels\",\n \"pod is deleted in 30 seconds. The Kubelet will react\",\n \"a list of source and destination paths to copy from\",\n \"This is optional. type: complex contains: git: description: - Git\",\n \"from the API. In the presence of network partitions, this\",\n \"will be returned. type: bool controller: description: - If true,\",\n \"env: description: - env contains additional environment variables you want\",\n \"a user needs \\\"delete\\\" permission of the owner, otherwise 422\",\n \"more than once in an array of causes due to\",\n \"to terminate the build; value must be positive integer type:\",\n \"to push the output of this build to. Kind must\",\n \"must be defined type: bool force_pull: description: - forcePull describes\",\n \"contains: {} deletion_grace_period_seconds: description: - Number of seconds allowed for\",\n \"be empty. Must be a DNS_LABEL. Cannot be updated. type:\",\n \"the build pod to fit on a node If nil,\",\n \"the referent. type: str name: description: - Name of the\",\n \"to the name of the container that triggered the event)\",\n \"and extract it as the source. The custom strategy receives\",\n \"to all clients. type: list contains: name: description: - name\",\n \"\\\\ are: ssh-privatekey.\\\" type: complex contains: name: description: - Name\",\n \"Read-only. type: complex contains: {} finalizers: description: - Must be\",\n \"this value is empty there is no information available. type:\",\n \"complex contains: author: description: - author is the author of\",\n \"and namespace in different clusters. This field is not set\",\n \"paths to copy from the image. type: list contains: destination_dir:\",\n \"str metadata: description: - Standard list metadata. type: complex contains:\",\n \"will be removed from the system. Only set when deletionTimestamp\",\n \"build. type: int kind: description: - Kind is a string\",\n \"str creation_timestamp: description: - CreationTimestamp is a timestamp representing the\",\n \"some well-defined way of referencing a part of an object.'\",\n \"If no triggers are defined, a new build can only\",\n \"does not override it. type: str status: description: - 'Status\",\n \"of compute resources allowed. type: complex contains: str, str requests:\",\n \"of the referent. type: str secrets: description: - secrets is\",\n \"the system. Read-only. Null for lists. type: complex contains: {}\",\n \"the build source, may be one of 'Source', 'Dockerfile', 'Binary',\",\n \"or 'Images' type: str run_policy: description: - RunPolicy describes how\",\n \"value defines the literal value of the label. type: str\",\n \"- The kind attribute of the resource associated with the\",\n \"your strategy base image and additional ENV stanzas from your\",\n \"clients. type: list contains: name: description: - name of the\",\n \"the ImageStreamTag will be looked for in the namespace of\",\n \"be allowed to use secrets referenced by the ServiceAccount type:\",\n \"the context (contextDir). If both JenkinsfilePath & Jenkinsfile are both\",\n \"made, if any. type: str uid: description: - UID of\",\n \"- items is a list of build configs type: list\",\n \"request. type: str creation_timestamp: description: - CreationTimestamp is a timestamp\",\n \"- Specify whether the ConfigMap or it's key must be\",\n \"system will populate this list with the current set of\",\n \"contains: allow_env: description: - allowEnv determines whether the webhook can\"\n]","isConversational":false}}},{"rowIdx":199,"cells":{"ngram":{"kind":"list like","value":["element break else: logger.info(\"region_code NOT found\") return_info = \"Region code","'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*'","str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' },","os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are not allowed, get out!\" if","region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try: json_data = get_json()","region_code: logger.info(\"region_code found\") http_code = 200 return_info_final['request'] = { \"request_status\":","<reponame>groorj/cloud-regions import json import logging import os import inspect import","logger.info(\"region_code found\") http_code = 200 return_info_final['request'] = { \"request_status\": \"Success\"","getting Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\"json_data:","code NOT found.\", \"http_error_code\": http_code } return create_response_new(http_code, return_info_final) #","validate access def validate_access(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED:","= \"You are not allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] ==","file def get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try: response =","= event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try: json_data = get_json() except","logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access","import logging import os import inspect import urllib import urllib.request","access def validate_access(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\",","{ \"request_status\": \"Fail\", \"error_message\": \"Region code NOT found.\", \"http_error_code\": http_code","except ValueError as err: return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\":","logger.debug(\"json_data: [%s]\", json_data) http_code = 200 return_info_final['request'] = { \"request_status\":","logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context):","== 'true': logger.info(\"Restricted access is enabled\") logger.info(\"Value for header [%s]","\"error_message\": \"Region code NOT found.\", \"http_error_code\": http_code } return create_response_new(http_code,","logger.info(\"Restricted access is enabled\") logger.info(\"Value for header [%s] is: [%s]\",","create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code),","'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json file","are not allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted","try: validate_access(event, context) except ValueError as err: return_info_final['request'] = {","\"You are not allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true':","region info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try: json_data","[%s]\", element['code'], region_code) if element['code'] == region_code: logger.info(\"region_code found\") http_code","os import inspect import urllib import urllib.request from urllib.error import","= \"Region code NOT found.\" http_code = 404 return_info_final['request'] =","HTTPError # logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level)","\"Error getting Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final)","access is NOT enabled\") # create response def create_response_new(status_code, message_body):","json.loads(response.read()) return json_data # entry point -> return region info","if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access is enabled\") logger.info(\"Value for","break else: logger.info(\"region_code NOT found\") return_info = \"Region code NOT","logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You","return_info_final['data'] = element break else: logger.info(\"region_code NOT found\") return_info =","# create response def create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name)","function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are","= 500 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Error getting","= json.loads(response.read()) return json_data # entry point -> return region","[%s]\", json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data)) for element in json_data['data']:","import inspect import urllib import urllib.request from urllib.error import HTTPError","json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data)) for element in json_data['data']: #","{ 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin':","err: # http_code = err.code http_code = 500 return_info_final['request'] =","} return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code = 200","NOT found.\" http_code = 404 return_info_final['request'] = { \"request_status\": \"Fail\",","inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are not allowed,","import HTTPError # logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL'])","} # download json file def get_json(): logger.debug(\"Inside function: [%s]\",","context) except ValueError as err: return_info_final['request'] = { \"request_status\": \"Fail\",","try: json_data = get_json() except HTTPError as err: # http_code","else: logger.info(\"region_code NOT found\") return_info = \"Region code NOT found.\"","provided is valid\") else: logger.info(\"Restricted access is NOT enabled\") #","# download json file def get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name)","} return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else: logger.info(\"region_code","[%s]\", region_code) try: json_data = get_json() except HTTPError as err:","error_message) http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\"Key provided","logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the","create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code = 200 return_info_final['request'] =","json file def get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try: response","context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message =","is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided","not valid\") logger.debug(\"Error: [%s]\", error_message) http_code = 403 raise ValueError(http_code,","json_data['data']: # logger.debug(\"code: [%s] && region_code: [%s]\", element['code'], region_code) if","entry point -> return region info def get_all_regions_info(event, context): logger.debug(\"Inside","HTTPError as err: # http_code = err.code http_code = 500","raise ValueError(http_code, error_message) else: logger.info(\"Key provided is valid\") else: logger.info(\"Restricted","found\") http_code = 200 return_info_final['request'] = { \"request_status\": \"Success\" }","NOT found.\", \"http_error_code\": http_code } return create_response_new(http_code, return_info_final) # entry","out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access is enabled\") logger.info(\"Value","= 200 return_info_final['request'] = { \"request_status\": \"Success\" } return_info_final['info'] =","response def create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return {","err.args[0] } return create_response_new(err.args[0], return_info_final) # get region info region_code","http_code = 500 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Error","return region info def get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name)","\"request_status\": \"Fail\", \"error_message\": \"Error getting Regions information.\", \"http_error_code\": err.code }","return create_response_new(err.args[0], return_info_final) # get region info region_code = event['pathParameters']['region_code']","return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code = 200 return_info_final['request']","\"error_message\": err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get","404 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Region code NOT","[%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key","return json_data # entry point -> return region info def","validate_access(event, context) except ValueError as err: return_info_final['request'] = { \"request_status\":","def create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode':","error_message = \"You are not allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED']","create_response_new(err.args[0], return_info_final) # get region info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code:","err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get region","return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else: logger.info(\"region_code NOT","is not valid\") logger.debug(\"Error: [%s]\", error_message) http_code = 403 raise","else: logger.info(\"Restricted access is NOT enabled\") # create response def","event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not valid\") logger.debug(\"Error: [%s]\",","return region info def get_region_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name)","get region info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try:","Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\"json_data:","'true': logger.info(\"Restricted access is enabled\") logger.info(\"Value for header [%s] is:","except HTTPError as err: # catch HTTP error logger.debug(\"HTTP error:","} return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final)","return create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data): [%s]\",","info try: json_data = get_json() except HTTPError as err: #","\"error_message\": \"Error getting Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code,","import os import inspect import urllib import urllib.request from urllib.error","json_data = get_json() except HTTPError as err: # http_code =","= 404 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Region code","http_code = 200 return_info_final['request'] = { \"request_status\": \"Success\" } return_info_final['info']","return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final) #","= 403 raise ValueError(http_code, error_message) else: logger.info(\"Key provided is valid\")","[%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': {","urllib.request from urllib.error import HTTPError # logger logger = logging.getLogger()","\"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return","# entry point -> return region info def get_region_info(event, context):","\"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final)","as err: # http_code = err.code http_code = 500 return_info_final['request']","def get_region_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {}","} return create_response_new(http_code, return_info_final) # entry point -> return region","from urllib.error import HTTPError # logger logger = logging.getLogger() logger_level","function: [%s]\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as","return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Region code NOT found.\",","info def get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final =","found.\" http_code = 404 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\":","to this resource try: validate_access(event, context) except ValueError as err:","return_info_final) # get regions info try: json_data = get_json() except","regions info try: json_data = get_json() except HTTPError as err:","os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access is enabled\") logger.info(\"Value for header","# get regions info try: json_data = get_json() except HTTPError","err: return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0]","import json import logging import os import inspect import urllib","create_response_new(http_code, return_info_final) # entry point -> return region info def","} return create_response_new(err.args[0], return_info_final) # get regions info try: json_data","os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not valid\") logger.debug(\"Error: [%s]\", error_message) http_code","logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\"Inside function:","return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Error getting Regions information.\",","[%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are not","validate_access(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message","} return create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data):","return_info_final) # entry point -> return region info def get_all_regions_info(event,","\"request_status\": \"Fail\", \"error_message\": \"Region code NOT found.\", \"http_error_code\": http_code }","# logger.debug(\"code: [%s] && region_code: [%s]\", element['code'], region_code) if element['code']","= { \"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] =","= element break else: logger.info(\"region_code NOT found\") return_info = \"Region","logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are not allowed, get","logger.info(\"Restricted access is NOT enabled\") # create response def create_response_new(status_code,","create response def create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return","return_info = \"Region code NOT found.\" http_code = 404 return_info_final['request']","\"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get region info","else: logger.info(\"Key provided is valid\") else: logger.info(\"Restricted access is NOT","urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP error logger.debug(\"HTTP","{ 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json","= {} # validate the access to this resource try:","\"http_error_code\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code","found\") return_info = \"Region code NOT found.\" http_code = 404","'Access-Control-Allow-Origin': '*' }, } # download json file def get_json():","return_info_final['request'] = { \"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data']","err.code } return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code =","information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\",","logger.debug(\"type(json_data): [%s]\", type(json_data)) for element in json_data['data']: # logger.debug(\"code: [%s]","-> return region info def get_region_info(event, context): logger.debug(\"Inside function: [%s]\",","this resource try: validate_access(event, context) except ValueError as err: return_info_final['request']","return_info_final) # logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data)) for","'*' }, } # download json file def get_json(): logger.debug(\"Inside","[%s]\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err:","500 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Error getting Regions","err: # catch HTTP error logger.debug(\"HTTP error: [%s]\", err) raise","for header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] !=","\"Fail\", \"error_message\": \"Error getting Regions information.\", \"http_error_code\": err.code } return","return create_response_new(http_code, return_info_final) # entry point -> return region info","# catch HTTP error logger.debug(\"HTTP error: [%s]\", err) raise json_data","message_body): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body':","logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body),","type(json_data)) for element in json_data['data']: # logger.debug(\"code: [%s] && region_code:","403 raise ValueError(http_code, error_message) else: logger.info(\"Key provided is valid\") else:","[%s]\", error_message) http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\"Key","[%s]\", type(json_data)) for element in json_data['data']: # logger.debug(\"code: [%s] &&","return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json',","catch HTTP error logger.debug(\"HTTP error: [%s]\", err) raise json_data =","event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not valid\")","region_code) if element['code'] == region_code: logger.info(\"region_code found\") http_code = 200","region info def get_region_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final","= { \"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0] } return","create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data))","} return create_response_new(err.args[0], return_info_final) # get region info region_code =","= get_json() except HTTPError as err: # http_code = err.code","is NOT enabled\") # create response def create_response_new(status_code, message_body): logger.debug(\"Inside","is enabled\") logger.info(\"Value for header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']])","except HTTPError as err: # http_code = err.code http_code =","found.\", \"http_error_code\": http_code } return create_response_new(http_code, return_info_final) # entry point","return_info_final) logger.debug(\"json_data: [%s]\", json_data) http_code = 200 return_info_final['request'] = {","get_json() except HTTPError as err: # http_code = err.code http_code","allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access is","return create_response_new(err.args[0], return_info_final) # get regions info try: json_data =","if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not valid\") logger.debug(\"Error:","response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP","json_data['info'] return_info_final['data'] = element break else: logger.info(\"region_code NOT found\") return_info","in json_data['data']: # logger.debug(\"code: [%s] && region_code: [%s]\", element['code'], region_code)","\"http_error_code\": http_code } return create_response_new(http_code, return_info_final) # entry point ->","access is enabled\") logger.info(\"Value for header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'],","= logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def","validate the access to this resource try: validate_access(event, context) except","the access to this resource try: validate_access(event, context) except ValueError","= logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\"Inside","return_info_final = {} # validate the access to this resource","200 return_info_final['request'] = { \"request_status\": \"Success\" } return_info_final['info'] = json_data['info']","{ \"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data']","logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data)) for element in","# logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) #","def get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {}","info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try: json_data =","NOT enabled\") # create response def create_response_new(status_code, message_body): logger.debug(\"Inside function:","inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access to this","get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} #","[%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is","\"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) #","import urllib.request from urllib.error import HTTPError # logger logger =","http_code } return create_response_new(http_code, return_info_final) # entry point -> return","# http_code = err.code http_code = 500 return_info_final['request'] = {","json_data = json.loads(response.read()) return json_data # entry point -> return","'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, }","urllib import urllib.request from urllib.error import HTTPError # logger logger","\"Region code NOT found.\" http_code = 404 return_info_final['request'] = {","get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access is enabled\")","is valid\") else: logger.info(\"Restricted access is NOT enabled\") # create","get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except","# logger.debug(\"type(json_data): [%s]\", type(json_data)) for element in json_data['data']: # logger.debug(\"code:","\"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code,","# validate access def validate_access(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name)","info def get_region_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final =","logger.debug(\"HTTP error: [%s]\", err) raise json_data = json.loads(response.read()) return json_data","json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } #","valid\") logger.debug(\"Error: [%s]\", error_message) http_code = 403 raise ValueError(http_code, error_message)","logger.info(\"Key provided is valid\") else: logger.info(\"Restricted access is NOT enabled\")","inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: #","def get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL'])","event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code) try: json_data = get_json() except HTTPError","{ \"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element","region_code) try: json_data = get_json() except HTTPError as err: #","json import logging import os import inspect import urllib import","\"Fail\", \"error_message\": \"Region code NOT found.\", \"http_error_code\": http_code } return","= err.code http_code = 500 return_info_final['request'] = { \"request_status\": \"Fail\",","= urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP error","entry point -> return region info def get_region_info(event, context): logger.debug(\"Inside","if element['code'] == region_code: logger.info(\"region_code found\") http_code = 200 return_info_final['request']","[%s]\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \"You are not allowed, get out!\"","http_code = 404 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": \"Region","logger.debug(\"Error: [%s]\", error_message) http_code = 403 raise ValueError(http_code, error_message) else:","get regions info try: json_data = get_json() except HTTPError as","as err: return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\":","return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0] }","logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\"Inside function: [%s]\",","Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\",","# logger.debug(\"json_data: [%s]\", json_data) # logger.debug(\"type(json_data): [%s]\", type(json_data)) for element","not allowed, get out!\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\"Restricted access","http_code = err.code http_code = 500 return_info_final['request'] = { \"request_status\":","logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError","return_info_final) # get region info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\",","http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\"Key provided is","= json_data['info'] return_info_final['data'] = element break else: logger.info(\"region_code NOT found\")","point -> return region info def get_region_info(event, context): logger.debug(\"Inside function:","[%s] && region_code: [%s]\", element['code'], region_code) if element['code'] == region_code:","download json file def get_json(): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) try:","ValueError(http_code, error_message) else: logger.info(\"Key provided is valid\") else: logger.info(\"Restricted access","\"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else:","information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\"json_data: [%s]\", json_data)","err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions","logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate","inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type':","logger.info(\"Value for header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]","HTTP error logger.debug(\"HTTP error: [%s]\", err) raise json_data = json.loads(response.read())","= json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final) # End;","{ \"request_status\": \"Fail\", \"error_message\": err.args[1], \"http_error_code\": err.args[0] } return create_response_new(err.args[0],","function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access","point -> return region info def get_all_regions_info(event, context): logger.debug(\"Inside function:","err.code http_code = 500 return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\":","raise json_data = json.loads(response.read()) return json_data # entry point ->","code NOT found.\" http_code = 404 return_info_final['request'] = { \"request_status\":","urllib.error import HTTPError # logger logger = logging.getLogger() logger_level =","[%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access to","# get region info region_code = event['pathParameters']['region_code'] logger.debug(\"region_code: [%s]\", region_code)","os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not","try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch","logging import os import inspect import urllib import urllib.request from","as err: # catch HTTP error logger.debug(\"HTTP error: [%s]\", err)","get_region_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} #","NOT found\") return_info = \"Region code NOT found.\" http_code =","}, } # download json file def get_json(): logger.debug(\"Inside function:","access to this resource try: validate_access(event, context) except ValueError as","enabled\") logger.info(\"Value for header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if","element in json_data['data']: # logger.debug(\"code: [%s] && region_code: [%s]\", element['code'],","json_data) http_code = 200 return_info_final['request'] = { \"request_status\": \"Success\" }","!= os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\"Key provided is not valid\") logger.debug(\"Error: [%s]\", error_message)","== region_code: logger.info(\"region_code found\") http_code = 200 return_info_final['request'] = {","logger.debug(\"code: [%s] && region_code: [%s]\", element['code'], region_code) if element['code'] ==","err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions info try:","= { \"request_status\": \"Fail\", \"error_message\": \"Region code NOT found.\", \"http_error_code\":","header [%s] is: [%s]\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\"headers\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']:","region info def get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final","logger.info(\"region_code NOT found\") return_info = \"Region code NOT found.\" http_code","json_data # entry point -> return region info def get_region_info(event,","err) raise json_data = json.loads(response.read()) return json_data # entry point","ValueError as err: return_info_final['request'] = { \"request_status\": \"Fail\", \"error_message\": err.args[1],","error: [%s]\", err) raise json_data = json.loads(response.read()) return json_data #","function: [%s]\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers':","create_response_new(err.args[0], return_info_final) # get regions info try: json_data = get_json()","'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json file def","# validate the access to this resource try: validate_access(event, context)","'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download","logger.info(\"Key provided is not valid\") logger.debug(\"Error: [%s]\", error_message) http_code =","logger.debug(\"region_code: [%s]\", region_code) try: json_data = get_json() except HTTPError as","\"http_error_code\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions info","error logger.debug(\"HTTP error: [%s]\", err) raise json_data = json.loads(response.read()) return","\"request_status\": \"Success\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element break","getting Regions information.\", \"http_error_code\": err.code } return create_response_new(http_code, return_info_final) #","element['code'], region_code) if element['code'] == region_code: logger.info(\"region_code found\") http_code =","def validate_access(event, context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) logger.debug(\"RESTRICTED_ACCESS_ENABLED: [%s]\", os.environ['RESTRICTED_ACCESS_ENABLED'])","= { \"request_status\": \"Fail\", \"error_message\": \"Error getting Regions information.\", \"http_error_code\":","context): logger.debug(\"Inside function: [%s]\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate","inspect import urllib import urllib.request from urllib.error import HTTPError #","&& region_code: [%s]\", element['code'], region_code) if element['code'] == region_code: logger.info(\"region_code","for element in json_data['data']: # logger.debug(\"code: [%s] && region_code: [%s]\",","provided is not valid\") logger.debug(\"Error: [%s]\", error_message) http_code = 403","element['code'] == region_code: logger.info(\"region_code found\") http_code = 200 return_info_final['request'] =","valid\") else: logger.info(\"Restricted access is NOT enabled\") # create response","region_code: [%s]\", element['code'], region_code) if element['code'] == region_code: logger.info(\"region_code found\")","# entry point -> return region info def get_all_regions_info(event, context):","enabled\") # create response def create_response_new(status_code, message_body): logger.debug(\"Inside function: [%s]\",","err.code } return create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\", json_data) #","\"http_error_code\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\"json_data: [%s]\", json_data)","[%s]\", json_data) http_code = 200 return_info_final['request'] = { \"request_status\": \"Success\"","{ \"request_status\": \"Fail\", \"error_message\": \"Error getting Regions information.\", \"http_error_code\": err.code","resource try: validate_access(event, context) except ValueError as err: return_info_final['request'] =","{} # validate the access to this resource try: validate_access(event,","import urllib import urllib.request from urllib.error import HTTPError # logger","logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event,","HTTPError as err: # catch HTTP error logger.debug(\"HTTP error: [%s]\",","error_message) else: logger.info(\"Key provided is valid\") else: logger.info(\"Restricted access is","[%s]\", err) raise json_data = json.loads(response.read()) return json_data # entry","-> return region info def get_all_regions_info(event, context): logger.debug(\"Inside function: [%s]\",","\"Region code NOT found.\", \"http_error_code\": http_code } return create_response_new(http_code, return_info_final)"],"string":"[\n \"element break else: logger.info(\\\"region_code NOT found\\\") return_info = \\\"Region code\",\n \"'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*'\",\n \"str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' },\",\n \"os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are not allowed, get out!\\\" if\",\n \"region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data = get_json()\",\n \"region_code: logger.info(\\\"region_code found\\\") http_code = 200 return_info_final['request'] = { \\\"request_status\\\":\",\n \"<reponame>groorj/cloud-regions import json import logging import os import inspect import\",\n \"logger.info(\\\"region_code found\\\") http_code = 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\"\",\n \"getting Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data:\",\n \"code NOT found.\\\", \\\"http_error_code\\\": http_code } return create_response_new(http_code, return_info_final) #\",\n \"validate access def validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED:\",\n \"= \\\"You are not allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] ==\",\n \"file def get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response =\",\n \"= event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data = get_json() except\",\n \"logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access\",\n \"import logging import os import inspect import urllib import urllib.request\",\n \"access def validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\",\",\n \"{ \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT found.\\\", \\\"http_error_code\\\": http_code\",\n \"except ValueError as err: return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\":\",\n \"logger.debug(\\\"json_data: [%s]\\\", json_data) http_code = 200 return_info_final['request'] = { \\\"request_status\\\":\",\n \"logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context):\",\n \"== 'true': logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value for header [%s]\",\n \"\\\"error_message\\\": \\\"Region code NOT found.\\\", \\\"http_error_code\\\": http_code } return create_response_new(http_code,\",\n \"logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value for header [%s] is: [%s]\\\",\",\n \"create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code),\",\n \"'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json file\",\n \"are not allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted\",\n \"try: validate_access(event, context) except ValueError as err: return_info_final['request'] = {\",\n \"\\\"You are not allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true':\",\n \"region info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data\",\n \"[%s]\\\", element['code'], region_code) if element['code'] == region_code: logger.info(\\\"region_code found\\\") http_code\",\n \"os import inspect import urllib import urllib.request from urllib.error import\",\n \"= \\\"Region code NOT found.\\\" http_code = 404 return_info_final['request'] =\",\n \"HTTPError # logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level)\",\n \"\\\"Error getting Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final)\",\n \"access is NOT enabled\\\") # create response def create_response_new(status_code, message_body):\",\n \"json.loads(response.read()) return json_data # entry point -> return region info\",\n \"if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value for\",\n \"break else: logger.info(\\\"region_code NOT found\\\") return_info = \\\"Region code NOT\",\n \"logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You\",\n \"return_info_final['data'] = element break else: logger.info(\\\"region_code NOT found\\\") return_info =\",\n \"# create response def create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name)\",\n \"function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are\",\n \"= 500 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting\",\n \"= json.loads(response.read()) return json_data # entry point -> return region\",\n \"[%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element in json_data['data']:\",\n \"import inspect import urllib import urllib.request from urllib.error import HTTPError\",\n \"json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element in json_data['data']: #\",\n \"{ 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin':\",\n \"err: # http_code = err.code http_code = 500 return_info_final['request'] =\",\n \"} return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code = 200\",\n \"NOT found.\\\" http_code = 404 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\",\",\n \"inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are not allowed,\",\n \"import HTTPError # logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL'])\",\n \"} # download json file def get_json(): logger.debug(\\\"Inside function: [%s]\\\",\",\n \"context) except ValueError as err: return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\",\",\n \"try: json_data = get_json() except HTTPError as err: # http_code\",\n \"else: logger.info(\\\"region_code NOT found\\\") return_info = \\\"Region code NOT found.\\\"\",\n \"provided is valid\\\") else: logger.info(\\\"Restricted access is NOT enabled\\\") #\",\n \"# download json file def get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name)\",\n \"} return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else: logger.info(\\\"region_code\",\n \"[%s]\\\", region_code) try: json_data = get_json() except HTTPError as err:\",\n \"error_message) http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\\\"Key provided\",\n \"logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the\",\n \"create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code = 200 return_info_final['request'] =\",\n \"json file def get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response\",\n \"context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message =\",\n \"is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided\",\n \"not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code = 403 raise ValueError(http_code,\",\n \"json_data['data']: # logger.debug(\\\"code: [%s] && region_code: [%s]\\\", element['code'], region_code) if\",\n \"entry point -> return region info def get_all_regions_info(event, context): logger.debug(\\\"Inside\",\n \"HTTPError as err: # http_code = err.code http_code = 500\",\n \"raise ValueError(http_code, error_message) else: logger.info(\\\"Key provided is valid\\\") else: logger.info(\\\"Restricted\",\n \"found\\\") http_code = 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" }\",\n \"NOT found.\\\", \\\"http_error_code\\\": http_code } return create_response_new(http_code, return_info_final) # entry\",\n \"out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value\",\n \"= 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] =\",\n \"response def create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return {\",\n \"err.args[0] } return create_response_new(err.args[0], return_info_final) # get region info region_code\",\n \"http_code = 500 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error\",\n \"return region info def get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name)\",\n \"\\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions information.\\\", \\\"http_error_code\\\": err.code }\",\n \"return create_response_new(err.args[0], return_info_final) # get region info region_code = event['pathParameters']['region_code']\",\n \"return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code = 200 return_info_final['request']\",\n \"\\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get\",\n \"404 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT\",\n \"[%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key\",\n \"return json_data # entry point -> return region info def\",\n \"validate_access(event, context) except ValueError as err: return_info_final['request'] = { \\\"request_status\\\":\",\n \"def create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode':\",\n \"error_message = \\\"You are not allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED']\",\n \"create_response_new(err.args[0], return_info_final) # get region info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code:\",\n \"err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get region\",\n \"return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else: logger.info(\\\"region_code NOT\",\n \"is not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code = 403 raise\",\n \"else: logger.info(\\\"Restricted access is NOT enabled\\\") # create response def\",\n \"event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not valid\\\") logger.debug(\\\"Error: [%s]\\\",\",\n \"return region info def get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name)\",\n \"get region info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try:\",\n \"Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data:\",\n \"'true': logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value for header [%s] is:\",\n \"except HTTPError as err: # catch HTTP error logger.debug(\\\"HTTP error:\",\n \"} return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final)\",\n \"return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\",\",\n \"info try: json_data = get_json() except HTTPError as err: #\",\n \"\\\"error_message\\\": \\\"Error getting Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code,\",\n \"import os import inspect import urllib import urllib.request from urllib.error\",\n \"json_data = get_json() except HTTPError as err: # http_code =\",\n \"= 404 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code\",\n \"http_code = 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" } return_info_final['info']\",\n \"return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final) #\",\n \"= 403 raise ValueError(http_code, error_message) else: logger.info(\\\"Key provided is valid\\\")\",\n \"[%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': {\",\n \"urllib.request from urllib.error import HTTPError # logger logger = logging.getLogger()\",\n \"\\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return\",\n \"# entry point -> return region info def get_region_info(event, context):\",\n \"\\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final)\",\n \"as err: # http_code = err.code http_code = 500 return_info_final['request']\",\n \"def get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {}\",\n \"} return create_response_new(http_code, return_info_final) # entry point -> return region\",\n \"from urllib.error import HTTPError # logger logger = logging.getLogger() logger_level\",\n \"function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as\",\n \"return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT found.\\\",\",\n \"info def get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final =\",\n \"found.\\\" http_code = 404 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\":\",\n \"to this resource try: validate_access(event, context) except ValueError as err:\",\n \"return_info_final) # get regions info try: json_data = get_json() except\",\n \"regions info try: json_data = get_json() except HTTPError as err:\",\n \"os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access is enabled\\\") logger.info(\\\"Value for header\",\n \"# get regions info try: json_data = get_json() except HTTPError\",\n \"err: return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0]\",\n \"import json import logging import os import inspect import urllib\",\n \"create_response_new(http_code, return_info_final) # entry point -> return region info def\",\n \"} return create_response_new(err.args[0], return_info_final) # get regions info try: json_data\",\n \"os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code\",\n \"logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\\\"Inside function:\",\n \"return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions information.\\\",\",\n \"[%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are not\",\n \"validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message\",\n \"} return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data):\",\n \"return_info_final) # entry point -> return region info def get_all_regions_info(event,\",\n \"\\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT found.\\\", \\\"http_error_code\\\": http_code }\",\n \"# logger.debug(\\\"code: [%s] && region_code: [%s]\\\", element['code'], region_code) if element['code']\",\n \"= { \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] =\",\n \"= element break else: logger.info(\\\"region_code NOT found\\\") return_info = \\\"Region\",\n \"logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are not allowed, get\",\n \"logger.info(\\\"Restricted access is NOT enabled\\\") # create response def create_response_new(status_code,\",\n \"create response def create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return\",\n \"return_info = \\\"Region code NOT found.\\\" http_code = 404 return_info_final['request']\",\n \"\\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get region info\",\n \"else: logger.info(\\\"Key provided is valid\\\") else: logger.info(\\\"Restricted access is NOT\",\n \"urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP error logger.debug(\\\"HTTP\",\n \"{ 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json\",\n \"= {} # validate the access to this resource try:\",\n \"\\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code\",\n \"found\\\") return_info = \\\"Region code NOT found.\\\" http_code = 404\",\n \"'Access-Control-Allow-Origin': '*' }, } # download json file def get_json():\",\n \"return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data']\",\n \"err.code } return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code =\",\n \"information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\",\",\n \"logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element in json_data['data']: # logger.debug(\\\"code: [%s]\",\n \"-> return region info def get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\",\",\n \"this resource try: validate_access(event, context) except ValueError as err: return_info_final['request']\",\n \"return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for\",\n \"'*' }, } # download json file def get_json(): logger.debug(\\\"Inside\",\n \"[%s]\\\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err:\",\n \"500 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions\",\n \"err: # catch HTTP error logger.debug(\\\"HTTP error: [%s]\\\", err) raise\",\n \"for header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] !=\",\n \"\\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions information.\\\", \\\"http_error_code\\\": err.code } return\",\n \"return create_response_new(http_code, return_info_final) # entry point -> return region info\",\n \"# catch HTTP error logger.debug(\\\"HTTP error: [%s]\\\", err) raise json_data\",\n \"message_body): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body':\",\n \"logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body),\",\n \"type(json_data)) for element in json_data['data']: # logger.debug(\\\"code: [%s] && region_code:\",\n \"403 raise ValueError(http_code, error_message) else: logger.info(\\\"Key provided is valid\\\") else:\",\n \"[%s]\\\", error_message) http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\\\"Key\",\n \"[%s]\\\", type(json_data)) for element in json_data['data']: # logger.debug(\\\"code: [%s] &&\",\n \"return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json',\",\n \"catch HTTP error logger.debug(\\\"HTTP error: [%s]\\\", err) raise json_data =\",\n \"event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not valid\\\")\",\n \"region_code) if element['code'] == region_code: logger.info(\\\"region_code found\\\") http_code = 200\",\n \"region info def get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final\",\n \"= { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] } return\",\n \"create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data))\",\n \"} return create_response_new(err.args[0], return_info_final) # get region info region_code =\",\n \"= get_json() except HTTPError as err: # http_code = err.code\",\n \"is NOT enabled\\\") # create response def create_response_new(status_code, message_body): logger.debug(\\\"Inside\",\n \"is enabled\\\") logger.info(\\\"Value for header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']])\",\n \"except HTTPError as err: # http_code = err.code http_code =\",\n \"found.\\\", \\\"http_error_code\\\": http_code } return create_response_new(http_code, return_info_final) # entry point\",\n \"return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data) http_code = 200 return_info_final['request'] = {\",\n \"get_json() except HTTPError as err: # http_code = err.code http_code\",\n \"allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access is\",\n \"return create_response_new(err.args[0], return_info_final) # get regions info try: json_data =\",\n \"if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not valid\\\") logger.debug(\\\"Error:\",\n \"response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP\",\n \"json_data['info'] return_info_final['data'] = element break else: logger.info(\\\"region_code NOT found\\\") return_info\",\n \"in json_data['data']: # logger.debug(\\\"code: [%s] && region_code: [%s]\\\", element['code'], region_code)\",\n \"\\\"http_error_code\\\": http_code } return create_response_new(http_code, return_info_final) # entry point ->\",\n \"access is enabled\\\") logger.info(\\\"Value for header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'],\",\n \"= logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def\",\n \"validate the access to this resource try: validate_access(event, context) except\",\n \"the access to this resource try: validate_access(event, context) except ValueError\",\n \"= logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\\\"Inside\",\n \"return_info_final = {} # validate the access to this resource\",\n \"200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info']\",\n \"{ \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data']\",\n \"logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element in\",\n \"# logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) #\",\n \"def get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {}\",\n \"info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data =\",\n \"NOT enabled\\\") # create response def create_response_new(status_code, message_body): logger.debug(\\\"Inside function:\",\n \"inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access to this\",\n \"get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} #\",\n \"[%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is\",\n \"\\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) #\",\n \"import urllib.request from urllib.error import HTTPError # logger logger =\",\n \"http_code } return create_response_new(http_code, return_info_final) # entry point -> return\",\n \"# http_code = err.code http_code = 500 return_info_final['request'] = {\",\n \"json_data = json.loads(response.read()) return json_data # entry point -> return\",\n \"'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, }\",\n \"urllib import urllib.request from urllib.error import HTTPError # logger logger\",\n \"\\\"Region code NOT found.\\\" http_code = 404 return_info_final['request'] = {\",\n \"get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access is enabled\\\")\",\n \"is valid\\\") else: logger.info(\\\"Restricted access is NOT enabled\\\") # create\",\n \"get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except\",\n \"# logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element in json_data['data']: # logger.debug(\\\"code:\",\n \"\\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code,\",\n \"# validate access def validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name)\",\n \"info def get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final =\",\n \"logger.debug(\\\"HTTP error: [%s]\\\", err) raise json_data = json.loads(response.read()) return json_data\",\n \"json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } #\",\n \"valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code = 403 raise ValueError(http_code, error_message)\",\n \"logger.info(\\\"Key provided is valid\\\") else: logger.info(\\\"Restricted access is NOT enabled\\\")\",\n \"inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: #\",\n \"def get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL'])\",\n \"event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data = get_json() except HTTPError\",\n \"{ \\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element\",\n \"region_code) try: json_data = get_json() except HTTPError as err: #\",\n \"json import logging import os import inspect import urllib import\",\n \"\\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT found.\\\", \\\"http_error_code\\\": http_code } return\",\n \"= err.code http_code = 500 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\",\",\n \"= urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch HTTP error\",\n \"entry point -> return region info def get_region_info(event, context): logger.debug(\\\"Inside\",\n \"if element['code'] == region_code: logger.info(\\\"region_code found\\\") http_code = 200 return_info_final['request']\",\n \"[%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED']) error_message = \\\"You are not allowed, get out!\\\"\",\n \"http_code = 404 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region\",\n \"logger.debug(\\\"Error: [%s]\\\", error_message) http_code = 403 raise ValueError(http_code, error_message) else:\",\n \"get regions info try: json_data = get_json() except HTTPError as\",\n \"as err: return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\":\",\n \"return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] }\",\n \"logger.setLevel(logger_level) # validate access def validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\",\",\n \"Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\",\",\n \"# logger.debug(\\\"json_data: [%s]\\\", json_data) # logger.debug(\\\"type(json_data): [%s]\\\", type(json_data)) for element\",\n \"not allowed, get out!\\\" if os.environ['RESTRICTED_ACCESS_ENABLED'] == 'true': logger.info(\\\"Restricted access\",\n \"http_code = err.code http_code = 500 return_info_final['request'] = { \\\"request_status\\\":\",\n \"logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError\",\n \"return_info_final) # get region info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\",\",\n \"http_code = 403 raise ValueError(http_code, error_message) else: logger.info(\\\"Key provided is\",\n \"= json_data['info'] return_info_final['data'] = element break else: logger.info(\\\"region_code NOT found\\\")\",\n \"point -> return region info def get_region_info(event, context): logger.debug(\\\"Inside function:\",\n \"[%s] && region_code: [%s]\\\", element['code'], region_code) if element['code'] == region_code:\",\n \"download json file def get_json(): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) try:\",\n \"ValueError(http_code, error_message) else: logger.info(\\\"Key provided is valid\\\") else: logger.info(\\\"Restricted access\",\n \"\\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element break else:\",\n \"information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) logger.debug(\\\"json_data: [%s]\\\", json_data)\",\n \"err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions\",\n \"logger logger = logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate\",\n \"inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type':\",\n \"logger.info(\\\"Value for header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]\",\n \"HTTP error logger.debug(\\\"HTTP error: [%s]\\\", err) raise json_data = json.loads(response.read())\",\n \"= json_data['info'] return_info_final['data'] = json_data['data'] return create_response_new(http_code, return_info_final) # End;\",\n \"{ \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1], \\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0],\",\n \"function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access\",\n \"point -> return region info def get_all_regions_info(event, context): logger.debug(\\\"Inside function:\",\n \"err.code http_code = 500 return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\":\",\n \"raise json_data = json.loads(response.read()) return json_data # entry point ->\",\n \"code NOT found.\\\" http_code = 404 return_info_final['request'] = { \\\"request_status\\\":\",\n \"urllib.error import HTTPError # logger logger = logging.getLogger() logger_level =\",\n \"[%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate the access to\",\n \"# get region info region_code = event['pathParameters']['region_code'] logger.debug(\\\"region_code: [%s]\\\", region_code)\",\n \"os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not\",\n \"try: response = urllib.request.urlopen(os.environ['AWS_REGIONS_JSON_URL']) except HTTPError as err: # catch\",\n \"logging import os import inspect import urllib import urllib.request from\",\n \"as err: # catch HTTP error logger.debug(\\\"HTTP error: [%s]\\\", err)\",\n \"get_region_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} #\",\n \"NOT found\\\") return_info = \\\"Region code NOT found.\\\" http_code =\",\n \"}, } # download json file def get_json(): logger.debug(\\\"Inside function:\",\n \"access to this resource try: validate_access(event, context) except ValueError as\",\n \"enabled\\\") logger.info(\\\"Value for header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if\",\n \"element in json_data['data']: # logger.debug(\\\"code: [%s] && region_code: [%s]\\\", element['code'],\",\n \"json_data) http_code = 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\" }\",\n \"!= os.environ['RESTRICTED_ACCESS_SECRET']: logger.info(\\\"Key provided is not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message)\",\n \"== region_code: logger.info(\\\"region_code found\\\") http_code = 200 return_info_final['request'] = {\",\n \"logger.debug(\\\"code: [%s] && region_code: [%s]\\\", element['code'], region_code) if element['code'] ==\",\n \"err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions info try:\",\n \"= { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Region code NOT found.\\\", \\\"http_error_code\\\":\",\n \"header [%s] is: [%s]\\\", os.environ['RESTRICTED_ACCESS_HTTP_HEADER'], event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']]) if event[\\\"headers\\\"][os.environ['RESTRICTED_ACCESS_HTTP_HEADER']] != os.environ['RESTRICTED_ACCESS_SECRET']:\",\n \"region info def get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final\",\n \"logger.info(\\\"region_code NOT found\\\") return_info = \\\"Region code NOT found.\\\" http_code\",\n \"json_data # entry point -> return region info def get_region_info(event,\",\n \"err) raise json_data = json.loads(response.read()) return json_data # entry point\",\n \"ValueError as err: return_info_final['request'] = { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": err.args[1],\",\n \"error: [%s]\\\", err) raise json_data = json.loads(response.read()) return json_data #\",\n \"function: [%s]\\\", inspect.currentframe().f_code.co_name) return { 'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers':\",\n \"create_response_new(err.args[0], return_info_final) # get regions info try: json_data = get_json()\",\n \"'application/json', 'Access-Control-Allow-Origin': '*' }, } # download json file def\",\n \"# validate the access to this resource try: validate_access(event, context)\",\n \"'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*' }, } # download\",\n \"logger.info(\\\"Key provided is not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code =\",\n \"logger.debug(\\\"region_code: [%s]\\\", region_code) try: json_data = get_json() except HTTPError as\",\n \"\\\"http_error_code\\\": err.args[0] } return create_response_new(err.args[0], return_info_final) # get regions info\",\n \"error logger.debug(\\\"HTTP error: [%s]\\\", err) raise json_data = json.loads(response.read()) return\",\n \"\\\"request_status\\\": \\\"Success\\\" } return_info_final['info'] = json_data['info'] return_info_final['data'] = element break\",\n \"getting Regions information.\\\", \\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) #\",\n \"element['code'], region_code) if element['code'] == region_code: logger.info(\\\"region_code found\\\") http_code =\",\n \"def validate_access(event, context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) logger.debug(\\\"RESTRICTED_ACCESS_ENABLED: [%s]\\\", os.environ['RESTRICTED_ACCESS_ENABLED'])\",\n \"= { \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions information.\\\", \\\"http_error_code\\\":\",\n \"context): logger.debug(\\\"Inside function: [%s]\\\", inspect.currentframe().f_code.co_name) return_info_final = {} # validate\",\n \"inspect import urllib import urllib.request from urllib.error import HTTPError #\",\n \"&& region_code: [%s]\\\", element['code'], region_code) if element['code'] == region_code: logger.info(\\\"region_code\",\n \"for element in json_data['data']: # logger.debug(\\\"code: [%s] && region_code: [%s]\\\",\",\n \"provided is not valid\\\") logger.debug(\\\"Error: [%s]\\\", error_message) http_code = 403\",\n \"element['code'] == region_code: logger.info(\\\"region_code found\\\") http_code = 200 return_info_final['request'] =\",\n \"valid\\\") else: logger.info(\\\"Restricted access is NOT enabled\\\") # create response\",\n \"region_code: [%s]\\\", element['code'], region_code) if element['code'] == region_code: logger.info(\\\"region_code found\\\")\",\n \"# entry point -> return region info def get_all_regions_info(event, context):\",\n \"enabled\\\") # create response def create_response_new(status_code, message_body): logger.debug(\\\"Inside function: [%s]\\\",\",\n \"err.code } return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data) #\",\n \"\\\"http_error_code\\\": err.code } return create_response_new(http_code, return_info_final) # logger.debug(\\\"json_data: [%s]\\\", json_data)\",\n \"[%s]\\\", json_data) http_code = 200 return_info_final['request'] = { \\\"request_status\\\": \\\"Success\\\"\",\n \"{ \\\"request_status\\\": \\\"Fail\\\", \\\"error_message\\\": \\\"Error getting Regions information.\\\", \\\"http_error_code\\\": err.code\",\n \"resource try: validate_access(event, context) except ValueError as err: return_info_final['request'] =\",\n \"{} # validate the access to this resource try: validate_access(event,\",\n \"import urllib import urllib.request from urllib.error import HTTPError # logger\",\n \"logging.getLogger() logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL']) logger.setLevel(logger_level) # validate access def validate_access(event,\",\n \"HTTPError as err: # catch HTTP error logger.debug(\\\"HTTP error: [%s]\\\",\",\n \"error_message) else: logger.info(\\\"Key provided is valid\\\") else: logger.info(\\\"Restricted access is\",\n \"[%s]\\\", err) raise json_data = json.loads(response.read()) return json_data # entry\",\n \"-> return region info def get_all_regions_info(event, context): logger.debug(\\\"Inside function: [%s]\\\",\",\n \"\\\"Region code NOT found.\\\", \\\"http_error_code\\\": http_code } return create_response_new(http_code, return_info_final)\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":1,"numItemsPerPage":100,"numTotalItems":12866649,"offset":100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NjI4MjAyMCwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMCIsImV4cCI6MTc3NjI4NTYyMCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.PiDyhaLP39KN-zvNquuEotx7Gjjfi_a5Bee7LftxApflREF3ot1P2zoLE7Iwg94cDLi9YMDKsfmpj6CY062EBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":126831957853}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 82k |
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[
"COMPONENT_PROPERTIES file.\"\"\" from ogs5py.fileclasses.base import BlockFile class MCP(BlockFile): \"\"\" Class",
"- MINERAL_DENSITY - MOBILE - MOLAR_DENSITY - MOLAR_VOLUME - MOLAR_WEIGHT",
"] ] STD = {} def __init__(self, **OGS_Config): super().__init__(**OGS_Config) self.file_ext",
"\"\"\" Class for the ... |
[
"assignment refs assignment = self.build_role_assignment_entity( role_id=role['id'], domain_id=domain['id'], user_id=user['id'], inherited_to_projects=True) assignment_names",
"put on 'links': {'assignment': link} assignment_link = self.build_role_assignment_link(**filters) # Expand",
"{ 'project_id': self.proj... |
[
"author=\"<NAME>\", author_email='<EMAIL>', url='http://github.com/aaren/notedown', install_requires=['ipython', ], entry_points={ 'console_scripts': [ 'notedown =",
"__version__ exec(open('./_version.py').read()) setup( name=\"notedown\", version=__version__, description=\"Convert markdown to IPython notebook.\"... |
[
"complete: {:.6f}s\".format(end-start)) print(\"Courses extracted: \", len(data_to_csv)) page += 1 export_to_csv(data_to_csv)",
"# Multithread only the work: # Tuning is required to",
"funtion which will push it to the Cassandra Database def",
"ProcessPoolExecutor, as_completed, ThreadPoolExecutor import time... |
[
"def create_template( constructor, template_path: Optional[List[str]] = None, out_path: Optional[OutPath] =",
"class ConvertDict(Template): module_name: str = attr.ib() @attr.s class SQLAlchemyModel(Template): module_name:",
"attr.ib() entity: Entity = attr.ib() restplus_template: str = attr.ib() TypeOption:",
... |
[
"def _on_selected_global_mix_mode_changed(self, mode): self.notify_global_mix_mode() @listenable_property def device_mode(self): return self._device_modes.selected_mode @listens(b'selected_mode')",
"ableton.v2.base import listenable_property, listens, EventObject class ModeCollector(EventObject): def __init__(sel... |
[
"corpora, models, similarities, matutils,utils from gensim.models import KeyedVectors import numpy",
"word_vectors.similarity('woman', 'man') #print word_vectors.most_similar(positive=['san_francisco']) print word_vectors.most_similar(positive=['SURGICAL']) #word_vectors.similarity(testString[0],testString[1]) ''... |
[
"'sword', ], 'https://m.wuxiaworld.co/': [ 'https://m.wuxiaworld.co/Reincarnation-Of-The-Strongest-Sword-God/', ], 'https://meionovel.id/': [ 'https://meionovel.id/novel/the-legendary-mechanic/', ],",
"], 'http://liberspark.com/': [ 'http://liberspark.com/novel/black-irons-glory' ], 'https://webnovelindonesia.com... |
[
"= {} ref_counts = {} for ref in refs: ref_dict,",
"with new words in dictionary''' for word in dictionary: text",
"for word in dictionary: text = text.replace(word, dictionary[word]) return text",
"name = get_ref_tag_name(tag) spec_ref = get_spec_ref(text, name) text = text.replace(tag,",
"name\\=.+?\\/\\>... |
[
"'body:' body_lookup_pattern = body_key_prefix + '*' body_expiration = 10 #",
"self._get_default(2) def _get_default(self, index) -> BodyObject: return self.__default_bodies[index] @staticmethod def",
"\"\"\" return json.loads( self._scripts.update_records(keys=known_bodies_keys, args=[max_capacity, self.body_l... |
[
"forms from django.utils.translation import ugettext_lazy as _ from djangocms_baseplugins.baseplugin import",
"import defaults from djangocms_baseplugins.baseplugin.cms_plugins import BasePluginMixin from djangocms_baseplugins.baseplugin.utils import get_fields_from_fieldsets,",
"name = _(u'Spacer') render_temp... |
[
"= c / v return (h, s, v) def hsv_to_rgb(hsv):",
"255. g /= 255. b /= 255. mmax = max(r,",
"if np.any(rgbv > 1.): rgbv = rgbv / 255. yuv",
"= 0. elif (mmax == r): hp = ((g -",
"hp = ((b - r) / c) + 2 elif",
"\"\"\"Convert Y'UV colors to RGB colors.\"\"\" yuvv = np.array(yuv).reshape(3, 1)",
"yuv = np.do... |
[
"run('ls', return_output=True) assert out == 'hello.txt\\n' out = run(['cat', 'hello.txt'],",
"run, silent_run, RunError from utils.fs import in_temp_dir def test_run(capsys): with",
"= run(['cat', 'hello.txt'], return_output=True) assert out == 'hello\\nworld\\n' with pytest.raises(RunError):",
"out == 'hell... |
[
"``http(s)`` URL. Raises: ValueError: If the input URL is not",
"import absolute_import, division, print_function, unicode_literals import logging log = logging.getLogger(__name__)",
"if url.startswith(\"ws:\"): reprotocoled = \"http\" + url[2:] elif url.startswith(\"wss:\"): reprotocoled",
"protocol \" + url... |
[
"tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5, scope='Conv2d_0b_5x5') with tf.variable_scope('Branch_2'): tower_conv2_0 =",
"2.0 (the \"License\"); # you may not use this file",
"288, 3, scope='Conv2d_0b_3x3') tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2, padding=padding,",
"activation_fn a... |
[
"major, minor, patch def get_app_name(self) -> str: sw, response =",
"button.right_click() # custom contracts if (len(transaction.signers) > 0 and payloads.WitnessScope.CUSTOM_CONTRACTS",
"of uncompressed key return response def sign_tx(self, bip44_path: str, transaction:",
"bip44_path: str, transaction: payl... |
[
"long long*)address; unsigned long long old = *address_as_ull, assumed; do",
"''' #if __CUDA_ARCH__ < 600 __device__ double atomicAdd(double* address, double",
"long*)address; unsigned long long old = *address_as_ull, assumed; do {",
"return clpy.isscalar(x) or (clpy.sparse.base.isdense(x) and x.ndim == 0) de... |
[
"pytest import raises from discopy.cartesian import * def test_Box_repr(): f",
"test_Function_tensor_err(): f = Function(2, 1, lambda x, y: x +",
"assert str(err.value) == messages.expected_input_length(f, values) def test_Function_then(): f, g =",
"Function.id(3)(1, 2, 3)\\ == Function.id(0).tensor(*(3 * [Fu... |
[
"mode. nextDoc=_(\"moves to the next heading at level 5\"), #",
"= ( # Translators: The label of a radio button",
"if possible. # Find the next/previous focusable node. try: item",
"elements list to place its own selection. def __init__(self,itemType,document): \"\"\"",
"Note that parentElements will be emp... |
[
"0)) design = draw.translate(design, p.position_x, p.position_y) pocket = draw.rotate(pocket, p.rotation,",
"in the root directory # of this source tree or",
"import numpy as np #from ... import config #if not",
"design = draw.rotate(design, p.rotation, origin=(0, 0)) design = draw.translate(design, p.positio... |
[
"-> [bS, max_len_header, mL_n] att_h = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) #",
"1): pr_wo_temp = [[i_op] * self.max_where_number] * bS # wv",
"wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # idx11[0] # get wv_str temp_pr_wv_str, _",
") if show_p_wv: # p = [b, hea... |
[
"loop.create_server(app.make_handler(), '127.0.0.1', 9000) logging.info('server started at http://127.0.0.1:9000...') return srv loop",
"'127.0.0.1', 9000) logging.info('server started at http://127.0.0.1:9000...') return srv loop =",
"def index(request): return web.Response(body=b'<h1>Awesome</h1>') async def ... |
[
"np import matplotlib.pyplot as plt import flopy fb = flopy.modflow.Modflow.load('freyberg',",
"colorbar=True) fb.dis.botm.plot(grid=True, colorbar=True) fb.dis.plot() plt.show() fb.dis.plot() plt.show() fig = plt.figure(figsize=(8,",
"import print_function import os import numpy as np import matplotlib.pyplot"... |
[
"import SampleTransformer class SampleTransformerFactory(components.Factory): name_to_class_mapping = {\"identity\": identity.IdentitySampleTransformer} __all__ =",
"import identity from chia.components.sample_transformers.sample_transformer import SampleTransformer class SampleTransformerFactory(components.Facto... |
[
"[] names.append('singi') names.append('lily') names.append('sam') print('I find a big dining-table,I can",
"names.append('lily') names.append('sam') print('I find a big dining-table,I can invite more",
"',would you like to have dinner with me ?' print(names[0]+greets)",
"'fangsi') names.append('zhangqing') g... |
[
"= Slurper(TEST_PACKAGE_REPO_NAME) version = slurper.get_latest_version_number(TEST_PACKAGE_REPO_NAME) package, created = slurper.get_or_create_package(TEST_PACKAGE_REPO_NAME, version)",
"version) # fetch the package for testing package = Package.objects.get(title=TEST_PACKAGE_REPO_NAME)",
"slurper.get_latest_v... |
[
"or :class:`DayOfWeek <azure.mgmt.logic.models.DayOfWeek>` :param occurrence: The occurrence. :type occurrence: int",
":type day: str or :class:`DayOfWeek <azure.mgmt.logic.models.DayOfWeek>` :param occurrence: The occurrence.",
"the MIT License. See License.txt in the project root for",
"the week. Possible v... |
[
"try: r = self.f(Series([])) except Exception: pass else: should_reduce =",
"ares > 1: raise ValueError(\"too many dims to broadcast\") elif",
"just ignore if not self.ignore_failures: raise except ZeroDivisionError: # reached",
"and not self.dtypes.apply(is_extension_array_dtype).any() # Disallow complex_int... |
[
"# -*- coding: utf-8 -*- \"\"\" @date: 2021/5/16 下午10:22 @file:",
"== (1, 1000) def test_shufflenet(): cfg.merge_from_file('configs/benchmarks/shufflenet/shufflenet_v1_3g2x_zcls_imagenet_224.yaml') print(cfg) model = build_recognizer(cfg,",
"model(data)[KEY_OUTPUT] print(outputs.shape) assert outputs.shape == (... |
[
"'/out/?delimiter=%2F&max-keys=100&prefix=testsync.txt%2F', 404) def method(request): if request.headers['Content-Length'] != '3': sys.stderr.write('Did not",
"gdal.Sync('vsifile.py', '/vsis3/do_not/exist', options=options) handler = webserver.SequentialHandler() handler.add('GET', '/out/', 200) handler.add('GET'... |
[
"@pytest.mark.parametrize( ('input_s', 'expected'), ( (INPUT_S, EXPECTED), ), ) def test(input_s:",
"lines[0].split(\",\")) for d in range(80): numbers2 = Counter({8: numbers[0], 6:",
") def test(input_s: str, expected: int) -> None: assert compute(input_s)",
"= 5934 @pytest.mark.parametrize( ('input_s', 'exp... |
[
"from selenium import webdriver browser = webdriver.Firefox() browser.get('http://localhost:8000') assert 'Django'",
"import webdriver browser = webdriver.Firefox() browser.get('http://localhost:8000') assert 'Django' in browser.title",
"selenium import webdriver browser = webdriver.Firefox() browser.get('http:... |
[
"csv.reader(stdin) writer = csv.writer(stdout) for row in reader: try: row[-1]",
"first_char_last_column.py Should print \"100,2\\n0,\" \"\"\" import csv from sys import",
"sys import stdin, stdout def main(): reader = csv.reader(stdin) writer",
"print \"100,2\\n0,\" \"\"\" import csv from sys import stdin, s... |
[
"--check .\") @p.command def mypy(self) -> None: pass run(\"mypy .\")",
"pass run(\"mypy .\") @p.command def generate_version(self) -> None: import toml",
"typing import Any, Dict, List, Optional, Tuple from envo import",
"import Path from typing import Any, Dict, List, Optional, Tuple",
"build\") @p.comman... |
[
"revc timedout:', e) else: print('client says: %s' % msg) srv.send('hi",
"ctx = zmq.Context() srv = ctx.socket(zmq.REP) srv.bind(ADDR) #srv.setsockopt(zmq.RCVTIMEO, 3000); while",
"print('zmq socket revc timedout:', e) else: print('client says: %s' %",
"= ctx.socket(zmq.REP) srv.bind(ADDR) #srv.setsockopt(zmq... |
[
"to affect non-idempotent semantic analysis behavior. TODO: It would be",
"node.is_new_def): # Remove defined attributes so that they can recreated",
"changes can be made in semantic analysis passes 2 and",
"even though some identities are preserved. \"\"\" import contextlib from",
"node.is_new_def = False ... |
[
"os.getcwd() sys.path.append(os.path.join(os.path.dirname(cwd_path), 'rt-thread', 'tools')) # BSP dist function def dist_do_building(BSP_ROOT,",
"cwd_path = os.getcwd() sys.path.append(os.path.join(os.path.dirname(cwd_path), 'rt-thread', 'tools')) # BSP dist function",
"BSP dist function def dist_do_building(BS... |
[
"select a version of the method to call based on",
"name, bases, attr_dict): \"\"\"Clear out the cached locals dict once",
"specs. Packages are instantiated with a particular spec, and you",
"# will be the one 'wrapped'. wrapped_method = self.method_list[0][1] #",
"that this works for methods other than ins... |
[
"# else, OS==\"win\" 'variables': { 'config_h_dir': 'vsprojects', # crafted for",
"treatment is based # on the path to the .proto",
"a constant exactly). # I'm not sure how to convince",
"], }, }] ], 'targets': [ # The \"lite\" lib",
"# compiler itself (protoc) falls into that category. # #",
"but it does... |
[
"# 7*1 = 7 class Student: def __init__(self, fname): self.fname",
"0) # Red rgb(0, 0, 0) # Black rgb(0, 255,",
"Student: def __init__(self, fname): self.fname = fname def greet(self, fname):",
"fname): return f\"Hello, {fname}\" class BatchA(Student): def __init__(self, lname): self.lname",
"White rgb(255, ... |
[
"front right, back left and back right sides. def containers():",
"Create the a5 booklet's name a5_booklet_name = Path(input_file).stem + \"_as_a5_booklet\"",
"packet = BytesIO() cover_canvas = canvas.Canvas(packet, pagesize=A4) width, height =",
"= BytesIO() diary_canvas = canvas.Canvas(packet, pagesize=A5) ... |
[
"= self.detect_url(target) elif type(url) != bool: raise TypeError('url kwarg must",
"url flag that tells the extractor whether or not the",
"self.url: raw_data = urllib2.urlopen(self.target).read().decode('utf-8-sig').rstrip() else: with open(self.target, 'r') as f:",
"target = 'http://' + target host, path ... |
[
"1.2e-3, # [Bohr|rad] } from pyscf.geomopt import berny_solver newmol =",
"[Bohr|rad] } from pyscf.geomopt import berny_solver newmol = berny_solver.optimize(method, **conv_params)",
"atom_coords = mol.atom_coords() atom_coords[atmlst] = coords / lib.param.BOHR return atom_coords",
"and not g_scanner.converge... |
[
"the License. from taf.foundation.utils import ConnectionCache class AUT(object): cache =",
"data from AUT' ) def _create_instance(self, name, **kwargs): raise NotImplementedError(",
"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law",
"from taf.foundation.utils import Conne... |
[
"crypto_tools.get_param_json_data(\"alphabets.json\", lang) num_data = vigenere_str_to_list(data, vigenere_dict) num_key = vigenere_str_to_list(key, vigenere_dict)",
"val in num_result: result_str += vigenere_dict[val] return result_str @crypto_tools.file_manipulation() def",
"and encrypt != \"decrypt\": raise ... |
[
"unused_contexts or not index_contexts: result |= context.py__getitem__( ContextSet(unused_contexts), contextualized_node )",
"ContextName(self, wrapped_name.tree_name) else: from jedi.evaluate.compiled import CompiledContextName return CompiledContextName(self, wrapped_name.string_name)",
"ContextSet(unused_co... |
[
"import Tuple import click from .cmd import KiwiCommandType, KiwiCommand from",
"nargs=-1, ) @click.argument( \"compose_cmd\", metavar=\"COMMAND\", ) @kiwi_command( short_help=\"Run docker-compose command\",",
"\"compose_cmd\", metavar=\"COMMAND\", ) @kiwi_command( short_help=\"Run docker-compose command\", # i... |
[
"True): n1 = e.node1 n2 = e.node2 if n1 ==",
"prop): return prop in self.props class Node(PropertyContainer): pass class Edge(PropertyContainer):",
"property\".format(Network.FRIEND_PROP)) for e in self.edges: if (e.node1 is person1 and",
"is a string argument\".format(Network.NAME_PROP)) for n in self.nodes:... |
[] |
[
"def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) + k for",
"= \"1\", 1 while i < n: s = gen(s)",
"class Solution: def countAndSay(self, n): def gen(s): return \"\".join(str(len(list(g))) +",
"import groupby class Solution: def countAndSay(self, n): def gen(s): return",
"in groupby(s... |
[
"+ '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir",
"= model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 #",
"base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT",
"= model_dir + '/bert_co... |
[
"3, np.nan, 1, 4], [2, np.nan, 1, 3, 4, np.nan]",
"from hidrokit.prep import read import numpy as np import pandas",
"'F'] ) A_date = A.set_index(pd.date_range(\"20190617\", \"20190619\")) res_A_number = {'A': [1],",
"= pd.DataFrame( data=[ [1, 3, 4, np.nan, 2, np.nan], [np.nan,",
"A = pd.DataFrame( data=[ ... |
[
"department_blueprint = Blueprint('department', __name__, url_prefix=url_prefix) department_controller = DepartmentController(request) @department_blueprint.route('/', methods=['GET'])",
"= DepartmentController(request) @department_blueprint.route('/', methods=['GET']) @Auth.has_permission('view_department') @swa... |
[
"g.compile(loss=loss_mse_ssim, optimizer=optimizer_g) lr_controller = ReduceLROnPlateau(model=g, factor=lr_decay_factor, patience=10, mode='min', min_delta=1e-4, cooldown=0,",
"+ '%d.png' % iter) plt.close() else: if validate_num < validate_path.__len__():",
"<reponame>ikecoglu/DL-SR import argparse from keras ... |
[
"that users can still interact with the chart during the",
"<filename>catalyst/exchange/live_graph_clock.py import pandas as pd from catalyst.constants import LOG_LEVEL from",
"using the 'animate' callback of Matplotlib. We need to direct",
"which is a wrapper around time.sleep() used in the SimpleClock.",
... |
[
"print(\"Resultado: Fallido\") print(\"Nombre del test: \" + inspect.currentframe().f_code.co_name) print(\"Descripcion: Usuario",
"el detalle de la tarea\") print(\"-----------\") def test_create_task_with_authenticated_user(self): self.client.login(username='test_user', password='<PASSWORD>')",
"usuario no au... |
[
"from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/seidel-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/seidel-2d/tmp_files/6745.c') procedure('kernel_seidel_2d') loop(0) known(' n",
"destination('... |
[
"async with aiohttp.ClientSession() as client: async with client.post( url, data=data,",
"return await request(f'{base_url}/sendMessage', payload) async def answer_callback_query( base_url, callback_query_id, text,",
"Check response status json_response = await resp.json() return json_response async",
"aiohtt... |
[
"Platform from nautobot_device_onboarding.exceptions import OnboardException from nautobot_device_onboarding.helpers import onboarding_task_fqdn_to_ip from",
"(c) 2020-2021 Network To Code Licensed under the Apache License,",
"DeviceRole.objects.create(name=\"Firewall\", slug=\"firewall\") self.platform1 = Plat... |
[
"= struct.unpack(\"<Q\", h)[0] w = file.read(8) w = struct.unpack(\"<Q\", w)[0]",
"img = np.array([r,g,b]).transpose(1,0).reshape(h,w,c) print(img.shape) plt.imshow(img) plt.show() def read_label(path, ground_truth=None): with",
"file: n = file.read(8) n = struct.unpack(\"<Q\", n)[0] c =",
"print(len(label)) ... |
[
"self.assertRaises(ValueError, PiezoTensor.from_voigt, bad_voigt) self.assertArrayEqual(self.voigt_matrix, pt.voigt) if __name__ == \"__main__\": unittest.main()",
"3)) self.assertRaises(ValueError, PiezoTensor, bad_dim_array) def test_from_voigt(self): bad_voigt = np.zeros((3, 7))",
"Team. # Distributed under ... |
[
"\"\"\"Checks access of instance files on the host. :param instance:",
"NotImplementedError() def host_power_action(self, action): \"\"\"Reboots, shuts down or powers up",
"context: security context :instance: instance object that was migrated :block_device_info:",
"%s\", str(event)) return if not isinstance(... |
[
"= 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId) self.whisperAvatarName = None self.whisperAvatarId",
"self.whisperAvatarId = None self.toPlayer = 0 elif self.whisperAvatarId: self.chatMgr.sendWhisperString(text, self.whisperAvatarId)",
"0.71) self.whisperAvatarName = N... |
[
"log.fit(features, label) _ = joblib.dump(log, pkl, compress=9) if __name__ ==",
"Only computes a batch-wise average of precision. Computes the precision,",
"DNN will be saved. :return: None ''' model = Sequential()",
"precision(y_true, y_pred): \"\"\"Precision metric. Only computes a batch-wise average of",
... |
[
"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law",
"TaskState.Value('TASK_KILLED') self.callback_called = True mock_time = mock.Mock(spec=time) status_manager = StatusManager(checker,",
"# # Licensed under the Apache License, Version 2.0 (the",
"compliance with the License... |
[
"to the principal impact direction. for veh_striking in veh_striking_list: if",
"conservation. veh_y1 = veh_k[0] ** 2 / (veh_a1 ** 2",
"+ np.pi / 2)) veh_RDS = V1_v * np.sin(delta_angle_2) veh_e",
"+ veh_m[1] * veh_y2) delta_v2 = (1 + veh_e) *",
"= np.abs(veh_cgs[0] - veh_striking[3]) veh_RDS = np.abs(V2_v ... |
[
"randomly per batch ('NV')\", ) parser.add_argument( \"--freeze_enc\", action=\"store_true\", default=None, help=\"Freeze",
") # (SB, NS, 3, H, W) src_poses = util.batched_index_select_nd(all_poses,",
"= util.args.parse_args(extra_args, training=True, default_ray_batch_size=128) device = util.get_cuda(args.gpu_... |
[
"import QtPluginSorter from .qt_plugin_report import QtPluginErrReporter # TODO: add error",
"= ['-m', 'pip', 'install', '--upgrade'] if running_as_bundled_app() and sys.platform.startswith('linux'): cmd",
"return # including summary here for sake of filtering below.",
"output_widget self.process.setParent(ou... |
[
"= ('create_partial_webhook_from_id', ) from scarletio import export from ..core import",
"channel's identifier. Defaults to `0`. Returns ------- webhook : ``Webhook``",
"given `webhook_id` already exists, then returns that instead. Parameters ----------",
"a partial webhook from the given parameters. If the ... |
[
"'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2,",
"def getWordScore(word, n): \"\"\" Returns the score for a word.",
"is worth 3, D is worth 2, E is worth",
"0 \"\"\" total_points = 0 for letter in word: total_points",
"if all n letters are used on the first turn.",
"total_points += SCRABBLE_LETTER_VALUES[letter... |
[
"row in data: f = open(filename+'.tex','r+') old = f.readlines() if",
"f.writelines(old[offset:]) f.close() # Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv",
"# Compilar arquivo LaTeX try: os.system('pdflatex '+filename+'.tex') os.system('mv '+filename+'.pdf '+filename+'_'+row... |
[
"\"r\", chunk_size=chunk_size) as f: for chrom in f.get_chromosomes(): print(f\"{chrom}: {f[chrom].get_number_of_chunks()}\")",
"import Path from meth5.meth5 import MetH5File def main(m5file:Path, chunk_size:int, quiet:bool):",
"from pathlib import Path from meth5.meth5 import MetH5File def main(m5file:Path,",
... |
[
"for this year filepatt = \"quev-a-{}{}-*.tif\".format(region, year2) globpatt = os.path.join(indir,",
"quarter = args.quarter # region list (LAEA regions only) valid_region_list",
"means: {}\".format(dst_path)) dst_ds = driver.Create(dst_path, nx, ny, 1, gdal.GDT_Float32) dst_data",
"= dbmean.max() dbmean_me... |
[
"', end='') termo += razão cont += 1 print('Pausa') mais",
"cont += 1 print('Pausa') mais = int(input('Quantos termos você quer",
"priemiro termo da PA: ')) razão = int(input('Digite a razão",
"a razão da PA: ')) termo = primeiro cont =",
"while mais != 0: total += mais while cont <=",
"= int(input('Digit... |
[
"- handle DataObject(\\'{data_object.name}\\')') else: self.logger.info(f'ObjectHandler - [root] handle DataObject(\\'{data_object.name}\\')') values",
"\\'{results[value.name]}\\'' ) else: error = f'ObjectHandler - invalid value type",
"results[value.name] = self.__handle_value(value.query, node) if results[va... |
[
"MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO",
"\"datetime\": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), \"status\": DataTraceFinishStatus.STATUS, \"status_alias\": DataTraceFinishStatus.STATUS_ALIAS, } ) return [create_trace_dict]",
"substantial portions o... |
[
"Doc2Vec import gensim.models.doc2vec from collections import OrderedDict import multiprocessing from",
"alldocs if doc.split == 'test'] #doc_list = alldocs[:] # for",
"# # with open('/'.join([dirname, output]), 'w') as n: # n.write(temp_norm)",
"f.write(num_line) # #SentimentDocument = namedtuple('SentimentD... |
[
"range(10)] self.assertEqual(Task.find(5).id(), 5) self.assertEqual(Task.find(5), tasks[5]) if __name__ == '__main__': unittest.main()",
"<gh_stars>1-10 import unittest from caravan.task import Task from caravan.tables import",
"[Task.create(\"echo %d\" % i) for i in range(10)] self.assertEqual(Task.find(5).id(... |
[
"self.get(\"allowed_domains\", default=None) if allowed_domains is not None: return allowed_domains.split(',') def",
"self._get_bool(\"response_body\", defaults.RESPONSE_BODY_ENABLED) def get_request_body(self): return self._get_bool(\"request_body\", defaults.REQUEST_BODY_ENABLED) def get_images(self): return",
... |
[
"r = s/3 print(r) # code.... ''' def average(): a=1",
"s/3 print(r) # code.... ''' def average(): a=1 b=2 c=3",
"a=1 b=2 c=3 s=a+b+c r=s/3 print(r) average() ''' ''' #input",
"#parameter #argument def average(a,b,c): s=a+b+c r=s/3 print(r) average(10,20,30) ''' def",
"average(a, b, c): s = a+b+c r = s/3 ret... |
[
"python # runs after the job (and after the default",
"start, end and strand columns.\") error_map[name] = error_msg data_param_names.add( name",
"param in page_param_map.items(): if isinstance( param, DataToolParameter ): # for",
"not None: dbkeys.add( param_values[name].dbkey ) data_params += 1 # check",
... |
[
"self.sync_all() assert(len(self.nodes[0].listtransactions(\"watchonly\", 100, 0, False)) == 0) check_array_result(self.nodes[0].listtransactions(\"watchonly\", 100, 0,",
"send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress(\"from1\")",
"0, Fal... |
[
"Invalid entry lines.append(line) continue if comps[1] == name or comps[0]",
"comps[1] == name and comps[0] == device: continue else: if",
"automaster(config) if name not in contents: return True # The",
"comps = re.sub(r\"\\s+\", \" \", line).split() nod = __salt__['cmd.run_stdout']('ls -l",
"{1}\" raise C... |
[
"obj.created_by: obj.created_by = request.user return super().save_model(request, obj, form, change) class",
"class PhotoAdmin(EONBaseAdmin): # TODO Add Proper List Display pass admin.site.register(Topic,",
"from base.models import Topic, Photo class EONBaseAdmin(admin.ModelAdmin): def get_changeform_initial_da... |
[
"django.db import models from django.utils.translation import ugettext_lazy as _ class",
"########################################################################## from django.db import models from django.utils.translation import ugettext_lazy as",
"TERENA by <NAME>, DAASI International GmbH, http://www.daasi.... |
[
"logger.info('KeyboardInterrupt received, ending...') self.alive = False if self.core.useHotReload: self.dump_login_status() logger.info('Bye.')",
"except_self(self, chats_or_dicts): \"\"\" 从聊天对象合集或用户字典列表中排除自身 :param chats_or_dicts: 聊天对象合集或用户字典列表 :return: 排除自身后的列表 \"\"\"",
"loginCallback=login_callback, exitCal... |
[
"up configuration variables __all__ = ['custom_viewer', 'qglue', 'test'] import os",
"detect # the default excepthook is in place and override",
"configuration file from .config import load_configuration env = load_configuration() from",
"import MatplotlibBackendSetter sys.meta_path.append(MatplotlibBackendSe... |
[] |
[
"Use our new middleware instead of the old one classes",
"will eventually be backported into the main settings file, but",
"classes.remove(mw) else: log.warning('Failed to remove middleware: %s', mw) return classes",
"them to be able to run the site with the",
"'readthedocs.proxito.urls' USE_SUBDOMAIN = Tru... |
[
"0, 0, 1, 1, 1] clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'), param_grid=param_grid, cv=3)",
"by fits # to individual folds will be caught and",
"predict def score(self, X=None, Y=None): if self.foo_param > 1: score",
"def test_grid_search_groups(): # Check if ValueError (when groups is None)",
... |
[
"Generated by Django 3.2.7 on 2021-09-13 03:52 import uuid from",
"POS?\", max_length=100)), ], options={ \"ordering\": [\"-zettle_created\"], \"get_latest_by\": [\"zettle_created\"], }, ),",
"the total amount which is VAT\", max_digits=12, ), ), (",
"models.DecimalField( decimal_places=2, help_text=\"The par... |
[
"== \"=\": sub += next_char new.append(next_char) return sub new.append(lexer.get_char()) return",
"* 4 - 2 do skip endwhile else x :=",
"endif; y := 1\" input_string = line.strip(\"\\n\") lexer = Lexer(input_string)",
"current char. curr_char = lexer.get_char() while (curr_char != None): while",
"lexer)) c... |
[
"sys sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv)",
"sys.path.append('/usr/local/lib/python3.4/site-packages/') from warc_dedup import deduplicate def main(): if len(sys.argv) ==",
"if len(sys.argv) == 1: raise Exception('Please provi... |
[
"url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL =",
"xmltodict.parse(curve.content) # This is based around retrieving the n last",
"f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, re... |
[
"webhook verify token can be set here, if set as",
"change various variables here to customize and set up the",
"Mattermost server '''----------------------------Flask Configuration----------------------------''' #set external IP for the Flask",
"as a String mm_channel = None #Set a custom Username",
"custo... |
[] |
[
"import matplotlib.pyplot as plt import cv2.cv2 as cv from functools",
"= np.min(img) pmax = np.max(img) img = ((img - pmin)",
"# -*- encoding:utf-8 -*- # @Time : 2021/1/3 15:15 #",
"for det_cls in result] result = np.vstack(result) _, ids =",
"= 0 image_name = '.' # '.' is surely exist,",
"encoding:utf-8... |
[
"\"\"\"get the margin of the movement\"\"\" self._check_open() return num(self.api.css1(\"span.cfd-order-info-item-value\").text) def",
"dom = self.browser return expect(dom.is_element_present_by_css, args=[css_path]) def elXpath(self, xpath, dom=None):",
"True elif title == \"Maximum Quantity Limit\": raise ex... |
[
"finishes, all files injected will be truncated to zero length.",
"is specified \"spec.containers[2]\" (container with index 2 in this pod).",
"be set by external tools to store and retrieve arbitrary",
"sourceStrategy holds the parameters to the Source build strategy. type:",
"30 seconds, the Kubelet will ... |
[
"element break else: logger.info(\"region_code NOT found\") return_info = \"Region code",
"'statusCode': str(status_code), 'body': json.dumps(message_body), 'headers': { 'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*'",
"str(status_code), 'body': json.dumps(message_body), 'headers': { 'Co... |